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Table 1.  The Health Care Payment LAN Frameworka
The Health Care Payment LAN Frameworka
Table 2.  Quality Rating Scheme for Studies and Other Evidence Modified From the Oxford Centre for Evidence-Based Medicine Rating Frameworka
Quality Rating Scheme for Studies and Other Evidence Modified From the Oxford Centre for Evidence-Based Medicine Rating Frameworka
Table 3.  Results of Systematic Review Organized by the LAN Framework for APMs
Results of Systematic Review Organized by the LAN Framework for APMs
Table 4.  General Characteristics of the Study Publications and APMs Included in this Literature Review
General Characteristics of the Study Publications and APMs Included in this Literature Review
Table 5.  Summary of 17 APM Implementation Findings Based on Outcomes Examined
Summary of 17 APM Implementation Findings Based on Outcomes Examined
1.
Centers for Medicare & Medicaid Services. NHE fact sheet. Updated March 24, 2020. Accessed June 18, 2020. https://www.cms.gov/research-statistics-data-and-systems/statistics-trends-and-reports/nationalhealthexpenddata/nhe-fact-sheet.html
2.
Shrank  WH, Rogstad  TL, Parekh  N.  Waste in the US health care system: estimated costs and potential for savings.   JAMA. 2019;322(15):1501-1509. doi:10.1001/jama.2019.13978PubMedGoogle Scholar
3.
Papanicolas  I, Woskie  LR, Jha  AK.  Health care spending in the United States and other high-income countries.   JAMA. 2018;319(10):1024-1039. doi:10.1001/jama.2018.1150PubMedGoogle Scholar
4.
Song  Z, Navathe  AS, Emanuel  EJ, Volpp  KG.  Incorporating value into physician payment and patient cost sharing.   Am J Manag Care. 2018;24(3):126-128.PubMedGoogle Scholar
5.
Centers for Medicare & Medicaid Services. APMs overview. Accessed March 12, 2019. https://qpp.cms.gov/apms/overview
6.
Centers for Medicare & Medicaid Services (CMS), HHS.  Medicare Program; CY 2018 updates to the Quality Payment Program; and Quality Payment Program: extreme and uncontrollable circumstance policy for the transition year.   Fed Regist. 2017;82(220):53568-54229.PubMedGoogle Scholar
7.
Muhlestein  D, Burton  N, Win  L. The changing payment landscape of current cms payment models foreshadows future plans. Health Affairs Blog. Published February 3, 2017. doi:10.1377/hblog20170203.058589
8.
Muhlestein  D, McClellan  MB.  Accountable care organizations in 2016: private and public-sector growth and dispersion.  Health Affairs Blog. Published April 21, 2016. doi:10.1377/hblog20160421.054564
9.
Kessler  RC, Berglund  P, Demler  O, Jin  R, Merikangas  KR, Walters  EE.  Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication.   Arch Gen Psychiatry. 2005;62(6):593-602. doi:10.1001/archpsyc.62.6.593PubMedGoogle Scholar
10.
Katon  WJ, Lin  EHB, Von Korff  M,  et al.  Collaborative care for patients with depression and chronic illnesses.   N Engl J Med. 2010;363(27):2611-2620. doi:10.1056/NEJMoa1003955PubMedGoogle Scholar
11.
Mechanic  D, Olfson  M.  The relevance of the Affordable Care Act for improving mental health care.   Annu Rev Clin Psychol. 2016;12:515-542. doi:10.1146/annurev-clinpsy-021815-092936PubMedGoogle Scholar
12.
Mauri  A, Harbin  H, Unutzer  J, Carlo  AD, Ferguson  R, Schoenbaum  M. Payment reform and opportunities for behavioral health: alternative payment model examples. Published September 2017. Accessed June 18, 2020. https://www.scattergoodfoundation.org/wp-content/uploads/yumpu_files/Scattergood_APM_Final_digital.pdf
13.
Bailitt Health. Categorizing value-based payment models according to the LAN Alternative Payment Model framework: examples of payment models by category. Published February 2018. Accessed June 18, 2020. http://shvs.org/wp-content/uploads/2018/02/SHVS_APM-Categorization_Brief-Final.pdf
14.
Health Care Payment Learning and Action Network. What is the Health Care Payment Learning and Action Network? Accessed September 20, 2018. https://hcp-lan.org
15.
Yuan  B, He  L, Meng  Q, Jia  L.  Payment methods for outpatient care facilities.   Cochrane Database Syst Rev. 2017;3(3):CD011153. doi:10.1002/14651858.CD011153.pub2PubMedGoogle Scholar
16.
Catalano  R, Libby  A, Snowden  L, Cuellar  AE.  The effect of capitated financing on mental health services for children and youth: the Colorado experience.   Am J Public Health. 2000;90(12):1861-1865. doi:10.2105/AJPH.90.12.1861PubMedGoogle Scholar
17.
Catalano  RA, Coffman  JM, Bloom  JR, Ma  Y, Kang  SH.  The impact of capitated financing on psychiatric emergency services.   Psychiatr Serv. 2005;56(6):685-690. doi:10.1176/appi.ps.56.6.685PubMedGoogle Scholar
18.
Stewart  RE, Lareef  I, Hadley  TR, Mandell  DS.  Can we pay for performance in behavioral health care?   Psychiatr Serv. 2017;68(2):109-111. doi:10.1176/appi.ps.201600475Google Scholar
19.
Health Care Payment Learning and Action Network. Alternative payment model: APM framework. Accessed July 9, 2019. http://hcp-lan.org/workproducts/apm-refresh-whitepaper-final.pdf
20.
Centre for Evidence-Based Medicine. OCEBM levels of evidence. Published May 1, 2016. Accessed November 30, 2018. https://www.cebm.net/2016/05/ocebm-levels-of-evidence/
21.
JAMA Network Open. Instructions for authors. Accessed December 17, 2019. https://jamanetwork.com/journals/jamanetworkopen/pages/instructions-for-authors
22.
Petticrew  M, Roberts  H.  Evidence, hierarchies, and typologies: horses for courses.   J Epidemiol Community Health. 2003;57(7):527-529. doi:10.1136/jech.57.7.527PubMedGoogle Scholar
23.
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24.
