Integrating Behavioral Health into Primary Care – Thought Leaders in Population Health
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The document outlines the integration of behavioral health into primary care, emphasizing its clinical, legal, social, and financial impacts on chronic medical conditions. It discusses challenges faced in improving health outcomes for patients with comorbidities and suggests strategies for enhancing healthcare management through better data utilization and technology application. Additionally, it highlights the significant cost burdens associated with unmanaged behavioral health conditions and the importance of collaboration in care delivery.
![purchasers of addiction treatment services. As
such, they will now play a regulatory role analo-
gous to the one they have played with the mental
health treatment system.
While Medicaid agencies consider how to op-
timally allocate resources for addiction treat-
ment, differences in the severity of need, as il-
lustrated in mental health, will complicate these
financing and regulatory decisions. Medicaid
agencies must develop strategies to support a
continuum of care that effectively responds to
people with diverse addiction-related needs.
State Medicaid programs will need to create reg-
ulatory policies that set clear rules to improve
quality while remaining flexible and nimble to
adjust to different patient needs.
Any major purchaser such as Medicaid will be
able to create important incentives to develop
new innovative delivery model reforms. It is
hoped that states will consider the power of this
leverage prospectively to provide efficient, high-
quality addiction treatment services instead of
responding retrospectively to unintended prob-
lems that so often arise with the infusion of new
funding. ▪
The authors acknowledge support from
the National Institute on Drug Abuse
(Grant No. R01 DA034634-01) for its
project, “The impact of health reform on
outpatient substance abuse treatment
programs,” although the
recommendations and responsibility for
any errors rest solely with the authors.
The authors also acknowledge helpful
feedback from the editors of Health
Affairs and two anonymous reviewers.
NOTES
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37 Butler M, Kane RL, McAlpine D,
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Vulnerable Populations
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![affected: There are also adverse impacts on the
health of their caregivers. Caring for a person
with a chronic, disabling noncommunicable dis-
ease or mental disorder, such as cancer or de-
mentia, is stressful and associated with an in-
creased risk of chronic health problems,
including depression, hypertension, sleeping
problems, and fatigue; increased use of psycho-
tropic drugs; and premature mortality.12
The in-
direct costs of such uncompensated caregiving
are also considerable. These impacts on care-
givers, who are often members of the patient’s
household, can lead to the clustering of noncom-
municable diseases and mental disorders within
households, creating sick households.
Addressing Comorbidities: The
Evidence
There is a robust evidence base testifying to the
effectiveness and cost-effectiveness of a range of
mental disorder interventions, including medi-
cines, psychological treatments, and social inter-
ventions.13
This evidence also demonstrates the
effectiveness of the delivery of psychosocial in-
terventions by nonspecialist health workers in
routine primary care platforms in low- and mid-
dle-income countries, and the effectiveness of
collaborative care models in these countries.
In this delivery model, front-line care, consisting
of tasks such as screening, case management,
and the provision of psychosocial interventions,
is delivered by nonspecialist health workers in
partnership with primary care physicians, men-
tal health professionals, or both.14,15
There is a
small but consistent evidence base that testifies
to the cost-effectiveness of such task sharing,
despite the fact that such models entail addition-
al expenditures because they require additional
human resources.16
Depression A separate body of evidence, al-
most all of which is from high-income countries,
specifically evaluates the integration of effective
interventions for mental disorders with the care
of people with noncommunicable diseases—in
particular, the management of depression that
is comorbid with diabetes or coronary artery dis-
ease. Among patients with coronary artery dis-
ease, both psychological and pharmacological
(selective serotonin reuptake inhibitor [SSRI]
antidepressants) interventions have a modest
beneficial effect on depression.17
Some trials of pharmacological interventions
in patients with coronary artery disease have
shown a reduction in rates of hospitalization
and emergency department visits,17
while some
trials of psychological interventions have also
shown a reduction of cardiac mortality.18
In gen-
eral, for the effective treatment of depression
that is comorbid with coronary artery disease,
there seems to be no difference across various
types of psychological treatments or across vari-
ous SSRI antidepressants.17
Similarly, there is a promising evidence base
for the benefits of integrated care on both mental
health and physical health outcomes in people
with diabetes and depression. A systematic re-
view of collaborative care for patients with these
conditions provides clear evidence to support its
effectiveness in improving depression outcomes
and improved adherence to treatment for both
depression and diabetes.19
Two recent trials evaluated collaborative care
for multiple noncommunicable diseases (coro-
nary artery disease, diabetes, or both) and de-
pression and provided evidence that such care is
of particular relevance to primary care practice,
where multiple morbidities are common. These
two trials, one from the United Kingdom20
and
one from the United States,21
reported signifi-
cantly superior health outcomes for patients in
a collaborative care intervention group, com-
pared to those in the control group.
In the US trial, carried out in fourteen primary
care clinics in Washington State,22
214 patients
who suffered from depression and from coro-
nary artery disease, diabetes, or both worked
collaboratively with nurses and primary care
physicians to set individualized clinical and
self-care goals. In two or three weekly structured
visits by patients to primary care settings, nurses
monitored the level of depression, control of the
noncommunicable disease or diseases, and ad-
herence to interventions. First-line medications
included diuretics and angiotensin-converting-
enzyme inhibitors for hypertension, statins for
hyperlipidemia, metformin for hyperglycemia,
and citalopram or bupropion for depression.
Comorbidities lead to
poorer quality of care,
higher health care
costs, and poorer
outcomes for both the
mental disorder and
the noncommunicable
disease.
September 2015 34:9 Health Affairs 1501
by CHARLES COLEMAN
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September 2015 34:9 Health Affairs 1505
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Integrating Behavioral Health into Primary Care – Thought Leaders in Population Health
- 1. POPULATION HEALTH thought leaders in identifying implementation tactics The Challenges and Rewards of Integrating Behavioral Health into Primary Care October 13, 2015
- 2. This presentation has been provided for informational purposes only and is not intended and should not be construed to constitute legal advice. Please consult your attorneys in connection with any fact-specific situation under federal, state, and/or local laws that may impose additional obligations on you and your company. Cisco WebEx can be used to record webinars/briefings. By participating in this webinar/briefing, you agree that your communications may be monitored or recorded at any time during the webinar/briefing. Attorney Advertising
- 3. POPULATION HEALTH thought leaders in Identifying implementation tactics We hope you join us for our upcoming webinars. For additional details, to register or to watch the recordings of previous sessions, visit www.ebgadvisors.com! Upcoming Webinars! 3
- 4. POPULATION HEALTH thought leaders in Identifying implementation tactics Mark E. Lutes Member of the Firm Chair of the Board of Directors Epstein Becker Green [email protected] Charles A. Coleman, PhD Senior Sponsor of IBM’s Population Health Insights and Programs Management Healthcare Solutions Board Behavioral and Mental Health Programs [email protected] Steven R. Daviss, MD, DFAPA Chief Medical Information Officer at M3 Information, LLC [email protected] @HITshrink Webinar Presenters 4
- 5. POPULATION HEALTH thought leaders in Identifying implementation tactics This session will discuss the advances in policy and practice regarding the integration of behavioral health with physical health, as well as some of the gaps in identifying, aggregating, and analyzing data critical to a more holistic and comprehensive view of the individual. In addition, the speakers will: Identify the clinical, legal, social, and financial impacts of behavioral health disorders on chronic medical conditions. Describe the challenges involved in improving clinical and financial outcomes in patients with chronic medical conditions who also have behavioral health symptoms and/or conditions. Demonstrate the rewards for implementing new information technology applications and analysis for better clinical and financial outcomes for these specific populations. Presentation Overview 5
- 6. POPULATION HEALTH thought leaders in Identifying implementation tactics Integration: Tearing down silos – Social: Cartesian dualism, stigma, discrimination – Legal: mental health records, consent, HIPAA, 42CFR Part 2, federal/state laws, privacy – Clinical: primary/specialist care, settings, whole person, multidimensional – Technical: EHR, HIE, DS4P, C2S, data rights, breach, analytics, real- time CDS – Financial: MCO/MBHO, payers, claims, dx, shared savings, dx, outcomes* Why integration? 6
- 7. POPULATION HEALTH thought leaders in Identifying implementation tactics The Problems are the Opportunities for Improving Mental Health Care Management Source: NAMI, APA, CMS, Project Red mood disorders are treated by Primary Care doctors 35% Of patients with a chronic illness have a mental illness 26% Americans 18 and older suffer from a diagnosable mental disorder 40million US adults (18-54) have an anxiety disorder in given year 2X Costs Unmanaged patients with mental illnesses Cost Payers more than double to manage chronic conditions $3T Global disease burden for Mental illnesses due to disability #1 cost for businesses (WHO) 28% Of patients re-admitted to hospitals related to mental illness 7
- 8. POPULATION HEALTH thought leaders in Identifying implementation tactics What’s worse is that across these top 9 chronic conditions, depression and anxiety go UNDIAGNOSED 85% of the time. Medical Costs per Disease State Chronic Medical Condition PMPM With Behavioral Condition PMPM Without Behavioral Condition % Treated For Depression or Anxiety Expected Depression or Anxiety Prevalence % Missed Arthritis $871.88 $564.76 7.1% 32.3% 77.9% Asthma $861.99 $470.05 6.8% 60.5% 88.8% Cancer (Malignant) $1,180.96 $1,018.45 5.7% 39.8% 85.7% Chronic Pain $1,210.56 $884.70 5.9% 61.2% 90.4% Coronary Artery $1,305.00 $958.34 5.7% 48.2% 88.1% Diabetes $1,110 $828.18 5.2% 30.8% 83.2% Heart Failure $2,242.85 $1,888.11 7.0% 43.8% 84.1% Hypertension $880.33 $588.04 5.5% 30.5% 82.0% Ischemic Stroke $1,461.57 $1,254.68 7.7% 52.4% 85.2% Source: http://www.ncbi.nlm.nih.gov/ Cost Burdens from unrecognized/undiagnosed/Mental Health Cases. 8
- 9. POPULATION HEALTH thought leaders in Identifying implementation tactics Slide courtesy of Ben Druss MD Life expectancy cut by up to 25 years 9
- 10. POPULATION HEALTH thought leaders in Identifying implementation tactics Physical Diagnosis Mental Disorder Mental Health Affects Clinical Conditions and Outcomes in a BIG WAY 10
- 11. POPULATION HEALTH thought leaders in Identifying implementation tactics Relative risk of medical admission with & without MH and SU comorbidity None +MH +SU +MH+SU Diabetes RelativeRisk 11 -- Maryland Medicaid Adults, 2011
- 12. POPULATION HEALTH thought leaders in Identifying implementation tactics None +MH +SU +MH+SU COPD Asthma Pneumonia NOS Bronchitis RelativeRisk 12 -- Maryland Medicaid Adults, 2011 Relative risk of medical admission with & without MH and SU comorbidity
- 13. POPULATION HEALTH thought leaders in Identifying implementation tactics None +MH +SU +MH+SU Cellulitis Septicemia RelativeRisk 13 Relative risk of medical admission with & without MH and SU comorbidity -- Maryland Medicaid Adults, 2011
- 14. POPULATION HEALTH thought leaders in Identifying implementation tacticsSlide courtesy of Wayne Katon MD Mitchell et al, J Hosp Med 2010 Depression increases risk of 30-day readmission by nearly 40% 14
- 15. POPULATION HEALTH thought leaders in Identifying implementation tactics $0 $5,000 $10,000 $15,000 $20,000 Cancer CHF Diabetes COPD Asthma Source: Cartesian Solutions, consolidated health claims data Increased costs of chronic medical conditions with comorbid mental illness Dollars with mental illness without 15
- 16. POPULATION HEALTH thought leaders in Identifying implementation tactics Behavioral Health Co-Morbidities Have Significant Impact on Healthcare Costs $8,000 $9,488 $8,788 $9,498 $15,691 $24,598 $24,927 $24,443 $36,730 $35,840 Asthma and/or COPD Congestive Heart Failure Coronary Heart Disease Diabetes Hypertension AnnualPerCapitaCosts No Mental Illness and No Drug/Alcohol Abuse With Undiagnosed and/or Untreated MI and Drug/Alcohol Abuse Impact on Chronic Healthcare Costs 16
- 17. POPULATION HEALTH thought leaders in Identifying implementation tactics Melek, Norris, & Paulus. Economic impact of integrated medical-behavioral healthcare: Implications for Psychiatry. Milliman, 2014 17
- 18. POPULATION HEALTH thought leaders in Identifying implementation tactics Breakdown of U.S. Population, Estimated Annual Costs, and Cost Reduction from Managing Behavioral Hea lth Conditions Total U.S. Populati on 321,043,000 1 Total Adult U.S. Populat ion 234,000,000 2 Estimated Monthly Per Patient Cost Reducti on Estimated Total Annual Cost Reduction Population Total Population Percentage Population SubtotalBreakdown by Chronic Disease Diabetes COPD CHF Asthma Cancer Arthritis Hypertension Ischemic Heart Disease 35,314,730 14,742,000 5,100,000 25,683,440 14,483,830 53,118,000 68,094,000 14,040,000 25.6% 31.3% 36.8% 53.7% 34.1% 25.2% 25.0% 44.7% 9,040,571 4,614,246 1,876,800 13,792,007 4,938,986 13,385,736 17,023,500 282 412 355 392 163 307 292 207 30,603,055,674 22,795,667,229 7,989,312,384 64,867,672,000 9,631,615,437 49,332,326,884 59,709,585,780 3 4 5 6 7 8 9 10 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 6,275,880 11 15,581,001,758 Total for Top 8 Dis eases 230,576,000 * 70,947,726 * 260,510,237,146 * Costs to Tr eat As % of C osts Cost to Tre at Potential Savings Milliman Estimate Unutzer Collaborative Model Estim ate 16.00% 10.00% 41,681,637,943 * 26,051,023,715 * $218,828,599,203 $234,459,213,432 12 13 * Actual amounts are less because some patients have more than one ch ronic condition. Population Clock, U.S. Census Bureau, Retrieved March 6, 2015 Chronic Disease Overview, Center for Disease Control and Prevention U.S. Diabetes Rate Levels off in 2011, Gallop.com, December 16, 2011, Retrieved May 8, 2013 6.3% of U.S. Adults Report Having COPD, Center for Disease Control and Prevention (only includes reported cases) Heart Failure Fact Sheet, Centers for Disease Control and Prevention Asthma Statistics, American Academy of Allergy Asthma & Immunology Cancer Prevalence: How Many People Have Cancer?, American Cancer Society CDC: Center for Disease Control and Prevention; (Arithitis: 2010--2012 -- Data and Statistics) Center for Disease Control and Prevention; Hypertension among adults in the United States; National Health and Nutrition Examination Surve y, October 2013 CDC: Center for Disease Control and Prevention; (Morbidity and Mortality Weekly Report: October 14, 2011) Based on OptumHealth Report: Co--Morbid Behavioral Health Conditions in PCP Practice Milliman, 2013. Economic impact of integrated medical--behavioral healthcare Unutzer, 2008. American Journal of Managed Care 1 2 3 4 5 6 7 8 9 10 11 12 13 Confidential Property of M-3 Information, LLC. Do not distribute or reproduce without permission. Undiagnosed Behavioral Health Conditions This is a BIG number in COST SAVINGS. 18
- 19. POPULATION HEALTH thought leaders in Identifying implementation tactics 2013 National Survey of ACOs, n=257 …we found that the decision to pursue integrated models depends powerfully on the design of the ACO payment model, details of contracts, and the quality measures used in contracts. “ ” 19
- 20. POPULATION HEALTH thought leaders in Identifying implementation tactics Effect of Collaborative Care for Depression on Risk of Cardiovascular Events: Data From the IMPACT Randomized Controlled Trial 20 Stewart, Perkins, & Callahan, Psychosomatic Med 2014 76:129
