Each year 46 million Americans with uncontrolled hypertension (HT) make well over 150 million office visits to about 150,000 primary care physicians (PCPs). Each of these visits represents an opportunity to intensify HT therapy, but over 80% of the time, no change in therapy is made. These data suggest that an effective HT improvement intervention must reduce therapeutic inertia in primary care, and be simple and inexpensive enough to facilitate broad dissemination. Personalized Physician Learning (PPL) is a powerful strategy to change behavior that has yet to be applied to HT care provided by PCPs. In this project we assess the impact of two PPL interventions that differ in how they identify patterns of physician decision making in HT care. The first intervention, REAL-PPL, uses real EMR-derived data to direct the personalized learning intervention. The second intervention, SIM-PPL, uses physician performance on simulated cases to direct the personalized learning intervention. Both interventions are delivered via the web to PCPs who complete a minimum of 12 simulated HT learning cases that address therapeutic inertia and each physician's specific identified failures of decision making in HT care. The learning cases used in both interventions embody principles of adaptive learning, and provide three kinds of learning feedback (of actual BP values, graphic data displays, and specific clinical management suggestions) an average of 15 times per learning case. To test the hypothesis that these interventions improve HT control, we group randomize 39 clinics with their 120 PCPs and 6,000 adult patients with uncontrolled HT to one of three study arms: (a) REAL+PPL Intervention, (b) SIM+PPL Intervention, or (c) No Intervention (control group). Hierarchical logistic models (MLwiN) that accommodate nested data are used to compare each intervention to control group, and to evaluate differences between the REAL+PPL and SIM+PPL interventions. Secondary analysis quantifies the impact of interventions on therapeutic inertia and on a defined set of specific failures of physician decision making in HT care. Formal cost-effectiveness analysis provides key information to guide dissemination of interventions, if successful. Dissemination is supported by (a) the low cost and easy transportability of SIM+PPL, (b) the appeal of REAL+PPL to medical groups with EMRs, (c) the prevalence, clinical complications, and costs of uncontrolled HT, and (d) participation of PCP and managed care leaders in the project. Results have the potential to improve the care of millions of Americans who silently suffer the ravages of one of the most devastating and hard to control chronic diseases of all time.A major obstacle to better hypertension care in the United States is failure of physicians to intensify therapy in a timely and effective way.
This project takes advantage of electronic medical record technology to (a) assess physician patterns of hypertension care, (b) based on each physician's observed patterns of care, develop and deliver via the Web a sophisticated physician-specific set of learning interventions, and (c) assess the impact of these learning interventions on quality of care delivered to real patients with hypertension. Results have the potential to improve the care of millions of Americans who silently suffer the ravages of one of the most devastating and hard to control chronic diseases of all time.
|O'Connor, Patrick J; Magid, David J; Sperl-Hillen, JoAnn M et al. (2014) Personalised physician learning intervention to improve hypertension and lipid control: randomised trial comparing two methods of physician profiling. BMJ Qual Saf 23:1014-22|