quality iq patient simulation physician practice measurement and engagement

Conditions: Diabetes; Hypertension; Depression; Osteoarthritis; Asthma; Pain; Heart Failure

Summary: this study will test the quality of physician care decisions using a patient-simulation based measurement and feedback approach that combines multiple-choice care decisions with real-time, personalized scoring and feedback. the study will also measure the impact of gaming-inspired competition and motivation, including a weekly leaderboard, to improve evidence-based care decisions. in addition, the study the test the impact of cme and moc credits on participant engagement in the process.

primary care providers (pcps) make many of the most important care decisions, especially for patients with chronic conditions and multiple co-morbidities. studies have confirmed that unwarranted variation is common among pcps, with high level of variation in care documented between urban and rural practices, across regions, and even among providers within a single healthcare system. the investigators' previous work has shown that patient simulations can rapidly and reliably measure unwarranted practice variation among providers. in addition, published work shows that patient simulations, when administered serially and combined with customized feedback on improvement opportunities can reduce practice variation and improve performance on patient-level quality measures. given the large scope of unwarranted variation in medical practice, there is a need for scalable approaches to measure care decisions, provide feedback on improvement opportunities and benchmark performance to peers. this study seeks to evaluate the impact of measurement, feedback and competition on evidence-based care decisions made by primary care providers across the country. it is a randomized, controlled trial with multiple measurements across key domains of clinical care. all participants are asked to care for simulated patients designed to look like typical patients seen in a primary care practice. in each case, providers will answer multiple-choice questions about their preferred course of action to work-up, diagnose and treat patients in the primary care setting. after each question, providers will receive evidence-based feedback, including references, on the appropriateness of each of their care decisions. feedback will be supported with relevant reference to evidence-based guidelines, including national mips quality measures. all participants will receive the following interventions: * feedback on care decisions made in each quality iq case, which will identify correct care, unneeded care, or gaps in care. this feedback will recommend or reinforce evidence-based care decisions and includes references. * all cases will be scored against evidence-based criteria. for each case, providers will start with 100 base points. correct care decisions will add to that total, while unnecessary care decisions will subtract from that total. a weekly leaderboard will be posted online, allowing participants to see how they are performing relative to their peers across the country. participants will have the opportunity to select a unique username or an anonymous user id to be identified on the leaderboard, to maintain anonymity. half of the recruits will be offered category i cme credit approved by the university of california, san francisco school of medicine (ucsf) which has been accredited by the accreditation council of continuing medical education to provide cme for physicians and moc points in the abim's moc program.

Eligibility criteria:
inclusion criteria: 1. board-certified in internal medicine or family medicine 2. minimum patient panel size of 1,500 patients 3. english-speaking 4. access to the internet 5. informed, signed and voluntarily consented to be in the study exclusion criteria: 1. not board certified in either internal medicine or family medicine 2. patient panel size less than 1,500 patients 3. non-english speaking 4. unable to access the internet 5. does not voluntarily consent to be in the study