- 21. POPULATION HEALTH thought leaders in Identifying implementation tactics N=551 depressed pts 60+ yrs > randomized Reference 21
- 22. POPULATION HEALTH thought leaders in Identifying implementation tactics Collaborative Care Model Source: Unutzer, UW 22
- 23. POPULATION HEALTH thought leaders in Identifying implementation tactics Gaynes et al, Ann Fam Med 2010 Anxiety disorders are twice as common as depression in primary care. Anxiety Depression Bipolar 23
- 24. POPULATION HEALTH thought leaders in Identifying implementation tactics Risks of not addressing multidimensional behavioral health: misdiagnosis – mistreatment - cost - safety 24
- 25. POPULATION HEALTH thought leaders in Identifying implementation tactics M3 Checklist validated in 2009 UNC study Published in March 2010 Annals of Family Medicine n=647 adults in an academic family medicine clinic 29 items validated against the MINI overall sensitivity & specificity = 0.83 & 0.76 -Depr = 0.84 & 0.80 -Bip = 0.88 & 0.70 -Anx = 0.82 & 0.78 -PTSD = 0.88 & 0.76 3-5 minutes to complete 25
- 26. POPULATION HEALTH thought leaders in Identifying implementation tactics M3 Clinician Report measured care monitor trend fit in workflow patient engagement aids communication team-based care 26
- 27. POPULATION HEALTH thought leaders in Identifying implementation tactics Begins with access to expanded structured and unstructured data and data sets from a variety of sources Requires data aggregation, semantic interoperability / integration and an HIE using a data model that includes mental health Provides meeting Meaningful Use requirements for PCPs Supports implementing Population Health Management and 360-Care Coordination emanating from Primary Care Enables finding, understanding, engaging and improving treatment and therapy for high-risk populations while lowering costs through • sophisticated analytics and patient risk/similarity algorithms • new patient engagement tools and mobile apps • integrated care plans (clinical / behavioral / social / mental health) • evidence-based, proactive interventions using predictive models • enhanced care coordination to improve outcomes and lower costs Leveraging Integrated Clinical & Behavioral / Mental Health Data for Improved Population Health Strategies 27
- 28. POPULATION HEALTH thought leaders in Identifying implementation tactics US Industry Drivers Behind Emerging Healthcare Solutions 2015-2017 HIMSS-7 Triple Aim Quality/Safety Access Cost Meaningful Use 1,2,3 Agency for Healthcare Research & Quality Clinical Guidelines 28
- 29. POPULATION HEALTH thought leaders in Identifying implementation tactics Meaningful Use 2015-2017 2016 Guidelines 1. Pop Health Mgt 2. Care Coordination 3. Patient Engagement 4. Clinical Decision Support 29
- 30. POPULATION HEALTH thought leaders in Identifying implementation tactics The Big Game Changer . . . Great U.S. EHR Expansion Source: CDC NCHS Data Brief #143, Jan 2014 Rapid Growth in the past two years. Current leading estimates report an 86% rate of adoption. Actually, we’ll be 90% the end of this year 30 Percentage of physicians with EHR system, 2013
- 31. POPULATION HEALTH thought leaders in Identifying implementation tactics Downstream Effects of EHR Use EHR Use EHR Database Analytics Platform Meaningful Use Compliance Population Health/Clinical Quality Reporting Business Intelligence/Key Performance Indicators 31
- 32. POPULATION HEALTH thought leaders in Identifying implementation tactics Content Analytics Healthcare Accelerators drive overall time to value through the following: • Annotators focused on extracting medical terms • Approximately 800 pre-built rules developed in IBM Content Analytics Studio • Extracted concepts, including diagnoses, procedures, labs, and population health measures • The transformation of unstructured data to CPT, ICD-9, and SNOMED-CT codes • The detection of negations • The identification, coding and uploading of family histories IBM Watson Healthcare Content Analytics: Configurable Healthcare Accelerators provide comprehensive NLP 32
- 33. POPULATION HEALTH thought leaders in Identifying implementation tactics Structured EHR Data Meaningful Use Compliance Enhanced Clinical Reporting Accurate Business Intelligence IBM Watson Healthcare Content Analytics: Enhancing EHR usability through advanced natural language processing (NLP) Unstructured Free Text Watson Content Analytics Watson Content Analytics Watson Content Analytics Watson Content Analytics Until now structured EHR data has required manual entry. IBM software changes this. It analyzes doctors’ notes, extracting structured clinical findings for upload into patient records, automatically adding industry standard diagnoses, clinical observations, and treatment codes. This will significantly simplify administrative processes and improve patient outcomes. 33
- 34. POPULATION HEALTH thought leaders in Identifying implementation tactics IBM Unified Data Model includes Mental Health Healthcare Data Models are often used as foundational prerequisite frameworks for accessing, integrating, staging and managing comprehensive healthcare-related data across the spectrum of care to include mental health disorders and substance abuse. Content that describes mental health has been added to UDMH to cover: • Clinical Patient History • Mental Health History • Substance Abuse History • Criminal Justice History • Socioeconomic History Updates to the existing model content have been made under: • Care Plan • Care Team • Discharge • Episode of Care • Risk Assessment • Program Activity • Care Interaction • Care Management Crisis Plan • Contingency Plan and Actions • Court Ordered Care 34
- 35. POPULATION HEALTH thought leaders in Identifying implementation tactics Utilizing Data Models and Registries for Promoting Insightful, Integrated Care By integrating MENTAL HEALTH / BEHAVIORIAL HEALTH data with clinical, social, genomic and cost-of-care and outcomes data, a much more comprehensive “picture” of the correlation and causal relationships among these data sets can be used to more precisely and efficiently diagnose and treat individuals and populations with mental health disorders and chronic diseases. 35
- 36. POPULATION HEALTH thought leaders in Identifying implementation tactics CCD – New Behavioral Health The Continuity of Care Document (CCD) specification is an XML-based markup standard intended to specify the encoding, structure, and semantics of a patient summary clinical document for exchange. It provides a snapshot in time containing the pertinent clinical, demographic, and administrative data for a specific patient. This standard helps to promote interoperability between participating systems/organizations such as Personal Health Record Systems (PHRs), Electronic Health Record Systems (EHRs), Practice Management Application, Criminal Justice System, Education System. The CCD Behavioral Health uses all of the subjects plus other subjects which are important in behavioral health such as substance of abuse, criminal justice, homelessness, income etc. A new supportive content package called Continuity of Care Document (CCD) – Behavioral Health has been added to IGC with mappings to IBM UDMH content. 36
- 37. POPULATION HEALTH thought leaders in Identifying implementation tactics Population Health Assessment IBM Explorys Program Framework Population Assessment Know their past and future utilization, their risks, and which are can be mitigated given your network’s capabilities. Population Assessment • Demographics & health status • Geographic coverage • Share of chart Historical Utilization • By service line • By health status Risk and Projected Utilization • PMPM by condition and type Identify the Best Opportunities • To eliminate waste • To reduce variation • To close care gaps • To improve outcomes 37
- 38. POPULATION HEALTH thought leaders in Identifying implementation tactics Representative Output of At-Risk Populations with Co-Morbidities and Identified Mental Health Disorders 680 452 370 225 190 164 85 52 45 38 36 21 12 0 100 200 300 400 500 600 700 800 Selected Conditions 1.Diabetes Mellitus Type 2 & Unspec Type Maintenance 2.Hypertension 3.Cancer 4.Congestive Heart Failure 5.COPD 6.Depression 7.Anxiety 8.Bi Polar Disorder 9.PTSD 1 2 3 4 5 6 7 8 9 N = 1917 Patients with Chronic Conditions N = 346 Patients Diagnosed with Mental Health Disorder 18% of this Patient Population has co- morbidities that include one or more diagnosed mental illnesses. How many remain undiagnosed? How will we know? 38
- 39. POPULATION HEALTH thought leaders in Identifying implementation tactics Based on this patient’s personalized profile … Find the most similar patients (or dynamic cohort) from entire population Analyze what happened with the cohort and reasons why (30,000+ dimensions) Predict the probability of the desired outcome for this patient Create personalized care plan based on unique needs of this patient Summary View of How Similarity and At-Risk Analytics Work Desired Outcomes Historical Observation Window Prediction Window This Patient’s Longitudinal Data Predicted Outcome For This Patient Dynamic Cohort Longitudinal Data with Outcomes 39
- 40. POPULATION HEALTH thought leaders in Identifying implementation tactics Patient Risk Stratification: As a part of delivering customized care, each patient is assigned to a risk group within the Care Coordination Solution X1 Q X2 Q … Xn Q X1 1 X2 1 … Xn 1 X1 2 X2 2 … Xn 2 … X1 K X2K … XnKX Patient Data Risk Predictors Risk Cohorts Predicted Risk Historical Patient Population The Care Coordination Solution has data from medical records and patient profiles of thousands of previous patients with similar diagnoses. Regression, data mining and text mining tools are used to identify risk factors from the accumulated data that are predictive of patient outcome, and construct risk prediction models to predict overall risk level of a patient. Clustering techniques are used to stratify patients into different risk groups based on risk factors and level. When a patient is discharged, medical records and patient profile data is collected, and analyzed against the risk prediction and stratification models to assign patient to appropriate risk group. Actions Based on historical data of likeness among related cohorts of patients, risk grouping algorithms (“groupers”) are applied to the individual patients across a panel of similar patients extracted from the historical LPR data and applied to the selection of the most successful (historical) care plan. Patient Similarity and Risk Indices Based on patient data derived from historical sources (EMR, EMR-captured data, age/gender/lifestyle demo-graphics, diagnoses, procedures, labs, vitals, medications, disease registries, unstructured notes and observations), patient similarity cohorts are constructed to match with the current patient’s past history and current diseases. X1 1 X2 1 … Xn 1 X1 2 X2 2 … Xn 2 … X1 K X2K… XnK X 1 2 43 1 2 3 4 40
- 41. POPULATION HEALTH thought leaders in Identifying implementation tactics Care Plan Creation & Selection: Similar characteristics of a risk group drive the selection of a specific care plan for the patient Patient’s MH & Clinical Data Cohort of Similar Patients Care Plan A Similarity Metric Care Plan B Care Plan C X X Outcomes Index Patient The Care Coordination Solution collects EMR and other relevant patient data from provider notes/ inputs/ outcomes. This data is combined into a single Similarity Metric that is used to compare the discharged patient against the overall population of past patients in the system. A Cohort of similar historical patients based on the Similarity Metric is identified. The outcome of Care Plans that have been assigned to those historical patients is evaluated. Finally a care plan is recommended that has proven to be the most likely to lead to a positive outcome for patients in that risk group. Action Based on changes in patient’s vitals and responses to therapies, patient’s risk profile is dynamically updated and the deviation from care plan calibrations are reflected in alerts to the care coordination team for interventions. Recommendations may also be generated as part of a DSS created by a library of evidence-based outcome indicators. 1 2 54 3 1 2 3 4 5 Care Plan C1 Care Plan A1 Care Plan B1 Patient Data such as longitudinal patient medical records for similar patients and the index patient and medical records with sufficient history to be able to assess patient trajectories. 41
- 42. POPULATION HEALTH thought leaders in Identifying implementation tactics Population Management Provide targeted information and directives for care coordinators, providers, and patients to drive performance. Registries & Work Lists to… • Mitigate time-sensitive risks of unnecessary utilization and poor outcomes • Proactively manage diseases • Meet performance goals and objectives of programs Workflow • Integrated into the daily process of care coordinators and providers • Automated assignment, alerts, notes, and reminders Engagement • Communicate via integrated 3rd party portal, telephone, and letters. Population Health Management Population Assessment Know their past and future utilization, their risks, and which are can be mitigated given your network’s capabilities. Population Assessment • Demographics & health status • Geographic coverage • Share of chart Historical Utilization • By service line • By health status Risk and Projected Utilization • PMPM by condition and type Identify the Best Opportunities • To eliminate waste • To reduce variation • To close care gaps • To improve outcomes IBM Explorys Program Framework 42
- 43. POPULATION HEALTH thought leaders in Identifying implementation tactics Phytel PHM Platform Integration Engine | Protocol Engine | Communication Engine | Predictive Modeling Patient Registry External Systems EMR | PM | HIS | eRx | Lab | Claims OutreachTM TransitionTMRemindTM Fully Automated Population Engagement InsightTM CoordinateTM Team Directed Solutions EngageTM Individual Tools 6 PCMH Role-Based Population Health Management Patient Engagement Platform Patient Service Representative Vice President Quality Physician Care Manager Case Manager 43
- 44. POPULATION HEALTH thought leaders in Identifying implementation tactics Fully Automated Population Engagement Outreach, Remind, Transition Phytel will identify and communicate with... - all the patients who need to book an appointment - all the patients with appointments next week - all the patients discharged yesterday Team Directed Tools Coordinate, Insight I want to find... - all the patients that meet a quality objective - all the patients that need to come to a group visit - all the patients coming in tomorrow Individual Engagement Engage I am working with Jane Doe to help her achieve her health goals and lower her health risks (costs) 44
- 45. POPULATION HEALTH thought leaders in Identifying implementation tactics Poor Care Coordination Costs 12 Billion! Poor coordination of care cost an estimated $25 billion to $45 billion dollars per year (Donald M. Berwick, 2012). At least $12 billion of that total is considered avoidable (Health Affairs, 2012) Moreover, poor care coordination often result in reduced client outcomes. The most common adverse effects associated with poor transitions are injuries due to medication errors, complications from procedures, infections and falls. These poor transitions often occur due to lack of information sharing. (Health Affairs, 2012): Key reasons for behavioral health readmissions following: Medication non-adherence Lack of engagement in outpatient services Substance abuse 45
- 46. POPULATION HEALTH thought leaders in Identifying implementation tactics Population Health Program Measurement Performance Measurement Up-to-date network-wide reporting and measures relative to performance targets, program return-on-investment, and pinpointed opportunities for continued improvement. Role Specific Dashboards for… • Leadership • Care coordinators • Providers Program Specific Measure Libraries • ACO (commercial and Medicare) • Medicare Advantage • Direct-to-Employer • Provider-based HEDIS • Inpatient quality and efficiency • Optimized utilization Pre-built Reports & Data Marts • Payer/plan submission • Provider scorecards and performance plans • HCC and proper coding opportunities • Contract performance IBM Explorys Program Framework 46
- 47. POPULATION HEALTH thought leaders in Identifying implementation tactics Citizens, Family Members and Concerned Care Givers—have a path to their PCP who can screen and treat family members exhibiting early signs of mental illnesses Taxpayer’s—money more efficiently used to treat the whole person—physical AND mental health—thereby saving BILLIONS in undiagnosed mental conditions, mismatched therapies, and ineffective and conflicting treatments Criminal Justice—by diagnosing those non-violent prisoners with harmless mental disorders and putting them into community-based treatment programs at far less cost to tax payers and far better outcomes for individuals. CMS & All Payers—Reduced payouts for prolonged, ineffective clinical care– by integrating mental health into the clinical diagnoses and treatment for those with chronic conditions—is money well saved Providers—are enabled and empowered to treat the entire patient Patients—Better care is delivered when care is inclusive of behavioral and mental health considerations Employers & GDP—even the economy gets happier and healthier with fewer employee missed days, sick leave, hospitalizations, incarcerations, suicides, shootings and legal entanglements Our Rewards for Implementing Integrated Care . . . 47
