Shared Decision Making in Diabetes: What, Why, and How? Nilay D. Shah Mayo Clinic Rochester, Minnesota, USA

Disclosures Funding provided by: AHRQ: R18 HS019214; R18 HS018339 NIDDK: R34 DK84009 Foundation for Informed Medical Decision Making (FIMDM) American Diabetes Association (ADA) Mayo Clinic Foundation for Medical Education and Research Mayo Clinic CCaTS

Decision making models Approaches Parental Clinician-as-perfect agent Shared decision-making Informed Direction and amount of information flow about options Clinician Patient Clinician Patient Clinician Patient Direction of information flow about values and preferences Clinician Patient Clinician Patient Deliberation Clinician Clinician, Patient Patient Decider Consistent with EBM principles No when decision is not purely technical and there are options Yes empathic Modified from Charles C et al

Opportunities for SDM in practice When pros and cons are closely balanced When pros>cons only if patients adhere When pros and cons are not well known

What if patients drove the process? (1) What are my options? What happens if I do nothing else? (2) What are the risks and benefits of each option? (3) How likely are these risks and benefits to happen? Shepherd HL, et al. Patient Educ Couns (2011), doi:10.1016/j.pec.2011.07.022

Shared Decision Making Why do it? Payment and policy Efficiency – time, cost, utilization Patient Safety – misdiagnosis of patient preferences leads to unwanted or unneeded tests and treatments Patient Engagement – what would the patient choose if the patient knew what clinician knows Patient Experience – satisfaction Ethics – right thing to do

The body of evidence Systematic review of 115 RCTs Compared to usual care, decision aids: Increase patient involvement by 34% (+++-) Increase patient knowledge of options by 13% (++++) Increase consultation time by ~2.6 minutes Reduce decisional conflict by ~7% Reduce % undecided by 40% No consistent effect on choice, adherence, health outcomes or costs A) Criteria involving decision attributes: Decision aids performed better than usual care interventions by increasing knowledge (MD 13.77 out of 100; 95% confidence interval (CI) 11.40 to 16.15; n = 26). When more detailed decision aids were compared to simpler decision aids, the relative improvement in knowledge was significant (MD 4.97 out of 100; 95% CI 3.22 to 6.72; n = 15). Exposure to a decision aid with expressed probabilities resulted in a higher proportion of people with accurate risk perceptions (RR 1.74; 95% CI 1.46 to 2.08; n = 14). The effect was stronger when probabilities were expressed in numbers (RR 1.93; 95% CI 1.58 to 2.37; n = 11) rather than words (RR 1.27; 95% CI 1.09 to 1.48; n = 3). Exposure to a decision aid with explicit values clarification compared to those without explicit values clarification resulted in a higher proportion of patients achieving decisions that were informed and consistent with their values (RR 1.25; 95% CI 1.03 to 1.52; n = 8). B) Criteria involving decision process attributes: Decision aids compared to usual care interventions resulted in: a) lower decisional conflict related to feeling uninformed (MD -6.43 of 100; 95% CI -9.16 to -3.70; n = 17); b) lower decisional conflict related to feeling unclear about personal values (MD -4.81; 95% CI -7.23 to -2.40; n = 14); c) reduced the proportions of people who were passive in decision making (RR 0.61; 95% CI 0.49 to 0.77; n = 11); and d) reduced proportions of people who remained undecided post-intervention (RR 0.57; 95% CI 0.44 to 0.74; n = 9). Decision aids appear to have a positive effect on patient-practitioner communication in the four studies that measured this outcome. For satisfaction with the decision (n = 12) and/or the decision making process (n = 12), those exposed to a decision aid were either more satisfied or there was no difference between the decision aid versus comparison interventions. There were no studies evaluating the decision process attributes relating to helping patients to recognize that a decision needs to be made or understand that values affect the choice. C) Secondary outcomes Exposure to decision aids compared to usual care continued to demonstrate reduced choice of: major elective invasive surgery in favour of conservative options (RR 0.80; 95% CI 0.64 to 1.00; n = 11). Exposure to decision aids compared to usual care also resulted in reduced choice of PSA screening (RR 0.85; 95% CI 0.74 to 0.98; n = 7). When detailed compared to simple decision aids were used, there was reduced choice of menopausal hormones (RR 0.73; 95% CI 0.55 to 0.98; n = 3). For other decisions, the effect on choices was variable. The effect of decision aids on length of consultation varied from -8 minutes to +23 minutes (median 2.5 minutes). Decision aids do not appear to be different from comparisons in terms of anxiety (n = 20), and general health outcomes (n = 7), and condition specific health outcomes (n = 9). The effects of decision aids on other outcomes (adherence to the decision, costs/resource use) were inconclusive. Stacey D et al. Cochrane review 2014

Glasziou and Haynes ACP JC 2005 EBM KT Glasziou and Haynes ACP JC 2005

“There are now 75 trials and 11 systematic reviews of trials, per day…” Bastian et. al, 2010 PLoS Medicine

Source: IOM, Best Care at Lower Costs

Imagine…. 62-year old woman…. Diabetes: Metformin 2x/day, SU 1x/day Hypertension: Diuretic and ACE-I 1/day Hypercholesterolemia: statin 1/day Osteoporosis: Bisphosphonate 1/week Chronic pain: NSAID 2x/day Asthma: oral leukotriene 1x/day OTC: Aspirin 1x/day Other health care requirements: testing and screening; specialists Caregiver...

