April 10 @ 4:00 – 5:00 pm
Speaker: Eric Eisenstein, DBA
Presented from Duke University
Broadcast Link: Seminar
Suboptimal care transitions constitute a significant cause of morbidity, mortality, and excess health care costs in the United States. Since patients with chronic conditions and/or mental illnesses often require care from multiple care providers over disparate care settings, the negative consequences of fragmented care transitions are felt most acutely by these individuals. Because suboptimal care transitions frequently are associated with inadequate care coordination and ineffective communication among providers, patients, and their caregivers, the use of health information technology (HIT) has been identified as a promising strategy for improving the quality and safety of health care for patients with chronic health conditions.
A randomized trial was conducted of HIT-facilitated care transitions among Medicaid beneficiaries in six North Carolina counties. Patients were assigned to (1) usual care (n=2281), (2) clinical decision support (CDS) care transition messaging to patients and their medical homes (n=2240), or (3) CDS care transition messaging to patients, their medical homes, and their care managers (n=3482). This study sought to increase knowledge and understanding regarding the use of CDS for improving clinical and economic outcomes within a vulnerable population with chronic disease and/or mental illness; and to demonstrate a generalizable approach in a community setting that can be replicated at other sites.
Dr. Eisenstein is a member of the Duke Clinical Research Institute’s Outcomes Research and Assessment Group, with a special interest in understanding the relationships between complex interventions in health care systems and the long-term clinical and economic outcomes of patients. In addition to his work in traditional health technology evaluation, Dr. Eisenstein has an interest in evaluating information technologies as interventions in health care systems. In this regard, he has collaborated in the design and conduct of large-scale, randomized clinical trials to evaluate clinical decision support systems. The research objective in these studies has been to develop methods for evaluating health information technologies in practice-based settings using a “tool kit” of inexpensive, yet highly scalable methods that make use of data sets created as a byproduct of normal clinical and administrative operations. The use of these evaluation methods has been demonstrated in four clinical trials that include care process, clinical, economic, and quality of life measurements.