- 48. POPULATION HEALTH thought leaders in Identifying implementation tactics Standardized Mental Health Screening & Assessments-- M3, PHQ-9— should be intro- duced and ad- ministered as part of SOP of Dx and treatment. Analytics are used to segment and stratify. Sophisticated analytics and data from NLP aid Care Coaches focused on Risk- Stratified Cohorts and Equipped with Advanced Care Coordination Technologies and integrated Care Plans. Multiple, Networked points of access to coordinated care are connected with all care- givers at multiple sites working from the same master patient record. Inclusive of care givers knowledge- able of mental health and behavioral health strategies who can perform tests and educate teams. to include preventive interventions. System-wide data and process management required for fully- integrated care provisioning , delivery . assessment, care coordination, outcomes analysis and quality and cost reporting. Solving the Problem Requires Integrated Care—Clinical, Physical, Social, and Mental Template Adapted from Kaiser.org & Stratification 48
- 49. POPULATION HEALTH thought leaders in Identifying implementation tactics Integration of clinical (physical) data and mental health (behavioral) data is an imperative for identifying those most-at-risk members and patients with multiple chronic diseases and mental health disorders. Implementing new provider-based data models that are inclusive of mental health and behavioral health “markers” is the most expedient way to aggregate and organize both structured and unstructured data for creating personalized care plans and managing complex cases. Incorporating simple mental health tests and screening tools into regularly scheduled “check ups” provides practitioners and care teams with actionable insights into the causal relationships between physical health and mental health. Mental health screening tests such as M3 can be self- administered and serve as a means to keep the patient engaged in his/her wellbeing. Primary Care Physicians working in a collaborative PCMH environment that includes mental health can serve many more patients more effectively at less cost using new care management technologies and advanced decision support tools such as Watson Advisor. Integrating Mental/ Behavioral Health into Primary Care: Data and Technology Summary 49
- 50. POPULATION HEALTH thought leaders in Identifying implementation tactics Technology • Implementation • Data model • Workflow • Public Health – managing populations • Real-time clinical decision support • Predictive analytics • Risk stratification Triple Aim • improved quality, better health, longer lives • lower costs, better resource use • patient engagement, whole person Rewards from new information technology 50
- 51. POPULATION HEALTH thought leaders in Identifying implementation tactics Social: Cartesian dualism, stigma, discrimination Legal: mental health records, consent, HIPAA, 42CFR Part 2, federal/state laws, privacy Clinical: primary/specialist care, settings, whole person, multidimensional BH Technical: EHR, HIE, DS4P, C2S, data rights, breach, analytics, real-time clinical decision support Financial: MCO/MBHO, payers, claims, dx, shared savings, dx, outcomes* Long road to bridge the mind-body gap 51
- 52. POPULATION HEALTH thought leaders in Identifying implementation tactics Mark E. Lutes Member of the Firm Chair of the Board of Directors Epstein Becker Green [email protected] Charles A. Coleman, PhD Senior Sponsor of IBM’s Population Health Insights and Programs Management Healthcare Solutions Board Behavioral and Mental Health Programs [email protected] Steven R. Daviss, MD, DFAPA Chief Medical Information Officer at M3 Information, LLC [email protected] @HITshrink Questions? 52
- 53. POPULATION HEALTH thought leaders in Identifying implementation tactics We hope you join us for our upcoming webinars. For additional details, to register, or to watch the recordings of previous sessions, visit www.ebgadvisors.com! Upcoming Webinars! 53
- 54. POPULATION HEALTH thought leaders in Identifying implementation tactics Additional Resources 54
- 55. IBM Software White paper IBM Unified Data Model for Healthcare Using Data to Deliver Mental Healthcare in the Community IBM Analytics
- 56. Mental Health Mental health problems are prevalent in modern society and appear to be on the increase. This can be partly due to greater awareness of their existence and a growing ability and willingness to discuss what was often stigmatized in the past. According to the National Institute of Mental Health (NIMH) 18.5% of the adult civilian population of the US has some mental health problem with 4.2% of the population having a serious mental illness1 . These 2013 figures, while stark, probably mask the true levels as they exclude a number of high risk cohorts such as military personnel, the homeless, and people already institutionalized or incarcerated. Similar statistical analysis from the UK for 2007 shows a similar picture with 17.6% of 16-64 year olds meeting at least one of the common mental health criteria2 . When adding the incidence of mental health among children and adolescents the picture does not improve. What does it cost? The NIMH estimated conservatively that the total cost of serious mental illness in 2002 was in excess of $300bn when both direct treatment costs and indirect cost such as disability benefit payments and loss of earnings are taken into account. Other anal- ysis shows that mental illness is the third most costly disease category in the US after heart conditions and trauma-related disor- ders, it is tied in third place with cancer. These figures suggest a need for better mental health management services for people in need. It would be impossible to manage such a large population or patients, 43.8 million adults in the US alone, with inpatient led care. Some patients do require hospital admission from time to time while others require long-term inpatient care or permanent institutionalization. But many others would benefit more from day-to-day care management strategies to help keep them well, in their communities. Community-base care is not only more cost effective, it is also recognized to be better for the patient in the vast majority of cir- cumstances. But to maximize the benefits of community-based care and minimize the incidence of crises that require emergency admissions, it is important that the patient has a well-defined care plan that is delivered by a well informed and coordinated care team. 2 IBM UDMH & Mental Health
- 57. The Care Team A community-based care plan for a patient requires a multidisciplinary care team that will usually include the patient’s regular family doctor, pharmacist, counselors, psychologists, psychiatrists, a hospital liaison, hospital consultants and usually at least one family member or close friend. This will provide the patient with a support structure ranging from a friend to talk to about any concerns through various levels of community-based primary care to help them identify any issues before they manifest themselves in some type of crisis. This approach should minimize the incidence of emergency admissions to inpatient care. When considering the nature of mental health and associated problems, it is not something episodic like the occasional bout of influenza that anybody might be subject to. It is a condition that a person who experiences mental health difficulties lives with every day. A problem might not be visible or obvious and it might not be causing any concern at a particular point in time but it always has the capacity to do so. To enable the patient to maximize their quality of life and minimize the impact of any mental health condition, they need to have the information to help them identify potential issues before they become a problem. Their care team also need comprehensive information on the patient’s health status and any environmental or other influencing factors that might indicate a potential risk. In many ways, the patient might themselves be considered the most important member of their own care team as an ability to trust team members and a willingness to openly discuss any concerns can avoid an exacerbation of a problem. Information is the key to this but it isn’t just the patient’s clinical records that are relevant. Many factors influence the potential for a person to experience a mental health problem or develop a serious mental illness. These same factors represent risks for a diagnosed mental health patient and need to be monitored to reduce the chances of a problem developing or worsening. Early intervention is better for the patient and faster and less expensive to deliver. Empowering the Patient and the Care Team 3 IBM UDMH & Mental Health
- 58. IBM Unified Data Model for Healthcare (UDMH) provides a blueprint for com- prehensive data warehouse business intelli- gence applications, such as mental health. It is a robust set of business and technical data models that can be extended and scaled to fit a healthcare organization’s unique envi- ronment, and offers significant competitive advantage. It offers the ability to create an analytical data store that connects to all of a health- care organizations critical data, across dis- parate systems and formats, across diverse departments and other data healthcare or- ganizations. It helps build a dynamic analytics environ- ment where data collected internally and externally is used to determine how to ar- range, align, deploy and improve care to patients. It forms the foundation of a true infor- mation management infrastructure where trusted, relevant information is available to the people who need it, when they need it, so that they can make better and timelier decisions. IBM Unified Data Model for Healthcare 4 IBM UDMH & Mental Health
- 59. Is there such a thing as a typical mental health patient? In the past, mental health patients might have been thought of only as people who were permanently institutional- ized. But today the reality is different. Today, a person with a mental health con- dition might be working in any type of job or profession and living a normal life for the vast majority of the time. There might be simple things, however, that plunge them into, for example, a serious state of depres- sion that can incapacitate them and make it difficult or impossible to function normally. A few of the obvious triggers are: Bereavement The loss of a loved one such as a spouse, a parent, a child, a sibling, or a lifelong friend is difficult for anyone to cope with but can act as a trigger to an exacerbation of an ex- isting mental health problem. Medication Many mental health patients are prescribed long-term medication to maintain their lifestyle free from the symptoms of their condition. One of the regular problems here is that when a patient is symptom free for an extended period, they might decide to stop taking their medication and trigger a relapse. Change in Circumstances A major change in circumstances such as loss of employment or a significant financial loss might trigger a relapse. Relationships A marital breakup or the breakup of a long- term relationship is often the cause of a mental health crisis. Of course the patient’s condition might have been contributory to the breakup. It is sometimes difficult to establish which factor was the cause and which the result. A Typical Mental Health Patient 5 IBM UDMH & Mental Health
- 60. Imagine for a moment that the patient’s primary care provider or family doctor, who is a member of the care team, has all of the patient’s medical records and is aware of the patient’s medication schedule. Know- ing that the patient did not pick up their monthly repeat prescription from the phar- macist would alert the doctor to a possible break in compliance with the patient’s med- ication plan and allow an intervention be- fore this becomes a problem. This of course requires coordination between the doctor and the patient’s pharmacist, two members of the care team. Being community-based the same family doctor would hear of any bereavement and a simple phone call to the patient might allow them to assess whether there is a po- tential problem and if intervention by a counselor would be justified. A friend, partner, or another care team member would be aware of changes in the patient’s circumstances and again could make the contact, be the shoulder to cry on, or suggest the patient consults a profession- al. The same could apply to a worsening of a relationship. Not Just a Mental Health Problem It is easy to fall into the trap of identifying or classifying people who have a mental health condition by their primary diagnosis. “Joe is a bipolar” or “Mary is schizophren- ic”. This is not something done so often with physical health conditions. This is be- cause we all experience general health prob- lems and expect to collect a few of them as we go through life. But the same applies to mental health. While mental illnesses are often chronic, long lasting conditions, a person does not acquire one of these and live with it in isola- tion. There is a well established high preva- lence of comorbitities among mental health patients where, for example, somebody with clinical depression might also be subject to anxiety or panic attacks. And this is not lim- ited to mental health conditions. Predicting and Preventing 6 IBM UDMH & Mental Health
- 61. Personal Health Information Clinical Records Mental health history Family history Medication history Substance abuse history Criminal justice history Socioeconomic history Care Team Practitioner Care Plan Roles Care Plan Assessments Contingency Plans Crisis Plans Treatment Programs Program Activities Care Interactions Patient There is also a high prevalence of men- tal health patients with comorbid physical health conditions. There is an added risk that one condition negatively affects the pa- tient’s ability to properly manage the other. For example, in their HEDIS Effectiveness of Care Behavioral Health measures, the National Committee for Quality Assurance include3 : • Cardiovascular Monitoring for Peo- ple with Cardiovascular Disease and Schizophrenia (SMC) • Diabetes Monitoring for People with Diabetes and Schizophrenia (SMD) • Diabetes Screening for People with Schizophrenia or Bipolar Disorder who are using Antipsychotic Medica- tions (SSD) This recognizes the added challenges of effective management of chronic conditions in a cohort of patients with mental health conditions. UDMH allows for the capture of all patient data including chronic conditions, comorbidities, and assessments of function- al status. 7 IBM UDMH & Mental Health Figure 1. Examples of mental health data structures supported by IBM UDMH
- 62. A UK-based statistical analysis identified a number of interesting correlations between the prevalence of mental health problems and various environmental and social fac- tors. Homelessness 70% of those accessing homelessness ser- vices had mental health problems and 64 % had an Alcohol or Other Drug (AOD) de- pendency. Unemployment Only 8.9% of adults in contact with sec- ondary mental health services were in paid employment. Low Income There is a strong correlation between low household income and mental health prob- lems. Incarceration A survey of newly sentenced prisoners iden- tified 16% with signs of psychosis and 50% with symptoms of anxiety or depression. Smoking Interestingly it was estimated that 42% of cigarettes smoked in England were smoked by people with a mental health problem (17.6% of the population). Which is cause and which is effect? These statistics, among others, clearly il- lustrate that mental health is not just about a clinical diagnosis with a single isolated treatment pathway. Delivering effective care is about understanding the patient as a person, knowing their physical and mental health histories, having up-to-date knowl- edge of their living circumstances, employ- ment, and income. Patient Information The provision of effective mental health- care for most patients is best done in a com- munity setting, with backup available from in hospital services only when required. A multi-disciplinary team is required to ad- dress the varied needs of the patient ranging from a calm conversation through profes- sional counseling to occasional admissions. To support this effort the team, with the permission and cooperation of the patient, need comprehensive and up-to-date in- formation and they need to be able to ef- fectively access this information when the need arises. What information does the care team need? Mental Health History Current and previous diagnoses, treatments and outcomes, known triggers for and ex- tent of exacerbations. Physical Health History Current and previous diagnoses of episod- ic and chronic conditions, treatments, and outcomes. Environmental and Social Risk Factors 8 IBM UDMH & Mental Health