Grieve  R, Sekhon  JS, Hu  TW, Bloom  JR.  Evaluating health care programs by combining cost with quality of life measures: a case study comparing capitation and fee for service.   Health Serv Res. 2008;43(4):1204-1222. doi:10.1111/j.1475-6773.2008.00834.xPubMedGoogle Scholar
25.
Norton  EC, Lindrooth  RC, Dickey  B.  Cost-shifting in managed care.   Ment Health Serv Res. 1999;1(3):185-196. doi:10.1023/A:1022377930099PubMedGoogle Scholar
26.
Rosenthal  MB.  Risk sharing and the supply of mental health services.   J Health Econ. 2000;19(6):1047-1065. doi:10.1016/S0167-6296(00)00063-1PubMedGoogle Scholar
27.
Scott  MA, Snowden  L, Libby  AM.  Effects of capitated mental health services on youth contact with the juvenile justice system.   J Am Acad Child Adolesc Psychiatry. 2002;41(12):1462-1469. doi:10.1097/00004583-200212000-00018PubMedGoogle Scholar
28.
Cuellar  AE, Libby  AM, Snowden  LR.  How capitated mental health care affects utilization by youth in the juvenile justice and child welfare systems.   Ment Health Serv Res. 2001;3(2):61-72. doi:10.1023/A:1011507117644PubMedGoogle Scholar
29.
Kaskie  B, Wallace  N, Kang  S, Bloom  J.  The implementation of managed behavioral healthcare in Colorado and the effects on older Medicaid beneficiaries.   J Ment Health Policy Econ. 2006;9(1):15-24.PubMedGoogle Scholar
30.
Libby  AM, Cuellar  A, Snowden  LR, Orton  HD.  Substitution in a Medicaid mental health carve-out: services and costs.   J Health Care Finance. 2002;28(4):11-23.PubMedGoogle Scholar
31.
Chou  AF, Wallace  N, Bloom  JR, Hu  T-W.  Variation in outpatient mental health service utilization under capitation.   J Ment Health Policy Econ. 2005;8(1):3-14.PubMedGoogle Scholar
32.
Kapur  K, Young  AS, Murata  D, Sullivan  G, Koegel  P.  The economic impact of capitated care for high utilizers of public mental health services: the Los Angeles PARTNERS program experience.   J Behav Health Serv Res. 1999;26(4):416-429. doi:10.1007/BF02287302PubMedGoogle Scholar
33.
Popkin  MK, Lurie  N, Manning  W,  et al.  Changes in the process of care for Medicaid patients with schizophrenia in Utah’s Prepaid Mental Health Plan.   Psychiatr Serv. 1998;49(4):518-523. doi:10.1176/ps.49.4.518PubMedGoogle Scholar
34.
Liu  CF, Manning  WG, Christianson  JB,  et al.  Patterns of outpatient use of mental health services for Medicaid beneficiaries under a prepaid mental health carve-out.   Adm Policy Ment Health. 1999;26(6):401-415. doi:10.1023/A:1021329722847PubMedGoogle Scholar
35.
Rapp  CA, Huff  S, Hansen  K.  The New Hampshire financing policy.   Psychiatr Rehabil J. 2003;26(4):385-391. doi:10.2975/26.2003.385.391PubMedGoogle Scholar
36.
Busch  AB, Huskamp  HA, McWilliams  JM.  Early efforts by Medicare accountable care organizations have limited effect on mental illness care and management.   Health Aff (Millwood). 2016;35(7):1247-1256. doi:10.1377/hlthaff.2015.1669PubMedGoogle Scholar
37.
Busch  AB, Huskamp  HA, Kreider  AR, McWilliams  JM.  Medicare accountable care organizations and antidepressant use by patients with depression.   Psychiatr Serv. 2017;68(11):1193-1196. doi:10.1176/appi.ps.201600538PubMedGoogle Scholar
38.
Beil  H, Feinberg  RK, Patel  SV, Romaire  MA.  Behavioral health integration with primary care: implementation experience and impacts from the State Innovation Model round 1 states.   Milbank Q. 2019;97(2):543-582. doi:10.1111/1468-0009.12379PubMedGoogle Scholar
39.
Ross  KM, Gilchrist  EC, Melek  SP, Gordon  PD, Ruland  SL, Miller  BF.  Cost savings associated with an alternative payment model for integrating behavioral health in primary care.   Transl Behav Med. 2019;9(2):274-291. doi:10.1093/tbm/iby054PubMedGoogle Scholar
40.
Garner  BR, Godley  SH, Bair  CML.  The impact of pay-for-performance on therapists’ intentions to deliver high-quality treatment.   J Subst Abuse Treat. 2011;41(1):97-103. doi:10.1016/j.jsat.2011.01.012PubMedGoogle Scholar
41.
Garner  BR, Godley  SH, Dennis  ML, Hunter  BD, Bair  CM, Godley  MD.  Using pay for performance to improve treatment implementation for adolescent substance use disorders: results from a cluster randomized trial.   Arch Pediatr Adolesc Med. 2012;166(10):938-944. doi:10.1001/archpediatrics.2012.802PubMedGoogle Scholar
42.
Garner  BR, Lwin  AK, Strickler  GK, Hunter  BD, Shepard  DS.  Pay-for-performance as a cost-effective implementation strategy: results from a cluster randomized trial.   Implement Sci. 2018;13(1):92. doi:10.1186/s13012-018-0774-1PubMedGoogle Scholar
43.
Lee  MT, Garnick  DW, O’Brien  PL,  et al.  Adolescent treatment initiation and engagement in an evidence-based practice initiative.   J Subst Abuse Treat. 2012;42(4):346-355. doi:10.1016/j.jsat.2011.09.005PubMedGoogle Scholar
44.
Shepard  DS, Calabro  JAB, Love  CT, McKay  JR, Tetreault  J, Yeom  HS.  Counselor incentives to improve client retention in an outpatient substance abuse aftercare program.   Adm Policy Ment Health. 2006;33(6):629-635. doi:10.1007/s10488-006-0054-2PubMedGoogle Scholar
45.
Vandrey  R, Stitzer  ML, Acquavita  SP, Quinn-Stabile  P.  Pay-for-performance in a community substance abuse clinic.   J Subst Abuse Treat. 2011;41(2):193-200. doi:10.1016/j.jsat.2011.03.001PubMedGoogle Scholar
46.
Unützer  J, Chan  YF, Hafer  E,  et al.  Quality improvement with pay-for-performance incentives in integrated behavioral health care.   Am J Public Health. 2012;102(6):e41-e45. doi:10.2105/ajph.2011.300555PubMedGoogle Scholar
47.
