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Challenging myths: Empathic decision making in usual clinical settings. Victor M. Montori, MD, MSc KER UNIT, Mayo Clinic. Decision making models. Modified from Charles C et al. Desired clinical behavior EMPATHIC DECISION MAKING Partnership Dance across models. http://kercards.e-bm.info. - PowerPoint PPT Presentation
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Victor M. Montori, MD, MScKER UNIT, Mayo Clinic
Challenging myths: Empathic decision making in usual clinical settings
Decision making models
Modified from Charles C et al
Approaches Parental Clinician-as-perfect agent
Shared decision-making Informed
Direction and amount of information flow about options
Clinician Patient Clinician Patient Clinician Patient Clinician Patient
Direction of information flow about values and preferences
Clinician Patient Clinician Patient Clinician Patient Clinician Patient
Deliberation Clinician Clinician Clinician, Patient Patient
Decider Clinician Clinician Clinician, Patient Patient
Consistent with EBM principles
No when decision is not purely technical and there
are optionsYes Yes Yes
Desired clinical behavior
EMPATHIC DECISION MAKING
1.Partnership 2.Dance across models
http://kercards.e-bm.info
Wiser Choices Programat Mayo Clinic’s KER UNIT
Settings (bold = RCT)Work Setting Policy Evaluation
Statin Choice Primary + specialty care
Effective care Feasible, effective, implemented in EHR, multicenter trial
DM2 Med Choice Primary care “Technical” care Feasible, effective, multicenter trial
Aspirin Choice Primary care (group) Effective care (but changed)
Not evaluated
Depression Choice Primary care Marketing Design phase
Genomic Choice Experimental Silent Design phase
Osteoporosis Choice Primary care Effective care Feasible, effective
ICD Choice Specialty care Preference sensitive Design phase
Smoking choice Primary care Effective care Design phase
Chest Pain Choice Emergency Effective care Feasible, effective, multicenter trial
AMI Choice Hospital ward Effective care Feasible, effective, multicenter trial
Hypertension e-primary care Effective care Design phase
Rosiglitazone General Effective care Not evaluated
Prostate General (tablet) Preference sensitive Design phase
Weymiller et al. Arch Intern Med 2007
Statin Choice
Osteoporosis Choice
Montori et al, AJM 2011
Mullan et al, Arch Intern Med 2009
Diabetes Medication Choice
AMI Choice
Chest Pain Choice
ParticipantsWork Age, mean
(range)Illness Clinician
satisfaction (%)*
Incremental time investment, median
Statin Choice 65 (55-80) Chronic, asymptomatic
74% 3.8 minutes (-2.9, 10.5)
Diabetes Medication Choice
62 (40-92) Chronic asymptomatic
90% 2.5 minutes
Osteoporosis Choice
67 (51-84) Chronic asymptomatic
75% 3.0 minutes (-56, 25)
Chest Pain Choice
54 (32-76) Acute, symptomatic
64% 1.6 minutes
AMI Choice 64 (40-85) Acute, symptomatic
NA NA
* Would like to use it again with other patients considering the same decision?
Success of the decision aid
Ethical Legal
Economic Effectiveness
Knowledge transferCreates a conversation
Fit
Implementation
UnderstandableDoable
Favorable
Fit for purpose, users, setting
http://normalizationprocess.org
Statin Decision Aid
Lessons learnt
User-centered design happens in the field, takes multiple iterations and expertise.
Testing decision aids in usual clinical settings is tough: decision moments are unpredictable.
Repeated use for chronic decisions has been difficult to study in efficacy trials.
MythsGoal and settings
1. Decision aids have no role in evidence-based care2. Decision aids support shared decision making3. Valid decision aids cannot be used in busy clinical
settings, such as primary care
Participants4. Clinicians would not want to use decision aids – they are
barriers to adoption of SDM5. Acutely ill patients are not good targets for SDM6. Elderly chronically ill patients cannot participate in SDM
Summary of 5 years of work
13 wiser choices decision aidsChronic and acute care
Primary and specialty careRural, urban, and academic
50+ sites200+ clinicians600+ patients
In trials!
http://kerunit.e-bm.orghttp://kercards.e-bm.infohttp://shareddecisions.mayoclinic.org
@vmontori