- 63. Medication History The patient’s current medication sched- ule as well as a history of medication and changes to their prescriptions including reasons for change. It is important to avoid represcribing a drug that was previously shown to affect the patient’s mental health. Details of the patient’s compliance with medication is also key to understanding how well the patient can manage their own health with or without intervention. Employment Details In all healthcare scenarios, knowledge of a person’s employment is relevant as it might influence their wellbeing, for example are they working with hazardous substances that might harm their respiratory system over time? Are they in a sedentary office job that means they get little physical exercise during the day? For mental health, it is even more impor- tant as the patient’s employment can expose them to harmful stress, it can leave them feeling demotivated, and the loss of em- ployment immediately exposes them to one of the high risk factors for a mental health episode. Housing Details Where is the patient living? Do they own their dwelling or is it rented? Are they se- cure in their accommodation for the fore- seeable future? Homelessness or the threat of homelessness might trigger a mental health problem. Social History Does the patient have a healthy social life, circle of friends? Do they participate in sports, hobbies pastimes? Do they have a balanced diet and do they get sufficient physical exercise? Does the patient smoke and if so, are they interested in quitting? Are they smoking more or less than they used to? Do they drink alcohol and, if so, how much? Are they drinking more or less than they used to? Substance Abuse The patient currently taking any illicit sub- stances, by what method and how much, how often? Have they taken such substanc- es in the past? Are they taking prescription medications other than those prescribed for them by their care team? Are they taking over the counter OTC medications, which ones, how often and how much? Law Enforcement Details Does the patient have any current interac- tions with law enforcement including the police, the courts, community service or- ders? Have they had any such interactions in the past including any custodial terms? Some of the information above can only be provided by the patient themselves or members of their families in a counseling environment, so a great deal of trust is re- quired between the patient and the care team to build a comprehensive picture. UDMH allows for the capture of patient demographics data including social, hous- ing, employment and details of any issues relating to substance abuse or law enforce- ment interactions. 9 IBM UDMH & Mental Health
- 64. Data Protection and Privacy Clearly every member of the team does not need access to every piece of informa- tion and a secure data protection regime is necessary to protect the patient’s right to privacy while ensuring effective care. How- ever it is by having a comprehensive view of all relevant data that the team can help the individual to manage their condition, effect early interventions where necessary, and avoid crises that might otherwise result in costly and invasive admissions. Unstructured Data Much of this information will be in the form of written notes and documents and might not be structured in the traditional sense but by using modern natural language processing techniques it is possible to iden- tify relevant content in such documents and to store them in such a way as to be easily retrieved when a team member needs to review the original note. Depending on the infrastructure within a healthcare facility, within a family doctor’s practice, or in a counselor’s office, practi- tioners might have to capture their patient’s information in an electronic health record (EHR), as electronic forms or unstructured electronic notes or as a paper record. EHRs are known as structured data sources and paper health records are known as unstruc- tured data sources. The electronic notes fall somewhere in between but are mainly con- sidered unstructured. For example, a practitioner might be able to enter information about the patient’s diag- nosis and treatment only in the electronic health record and the remaining infor- mation might be captured by using typed notes, which are added to the patient’s pa- per chart. Paper health records might include clini- cal notes which contain concerns that are raised by the patient such as “difficulty sleeping” or “loss of appetite”, which the doctor will use in reaching a diagnosis. They might also record other negative and positive type clinical observations such as, “problems at work” or “no problems at work” which can help to form the diagno- sis. The same might apply to discussions about family members’ health or changes in diet or exercise. Unstructured data sources have known associated accessibility issues, the simple logistics of being able to physically access a paper medical record or doctor’s note is just one example. 10 IBM UDMH & Mental Health
- 65. Accessing the all important data from an unstructured source poses many challenges and this is where Natural Language Pro- cessing (NLP) tools can play a huge role in resolving some of these problems. If the information that is embedded in these unstructured sources can be unlocked, it can greatly increase the care team’s knowl- edge of the patient. To this end, many healthcare organizations are now able to use NLP tools to intelligently browse free format text and even handwritten notes to extract key words and phrases in context that can indicate a problem or concern or a change in habit. Similar techniques can be applied to audio recordings of telephone consultations. Extracting meaningful insights from un- structured data sources in this manner al- lows a patient’s EHR to be updated with the identified data and the source of the new information shown so that practitioners are aware of the distinction between EHR up- dates consciously entered by a health pro- fessional and those that have been inferred from an unstructured source. But it is not just a case of extracting a few nuggets of wisdom from doctor’s notes to increment the EHR content. The ability to store the original material, electronic docu- ments, scanned images of paper documents, clinical images such as x-rays, and voice recordings adds even more patient knowl- edge. Now a practitioner reviewing a patient’s details might notice an earlier reference to a patient concern that was picked up by nat- ural language processing of clinical notes. They can then access the full document from which the information was extracted to better understand the circumstances of the particular patient encounter on which the notes were based. This type of information can help care team members, not only treat the patient’s prima- ry condition or current concern better, but identify early indicators of another develop- ing conditions or risk. UDMH supports the capture of the data interpretations that are extracted from these sources as well as an assessment of the level of confidence in the interpretation. It also includes links to the source document im- age so that the original can be reviewed. 11 IBM UDMH & Mental Health
- 66. UDMH Components Business Data Model The Business Data Model is a logical entity relationship model that represents the essential entities and relationships of the healthcare industry. It includes common design constructs that can be transformed into separate models for dedicated purposes such as an operational data store, data warehouses, data marts and data lakes. Atomic Warehouse Model The Atomic Warehouse Model is a logical, specialized model derived from the Business Data Model. It is optimized as a data repository which can hold long-term history, usually across the entire enterprise. Dimensional Warehouse Model The Dimensional Warehouse Model is a logical model derived from the Business Data Model and is an optimized data repository for supporting analytical queries. Business Terms Business terms define industry concepts in plain business language, with no modeling or abstraction involved. Business terms have a set of properties and are organized by business category. Clearly defined business terms help standardization and communication within an organization. Supportive Content Supportive Content represents data elements in the language of the source requirement. For example, requirements such as Health Level 7 (HL7), which is the standard series of predefined data formats for packaging and exchanging health care data in the form of messages transmitted between disparate IT systems, or HIPAA (Health Insurance Portability and Accountability Act.) The benefit of Supportive Content is in logically organizing the data requirements into cohesive groupings, and in translating requirement data needs into their support in the data warehouse model. Analytical Requirements Analytical Requirements enable rapid scoping and prototyping of data marts, which provide a subject-specific analytical layer in a data warehouse solution. With data warehouse modeling software, business users and analysts can use Business Solution Templates to quickly gather the reporting and analysis requirements of their organization. Business Terms A nalytical Requirem ents D im ensional W arehouse M odel A tom ic W arehouse M odel Supp ortive C ontent Project Scopes Business Data Model 12 IBM UDMH & Mental Health Figure 2. IBM UDMH components
- 67. Data Reservoir Sources IBM InfoSphere Information Server Data Warehouse Data Marts Raw Data A nalytical Requirem ents D im ensional W arehouse M odel A tom ic W arehouse M odel Supp ortive C ontent Project Scopes IBM DB2 BLU IBM PureData IBM InfoSphere Data Architect IBM InfoSphere Streams IBM BigInsights IBM DB2 IBM PureData IBM InfoSphere Data Stage IBM BigInsights Business Terms Business Data Model IBM IIGC Information Consumption Landing Zone Integrated Warehouse & Marts IBM Cognos IBM BigSheets IBM Data Explorer IBM SPSS IBM Phytel IBM Explorys IBM Watson Implementation A data warehouse is a central repository of summarized data from disparate internal operational systems and external sources. Operational and external source data is extracted, integrated, summarized and stored in a data warehouse that can be accessed by users in a consistent and subject-oriented format. Data organized around business entities is more useful for analysis than data committed to applications that support vertical functions of the business. The data warehouse is a single source of consolidated data that provides an enterprise-wide view of the business that becomes the main source of information for reporting and analyzing data marts that are usually departmental, line-of-business- oriented or business-function-oriented. The data warehouse overcomes limitations of older style decision-support systems. UDMH can be deployed on many software platforms but has been designed for use with IBM Software products. The data warehouse holds data about the business that can be used as the basis for supporting a detailed analysis of the areas of most concern to organizations today. This allows organizations to exploit the potential of information previously locked in legacy systems inaccessible to the business user. 13 IBM UDMH & Mental Health Figure 3. Typical IBM UDMH implementation architecture
- 68. Conclusion With mental healthcare requirements in- creasing through a growing number of patients being detected but with funding not keeping pace, we must use all of the resources available to deliver efficient and effective care. Capturing and retaining all data that is related to a patient including unstructured data and its interpretation enables healthcare professionals to have insight into the person as a whole and in the context of their living arrangements. This leads to more informed and more accurate diagnoses and better health man- agement at a lower total cost. Managing the patient and their conditions in the commu- nity is better for the patient, their family, and society. Mental health admissions to inpatient care are costly and while in some cases they are inevitable and necessary, it is better to keep the patient in a familiar community setting. The benefits are wide- spread: The Healthcare Provider sees better uti- lization of resources and the ability to treat more patients. The care team have more information to effectively monitor their pa- tients and they can plan a greater portion of their treatment as proactive care rather than reacting to exacerbations and crises. The Payer, whether public or private sees reduced total cost of care per person allow- ing more of the population to avail of care without increasing public funding or private premiums. The Patient sees an improvement in their health, better quality of life and they expe- rience support in a familiar environment that helps them deal with their mental and physical health issues. They spend less time overall in healthcare settings because they invest in maintaining their mental health rather than waiting for a problem to be- come a crisis and reacting to it. The patient’s Family and friends will see a more relaxed individual who can share their concerns more openly and reduce the ten- sions that sometimes prevail around a per- son with mental health issues. UDMH is designed exclusively for the healthcare industry and has support for many areas including mental health. It pro- vides a glossary of requirements, terms and concepts that can be clearly understood and communicated by both business and IT professionals, thereby helping to accelerate project scoping, appropriate reporting, data quality and data requirements, and identify- ing sources of data. Ultimately, it acts as a blueprint by defining the structures necessary to build effective data warehouse structures for mental health analytics and provides healthcare managers with critical prebuilt reporting templates that offer a wide and deep view of their business through key performance indica- tors (KPIs) and other measures. 14 IBM UDMH & Mental Health
- 69. References 1 National Institute of Mental Health – US Department of Health and Human Services. http://www.nimh.nih.gov/health/statistics/ index.shtml 2 Mental Health Network – NHS Confederation – Factsheet, Janu- ary 2014. http://www.nhsconfed.org/~/media/Confederation/Files/ Publications/Documents/facts-trends-mental-health-2014.pdf 3 National Committee for Quality Assurance (NCQA) - The Healthcare Effectiveness Data and Information Set (HEDIS) http://www.ncqa.org/ © Copyright IBM Corporation 2015 IBM Corporation Software Group Route 100 Somers, NY 10589 Produced in the United States of America September 2015 IBM, InfoSphere, Streams, DataStage, BigInsights, DB2, PureData, Cognos, SPSS and the IBM logo are trademarks of International Business Machines Corporation in the United States, other coun- tries, or both. Other company, product or service names may be trademarks or service marks of others. A current list of IBM trademarks is availa- ble on the web at “Copyright and trademark information” at. ibm. com/legal/copytrade/shtml. This document is current as of the initial date of publication and may be changed by IBM at any time. Not all offerings are available in every country in which IBM operates. THE INFORMATION IN THIS DOCUMENT IS PROVID- ED “AS-IS” WITHOUT ANY WARRANTY, EXPRESS OR IMPLIED, INCLUDING WITHOUT ANY WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND ANY WARRANTY OR CONDITION OF NON-INFRINGEMENT. IBM products are warranted according to the terms and conditions of the agreements under which they are provided. The client is responsible for ensuring compliance with laws and regulations applicable to it. IBM does not provide legal advice or represent or warrant its services or products will ensure that the client is in compliance with any law or regulation. Please Recycle IMW14851-USEN-00