Bao  Y, McGuire  TG, Chan  Y-F,  et al.  Value-based payment in implementing evidence-based care: the Mental Health Integration Program in Washington state.   Am J Manag Care. 2017;23(1):48-53.PubMedGoogle Scholar
48.
Schmutte  T, Van der Heide  L, Szczygiel  L, Phelan  A, Davidson  L, Plant  R.  A pay-for-performance initiative to reduce pediatric psychiatric inpatient length of stay.   Psychiatr Serv. 2019;70(2):156-158. doi:10.1176/appi.ps.201800190PubMedGoogle Scholar
49.
Stewart  MT, Horgan  CM, Garnick  DW, Ritter  G, McLellan  AT.  Performance contracting and quality improvement in outpatient treatment: effects on waiting time and length of stay.   J Subst Abuse Treat. 2013;44(1):27-33. doi:10.1016/j.jsat.2012.02.001PubMedGoogle Scholar
50.
McLellan  AT, Kemp  J, Brooks  A, Carise  D.  Improving public addiction treatment through performance contracting: the Delaware experiment.   Health Policy. 2008;87(3):296-308. doi:10.1016/j.healthpol.2008.01.010PubMedGoogle Scholar
51.
Haley  SJ, Dugosh  KL, Lynch  KG.  Performance contracting to engage detoxification-only patients into continued rehabilitation.   J Subst Abuse Treat. 2011;40(2):123-131. doi:10.1016/j.jsat.2010.09.001PubMedGoogle Scholar
52.
Commons  M, McGuire  TG, Riordan  MH.  Performance contracting for substance abuse treatment.   Health Serv Res. 1997;32(5):631-650.PubMedGoogle Scholar
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Lu  M.  Separating the true effect from gaming in incentive-based contracts in health care.   J Econ Manage Strategy. 1999;8(3):383-431. Accessed June 18, 2020. https://ideas.repec.org/a/bla/jemstr/v8y1999i3p383-431.htmlGoogle Scholar
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Lu  M, Ma  CT, Yuan  L.  Risk selection and matching in performance-based contracting.   Health Econ. 2003;12(5):339-354. doi:10.1002/hec.734PubMedGoogle Scholar
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Lu  M, Ma  CT.  Financial incentives and gaming in alcohol treatment.   Inquiry. 2006;43(1):34-53. doi:10.5034/inquiryjrnl_43.1.34PubMedGoogle Scholar
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Shen  Y.  Selection incentives in a performance-based contracting system.   Health Serv Res. 2003;38(2):535-552. doi:10.1111/1475-6773.00132PubMedGoogle Scholar
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Brucker  DL, Stewart  M.  Performance-based contracting within a state substance abuse treatment system: a preliminary exploration of differences in client access and client outcomes.   J Behav Health Serv Res. 2011;38(3):383-397. doi:10.1007/s11414-010-9228-5PubMedGoogle Scholar
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Stewart  MT, Reif  S, Dana  B,  et al.  Incentives in a public addiction treatment system: effects on waiting time and selection.   J Subst Abuse Treat. 2018;95:1-8. doi:10.1016/j.jsat.2018.09.002PubMedGoogle Scholar
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Barry  CL, Stuart  EA, Donohue  JM,  et al.  The early impact of the ‘Alternative Quality Contract’ on mental health service use and spending in Massachusetts.   Health Aff (Millwood). 2015;34(12):2077-2085. doi:10.1377/hlthaff.2015.0685PubMedGoogle Scholar
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Stuart  EA, Barry  CL, Donohue  JM,  et al.  Effects of accountable care and payment reform on substance use disorder treatment: evidence from the initial 3 years of the alternative quality contract.   Addiction. 2017;112(1):124-133. doi:10.1111/add.13555PubMedGoogle Scholar
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Donohue  JM, Barry  CL, Stuart  EA,  et al.  Effects of global payment and accountable care on medication treatment for alcohol and opioid use disorders.   J Addict Med. 2018;12(1):11-18. doi:10.1097/ADM.0000000000000368PubMedGoogle Scholar
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Rieckmann  T, Renfro  S, McCarty  D, Baker  R, McConnell  KJ.  Quality metrics and systems transformation: are we advancing alcohol and drug screening in primary care?   Health Serv Res. 2018;53(3):1702-1726. doi:10.1111/1475-6773.12716PubMedGoogle Scholar
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Centers for Medicare & Medicaid Services. Medicare Shared Savings Program. Updated June 1, 2020. Accessed June 18, 2020. https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/sharedsavingsprogram
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American Academy of Family Physicians. Medicare Shared Savings Program: accountable care organizations final rule. Published November 2, 2011. Accessed September 24, 2019. https://www.aafp.org/dam/AAFP/documents/advocacy/payment/medicare/ES-ACO-Rule-110211.pdf
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Berwick  DM.  Making good on ACOs’ promise—the final rule for the Medicare Shared Savings Program.   N Engl J Med. 2011;365(19):1753-1756. doi:10.1056/NEJMp1111671PubMedGoogle Scholar
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Center for Medicare & Medicaid Services. Alternative Payment Arrangements for the Pioneer ACO model. Accessed June 18, 2020. https://innovation.cms.gov/Files/x/Pioneer-ACO-Model-Alternative-Payment-Arrangements-document.pdf
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Rutledge  RI, Romaire  MA, Hersey  CL, Parish  WJ, Kissam  SM, Lloyd  JT.  Medicaid accountable care organizations in four states: implementation and early impacts.   Milbank Q. 2019;97(2):583-619. doi:10.1111/1468-0009.12386PubMedGoogle Scholar
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    Original Investigation
    Psychiatry
    July 23, 2020

