utilizing algorithms & systems of care: improving outcomes in mental health treatment
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Utilizing Algorithms & Systems of Care: Improving Outcomes in Mental Health Treatment. Neal Adams MD MPH Director of Special Projects California Institute for Mental Health. Objectives. At the conclusion of the training, participants will better understand…. - PowerPoint PPT PresentationTRANSCRIPT
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Utilizing Algorithms & Systems of Care:Improving Outcomes
in Mental Health Treatment
Neal Adams MD MPHDirector of Special Projects
California Institute for Mental Health
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Objectives
• At the conclusion of the training, participants will better understand….role of medication algorithms in overall
quality improvement experience to date in algorithm
implementationdata on apparent algorithm impactsthe role of psychoeducation in algorithms
and disease managementstakeholder concerns
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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 timerange from 11% to 79%
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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
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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
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Informed,ActivatedPatient
ProductiveInteractions
Prepared,ProactivePractice Team
Improved Outcomes
DeliverySystemDesign
DecisionSupport
ClinicalInformation
Systems
Self-Management
Support
Health System
Resources and Policies
Community
Health Care Organization
Chronic Care Model
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Chronic Illness Management Program ElementsGuidelines
Evidence-Based Planned Care
Adapted from Katon, W. et al., Gen Hosp Psychiatry, 19:169-178, 1997.
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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.
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Keys to Success
• Effective implementation knowledge, skills abilities and competenciesmodel/practice fidelity
• Requires redesign of system processes!!!workflowproject management
• Quality management is critical to successful implementation
• Change management attitudes and behavior
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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.
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Extreme Variability
Upper Control Limit
Lower Control Limit
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Quality Management
Upper Control Limit
Lower Control Limit
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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.
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Potential Benefits of Algorithms
• Patient condition = symptom severity + psychosocial functioning-- = Patient condition at initiation of treatment.
+ = Improvement during course of treatment.
Patient Condition
Time in Treatment
Algorithm
No Algorithm
++
––
Rush AJ, Crismon ML, et al. J Clin Psych 1998.
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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.
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Medication Algorithms
• Evidence based, expert consensus derivedStrategies (What treatments?)Tactics (How to treat?)
• Adult populationMajor depressive disorderSchizophreniaBipolar disorder
• Childhood disorders ADHDDepression
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Development Process
• Review of the evidence on a specific topic• Consensus panel process
academic content expertspracticing cliniciansconsumers/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
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Evidence Based Decision-Making
• Levels of evidenceLevel A
randomized, controlled clinical trialsLevel B
epidemiologic studies, cohort studies, retrospective analyses, etc.
Level Ccase reports, expert opinion
Crismon ML, et al. J Clin Psych 1999.
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Formulary Considerations
• Algorithms should drive formularyQuestion 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 addressedusing 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
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Monotherapy with agent withpositive efficacy/side effect profile
(chosen among list of Stage 1 meds)
Monotherapy with agent withpositive efficacy/side effect profile
(chosen among list of Stage 1 meds)
Monotherapy with alternate meds from above. May have added agents with less
favorable efficacy/side effect profile or new agent with limited clinical experience
Monotherapy with alternate meds from above. May have added agents with less
favorable efficacy/side effect profile or new agent with limited clinical experience
Patient with appropriate diagnoses,baseline evaluations, judged
suitable for algorithm
Stage 1
Stage 2
Exemplar Algorithm Strategies
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Different combination therapy than above (Medications with different mechanisms)
Different combination therapy than above (Medications with different mechanisms)
Other interventions as scientific data and clinical experience dictate
Other interventions as scientific data and clinical experience dictate
Exemplar Algorithm Strategies (cont’d)
(1) Different two-medication combination than above OR
(2) Triple medication combination
(1) Different two-medication combination than above OR
(2) Triple medication combination
(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
(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
Stage 3
Stage 4
Stage 5
Stage 6
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Tactical Issues
• How is the treatment stage optimally implemented?how often should the patient be seenhow 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?
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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
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TMAP Research
• GoalEvaluate 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.
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TMAP Comparison Groups
ALGO+ED
Schizophrenia
Bipolar disorder
Major depressive disorder
TAUinALGO clinic
TAUnonALGO
clinic
ALGO+ED
TAUinALGO clinic
TAUnonALGO
clinic
ALGO+ED
TAUinALGO clinic
TAUnonALGO
clinic
ED=education TAU=treatment-as-usual
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Selected TMAP
Results
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SCZ: Sum of Cognition Z Scores:
All Subjects
00
1
2
Baseline 1st Quarter 3rd Quarter
Sum
of z
Sco
res
TAUnonALGO (n=122)ALGO+ED (n=137)
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SCZ Adjusted Mean Symptoms (BPRS18): All Subjects
41.8
25
30
35
40
45
50
55
Baseline 1 2 3 4
TAUnonALO (n=144)ALO+ED (n=165)
Quarter
BPR
S1
8
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SCZ Adjusted Mean Symptoms (BPRS18)(Moderately Ill)
26.3
25
30
35
40
45
50
55
Baseline 1 2 3 4
Quarter
BPR
S 18
TAUnonALGO (n=12)
ALGO+ED (n=39)
Miller AL, et al. Schiz Bull (in press)
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SCZ Adjusted Mean Negative Symptoms (SANS) (Low Baseline Score)
Miller AL, et al. Schiz Bull (in press)
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TMAP Costs Compared with
Treatment as Usual
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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
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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 costsMDD – higher physician costs
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Value = Quality Cost
• Healthcare economicsvalue 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
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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
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From TMAP to CalMAP
• San Diego• Phase I
HumboldtKern
• Phase III• State Hospitals
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Adaptations
• Optimal Data Set decision support model
• Training• Implementation strategies• Fidelity measures
MedMAP study
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CompetencyKnowledge, skills and abilities
Project Management
work and business flow
Change Management
behavior and attitude
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Consumer Concerns
• Proscriptive treatmentLack of individualizationLack of choice
• ECT• Cultural/ethnic adaptation
cultural competence of psychoeducationEthnopsychopharmacotherapy
• Polypharmacy• Doctor to doctor variation in practice
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Provider Concerns
• Cookbook medicineToo proscriptiveLack of choiceLoss of professional autonomy
• BurdenIncreased tasksIncreased documentation
• Cost savings only
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Medi-Cal and DMH concerns
• Poor quality pharmacotherapy• Rising costs• Lack of practice standards• Maintenance of an open formulary• Improved continuity of care
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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