- 70. By Christina Andrews, Colleen M. Grogan, Marianne Brennan, and Harold A. Pollack Lessons From Medicaid’s Divergent Paths On Mental Health And Addiction Services ABSTRACT Over the past fifty years Medicaid has taken divergent paths in financing mental health and addiction treatment. In mental health, Medicaid became the dominant source of funding and had a profound impact on the organization and delivery of services. But it played a much more modest role in addiction treatment. This is poised to change, as the Affordable Care Act is expected to dramatically expand Medicaid’s role in financing addiction services. In this article we consider the different paths these two treatment systems have taken since 1965 and identify strategic lessons that the addiction treatment system might take from mental health’s experience under Medicaid. These lessons include leveraging optional coverage categories to tailor Medicaid to the unique needs of the addiction treatment system, providing incentives to addiction treatment programs to create and deliver high-quality alternatives to inpatient treatment, and using targeted Medicaid licensure standards to increase the quality of addiction services. M edicaid has played an impor- tant role during the past fifty years for low-income Ameri- cans needing mental health or addiction treatment. The mental health care and addiction treatment systems rely on Medicaid as a crucial financier of care. Nevertheless, Medicaid’s coverage for treatment of mental health and addictive dis- orders has diverged in important ways.Whereas Medicaid began as a small player in financing services for both types of disorders, it eventually grew to become the dominant source of funding for mental health treatment but has yet to reach that dominance in addiction treatment.1 Much has been written about Medicaid’s grow- ing role in financing mental health treatment. Less considered is the broader effect of these financing changes on the organization and qual- ity of the mental health treatment system for low-income people in the United States.2 Stake- holders in mental health treatment have been deliberate not only in using Medicaid to leverage new funds for treatment but also in using the program to increase the comprehensiveness of mental health services, elevate standards for mental health providers, and create meaningful alternatives to inpatient treatment. The Affordable Care Act (ACA) gives states the option to expand Medicaid eligibility to people younger than age sixty-five whose family in- comes are at or below 138 percent of the federal poverty level. In doing so, it creates an opportu- nity for the addiction treatment system to im- prove service access and quality. As a result of the ACA’s Medicaid eligibility expansion, along with the law’s mandate to provide addiction treatment coverage for newly eligible enrollees, Medicaid is expected to soon become the largest payer of addiction treatment.1 As such, Medicaid will obtain principal market power over major segments of the addiction treatment system in the United States and, therefore, have the ability to influence addiction treatment practices on a doi: 10.1377/hlthaff.2015.0151 HEALTH AFFAIRS 34, NO. 7 (2015): 1131–1138 ©2015 Project HOPE— The People-to-People Health Foundation, Inc. Christina Andrews is an assistant professor in the College of Social Work, University of South Carolina, in Columbia. Colleen M. Grogan (cgrogan@ uchicago.edu) is a professor in the School of Social Service Administration, University of Chicago, in Illinois. Marianne Brennan is a doctoral student in the School of Social Service Administration, University of Chicago. Harold A. Pollack is a professor in the School of Social Service Administration, University of Chicago. July 2015 34:7 Health Affairs 1131 Vulnerable Populations
- 71. broader scale than ever before. In this article we provide a brief history of financing for mental health and addiction treat- ment, and we highlight Medicaid’s contrasting role in paying for these services. We then show how Medicaid influenced reforms in the mental health delivery system, and we draw strategic lessons for addiction treatment in three main areas: leveraging optional coverage categories to tailor Medicaid to the unique needs of the addiction treatment system, providing incen- tives for addiction treatment programs to create and deliver high-quality alternatives to inpatient treatment, and using targeted Medicaid licen- sure standards to increase the quality of addic- tion services. These three strategies are consis- tent with the broader goal of integration of behavioral health services with mainstream medical care in the United States. A Tale Of Two Systems Medicaid has played a very different role in fi- nancing mental health versus addiction treat- ment for low-income individuals over the past thirty years. Both systems initially received little revenue from Medicaid.1 However, Medicaid’s role in financing mental health treatment has grown dramatically, while its role in financing addiction treatment has been more modest and constrained (Exhibit 1). Before 1965 the vast majority of mental health treatment services were paid for by states and administered under the authority of state mental health agencies. Medicaid changed this arrange- ment.3 In 1981 Medicaid represented only 16 per- cent of revenues received by state mental health agencies, the major providers of mental health treatment to low-income individuals. The vast majority of funding—73 percent—came from state general revenue and special funds. By 2010 Medicaid accounted for half of state mental health agency–controlled revenues and was re- sponsible for nearly two-thirds of all new state mental health agency spending between 2001 and 2010.4 Medicaid is now the largest purchaser of mental health treatment, accounting for nearly half of all public dollars and more than a quarter of all mental health spending (public and private combined).5 A principal driver of this growth has been ex- panded eligibility for people with mental health disorders. The creation of the Supplemental Security Income (SSI) program in 1972 and its linkage with Medicaid eligibility resulted in a major change in Medicaid’s role in covering peo- ple with mental health disorders severe enough to qualify as a disabling condition. Since the late 1980s mental health disorders have represented one of the fastest-growing categories of SSI eli- gibility. In 2009, 41 percent of all SSI beneficia- ries younger than age sixty-five qualified because of a mental disorder.6 A significant expansion in the scope of mental health treatment services covered by Medicaid has also increased its role. The addition of tar- geted case management in 1986 and the expan- sion of psychosocial rehabilitation under Medi- caid’s rehabilitation option in the early 1990s gave states important new options for providing intensive community-based supports to people with serious mental health disorders. Currently, almost all states have adopted these options. By 2005 targeted case management accounted for $2.9 billion in Medicaid expenditures, while ser- vices provided under the rehabilitative services option accounted for another $6.4 billion.7 Peo- ple with mental health disorders constitute close to three-quarters of service recipients under the rehabilitative services option and account for almost 80 percent of expenditures.8 These op- tions have enabled states to provide evidence- based practices such as assertive community treatment, medication management, and family psychoeducation.7 Medicaid has traditionally played a more modest role in financing addiction treatment. National spending for addiction treatment in- creased by roughly $15 billion between 1986 and 2009, representing an average annual rate of growth in nominal dollars of 4.4 percent.9 Medicaid spending on addiction treatment ser- vices also grew substantially during this period, from less than $1 billion to $5 billion. However, Exhibit 1 Medicaid Expenditures For Mental Health And Addiction Treatment Services For Selected Populations, 1986–2009 SOURCE Substance Abuse and Mental Health Services Administration. National expenditures for mental health services and substance abuse treatment, 1986–2009 (see Note 5 in text). NOTE Es- timates are inflation-adjusted (2009). Vulnerable Populations 1132 Health Affairs July 2015 34:7
- 72. because other funding sources for addiction treatment also increased during this time, Med- icaid’s share of spending remained steady at approximately 20 percent of total addiction out- lays. In contrast, state and local government funds for addiction treatment increased from $2.5 billion in 1986 to $7.5 billion in 2009 and continued to increase, albeit modestly, as a per- centage of overall spending.9 Unlike the financ- ing of mental health treatment, these state and local funds have remained the backbone of the publicly funded addiction treatment system. Before 1996 it seemed likely that Medicaid’s financing of addiction treatment would follow the same path that the program has taken in funding for mental health. Between 1990 and 1995 the number of people qualifying for SSI through an addiction-related disability in- creased by more than 500 percent. However, concerns emerged about rapid growth in the number of people enrolling in Medicaid as a result of addiction-related disability, which were rooted in a longer-standing controversy as to whether addiction should even be characterized as a disabling condition for the purpose of re- ceiving public aid.10 These concerns were embodied in the Contract with America Advancement Act of 1996, under which addiction became disallowed as a qualify- ing condition for federal disability programs. Consequently, the number of people with addic- tion disorders who qualified for Medicaid was greatly circumscribed. Although only about 200,000 recipients were immediately affected by this policy, because the new law restricted states’ ability to use Medicaid as a vehicle to cover and treat the majority of people with ad- diction disorders, its significance was, of course, much broader over time. When the ACA passed in 2010, for example, only about 20 percent of patients entering publically funded addiction treatment programs were covered by Medicaid.11 Moreover, coverage for addiction treatment within state Medicaid programs has been less comprehensive than mental health coverage. As recently as 2013 several states did not provide any coverage for addiction treatment, apart from federally mandated detoxification and short-term inpatient treatment. States can use the same options for addiction treatment that were used to establish expanded coverage for mental health treatment, including the rehabili- tative services, case management, and the com- munity-based services options. However, states’ take-up of optional coverage for addiction ser- vices has been highly variable. In 2003, the most recent year in which state Medicaid coverage for addiction was systematically reviewed, thirty states covered outpatient group counseling for substance abuse.12 Twenty-five states covered methadone maintenance. Twenty covered day treatment, and only seventeen covered bupre- norphine and naltrexone—evidence-based treat- ments for opioid addiction. The ACA And Addiction Treatment The ACA is expected to dramatically change the historic contrast between Medicaid’s role in fi- nancing mental health and in financing addic- tion treatment for low-income Americans.1,13,14 The law mandates Medicaid coverage for addic- tion treatment and prohibits limits on the provi- sion of addiction treatment services that is more restrictive than those for other medical services. The ACA also enables states to expand eligibility to all citizens with incomes at or below 138 per- cent of the federal poverty level. It therefore removes categorical federal restrictions on eligi- bility that have historically limited Medicaid en- rollment to children, parents, elderly, and indi- viduals with disabilities. Millions of low-income Americans who experience either of these dis- orders have become Medicaid-eligible in states that have embraced this eligibility expansion, and more are expected to do so in the years ahead.15 Now active in twenty-nine states and the District of Columbia, the Medicaid expan- sion will increase enrollment by 10.7 million peo- ple.15 Medicaid spending for addiction treatment is projected to double from $5 billion to $12 bil- lion by 2020, quickly making Medicaid the larg- est payer of addiction treatment in the country.1 Lessons For Medicaid’s Future In Addiction Treatment Because Medicaid has been the dominant funder in mental health treatment, reforms to the pro- gram have had wide-reaching implications for the entire system of mental health care. Below we discuss three broad lessons that the addiction Medicaid’s coverage for treatment of mental health and addictive disorders has diverged in important ways. July 2015 34:7 Health Affairs 1133
- 73. treatment system can learn from the successes and challenges of the mental health treatment system’s expansion within Medicaid. We do not mean to suggest that the mental health treat- ment system is without challenges or that the challenges facing the addiction treatment sys- tem are the same as those facing mental health treatment. Instead, we seek to point out strate- gies and successes of the mental health treat- ment system that may inform efforts to improve access and quality in addiction treatment, par- ticularly for low-income individuals, who make up the majority of Medicaid enrollees. Regulatory Flexibility The first lessonfrom the experience of the mental health treatment system highlights the importance of regulatory flexibility around Medicaid’s optional coverage categories. Since the 1980s, stakeholders in the mental health treatment system have skillfully crafted more responsive systems of care for mental health disorders. As noted, Medicaid’s optional benefits—targeted case management and rehabilitation—allow states to use Medicaid to finance a variety of community support pro- grams for people with serious mental health dis- orders, including intensive case management, crisis intervention, family psychosocial educa- tion, life skills training and social supports, assertive community treatment, community res- idential services, education and employment- related supports, and peer services.16,17 This flex- ibility was enormously important for expanding access to a range of services,18,19 and states’ take- up grew substantially over time. In 1988 only nine states covered psychosocial rehabilitation or targeted case management for people with mental health disorders; today nearly every state has adopted these options. More recently, states have sought to expand options for mental health treatment under Med- icaid’s 1915(c) home and community-based ser- vices (HCBS) waiver program. In the past, the HCBS waiver program was constrained in its ability to rebalance institutional care for people with severe mental illnesses toward more home and community-based models by Medicaid’s In- stitutions for Mental Disease (IMD) exclusion. This exclusion prohibits Medicaid coverage of working-age adults in IMDs, defined as nursing homes, hospitals, or other institutions of more than sixteen beds that are primarily engaged in the treatment of mental disorders. The IMD exclusion made it difficult for states to meet the cost-neutrality requirements of the HCBS waiver program for adults with severe mental illnesses. However, recent policy clarifications have expanded this option. Four states—Colorado, Connecticut, Montana, and Wisconsin—currently have HCBS waivers for adults with severe mental illnesses. Demonstra- tion projects, such as the Money Follows the Person Rebalancing Demonstration, have also played an important role in expanding Medi- caid’s role in mental health care, as have new options for home and community-based services authorized under the Deficit Reduction Act of 2005 and the ACA.7,20,21 In the contrasting case of addiction disorders, some states have made great progress toward using Medicaid options to expand coverage for these disorders. However, the overall national impact of these efforts has been limited. A mi- nority of states have actively pursued this strate- gy, in part because the largest group of people receiving addiction treatment in the United States prior to 2014 were uninsured. Yet many of the existing options within Medicaid can be used to expand addictioncoverage, including the rehabilitative services, case management, and HCBS options, and the 1115 waiver, which ena- bles states to implement five-year demonstration projects that incorporate coverage and eligibility expansions or service delivery model inno- vations. Learning from the experience of mental health coverage, stakeholders in addiction treatment can leverage the flexibility built into Medicaid’s optional benefit policies to advocate for coverage across the service continuum—from intensive outpatient treatment and crisis management to recovery-oriented services—to effectively man- age addiction as a chronic illness. Moreover, a substantial body of research supports the effica- cy and cost-effectiveness of medication-assisted addiction treatments.22,23 Especially since cur- rent adoption of such programs is sparse,24 Med- icaid coverage flexibility should include room for them. Relatedly, most states do not provide re- A growing body of evidence suggests that inpatient addiction treatment is no more effective than outpatient treatment for many patients. ◀ 6States In 2012 only six states required addiction treatment providers to possess a bachelor’s degree, and only one required a master’s degree. Vulnerable Populations 1134 Health Affairs July 2015 34:7