    Association of Alternative Payment and Delivery Models With Outcomes for Mental Health and Substance Use Disorders: A Systematic Review

    Author Affiliations
    • 1Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle
    • 2McLean Hospital, Harvard Medical School, Boston, Massachusetts
    • 3University of Washington School of Nursing, Seattle
    • 4Department of Health Care Policy, McLean Hospital, Harvard Medical School, Boston, Massachusetts
    JAMA Netw Open. 2020;3(7):e207401. doi:10.1001/jamanetworkopen.2020.7401
    Key Points español 中文 (chinese)

    Question  Are alternative health care payment and delivery models (APMs) associated with changes in service delivery or outcomes for mental health and/or substance use disorders (MH/SUDs) in the United States?

    Findings  This systematic review included 27 articles on 17 APM implementations in MH/SUD care. Some specific APMs (eg, pay-for-performance) have been associated with improved MH/SUD outcomes, while others (eg, APMs with shared savings) have not; broadly, clinical outcome data are lacking in evaluations of APMs.

    Meaning  This systematic review identified some evidence for APM effectiveness in MH/SUD care; further research is needed to identify successful program components and associations with clinical outcomes.

    Abstract

    Importance  Health care spending in the United States continues to grow. Mental health and substance use disorders (MH/SUDs) are prevalent and associated with worse health outcomes and higher health care spending; alternative payment and delivery models (APMs) have the potential to facilitate higher quality, integrated, and more cost-effective MH/SUD care.

    Objective  To systematically review and summarize the published literature on populations and MH/SUD conditions examined by APM evaluations and the associations of APMs with MH/SUD outcomes.

    Evidence Review  A literature search of MEDLINE, PsychInfo, Scopus, and Business Source was conducted from January 1, 1997, to May 17, 2019, for publications examining APMs for MH/SUD services, assessing at least 1 MH/SUD outcome, and having a comparison group. A total of 27 articles met these criteria, and each was classified according to the Health Care Payment Learning and Action Network’s APM framework. Strength of evidence was graded using a modified Oxford Centre for Evidence-Based Medicine framework.

    Findings  The 27 included articles evaluated 17 APM implementations that spanned 3 Health Care Payment Learning and Action Network categories and 6 subcategories, with no single category predominating the literature. APMs varied with regard to their assessed outcomes, funding sources, target populations, and diagnostic focuses. The APMs were primarily evaluated on their associations with process-of-care measures (15 [88.2%]), followed by utilization (11 [64.7%]), spending (9 [52.9%]), and clinical outcomes (5 [29.4%]). Medicaid and publicly funded SUD programs were most common, with each representing 7 APMs (41.2%). Most APMs focused on adults (11 [64.7%]), while fewer (2 [11.8%]) targeted children or adolescents. More than half of the APMs (9 [52.9%]) targeted populations with SUD, while 4 (23.5%) targeted MH populations, and the rest targeted MH/SUD broadly defined. APMs were most commonly associated with improvements in MH/SUD process-of-care outcomes (12 of 15 [80.0%]), although they were also associated with lower spending (4 of 8 [50.0%]) and utilization (5 of 11 [45.5%]) outcomes, suggesting gains in value from APMs. However, clinical outcomes were rarely measured (5 APMs [29.4%]). A total of 8 APMs (47.1%) assessed for gaming (ie, falsification of outcomes because of APM incentives) and adverse selection, with 1 (12.5%) showing evidence of gaming and 3 (37.5%) showing evidence of adverse selection. Other than those assessing accountable care organizations, few studies included qualitative evaluations.

    Conclusions and Relevance  In this study, APMs were associated with improvements in process-of-care outcomes, reductions in MH/SUD utilization, and decreases in spending. However, these findings cannot fully substitute for assessments of clinical outcomes, which have rarely been evaluated in this context. Additionally, this systematic review identified some noteworthy evidence for gaming and adverse selection, although these outcomes have not always been duly measured or analyzed. Future research is needed to better understand the varied qualitative experiences across APMs, their successful components, and their associations with clinical outcomes among diverse populations and settings.