What should be the A1c goal? Which agents to use?

Quality of care HbA1c Clinical inertia Technical decisions Report card

Will I live unhindered by complications? Will I live longer? Will I feel better? Will I live unhindered by complications?

For HbA1c to work... Is there a strong, consistent, independent association between HbA1c and patient important outcomes? Have RCTs across drug classes shown that improvement in HbA1c has consistently led to improvement in patient important outcomes? CAUSAL PATHWAY Patient important outcomes Tx HbA1c

Observational studies Consistent association between a 1% increase in HbA1c and 50% increase in risk of progression of retinopathy 20% increase in risk of macrovascular complications

20% diabetes trials in 2003 measured patient important outcomes Montori et al. Diabetes Care 2006

diabetes trials in future will measure 18% diabetes trials in future will measure patient important outcomes as primary endpoints Gandhi et al. JAMA 2008

Wasted or misallocated healthcare resources: Key problem: Do not follow advice Wasted or misallocated healthcare resources: US$ 290b (100b in avoidable hospitalizations) Poor health despite cost and side effects Complicated patient-clinician relationship Cutler and Everett NEJM 2010 10.1056/NEJMp1002305

Patient advisory groups Evidence synthesis Observation clinical encounters Initial prototype Designers Study team Patient advisory groups Clinicians Stakeholders Modified prototype Field testing Final Decision Aid Evaluation (trial)

Goal of our encounter tools Create a conversation Patient asks questions + formulates plan Tool must be quiet: share evidence + shut up. Goal for conversation: collaborative deliberation Preferences are constructed through discussion (trying on the options)

Diabetes Cards Nature of diabetes medication discussions Summarizing the research evidence Iterative process – Choice Architecture

“Baseball Cards”

“Narrative Cards”

Why not a benefits card? The impact of diabetes medications on patient important outcomes is unclear. Insulin and microvascular SU and insulin and macrovascular Glitazones and macrovascular Metformin and macrovascular

Incorporate patient preferences and context into clinical decisions

Incorporate research evidence and clinician’s expertise into patient decisions

Increased patient involvement More helpful Improved knowledge Increased patient involvement No difference in adherence (perfect adherence in control gr) No significant impact on HbA1c levels Mullan RJ et al. Archives of Internal Medicine 2009

Risk-Treatment Paradox Ko, Mamdani and Alter JAMA 2004

ACC/AHA Cholesterol Guidelines

ACC/AHA Cholesterol Guidelines Ioannidis JP. JAMA 2014

ACC/AHA Cholesterol Guidelines Pencina MJ. NEJM. 2014; March 19 online.

Improved Knowledge Risk estimation Comfort with the decision Total trust Action (70% fewer Rx in low risk patients) Short-term adherence First one to be developed. In endocrinology. Diabetes patients. Now in the EMR system at Mayo. Used more than 500 times.. Weymiller et al. Arch Intern Med 2007

Web Statin Choice

Web Statin Choice

IMPLEMENTATION

Implementing Statin Choice EMR Link Web EMR Documentation http://statindecisionaid.mayoclinic.org

Engaging the Practices “What is SDM?” “I already do SDM” Practice-based research network Clinical champions – relationship building

Engaging the Practices (2) Demos of the tools Voluntary participation by clinicians Flexible implementation – what works best for that clinic? Tie-in to ongoing quality improvement efforts

Barriers to Participating PRACTICE Time Value – what is the impact? “we already do this” Competing priorities Beliefs CLINICIAN Initiating this work

Clinician satisfaction (%)* Incremental time investment, median Participants Work Age, mean (range) Clinician satisfaction (%)* Incremental time investment, median Statin Choice 65 (55-80) 74% 3.8 minutes Diabetes Medication Choice 62 (40-92) 90% 2.5 minutes * Would like to use it again with other patients considering the same decision?

Challenges with evidence synthesis and changing evidence Lessons learnt User-centered design happens in the field, takes multiple iterations and expertise Challenges with evidence synthesis and changing evidence 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

The impact on improving adherence to medications is mixed Lessons learnt Decision aids have increased knowledge and patient involvement in the decision consistently The impact on improving adherence to medications is mixed Clinicians and patients have reported high-levels of satisfaction (in trial settings)

Work in progress Better understanding of the level of evidence necessary to embed into practice Challenges of broad implementation into routine practice and repeated use Right place and time to engage patients with chronic conditions

shah.nilay@mayo.edu http://shareddecisions.mayoclinic.org