- 74. imbursement for wraparound services associat- ed with opioid treatment programs such as ini- tial assessments, brief counseling, and follow-up with patients who are receiving these drugs. Alternatives To Inpatient Treatment The second lesson to be drawn from the mental health system relates to Medicaid’s capacity to promote broad-scale delivery system changes, particularly in spurring the creation of a high- quality alternative to treatment in inpatient and other institutionalized settings. Medicaid’s role in promoting community-based services for mental health was initially inadvertent. Medi- caid’s IMD exclusion strengthened incentives for states to shift patient care (and costs) from state-financed mental hospitals into Medicaid- reimbursable settings—such as nursing homes, community mental health centers, and general hospitals. Between 1955 and 1980 the resident census in state mental hospitals dropped by 75 percent. This early history played a critical role in making Medicaid the primary driver of mental health systems change.19 Medicaid also played a complementary role in helping states comply with a series of court de- cisions requiring greater emphasis on home and community-based services. Medicaid’s support for efforts such as Money Follows the Person and Balanced Incentives Program—policies ex- panded and supported by the ACA—is an impor- tant tool for many states seeking to meet their legal responsibilities to expand or improve their alternatives to institution-based care.25,26 Pro- grams such as assertive community treatment, covered by a number of state Medicaid pro- grams, provide intensive supports for people with severe mental illnesses, to enable them to manage their illnesses while remaining in the community.27 In crafting Medicaid addiction treatment cov- erage, states should consider how coverage de- sign can be used to promote high-quality, com- munity-based alternatives to inpatient addiction treatment for people with more severe addictive disorders. There will always be a need for in- patient addiction treatment services for people who require medically risky detoxification, are in crisis, or are experiencing severe symptoms. However, a growing body of evidence suggests that inpatient addiction treatment is no more effective than outpatient treatment for many pa- tients. Also, inpatient treatment is more costly, restrictive, and stigmatized than community- based addiction treatment.28 Nonetheless, de- spite this evidence, a large proportion of people receive addiction treatment in inpatient settings. In 2013 roughly 45 percent of people who re- ceived addiction treatment reported receiving it in a residential or inpatient setting.29 Expanding Treatment Options The third lesson concerns the power of public purchasers, namely Medicaid, to expand the supply of quali- fied treatment providers, and consequently, ele- vate the quality of services provided. As late as 1970 staffing in state mental hospitals consisted of a small number of professional mental health workers and a large number of less skilled cus- todial workers.3 However, as more institutional forms entered the mental health market, profes- sional staffing ratios increased.30 As community mental health centers emerged in response to deinstitutionalization and Medicaid dollars funded care in such centers, a whole new cadre of mental health professionals responded to this supply. These growing mental health profes- sions exerted political pressure on politicians to establish state licensing laws. By 1990 forty- two states had passed such laws, and eventually most states passed mandates that required insur- ers to cover mental health services provided by these professionals.3 Medicaid regulations for staff providing men- tal health treatment have also increased substan- tially. Under the rehabilitative services option, for example, the Centers for Medicare and Med- icaid Services requires that mental health treat- ment services be authorized by “licensed practi- tioners of the healing arts.” Most state Medicaid programs define licensed practitioners of the heal- ing arts as a licensed psychiatrist, psychologist, clinical social worker, or registered or advanced practice nurse. States also define the qualifica- tions of service providers under Medicaid, and most require that mental health providers pos- sess state board licensure as well as a master’s or doctoral degree from an accredited university in a relevant area of practice. States that cover para- professionals have also established minimal qualifications for this role.31 The evolution of licensing standards in the mental health treatment system differs markedly from that governing addiction treatment. Many The mental health and addiction treatment systems now face a common challenge to integrate with each other. July 2015 34:7 Health Affairs 1135
- 75. commentators and researchers express concerns about the overall quality of addiction treatment providers in the United States.13,14 While the number and type of addiction treatment pro- viders have similarly increased, fewer than half of addiction treatment providers have profes- sional degrees and any formal training or credentialing in addiction treatment.32 Most states have no or low licensure standards for addiction treatment providers. In 2012 only six states required addiction treatment providers to possess a bachelor’s degree, and only one state required a master’s degree.33 Fourteen states had no educational attainment requirements for li- censure whatsoever. As Medicaid’s role in addiction treatment ex- pands in the years ahead, there is an important opportunity to use the program to improve qual- ity. Medicaid can require formal training and licensure standards for staff who provide addic- tion treatment as a condition of Medicaid certi- fication. Because Medicaid is poised to become the largest payer of addiction treatment, most addiction treatment programs will need to re- main or become Medicaid certified to access this increasingly important revenue source.34 Such requirements could also help speed im- plementation of evidence-based addiction thera- pies, given evidence that licensed professionals are more likely than nonlicensed providers to endorse and use such therapies.33 For this strat- egy to be effective, many existing addiction treat- ment providers will need technical assistance and, ideally, additional financial resources to meet new Medicaid staffing requirements. The Common Challenge Of Integration The mental health and addiction treatment sys- tems have evolved into two different and largely separate systems over the past fifty years. These systems now face a common challenge to inte- grate with each other. Stakeholders across both systems recognize the high prevalence of co- occurring mental health and addiction disor- ders. The field has advanced treatment strategies that address both disorders in tandem. Unfortu- nately, funding regulations often conflict with integrated delivery approaches and have played a major role in hindering their proliferation.35 As Medicaid plays a greater role in financing addic- tion treatment, there will be new opportunities to improve access to integrated treatment for people who are newly eligible for Medicaid, as well as Medicaid enrollees in states that have expanded coverage for addiction treatment. Medicaid health homes provide a major new option with the potential to address long-stand- ing problems of segmentation in physical and behavioral health—including mental health and addiction treatment. At an enhanced federal matching rate for the first two years, state Med- icaid programs have the option to create health homes, which deliver coordinated care to enroll- ees with multiple chronic health conditions, in- cluding mental health and addiction disorders. The health home model has the potential to sig- nificantly improve coordination and integration of care and may become the primary model of treatment for people who have mental health or addiction treatment needs but require less inten- sive services.36 Both systems also face the challenge to inte- grate their services within primary care settings, where the vast majority of health care services to people with mental health or addiction use dis- orders will actually be provided or initiated. In- tegrating mental health and addiction treatment into primary carecan improve quality and reduce overall health care costs.37–39 Primary care pro- viders have made greater strides in integrating mental health treatment into their repertoire of services. General physicians provide an increas- ing share of mental health services.3 By the 1990s, 34 percent of mental health diagnoses came from general physicians, and prescribing of psychotropic medications in primary care has increased substantially. Assessment and treat- ment of addiction treatment has been less well integrated into primary care, in part as a result of restrictions on prescribing of addiction medi- cations.40 Conclusion Under the ACA Medicaid expansion, Medicaid agencies will become increasingly important Medicaid agencies must develop strategies to support a continuum of care that responds to people with diverse addiction-related needs. Vulnerable Populations 1136 Health Affairs July 2015 34:7
- 76. purchasers of addiction treatment services. As such, they will now play a regulatory role analo- gous to the one they have played with the mental health treatment system. While Medicaid agencies consider how to op- timally allocate resources for addiction treat- ment, differences in the severity of need, as il- lustrated in mental health, will complicate these financing and regulatory decisions. Medicaid agencies must develop strategies to support a continuum of care that effectively responds to people with diverse addiction-related needs. State Medicaid programs will need to create reg- ulatory policies that set clear rules to improve quality while remaining flexible and nimble to adjust to different patient needs. Any major purchaser such as Medicaid will be able to create important incentives to develop new innovative delivery model reforms. It is hoped that states will consider the power of this leverage prospectively to provide efficient, high- quality addiction treatment services instead of responding retrospectively to unintended prob- lems that so often arise with the infusion of new funding. ▪ The authors acknowledge support from the National Institute on Drug Abuse (Grant No. R01 DA034634-01) for its project, “The impact of health reform on outpatient substance abuse treatment programs,” although the recommendations and responsibility for any errors rest solely with the authors. The authors also acknowledge helpful feedback from the editors of Health Affairs and two anonymous reviewers. NOTES 1 Mark TL, Levit KR, Yee T, Chow CM. Spending on mental and substance use disorders projected to grow more slowly than all health spending through 2020. Health Aff (Mill- wood). 2014;33(8):1407–15. 2 Frank RG, Goldman HH, Hogan M. Medicaid and mental health: be careful what you ask for. Health Aff (Millwood). 2003;22(1):101–13. 3 Frank RG, Glied SA. Better but not well: mental health policy in the United States since 1950. Baltimore (MD): Johns Hopkins University Press; 2006. 4 Substance Abuse and Mental Health Services Administration. Controlled expenditures and revenues for mental health services, state fiscal year 2009 [Internet]. Rockville (MD): SAMHSA; 2014 [cited 2015 Jun 4]. p. 142. Available from: http://store.samhsa.gov/product/ Controlled-Expenditures-and- Revenues-for-Mental-Health- Services-State-Fiscal-Year-2009/ SMA14-4843 5 Substance Abuse and Mental Health Services Administration. National expenditures for mental health ser- vices and substance abuse treatment, 1986–2009 [Internet]. Rockville (MD): SAMHSA; 2013 [cited 2015 Jun 4]. p. 108. Available from: http://store.samhsa.gov/product/ National-Expenditures-for-Mental- Health-Services-and-Substance- Abuse-Treatment-1986-2009/ SMA13-4740 6 Garfield R. Mental health financing in the United States: a primer [In- ternet]. Washington (DC): Kaiser Commission on Medicaid and the Uninsured; 2011 Apr 1 [cited 2015 Jun 4]. p. 46. Available from: http:// kff.org/medicaid/report/mental- health-financing-in-the-united- states/ 7 Shirk C. Medicaid and mental health services [Internet]. Washington (DC): National Health Policy Forum; 2008 Oct 23 [cited 2015 Jun 4]. p. 19. Available from: http://www .nhpf.org/library/background- papers/BP66_MedicaidMental- Health_10-23-08.pdf 8 Crowley JS, O’Malley M. Medicaid’s rehabilitation services option: over- view and current policy issues [In- ternet]. Washington (DC): Kaiser Commission on Medicaid and the Uninsured; 2007 Jul 30 [cited 2015 Jun 4]. p. 24. Available from: http:// kff.org/medicaid/issue-brief/ medicaids-rehabilitation-services- option-overview-and-current/ 9 Levit KR, Mark TL, Coffey RM, Frankel S, Santora P, Vandivort- Warren R, et al. Federal spending on behavioral health accelerated during recession as individuals lost em- ployer insurance. Health Aff (Mill- wood). 2013;32(5):952–62. 10 Gresenz C, Watkins K, Podus D. Supplemental security income (SSI), disability insurance (DI), and sub- stance abusers. Community Ment Health J. 1998;34(4):337–50. 11 Substance Abuse and Mental Health Services Administration. The TEDS report: health insurance status of adult substance abuse treatment admissions aged 26 or older: 2011 [Internet]. Rockville (MD): SAMHSA; 2014 Feb 6 [cited 2015 Jun 4]. Available from: http://www .samhsa.gov/data/sites/default/ files/sr134-health-insurance-2014/ sr134-health-insurance-2014/sr134- health-insurance-2014.htm 12 Robinson G, Kaye N, Bergman D, Moreaux M, Baxter C. State profiles of mental health and substance abuse services in Medicaid [Inter- net]. Rockville (MD): Substance Abuse and Mental Health Services Administration; 2005 Jan [cited 2015 Jun 4]. p. 61. Available from: http://store.samhsa.gov/product/ State-Profiles-of-Mental-Health-and- Substance-Abuse-Services-in- Medicaid/NMH05-0202 13 Buck JA. The looming expansion and transformation of public substance abuse treatment under the Afford- able Care Act. Health Aff (Millwood). 2011;30(8):1402–10. 14 Humphreys K, Frank RG. The Af- fordable Care Act will revolutionize care for substance use disorders in the United States. Addiction. 2014; 109(12):1957–8. 15 Paradise J. Medicaid moving forward [Internet]. Washington (DC): Kaiser Commission on Medicaid and the Uninsured; 2015 Mar [cited 2015 Jun 4]. p. 12. Available from: http:// files.kff.org/attachment/issue-brief- medicaid-moving-forward 16 Smith G, Kennedy C, Knipper S, O’Brien J. Using Medicaid to sup- port working age adults with serious mental illnesses in the community: a handbook [Internet]. Washington (DC): Department of Health and Human Services, Office of the As- sistant Secretary for Planning and Evaluation; 2005 Jan [cited 2015 Jun 4]. p. 188. Available from: http://aspe.hhs.gov/daltcp/reports/ handbook.pdf 17 Koyanagi C, Semansky R. Recovery in the community: funding mental health rehabilitative approaches un- der Medicaid [Internet].Washington (DC): Judge David L. Bazelon Center for Mental Health Law; 2001 Nov [cited 2015 Jun 4]. p. 92. Available from: http://www.bazelon.org/ LinkClick.aspx?fileticket= S3P8OI01Rv0%3d&tabid=104 18 Koyanagi C, Goldman HH. The quiet success of the national plan for the chronically mentally ill. Hosp Com- July 2015 34:7 Health Affairs 1137
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Health homes for Medicaid beneficiaries with chronic conditions [Internet]. Washington (DC): Kaiser Commis- sion on Medicaid and the Unin- sured; 2012 Aug 1 [cited 2015 Jun 4]. p. 17. Available from: http://kff.org/ health-reform/issue-brief/health- homes-for-medicaid-beneficiaries- with-chronic/ 37 Butler M, Kane RL, McAlpine D, Kathol RG, Fu SS, Hagedorn H, et al. Integration of mental health/ substance abuse and primary care [Internet]. Rockville (MD): Agency for Healthcare Research and Quality; 2008 Oct [cited 2015 Jun 4]. p. 266. Available from: http://www.ahrq .gov/research/findings/evidence- based-reports/mhsapc-evidence- report.pdf 38 Friedmann PD, Zhang Z, Hendrickson J, Stein MD, Gerstein DR. Effect of primary medical care on addiction and medical severity in substance abuse treatment pro- grams. J Gen Intern Med. 2003; 18(1):1–8. 39 Weisner C, Mertens J, Parthasarathy S, Moore C, Lu Y. Integrating pri- mary medical care with addiction treatment: a randomized controlled trial. JAMA. 2001;286(14):1715–23. 40 Andrews CM, D’Aunno TA, Pollack HA, Friedmann PD. Adoption of evidence-based clinical innovations: the case of buprenorphine use by opioid treatment programs. Med Care Res Rev. 2014;71(1):43–60. Vulnerable Populations 1138 Health Affairs July 2015 34:7