    Introduction

    The continued growth in US health care spending1,2 and persistent suboptimal population health outcomes3 have spurred increasing interest by payers to tie clinician reimbursement to quality and value4 through alternative payment models (APMs). According to the Centers for Medicare & Medicaid Services (CMS), APMs provide financial incentives to encourage high-quality, cost-efficient care and can apply to a specific clinical condition, care episode, or population.5 APMs are heterogeneous and can be divided into groups based on the type of payment (eg, fee-for-service or population-level) and category of financial risk to the clinician or health care organization (ie, none, penalties, bonuses, or both). Given the potential of these models to shift the focus of care from volume to value, APMs have been the subject of several initiatives on the federal, state, and payer levels since the passage of the Patient Protection and Affordable Care Act in 2010.6-8

    Mental health and substance use disorders (MH/SUDs), also known as behavioral health disorders, commonly co-occur and are associated with total health care spending that is 2 to 3 times higher than the national average.9 APMs have the potential to facilitate more efficient, comprehensive, and team-based care10 for this patient population by effectively aligning incentives at the patient, clinician, and system levels.11 Although MH/SUDs have rarely been prioritized in contemporary APM implementations,12 a body of work is now emerging.

    To consolidate this literature, we conducted a systematic review with the primary aim of assessing the evidence base for the association of APMs with MH/SUD outcomes in the United States. We defined 5 outcome categories that are particularly relevant to MH/SUD care in the context of APMs, as follows: (1) process-of-care measures (to determine the extent to which clinical outcome–associated care processes are improved by APMs); (2) clinical outcomes; (3) spending; (4) utilization; and (5) adverse selection, patient dumping (ie, changes in the severity of patients treated in an APM context), or gaming (ie, falsification of outcomes because of APM incentives).

    Additionally, we classified APMs according to the Health Care Payment Learning and Action Network (LAN) framework,13,14 which defines 4 categories (LAN 1 to LAN 4) that indicate movement along the continuum of integrated payment and delivery (Table 1). To our knowledge, this is the first such comprehensive review in the published academic literature. A 2017 Cochrane Systematic Review assessed APMs for general outpatient medical services worldwide15 and included 2 MH/SUD studies performed in the United States, both of which were identified in our query and excluded because they did not meet our APM definition (ie, payments were not tied to quality or value).16,17 Another 2017 systematic review restricted its search to pay-for-performance studies.18 Our investigation adds substantially to the literature by including a variety of payment models, conducting a multidimensional analysis, and organizing APMs for MH/SUDs and their respective evaluations by model type and evidence quality using common frameworks.

    Methods

    After conducting a pilot search and systematic review on September 1, 2017, we performed the final search (with additional terms) on April 15, 2018. We updated the search on May 17, 2019. This investigation followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline.

    The final literature search included MEDLINE, PsychInfo, Scopus, and Business Source databases and was restricted to studies between January 1, 1997, and May 17, 2019. Final search terms included 2 additions from the pilot search (eAppendix in the Supplement). We also manually searched reference lists of articles identified through the systematic review for other relevant citations. The primary focus of the literature review was comparative studies examining the associations of APMs with MH/SUD outcomes. For inclusion, articles had to describe studies from the United States, be written in English, examine an MH/SUD APM (according to the LAN framework19), assess a defined MH/SUD outcome, and have a comparison or control group.

    For the first 100 study publications of the final literature search, 2 authors (A.D.C. and N.M.B.) reviewed titles and abstracts concurrently to identify articles for full review. After reaching concordance on article inclusions and exclusions, both authors independently reviewed half of the remaining article titles and abstracts. Articles that could not be excluded via this process were selected for full-text review. All discrepancies were resolved through email discussions and meetings. The senior author (A.B.B.) independently reviewed articles as needed.

    Because multiple studies were often conducted on the same APM implementation, the data source for each article was identified and matches were noted. This allowed for analysis at the levels of APM implementation and article. APMs were categorized according to the LAN framework,13,14 with final designation being assigned by discussion and complete agreement among 3 authors (A.D.C., N.M.B., and A.B.B.). We excluded APMs in the LAN 3N and 4N categories because they lacked incentives or requirements regarding treatment quality or value.

    Each article’s quality of study design was scored using a framework adapted from the Oxford Centre for Evidence-Based Medicine (OCEBM) (Table 2).20,21 The OCEBM framework alone is inadequate for evaluations of large, systems-level interventions used to inform health policy,22 scenarios in which randomized clinical trials are often infeasible. Evaluating these types of interventions requires additional design and analytic methods to mitigate threats to causal inference. Therefore, we augmented the OCEBM by dividing the level 3 rating (ie, natural experiments) into 3 groups based on the strength of the methods used to address threats of study bias or causal inference.23 In descending order of methodologic rigor, these were 3A, 3B, and 3C, with those in category 3C being at the highest risk of bias (Table 2).

    Statistical Analysis

    No statistical analyses were conducted. Therefore, no software was used and no prespecified level of statistical significance was set

    Results
    Evidence Base

    After removing duplicates, we identified 986 articles, 41 of which were classified as APMs. We excluded 13 because they were categorized LAN 4N16,17,24-34 and 1 because of concerns about data quality.35 This left 27 articles and 17 APM implementations in the final analysis. Details appear in the study flow diagram (eFigure in the Supplement). Each primary LAN APM category (ie, LAN 2 to LAN 4) was represented in the final sample, and 3 articles evaluated APMs in multiple LAN categories.36-38 No single primary LAN category predominated in the results (Table 3).36-62

