Transitioning From Volume to Value: One Academic Medical Center's Approach to Improving Population Health.

PROBLEM: The U.S. health care system is undergoing a major transformation. Clinical delivery systems are now being paid according to the value of the care they provide, in accordance with the Triple Aim, which incorporates improving the quality and cost of care and the patient experience. Increasingly, financial risk is being transferred from insurers to clinical delivery systems that become responsible for both episode-based clinical care and the longitudinal care of patients. Thus, these delivery systems need to develop strategies to manage the health of populations. Academic medical centers (AMCs) serve a unique role in many markets yet may be ill prepared for this transformation. APPROACH: In 2013, Oregon Health & Science University (OHSU) partnered with a large health insurer and six other hospitals across the state to form Propel Health, a collaborative partnership designed to deliver the tools, methods, and support necessary for population health management. OHSU also developed new internal structures and transformed its business model to embrace this value-based care model. OUTCOMES: Each Propel Health partner included the employees and dependents enrolled in its employee medical plan, for approximately 55,000 covered individuals initially. By 2017, Propel Health is expected to cover 110,000 individuals. Other outcomes to measure in the future include the quality and cost of care provided under this partnership. NEXT STEPS: Anticipated challenges to overcome include insufficient primary care networks, conflicting incentives, local competition, and the magnitude of the transformation. Still, the time is right for AMCs to commit to improving the health of populations.

Early experiences of accredited clinical informatics fellowships.

Since the launch of the clinical informatics subspecialty for physicians in 2013, over 1100 physicians have used the practice and education pathways to become board-certified in clinical informatics. Starting in 2018, only physicians who have completed a 2-year clinical informatics fellowship program accredited by the Accreditation Council on Graduate Medical Education will be eligible to take the board exam. The purpose of this viewpoint piece is to describe the collective experience of the first four programs accredited by the Accreditation Council on Graduate Medical Education and to share lessons learned in developing new fellowship programs in this novel medical subspecialty.

Clinical Decision Support Reduces Overuse of Red Blood Cell Transfusions: Interrupted Time Series Analysis.

BACKGROUND: Red blood cell transfusion is the most common procedure in hospitalized patients in the US. Growing evidence suggests that a sizeable percentage of these transfusions are inappropriate, putting patients at significant risk and increasing costs to the health care system. METHODS: We performed a retrospective quasi-experimental study from November 2008 until November 2014 in a 576-bed tertiary care hospital. The intervention consisted of an interruptive clinical decision support alert shown to a provider when a red blood cell transfusion was ordered in a patient whose most recent hematocrit was ≥21%. We used interrupted time series analysis to determine whether our primary outcome of interest, rate of red blood cell transfusion in patients with hematocrit ≥21% per 100 patient (pt) days, was reduced by the implementation of the clinical decision support tool. The rate of platelet transfusions was used as a nonequivalent dependent control variable. RESULTS: A total of 143,000 hospital admissions were included in our analysis. Red blood cell transfusions decreased from 9.4 to 7.8 per 100 pt days after the clinical decision support intervention was implemented. Interrupted time series analysis showed that significant decline of 0.05 (95% confidence interval [CI], 0.03-0.07; P < .001) units of red blood cells transfused per 100 pt days per month was already underway in the preintervention period. This trend accelerated to 0.1 (95% CI, 0.09-0.12; P < .001) units of red blood cells transfused per 100 pt days per month following the implementation of the clinical decision support tool. There was no statistical change in the rate of platelet transfusion resulting from the intervention. CONCLUSIONS: The implementation of an evidence-based clinical decision support tool was associated with a significant decline in the overuse of red blood cell transfusion. We believe this intervention could be easily replicated in other hospitals using commercial electronic health records and a similar reduction in overuse of red blood cell transfusions achieved.