- 78. At the Intersection of Health, Health Care and Policy doi: 10.1377/hlthaff.2015.0791 , 34, no.9 (2015):1498-1505Health Affairs Person-Centered Care Integrating Mental Health In Care For Noncommunicable Diseases: An Imperative For Vikram Patel and Somnath Chatterji Cite this article as: http://content.healthaffairs.org/content/34/9/1498.full.html available at: The online version of this article, along with updated information and services, is For Reprints, Links & Permissions: http://healthaffairs.org/1340_reprints.php http://content.healthaffairs.org/subscriptions/etoc.dtlE-mail Alerts : http://content.healthaffairs.org/subscriptions/online.shtmlTo Subscribe: written permission from the Publisher. All rights reserved. mechanical, including photocopying or by information storage or retrieval systems, without prior may be reproduced, displayed, or transmitted in any form or by any means, electronic orAffairs HealthFoundation. As provided by United States copyright law (Title 17, U.S. Code), no part of by Project HOPE - The People-to-People Health2015Bethesda, MD 20814-6133. Copyright © is published monthly by Project HOPE at 7500 Old Georgetown Road, Suite 600,Health Affairs Not for commercial use or unauthorized distribution by CHARLES COLEMAN on September 13, 2015Health Affairsbycontent.healthaffairs.orgDownloaded from by CHARLES COLEMAN on September 13, 2015Health Affairsbycontent.healthaffairs.orgDownloaded from
- 79. By Vikram Patel and Somnath Chatterji Integrating Mental Health In Care For Noncommunicable Diseases: An Imperative For Person-Centered Care ABSTRACT Mental disorders such as depression and alcohol use disorders often co-occur with other common noncommunicable diseases such as diabetes and heart disease. Furthermore, noncommunicable diseases are frequently encountered in patients with severe mental disorders such as schizophrenia. The pathways underlying the comorbidity of mental disorders and noncommunicable diseases are complex. For example, mental and physical noncommunicable diseases may have common environmental risk factors such as unhealthy lifestyles, and treatments for one condition may have side effects that increase the risk of another condition. Building on the robust evidence base for effective treatments for a range of mental disorders, there is now a growing evidence base for how such treatments can be integrated into the care of people with noncommunicable diseases. The best-established delivery model is a team approach that features a nonspecialist case manager who coordinates care with primary care physicians and specialists. This approach maximizes efficiencies in person-centered care, which are essential for achieving universal health coverage for both noncommunicable diseases and mental disorders. A number of research gaps remain, but there is sufficient evidence for policy makers to immediately implement measures to integrate mental health and noncommunicable disease care in primary care platforms. T he aging of populations around the world has been accompanied by marked increases in the burden of chronic noncommunicable dis- eases such as cardiovascular dis- ease, chronic respiratory conditions, cancer, diabetes, and musculoskeletal disease.1 With ef- fective interventions, mortality associated with many of these conditions has continued to fall. However, the interventions do not reach every- one and may not be universally affordable. Sev- eral studies from around the world reveal that up to half of the global population has at least one chronic condition and that nearly a quarter has more than one coexisting chronic condition.2 Alongside these daunting global health chal- lenges are those posed by the mounting burden of mental disorders, a heterogeneous group that includes some conditions—notably, depression and alcohol use disorders—that have exhibited some of the largest proportionate increases in global burden in the past two decades.1 Not sur- prisingly, given the high prevalence of both non- communicable diseases and mental disorders, comorbidity of these two groups of health con- ditions also occurs frequently.3 Estimates from the United States indicate that almost 30 percent of those living with a noncommunicable disease report a concurrent mental disorder.4 The prevalence of a mental disorder is elevated doi: 10.1377/hlthaff.2015.0791 HEALTH AFFAIRS 34, NO. 9 (2015): 1498–1505 ©2015 Project HOPE— The People-to-People Health Foundation, Inc. Vikram Patel (vikram.patel@ lshtm.ac.uk) is a professor of international mental health at the London School of Hygiene and Tropical Medicine, in the United Kingdom. He is based in New Delhi, India, where he serves as joint director of the Centre for Chronic Conditions and Injuries at the Public Health Foundation of India. Somnath Chatterji is a team leader in the Department of Health Statistics and Information Systems at the World Health Organization, in Geneva, Switzerland. 1498 Health Affairs September 2015 34:9 Systems Of Care by CHARLES COLEMAN on September 13, 2015Health Affairsbycontent.healthaffairs.orgDownloaded from
- 80. in people who live with noncommunicable diseases—especially those with multiple chronic conditions—compared to those without non- communicable disease. Conversely, more than two-thirds of people with a mental disorder have been shown to have at least one other chronic noncommunicable disease. The comorbidity be- tween noncommunicable diseases and mental disorders is particularly associated with a strong social gradient and is more common in those living in deprived areas than in residents of areas with more resources.5 The relationships between mental disorders and other noncommunicable diseases are com- plex and bidirectional3 (Exhibit 1). Poor mental health exacerbates a number of noncommunica- ble disease risk factors, including poor lifestyle choices leading to obesity, inactivity, and tobac- co use; poor health literacy; poor access to health promotion activities; and symptoms such as lack of motivation and energy. Heavy alcohol use, besides being frequently associated with a range of mental disorders, is also a major risk factor for cancer, cardiovascular disease, stroke, and liver disease and can compromise immune and cog- nitive functions—which in turn could further complicate the delivery of and adherence to com- plex treatment regimens for comorbid condi- tions. The adverse cardiometabolic reactions to drug treatments given for some mental dis- orders, notably schizophrenia, that lead to weight gain, hyperglycemia, and dyslipidemia could help explain the higher burden of noncom- municable diseases in these patients.6 Living with a chronic, painful, or disabling noncommunicable disease can, unsurprisingly, lead to increased stress and mental disorders. Noncommunicable diseases and mental dis- orders may have similar risk factors such as ge- netic determinants that increase susceptibility to cytokine-mediated inflammatory responses, along with adverse social and environmental de- terminants of both groups of conditions, such as childhood adversity and poverty. The Impact Of Comorbidity Among people living with noncommunicable diseases, comorbidity with a mental disorder of- ten has profound and detrimental impacts on health, including poorer glycemic control among people with diabetes and inadequate blood pressure control among people with hy- pertension, compared to people without comor- bidity. Such impacts are often due to a lack of compliance with treatment regimens that may be complex and necessitate lifestyle changes.6 Conditions such as panic attacks increase the risk of future cardiovascular events. Comorbid mental disorders lead to significant worseningof disability among those with noncommunicable diseases. Such comorbidity may have a synergis- tic deleterious effect, as the odds of severe dis- ability and resulting work absences among those with a mental disorder and a noncommunicable disease are greater than the sum of the odds for each of the single conditions.7 The burden of a mental disorder may also reduce a person’s abil- ity to adapt to symptoms of noncommunicable diseases. In addition to disability, poor mental health is also associated with higher mortality in people with noncommunicable diseases such as cardio- vascular disorders, stroke, and rheumatoid ar- Exhibit 1 The Mechanisms Of Comorbidity Of Mental Disorders With Other Noncommunicable Diseases SOURCE Authors’ analysis. September 2015 34:9 Health Affairs 1499 by CHARLES COLEMAN on September 13, 2015Health Affairsbycontent.healthaffairs.orgDownloaded from
- 81. thritis, compared to that of people without co- morbid mental disorders. Recent analyses have shown that mortality is significantly higher among people with mental disorders than among the general population, and that about two-thirds of mortality is due to natural causes that can primarily be attributed to non- communicable diseases, notably cardiovascular disease.8 These impacts are well illustrated in the multi- national Study on Global Ageing and Adult Health (SAGE) of the World Health Organization (WHO),9 which is one of the few primary data sources pertaining to health in low- and middle- income countries. The study shows that com- pared with a number of noncommunicable dis- eases, depression has a worse impact on overall health status (measured as a composite of the capacity to function in multiple domains of daily activities) (Exhibit 2). It also shows that when depression is comorbid with other noncommu- nicable diseases, it further worsens health significantly—especially for people with diabe- tes, chronic obstructive pulmonary disease, and stroke—even after age, sex, education, household wealth, and place of residence are controlled for in a multivariable regression analysis. Patients with comorbid mental disorders and noncommunicable diseases experience more complicated treatments and poorer treatment outcomes than do patients with isolated condi- tions. This is partly because those with co- morbidities have depressed motivation and im- paired memory, which interfere with their adherence to treatment. Another reason is the stigma associated with the mental disorder, which limits access to timely, appropriate, and patient-centered care. Consequently, patients with such comorbidities have higher rates of health care utilization but poorer overall quality of care, and they are more likely to use emergen- cy care, compared to those who have noncom- municable diseases without a comorbid mental disorder. Increased health care utilization and poorer quality of care have consequences for health care spending, potentially increasing both a patient’s costs and the likelihood of subsequent impover- ishment. As one example, data from the US Medical Expenditure Panel Survey observed that among obese adults, comorbidity with mental disorders was associated with higher total, out- patient, and pharmaceutical expenditures, com- pared to expenditures for those without such comorbidity.10 In the SAGE study, 23.6 percent of people di- agnosed with depression and hypertension had poorly controlledhypertension despite receiving treatment, compared to 16.8 percent of people with hypertension but no depression. This study also showed that depression, when comorbid with noncommunicable diseases, significantly increased the odds of contact with outpatient and inpatient services for people with diabetes, arthritis, angina, stroke, or chronic obstructive pulmonary disease and for those with multiple chronic conditions. Patients with severe mental disorders often have cardiovascular disease and diabetes that go unrecognized because of their difficulties in accessing appropriate health care and effectively communicating with their health care providers. Even when these conditions are recognized, patients often receive treatment that is not con- cordant with guidelines, in part because of a fragmented and specialist-dominated health care system. In short, comorbidities lead to poorer quality of care, higher health care costs, and poorer out- comes for both the mental disorder and the non- communicable disease. In low- and middle- income countries, these relationships are likely to be further complicated by the existence of chronic infectious diseases, notably HIV and AIDS, and by health systems that may not be equipped to deal with noncommunicable dis- eases or mental disorders because of low invest- ments in the health care delivery sector, limited human resource capacity, and low political will.11 It is also important to note that the impact of mental disorders and noncommunicable dis- eases extends beyond the people who are directly Exhibit 2 The Impact Of Noncommunicable Diseases, Depression, And Comorbidity On Health Status SOURCE Authors’ analysis of data from World Health Organization, Health statistics and information systems: WHO Study on global AGEing and adult health (SAGE) (Note 9 in text). NOTES Health status is measured as a composite of the capacity to function in multiple domains of daily activities. Higher negative values of the coefficient reflect worsening health. The whiskers indicate 95% confidence intervals. Systems Of Care 1500 Health Affairs September 2015 34:9 by CHARLES COLEMAN on September 13, 2015Health Affairsbycontent.healthaffairs.orgDownloaded from
- 82. affected: There are also adverse impacts on the health of their caregivers. Caring for a person with a chronic, disabling noncommunicable dis- ease or mental disorder, such as cancer or de- mentia, is stressful and associated with an in- creased risk of chronic health problems, including depression, hypertension, sleeping problems, and fatigue; increased use of psycho- tropic drugs; and premature mortality.12 The in- direct costs of such uncompensated caregiving are also considerable. These impacts on care- givers, who are often members of the patient’s household, can lead to the clustering of noncom- municable diseases and mental disorders within households, creating sick households. Addressing Comorbidities: The Evidence There is a robust evidence base testifying to the effectiveness and cost-effectiveness of a range of mental disorder interventions, including medi- cines, psychological treatments, and social inter- ventions.13 This evidence also demonstrates the effectiveness of the delivery of psychosocial in- terventions by nonspecialist health workers in routine primary care platforms in low- and mid- dle-income countries, and the effectiveness of collaborative care models in these countries. In this delivery model, front-line care, consisting of tasks such as screening, case management, and the provision of psychosocial interventions, is delivered by nonspecialist health workers in partnership with primary care physicians, men- tal health professionals, or both.14,15 There is a small but consistent evidence base that testifies to the cost-effectiveness of such task sharing, despite the fact that such models entail addition- al expenditures because they require additional human resources.16 Depression A separate body of evidence, al- most all of which is from high-income countries, specifically evaluates the integration of effective interventions for mental disorders with the care of people with noncommunicable diseases—in particular, the management of depression that is comorbid with diabetes or coronary artery dis- ease. Among patients with coronary artery dis- ease, both psychological and pharmacological (selective serotonin reuptake inhibitor [SSRI] antidepressants) interventions have a modest beneficial effect on depression.17 Some trials of pharmacological interventions in patients with coronary artery disease have shown a reduction in rates of hospitalization and emergency department visits,17 while some trials of psychological interventions have also shown a reduction of cardiac mortality.18 In gen- eral, for the effective treatment of depression that is comorbid with coronary artery disease, there seems to be no difference across various types of psychological treatments or across vari- ous SSRI antidepressants.17 Similarly, there is a promising evidence base for the benefits of integrated care on both mental health and physical health outcomes in people with diabetes and depression. A systematic re- view of collaborative care for patients with these conditions provides clear evidence to support its effectiveness in improving depression outcomes and improved adherence to treatment for both depression and diabetes.19 Two recent trials evaluated collaborative care for multiple noncommunicable diseases (coro- nary artery disease, diabetes, or both) and de- pression and provided evidence that such care is of particular relevance to primary care practice, where multiple morbidities are common. These two trials, one from the United Kingdom20 and one from the United States,21 reported signifi- cantly superior health outcomes for patients in a collaborative care intervention group, com- pared to those in the control group. In the US trial, carried out in fourteen primary care clinics in Washington State,22 214 patients who suffered from depression and from coro- nary artery disease, diabetes, or both worked collaboratively with nurses and primary care physicians to set individualized clinical and self-care goals. In two or three weekly structured visits by patients to primary care settings, nurses monitored the level of depression, control of the noncommunicable disease or diseases, and ad- herence to interventions. First-line medications included diuretics and angiotensin-converting- enzyme inhibitors for hypertension, statins for hyperlipidemia, metformin for hyperglycemia, and citalopram or bupropion for depression. Comorbidities lead to poorer quality of care, higher health care costs, and poorer outcomes for both the mental disorder and the noncommunicable disease. September 2015 34:9 Health Affairs 1501 by CHARLES COLEMAN on September 13, 2015Health Affairsbycontent.healthaffairs.orgDownloaded from