    More than half of the APMs (9 [52.9%]) targeted populations with SUDs, while 4 (23.5%) targeted MH populations, and the rest targeted MH/SUD broadly defined (Table 4). Most APMs focused on adults (11 [64.7%]), while fewer (2 [11.8%]) targeted children or adolescents. Medicaid and publicly funded SUD programs were most common, with each representing 7 APMs (41.2%). Most APM evaluations had an OCEBM rating of 3A (10 [58.8%]), although 2 were prospective controlled trials (1 [5.9%], OCEBM rating of 1; 1 [5.9%], OCEBM rating of 2). The APMs were primarily evaluated on their associations with process-of-care measures (15 [88.2%]), followed by utilization (11 [64.7%]), spending (9 [52.9%]), and clinical outcomes (5 [29.4%]). Only 8 APMs (47.1%) were evaluated for gaming or selection bias. When assessed, statistically significant process-of-care improvements were noted in 12 of 15 APMs (80.0%), while this was only true for 3 of 5 APMs (60.0%) with clinical outcomes and approximately half of the APMs with utilization and spending outcomes (5 of 11 [45.5%] and 4 of 8 [50.0%]). Detailed characteristics and outcomes of the 27 articles and 17 APM implementations are described in Table 5 and the eTable in the Supplement.

    Association of APMs With Outcomes
    LAN 2A: Foundational Payments for Infrastructure and Operations

    The Sustaining Healthcare Across Integrated Primary Care Efforts (SHAPE) study39 (OCEBM rating, 3A) evaluated the association of LAN 2A foundational payments with integrating MH/SUD services in primary care. Patients in the APM group were 3.5 times more likely to be screened for depression, 1.5 times more likely to be diagnosed with depression, and 1.3 times more likely to be diagnosed with anxiety. There were no differences for SUD found. Additionally, results showed a total cost savings of $17 per member per month and a net savings of more than $1 million during the study period.

    LAN 2C: Pay-for-Performance

    A total of 9 articles40-48 assessed 5 unique APM implementations (LAN 2C), 1 of which was a national, Substance Abuse and Mental Health Service Administration–funded pay-for-performance initiative using a cluster randomized clinical trial design that was evaluated in 4 separate publications,40-43 all with OCEBM ratings of 1. This program aimed to improve the implementation of the Adolescent Community Reinforcement Approach, an evidence-based SUD treatment model. The APM intervention resulted in therapists being more likely to demonstrate competence in delivering evidence-based treatment and in patients being more likely to receive the targeted dose,41 consistent with an earlier study examining therapist-level implementation of this treatment.40 Patients in the APM were also more likely to initiate SUD treatment, although there was no change in engagement.43 Findings were mixed on clinical outcomes, with 1 publication41 reporting no change in remission status, while another42 noted that receiving the targeted dose of the treatment (which was more likely in the APM group) was associated with a greater percentage of days abstinent. Additionally, patients in the APM group had lower MH/SUD treatment spending but higher spending when accounting for the cost of the APM itself.42

    Two studies46,47 examined an APM for MH/SUD integration that was implemented across a network of community health clinics in Washington state, both with OCEBM ratings of 3A. One study47 found that the APM was associated with a higher likelihood of having at least 1 follow-up contact, psychiatric case review, and Patient Health Questionnaire–9 assessment. Another study46 demonstrated that the APM was associated with more rapid depression symptom improvement as well as a higher likelihood of achieving treatment response and timely follow-up.

    Three other LAN 2C studies44,45,48 evaluated separate APMs. In a 2006 study,44 a counselor-level APM aiming to improve patient SUD treatment attendance (OCEBM rating, 3B) was associated with higher odds of completing at least 5 sessions but not the target number of 12 sessions. Another APM at a community SUD treatment clinic45 (OCEBM rating, 3C) was associated with patients attending more visits in the first 30 days and with higher 90-day treatment program retention without evidence of gaming. A third study examining the association of an APM with pediatric psychiatric hospitalization among children and adolescents with Medicaid coverage in Connecticut48 (OCEBM rating, 3C) found a nearly 25% decrease in length of stay in the final APM year compared with baseline, but no change in 7-day or 30-day readmission rates. While these latter 2 studies reported positive results, both were in OCEBM category 3C for study design quality.

    LAN 3A: APMs With Shared Savings

    In 2012, CMS implemented the Medicare Shared Savings Program (SSP) Accountable Care Organization (ACO) program.63 The SSP was primarily a 1-sided ACO model (ie, organizations share financial gains but not losses).64,65 In a series of 2 studies,36,37 both with OCEBM ratings of 3A, no differences in process-of-care outcomes, health care spending, service utilization, or patient characteristics relative to the comparison group (suggesting no selection bias) were found.36 Additionally, among beneficiaries diagnosed with depression, the SSP was associated with a decrease of 0.5% in antidepressant use and an increase of 0.4 days in the portion of days covered.37

    LAN 3B: APMs With Shared Savings and Downside Risk

    Alongside the SSP in 2012, CMS implemented the Pioneer ACO. Unlike the SSP, it initially included 2-sided risk (ie, organizations share financial gains and losses), and CMS preferentially selected organizations with existing infrastructure for case management and coordination.66,67 In a 2016 analysis36 (OCEBM rating, 3A), the Pioneer ACO was associated with a slight reduction in MH admissions and total MH spending in the first year of the program but not in the second. However, no changes were noted in utilization or processes-of-care outcomes. Among beneficiaries with depression, the Pioneer ACO was associated with slight increases in days of medication supplied in the first year and slight increases in the proportion of days supplied with antidepressants in the first 2 years. In a related study37 (OCEBM rating, 3A), there was no demonstrated change in the proportion of Pioneer ACO beneficiaries with depression using an antidepressant. Additionally, there was some evidence that the Pioneer ACO was associated with a reduction in the percentage of beneficiaries with MH diagnoses, although it is unlikely that these shifts contributed to reduced MH admissions and is unclear whether this finding represented intentional efforts.36

    Another study examined the associations of 3 different Medicaid ACOs with outcomes in MH/SUD populations in Maine (LAN 3A), Minnesota (LAN 3B), and Vermont (LAN 3A)38,68-70 (OCEBM rating, 3A). Minnesota’s ACO was associated with an increase in all-cause inpatient admissions in the MH/SUD population; Vermont’s ACO was associated with a decrease; and Maine’s ACO was associated with no change. All 3 ACOs were associated with reductions in emergency department visits, although none with changes in 30-day readmission rates. Maine’s ACO uniquely demonstrated a process-of-care measure improvement, finding an increase in 180-day antidepressant adherence among beneficiaries with depression. Vermont’s ACO was associated with lower total spending among beneficiaries with MH disorders, making it the only Medicaid ACO to hold that distinction.

