Utilizing Algorithms & Systems of Care: Improving Outcomes in Mental Health Treatment

1. Utilizing Algorithms & Systems of Care:Improving Outcomes in Mental Health Treatment Neal Adams MD MPH Director of Special Projects California Institute for Mental Health

2. Objectives • At the conclusion of the training, participants will better understand…. • role of medication algorithms in overall quality improvement • experience to date in algorithm implementation • data on apparent algorithm impacts • the role of psychoeducation in algorithms and disease management • stakeholder concerns

4. NEJM, June 2003 • Quality of Health Care Delivered to Adults in The United States • “the deficits in adherence to recommended processes for basic care pose serious threats to the health of the American public” • overall patients received recommended care only 55% of the time • range from 11% to 79%

6. Six Imperative Challenges in Redesigning Health Care • Redesign care processes • Effective use of information technologies • Knowledge and skills management • Development of effective teams • Coordination of care across patient conditions, services, & settings over time • Use of performance & outcome measures for CQI & accountability Institute of Medicine, Crossing the Quality Chasm, 2001

7. The Necessity of Process Improvement "The definition of insanity is… …continuing to do the same thing over and over again and expecting a different result.” Albert Einstein

8. Chronic Care Model Community Health System Health Care Organization Resources and Policies ClinicalInformationSystems Self-Management Support DeliverySystem Design Decision Support Prepared, Proactive Practice Team Informed, Activated Patient Productive Interactions Improved Outcomes

9. Chronic Illness Management Program Elements Guidelines Evidence-Based Planned Care Adapted from Katon, W. et al., Gen Hosp Psychiatry, 19:169-178, 1997.

10. CalMAP is an Illness Management Program • Evidence based algorithms • Uniform brief clinical rating scales • Optimal data set for decision support • Reduction in practice variability • Intensive patient/family education • increase participation in treatment and decision making • Clinical coordinator to enhance implementation and care Rush AJ, Crismon ML, et al J Clin Psych 2003.

11. Keys to Success • Effective implementation • knowledge, skills abilities and competencies • model/practice fidelity • Requires redesign of system processes!!! • workflow • project management • Quality management is critical to successful implementation • Change management • attitudes and behavior

12. Goals of Treatment Algorithms • Decrease variation in patient care • Provide framework for clinical decision- making • Deliver consistent treatment across clinicians and environments • Improve documentation of care • Improve patient outcomes Rush AJ, Crismon ML, et al. J Clin Psych 1998.

13. Extreme Variability Upper Control Limit Lower Control Limit

14. Quality Management Upper Control Limit Lower Control Limit

15. Goals of Treatment Algorithms (cont’d) • Provide basis for evaluating care • Provide basis for evaluating costs • Define costs related to specific treatments or outcomes • Provide metric for evaluating new treatments • Improve cost-effectiveness of care Gilbert D, et al. J Clin Psych 1998; Rush AJ, Crismon ML, et al. J Clin Psych 1998.

16. – – No Algorithm Patient Algorithm Condition + + Time in Treatment Potential Benefits of Algorithms • Patient condition = symptom severity + psychosocial functioning -- = Patient condition at initiation of treatment. + = Improvement during course of treatment. Rush AJ, Crismon ML, et al. J Clin Psych 1998.

17. Algorithm Philosophy • Goal of treatment should be remission • Most efficacious/safest treatments first • (i.e., evidence based) • Simplest interventions first • Subsequent interventions tend toward increased complexity and increased risk • Multiple options when appropriate • Patient preference Crismon ML, et al. J Clin Psych 1999.

18. Medication Algorithms • Evidence based, expert consensus derived • Strategies (What treatments?) • Tactics (How to treat?) • Adult population • Major depressive disorder • Schizophrenia • Bipolar disorder • Childhood disorders • ADHD • Depression

19. Development Process • Review of the evidence on a specific topic • Consensus panel process • academic content experts • practicing clinicians • consumers/family members • Present research evidence • Reaction panels • Discuss evidence & develop algorithms • Review and revise Crismon ML, et al. J Clin Psych 1999; Suppes T, et al. J Clin Psych 2002; Miller AL, et al. J Clin Psych 2004

20. Evidence Based Decision-Making • Levels of evidence • Level A • randomized, controlled clinical trials • Level B • epidemiologic studies, cohort studies, retrospective analyses, etc. • Level C • case reports, expert opinion Crismon ML, et al. J Clin Psych 1999.