- 83. Using motivational techniques, nurses helped patients solve problems and set goals for im- proved adherence and self-care. Once a patient achieved targeted levels for relevant outcomes, a nurse and the patient developed a maintenance plan that included stress reduction, behavioral goals, continued use of medications, and identi- fication of prodromal symptoms of deteriorating depression and glycemic control. The nurses then followed up with patients by telephone ev- ery four weeks to assess depression and review adherence and laboratory test results. Patients with worsening disease control were offered en- hanced follow-up. Compared to patients who re- ceived only the usual care from their physician, those who received the collaborative care inter- vention had greater overall improvement across hemoglobin A1c levels, low-density lipoprotein cholesterol levels, systolic blood pressure, and depression scores. This model of care, called TEAMCare,22 is now being rolled out in clinics and hospital systems in regions across the Unit- ed States and Canada. Conditions Other Than Depression There is less evidence on the impacts of integrating the care of mental disorders besides depression with noncommunicable disease care. However, there is a small evidence base to support integrating the prevention of noncommunicable disease risk factors, such as weight gain, in the manage- ment of people with serious mental disorders such as schizophrenia. Recent reviews23,24 have reported modest evidence of the effectiveness of lifestyle interventions such as changes in diet and physical activity and of switching from cer- tain antipsychotic medications to drugs such as aripiprazole that pose less risk of weight gain and other adverse effects. Few studies have evaluated interventions to address other cardiovascular disease risk factors in patients with serious mental disorders or in- terventions for people with noncommunicable diseases and comorbid alcohol use disorders. There have also been few studies that evaluated interventions with agents known to be effective in populations other than people with serious mental disorders, such as behavioral or pharma- cological interventions for tobacco cessation. Thus, integrating mental health care with non- communicable disease care should be viewed not only from the perspective of general medical care, but also in the context of psychiatric care, where many people with serious mental and sub- stance use disorders would expect to have their conditions managed. Ongoing Trials To Address Knowledge Gaps Two key research questions remain to be addressed. First, the existing evidence clearly points to the need to improve the effectiveness of interventions, both by improving the quality of available interventions and by identifying new interventions to enhance the modest effects observed in trials to date. Second, there is a need to evaluate approaches to the integration of mental health care and noncommunicable dis- ease care in more diverse contexts, particularly in low- and middle-income countries, and to in- tegrate with noncommunicable disease care the care of other mental disorders that have strong associations with noncommunicable diseases— notably, alcohol use disorders. Several ongoing trials in low- and middle-income countries prom- ise to generate evidence to address some of these knowledge gaps in the coming years. The m-WELLCARE program in India, sup- ported by the Wellcome Trust, is using a mobile health app for decision support and continuing care for people with diabetes or hypertension. It integrates the management of a range of co- morbidities, including depression and alcohol use disorders, into routine primary health care. The intervention is being evaluated in a cluster randomized controlled trial in two Indian states. South Africa’s Department of Health is pilot- ing the integration of noncommunicable disease care into routine primary health care in ten na- tional health insurance districts, one in each of the country’s provinces, with the goal of eventu- ally scaling the integration up to all districts. The screening and management tool used by nurses in the program is Primary Care 101, which is a symptom-based clinical management guideline that uses algorithms for managementof multiple common noncommunicable diseases. The Pro- gramme for Improving Mental Health Care (PRIME) program,25 supported by UK Aid, is specifically piloting the strengthening of the mental health component of this training for the management of multiple morbidities and has begun a pragmatic cluster randomized con- trolled trial in twenty public-sector primary care clinics in one district to assess mental and physi- cal health outcomes for depressed adults receiv- ing treatment for hypertension. The Integrating Depression and Diabetes Treatment (INDEPENDENT) project in India, supported by the US National Institute of Mental Health, is evaluating a version of the TEAMCare collaborative care intervention to address co- morbid depression and diabetes or cardiovascu- lar disease in India.21 In this study of patients with diabetes, comorbid depressive symptoms, and poor control of their cardiovascular risk, researchers are comparing standard noncom- municable disease care with a multifaceted intervention that uses nonphysician care co- ordinators who activate patients and encourage better self-care. The coordinators use a “smart” Systems Of Care 1502 Health Affairs September 2015 34:9 by CHARLES COLEMAN on September 13, 2015Health Affairsbycontent.healthaffairs.orgDownloaded from
- 84. electronic health record (EHR) that uses built-in decision algorithms to prompt physicians to pro- vide guideline-based care. Two monthly offline specialist supervision meetings each month are used to guide population health management and oversee care. The potential impacts of trials such as these include the opportunity to leverage patients’ ex- isting point of contact with the health system to simultaneously treat depressive symptoms and improve noncommunicable disease care. The trials also present an opportunity to identify ways to cost-effectively integrate the combined care delivery approach into health settings in the challenging health care milieu of low- and mid- dle-income countries. Implications For Policy Makers There is consistent, if modest, evidence17,18 of the effectiveness of SSRI antidepressant and struc- tured psychological interventions in reducing depressive and anxiety symptoms in people with coronary artery disease, diabetes, or both and comorbid depression, but less consistent evi- dence on the interventions’ impact in improving the noncommunicable disease outcomes. There is modest evidence23 of the health benefits of integrating noncommunicable disease care with care for serious mental disorders. Also, there is growing evidence21 that demonstrates how the care for these diverse conditions could be inte- grated into the same health care delivery setting. Such efficiencies point to the probability that integrated care is likely to be more cost-effective than vertical care models for specific disorders. However, beyond the beneficial effects of inte- grated careon economic orbiomedical outcomes is the impact on improving patient satisfaction and quality of life, and thereby achieving the goals of patient-centered health care.26 In most countries—both those in the low- and middle-income group and those in the high- income group—the management of mental dis- orders and noncommunicable diseases largely ignores the existence of multiple morbidities, in a single patient and in household members. This leads to poorer quality of care and higher levels of patient dissatisfaction and costs of care, resulting from fragmented disease-specific spe- cialist care.27 Patients are required to consult multiple specialists for each condition or, more commonly, are denied care for one or more of the coexisting conditions because physicians ig- nore those conditions that are outside their spe- cialties. The principles underlying effective integra- tion of care are consistent with the recommen- dations for the management of any chronic con- dition, which we call the 4C model. In this model, care is collaborative—that is, it involves a partner- ship among the patient, a nonspecialist case manager who delivers psychosocial interven- tions, a primary care physician, and providers of specialist services, and it emphasizes shared decision making and seamless communication; coordinated across health care delivery plat- forms, with integrated EHRs and liaison be- tween health care providers, multidisciplinary guidelines, and clearly defined care pathways; continuing, with an emphasis on proactive mon- itoring of health outcomes and regular reviews with specialists regarding patients who do not show clinical improvement; and centered on the patient, with an emphasis on promoting self- management and prioritizing patient-defined outcomes and delivery expectations.14 However, for successful integration to take place, policy makers and health programs will need to address a number of potential barriers and lessons learned from recent efforts.28 Truly integrated care involves more than locating health workers with diverse specialties in the same building. It also requires a systems ap- proach to implementation. Primary health workers—in particular, case managers, who are the critical human resources in integrated care—need competency-based training and su- pervision. Additionally, the major risks posed by integra- tion need to be explicitly addressed. These risks include primary health worker burnout and the possibility that with integrated care, the quality of care for mental disorders would be lower than that of care for other conditions.29 Above all, health workers at all levels need access to timely, useful data about patients in the form of integrated clinical information systems that can track individual patients across sectors of the health care system. New technologies—such as decision support algo- rithms enabled by mobile health, cloud-based The integration of the care of mental and physical comorbidities is relevant to many important global policy instruments. September 2015 34:9 Health Affairs 1503 by CHARLES COLEMAN on September 13, 2015Health Affairsbycontent.healthaffairs.orgDownloaded from
- 85. EHRs that can be accessed and updated by any provider, automated medication and appoint- ment reminders, and telemedicine-based super- vision by specialists—offer unique opportunities to address these barriers.30 Successful integration also requires attention to possible resistance from vested interests—in particular, medical specialists and the health care industry—seeking to promote a predomi- nantly biomedical and hospital-centric approach to care. Integration needs to happen across the entire spectrum of interventions, from prevention to management of disorders, and across all levels of care, from primary to tertiary. Such seamless integration would take into account the need for continuity of care, the fact that some people need long-term care, and communities’ legitimate ex- pectations for person-centered care. Finally, in- tegration takes time and typically involves a se- ries of developments spanning several years, with continuous loops of monitoring, evalua- tion, feedback, and service improvements. In September 2015 the United Nations is to convene an international meeting to finalize the Sustainable Development Goals, which col- lectively will represent a global consensus on the major challenges facing the planet.31 The draft proposals for the health goal call for the promo- tion of mental health and well-being and the prevention and treatment of substance abuse. Additionally, there is growing advocacy for in- cluding mental disorders in the noncommunica- ble disease targets.32 The UN General Assembly unanimously adopted a resolution endorsing universal health coverage as a global priority for sustainable development in 2012. Thus, it is likely that universal health coverage will be included as a specific target within the broader health related Sustainable Development Goals.31 In 2013 the World Health Assembly unanimously approved the WHO’s Comprehensive Mental Health Action Plan.33 The integration of the care of mental and physical comorbidities is relevant to all of these important global policy in- struments. Not only does integrated care provide a way to effectively address both noncommunicable dis- eases and mental disorders, but it also has the potential to produce efficiencies in health care delivery—for example, by providing care for mul- tiple conditions usingthe same human resources and a common primary care platform. Such effi- ciencies would increase the probability that in- terventions for both mental disorders and non- communicable diseases will be scaled up within universal health coverage. Conclusion Efficiencies arising from integrated primary care are essential both in high-income countries, where the costs of care for noncommunicable diseases and mental disorders are already very high and spiraling upward, and in low- and mid- dle-income countries, where large proportions of people with these conditions do not receive adequate care. Integration is key to improving the access to appropriate interventions for peo- ple with comorbid conditions, reducing the frag- mented manner in which care is delivered, and delivering care that is responsive to patients’ needs and expectations. Such an approach is consistent with the need for a person-centered approach to health care, which is particularly relevant in the area of chronic diseases in all countries.34 ▪ The views expressed in this article are those of the authors and do not necessarily represent the views or policies of the World Health Organization. Vikram Patel is supported by a Wellcome Trust Senior Research Fellowship (Grant No. 091834A) and is an investigator on the m-WELLCARE and the Programme for Improving Mental Health Care (PRIME) projects. The Study on Global Ageing and Adult Health (SAGE) is supported by the World Health Organization (WHO) and the US National Institute on Aging through interagency agreements (Nos. OGHA 04034785, YA1323-08-CN-0020, and Y1-AG-1005-01) with the WHO and through a research grant (Grant No. R01-AG034479). The authors acknowledge the contributions of Nicole Bergen and Emese Verdes to the preparation of this article. NOTES 1 Murray CJ, Vos T, Lozano R, Naghavi M, Flaxman AD, Michaud C, et al. Disability-adjusted life years (DALYs) for 291 diseases and inju- ries in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lan- cet. 2013;380(9859):2197–223. 2 Arokiasamy P, Uttamacharya JK, Biritwum RB, Yawson AE, Fan W, et al. The impact of multimorbidity on adult physical and mental health in low- and middle-income coun- tries: what does the Study on global AGEing and adult health (SAGE) reveal? BMC Med. 2015;13:178. 3 Prince M, Patel V, Saxena S, Maj M, Maselko J, Phillips MR, et al. No health without mental health. Lan- cet. 2007;370(9590):859–77. 4 Druss BG, Walker ER. Mental dis- orders and medical comorbidity. Synth Proj Res Synth Rep. 2011;21: 1–26. 5 Barnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B. Epide- miology of multimorbidity and im- plications for health care, research, and medical education: a cross- sectional study. Lancet. 2012; 380(9836):37–43. 6 Von Korff MR. Understanding consequences of mental-physical Systems Of Care 1504 Health Affairs September 2015 34:9 by CHARLES COLEMAN on September 13, 2015Health Affairsbycontent.healthaffairs.orgDownloaded from
- 86. comorbidity. In: Von Korff MR, Scott KM, Gureje O, editors. Global per- spectives on mental-physical co- morbidity in the WHO World Mental Health Surveys. New York (NY): Cambridge University Press; 2009. p. 193–209. 7 Moussavi S, Chatterji S, Verdes E, Tandon A, Patel V, Ustun B. Depression, chronic diseases, and decrements in health: results from the World Health Surveys. Lancet. 2007;370(9590):851–8. 8 Walker E, McGee RE, Druss BG. Mortality in mental disorders and global disease burden implications: A systematic review and meta- analysis. JAMA Psychiatry. 2015; 72(4):334–41. 9 World Health Organization. Health statistics and information systems: WHO Study on global AGEing and adult health (SAGE) [Internet]. Geneva: WHO; c 2015 [cited 2015 Jul 22]. Available from: http://www .who.int/healthinfo/sage/en 10 Shen C, Sambamoorthi U, Rust G. 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Available from: http://apps.who.int/ iris/bitstream/10665/155002/1/ WHO_HIS_SDS_2015.6_eng.pdf September 2015 34:9 Health Affairs 1505 by CHARLES COLEMAN on September 13, 2015Health Affairsbycontent.healthaffairs.orgDownloaded from