    LAN 4A: Condition-Specific Population-Based Payment

    Delaware implemented a LAN 4A APM in 2002, providing financial incentives to publicly funded outpatient SUD programs; 1 outcome measure was aimed at improving capacity utilization (ie, proportion of treatment capacity used), while 2 measures targeted active participation and completion of treatment.50 This APM was associated with reduced wait times and increased length of stay in the treatment programs (OCEBM rating, 3A).49 It was also associated with greater average SUD treatment capacity utilization and a higher proportion of patients meeting active participation requirements during a period when the population appeared to become more severely ill (OCEBM rating, 3B).50 In 2008, Delaware implemented a new LAN 4A APM, incentivizing the state’s sole detoxification vendor to maintain current detoxification unit capacity (of ≥90%) and increase the proportion of frequent detoxification patients who transition to SUD care after detoxification (OCEBM rating, 3C).51 While detoxification capacity was maintained and the rates of transition to outpatient care improved, only 8% of postdetoxification rehabilitation treatment episodes met the targeted length of stay (60 outpatient days or 30 residential days for most patients). There was no evidence of adverse selection.51

    Another group of 7 studies52-58 examined 2 iterations of a LAN 4A APM for SUD treatment in Maine. In the first phase, organization-level performance data were reviewed annually, leading to possible positive or negative contractual changes in the subsequent year. This version of the APM was associated with improvements in the efficiency and effectiveness of SUD programs (OCEBM rating, 3B).52 However, 2 subsequent evaluations (OCEBM ratings, 3B53 and 3A55) found that the APM was also associated with increases in gaming. Additionally, there was some evidence for selection bias, with the APM being associated with a lower proportion of clients with higher severity illness in ambulatory settings (OCEBM rating, 3A).56 Results of another evaluation54 found that the APM was associated with better clinician-patient matching and higher rates of referral (OCEBM rating, 3A), contrasting the earlier findings of possible patient dumping via selection bias. Building off these experiences, Maine subsequently implemented a second-generation APM that included base payments, incentive payments (including penalties), and quarterly (as opposed to annual) assessments. Two separate analyses (OCEBM ratings, 3B57 and 3A58) found that the revised APM was not associated with any changes in process-of-care measures or utilization; there was also no evidence for selection bias.

    LAN 4B: Comprehensive Population-Based Payment

    Three study publications59-61 (all OCEBM rating 3A) assessed the 3-year MH/SUD outcome associations of the Blue Cross/Blue Shield of Massachusetts Alternative Quality Contract (AQC), a LAN 4B commercial insurance ACO. The AQC compensated participating organizations via a risk-adjusted, prospective payment for all primary and specialty care, with bonuses available based on performance across 64 measures (most unrelated to MH/SUD care). Only 5 of the initial 12 AQC contracts included risk for MH/SUD services. The AQC was associated with slightly less MH service use among organizations with downside risk for MH/SUD but not among those without.59 The AQC was associated with no change in MH/SUD spending in organizations with and without downside MH/SUD risk.59 Among organizations facing MH/SUD risk, the AQC was associated with no change in SUD service use, spending, or quality measures.60 In organizations without MH/SUD risk, the AQC was associated with no change in engagement, a slight decrease in SUD medication use, and slight increases in the probability of SUD service use and treatment initiation. Overall, including organizations with and without MH/SUD risk, the AQC was not associated with any changes in SUD medication use or spending.61

    Implemented in 2012, Oregon’s Medicaid Coordinated Care Organizations were a LAN 4B APM implementation that included a prospectively paid global budget for physical health, MH/SUDs, and dental care. All participating organizations had the opportunity to receive annual incentive payments from a quality pool based on performance across 17 quality metrics (not all related to MH/SUD). The APMs have been associated with improved rates of screening and brief intervention but no changes in SUD identification or treatment initiation (OCEBM rating, 3B)62; additionally, screening has been associated with changes in initiating SUD treatment 6 or 12 months after implementation.62

    Discussion

    We identified 17 distinct APMs that were evaluated for their associations with MH/SUD care delivered across diverse settings and patient populations. Included APMs had a range of funding sources (eg, Medicare, Medicaid, state public health systems, commercial insurance) and covered various diagnostic groups (eg, severe mental illness, depression, SUDs) and age categories (child to adult). No single LAN category predominated the literature. More than 70% of APMs (12 of 17) underwent methodologically rigorous evaluations (ie, had OCEBM ratings of 3A or higher).

    Process-of-care measures predominated in the evaluations (nearly 90%), with direct clinical outcomes being examined much less commonly (nearly 30%). In an important note of caution, gaming of outcomes was noted in 1 APM,53-55 and 3 APMs49-51 were associated with changes in patient composition that could suggest selection bias, possibly erroneously inflating findings of improvement in some cases (although these changes were likely inconsequential in others36). Overall, only 8 of 17 APMs (47.1%) were evaluated for gaming or selection bias in any capacity.