21. Formulary Considerations • Algorithms should drive formulary • Question is not: ‘Is drug on formulary?’ • ‘When should it be used?’ • Acquisition cost vs health care costs? • acquisition cost should only considered after efficacy, safety, and tolerability are addressed • using preferred meds within an algorithm stage helps address both issues • Use of preferred meds when there is no clinical reason to use a different med

22. Exemplar Algorithm Strategies Patient with appropriate diagnoses, baseline evaluations, judged suitable for algorithm Monotherapy with agent with positive efficacy/side effect profile (chosen among list of Stage 1 meds) Stage 1 Monotherapy with alternate meds from above. May have added agents with less favorable efficacy/side effect profile or new agent with limited clinical experience Stage 2

23. Stage 3 Stage 4 Stage 5 Stage 6 Exemplar Algorithm Strategies(cont’d) (1) Monotherapy with different alternates(s) from above (May have more agents added to list) OR (2) Combination therapy with two agents with different mechanisms of action and favorable side effect profile when combined Different combination therapy than above (Medications with different mechanisms) (1) Different two-medication combination than above OR (2) Triple medication combination Other interventions as scientific data and clinical experience dictate

24. Tactical Issues • How is the treatment stage optimally implemented? • how often should the patient be seen • how should symptom improvement and side effects be monitored? • What are the critical decision points to make treatment decisions? • How long should treatment continue before declaring the treatment a failure? • How long should a successful treatment be continued? • how should a successful treatment be discontinued?

25. Characteristics of Algo Psychoeducation Program • Phased • simple to more complex • Targeted to individual needs • Multiple learning modalities • written, aural, visual, experiential • Repetition of key information • Individual and group formats • Consumer/family participation as educators • All materials available in Spanish

26. TMAP Research • Goal • Evaluate the clinical and economic outcomes of implementing an algorithm driven disease management program for the medical portion of care for individuals with bipolar disorder, major depressive disorder, or schizophrenia, treated in the public mental health sector, as compared with treatment as usual. Rush AJ, Crismon ML, et al. J Clin Psych 2003.

27. TMAP Comparison Groups TAUnonALGO clinic ALGO+ED TAUinALGO clinic Schizophrenia Bipolar disorder ALGO+ED TAUinALGO clinic TAUnonALGO clinic Major depressive disorder ALGO+ED TAUinALGO clinic TAUnonALGO clinic ED=education TAU=treatment-as-usual

28. Selected TMAP Results

29. SCZ: Sum of Cognition Z Scores: All Subjects

30. SCZ Adjusted Mean Symptoms (BPRS18): All Subjects BPRS18 Quarter

31. SCZ Adjusted Mean Symptoms (BPRS18)(Moderately Ill) Miller AL, et al. Schiz Bull (in press)

32. SCZ Adjusted Mean Negative Symptoms (SANS) (Low Baseline Score) Miller AL, et al. Schiz Bull (in press)

33. TMAP Costs Compared with Treatment as Usual

34. CalMAP Cost Calculations • Unit costs based upon VA regional charges • Includes organizational overhead and not just provider time • Includes costs for all patient encounters • Utilization based upon all administrative files, medical records review, and structured clinical interviews

35. Overall TMAP Costs • ALGO was associated with • higher medication costs (primarily due to increased potential for patient to possess an Rx) • greater frequency of physician visits, • but not necessarily higher physician costs: • BP - lower physician costs • SCZ - no difference in physician costs • MDD – higher physician costs

36. Value = Quality Cost • Healthcare economics • value is usually examined in terms of cost effectiveness • Cost effectiveness • can be increased by improving outcomes, by decreasing costs, or a combination of the two • Need to consider the difference in the outcomes and costs achieved with two different sets of interventions

37. SchizophreniaCost-Effectiveness • For BPRS as the clinical outcome, cost effectiveness is greater with ALGO intervention than TAU. • Cost effectiveness is even greater with cognition as the outcome

38. From TMAP to CalMAP • San Diego • Phase I • Humboldt • Kern • Phase III • State Hospitals

39. Adaptations • Optimal Data Set • decision support model • Training • Implementation strategies • Fidelity measures • MedMAP study

40. Competency Knowledge, skills and abilities Transformation Project Management work and business flow Change Management behavior and attitude

41. Consumer Concerns • Proscriptive treatment • Lack of individualization • Lack of choice • ECT • Cultural/ethnic adaptation • cultural competence of psychoeducation • Ethnopsychopharmacotherapy • Polypharmacy • Doctor to doctor variation in practice

42. Provider Concerns • Cookbook medicine • Too proscriptive • Lack of choice • Loss of professional autonomy • Burden • Increased tasks • Increased documentation • Cost savings only

43. Medi-Cal and DMH concerns • Poor quality pharmacotherapy • Rising costs • Lack of practice standards • Maintenance of an open formulary • Improved continuity of care

44. Conclusions • Algorithms provide a valuable tool in the management of chronic disease states • Implementation strategies and tactics are crucial to successful implementation • Best done in the context of a disease management program • System process redesign is likely necessary to successfully achieve implementation