    Among APMs demonstrating statistically significant results, some were large and clinically meaningful, while others were smaller, of more questionable clinical significance, or mixed in terms of clinical benefit. For example, the SHAPE APM, which integrated MH/SUD services into primary care, was associated with 3.5 times higher rates of screening for depression as well as clinically significant increases in the identification of depression and anxiety (1.5 times and 1.3 times higher, respectively).39 In contrast, while there were some positive results associated with the Massachusetts AQC among organizations in the APM who were not at financial risk for MH/SUD care (ie, higher probability of any SUD service use and increased SUD identification), the magnitudes of these increases were small, and they were coupled with some less desirable process-of-care outcome findings (ie, decreased SUD medication use and SUD care initiation).60

    Additionally, associations that seem small in nature may actually be clinically significant based on the prevalence of a condition or scale of the APM. For example, in the 2013 Medicare Shared Savings Program, the APM was associated with a decrease of 0.5% in antidepressant use and, among those taking antidepressants, an increase of 0.4 days in the proportion of days covered, both of which were statistically significant.37 Although the magnitudes appeared relatively small, the prevalence of depression, coupled with the fact that this was a large-scale evaluation of a Medicare APM, suggests that this intervention could have had a substantial population effect.

    Our findings highlight important gaps in the literature on APMs in MH/SUD care. First, we found few studies examining the effects of APMs on the MH/SUD care of children or adolescents. Second, although process-of-care measures are expected to be associated with clinical outcomes and are commonly used in APM evaluations for their feasibility, the association between these 2 outcome types is not always certain, often leaving the associations between APMs and clinical outcomes unclear. Although challenging,71,72 future work is needed to identify target clinical outcomes for APMs that are both clinically useful and pragmatic, particularly for those evaluated across systems. Third, given the complexity and varied characteristics of APMs, further research is needed to learn the key components of successful APMs through mixed methods. Fourth, fewer than half the APMs were evaluated for gaming or adverse selection; such evaluations should be routine to ensure that an apparent positive association of an APM is not a false-positive that was achieved through selection effects in the study population. To better inform policy and practice, future APM investigations should have at least the following elements: (1) rigorous design and analysis (ie, either a randomized clinical trial or natural experiment that includes robust observational design methods and sensitivity analyses, as in OCEBM category 3A), (2) an evaluation for possible gaming or adverse selection, (3) at least 1 clinical outcome, and (4) process-of-care measures that incentivize fidelity to evidence-based care (eg, collaborative care for depression46).

    It is noteworthy that qualitative themes and barriers identified in this systematic review were overwhelmingly from evaluations of ACOs (LAN 3 and 4) that spanned a variety of payers and populations, including Medicare, Medicaid, and commercial insurance.59-61,73,74 Some of these findings are likely generalizable beyond ACOs. For example, in earlier ACOs that were not designed solely to manage MH/SUD care, these services were not considered low-hanging fruit and were often not an early focus of coordination or integration efforts.73,74 It is therefore unsurprising that changes in MH/SUD process-of-care, utilization, or spending outcomes were limited or mixed in the early ACOs. We would expect similar findings in APMs other than ACOs that were not primarily intended to manage MH/SUD care.

    Additionally, qualitative findings from articles in this review demonstrate that even advanced APMs managed by well-resourced health systems may experience challenges in lowering utilization or spending, have difficulty improving care quality consistently over time, and require infrastructure investments. Although qualitative findings from ACO-focused evaluations offered some broadly applicable insights, the lack of comparable findings from evaluations of other APM categories is a notable gap in the literature.

    Limitations

    This study has limitations. Our findings are limited by the restriction to empirical evaluations with comparison or control groups, which may have missed descriptive or perspective publications on APMs. This decision was made to limit our review to the most methodologically rigorous studies. However, despite our exclusion criteria, other prior reviews have been less comprehensive.15,18

    Conclusions

    While APMs were often associated with improvements in process-of-care measures, they were less often associated with reduced MH/SUD utilization and spending. Additionally, more information is needed on clinical outcomes, which have rarely been evaluated in APMs. Studies to date have found some evidence for gaming and adverse selection in the APM context, highlighting the importance of including assessments for these potential phenomena in all future evaluations. Further research is needed to better understand the varied qualitative experiences across APMs, their successful ingredients, and the associations of these models with clinical outcomes across diverse populations and settings.

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    Article Information

    Accepted for Publication: April 4, 2020.

    Published: July 23, 2020. doi:10.1001/jamanetworkopen.2020.7401

    Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Carlo AD et al. JAMA Network Open.

    Corresponding Author: Andrew D. Carlo, MD, MPH, Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, 1959 NE Pacific St, PO Box 356560, Room BB1644, Seattle, WA 98191 (adc42@uw.edu).

    Author Contributions: Drs Carlo and Benson had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: All authors.

    Acquisition, analysis, or interpretation of data: Carlo, Benson, Busch.

    Drafting of the manuscript: Carlo, Benson, Chu.

    Critical revision of the manuscript for important intellectual content: Carlo, Benson, Busch.

    Administrative, technical, or material support: Carlo, Benson, Chu.

    Supervision: Busch.

    Conflict of Interest Disclosures: None reported.

    Funding/Support: Dr Carlo was supported by the National Institute of Mental Health (grant number, 5T32MH073553-16) and a National Institutes of Health–funded postdoctoral fellowship at the University of Washington entitled “Training Geriatric Mental Health Services Researchers” (project number, 6T32MH073553-15). Dr Benson was supported by the National Library of Medicine Biomedical Informatics and Data Science Research Training (grant number, T15 LM007092). Dr Busch was supported by the Brandeis/Harvard National Institute on Drug Abuse Center to Improve System Performance of Substance Use Disorder Treatment (grant number, P30 DA035772).

    Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

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