SEMINAR: "Supporting medication reconciliation and medication self-management: the implication of home health quality reporting requirements on the home care admission visit" — Ellen Bass
WHEN: April 8, 2021 3:00 pm-4:00 pmADD TO CALENDAR
The Departmental Seminar Series is open to all. U-M Industrial and Operations Engineering graduate students and faculty are especially encouraged to attend.
Supporting medication reconciliation and medication self-management: the implication of home health quality reporting requirements on the home care admission visit
In the United States, the landscape for the operations and evaluation of Medicare-certified home health agencies has changed radically in the last five years. In January 2016, the Center for Medicare & Medicaid Innovation Center launched the Home Health Value Based Purchasing Model (HHVBPM) and in November 2018, CMS finalized a case-mix classification model that went into effect January 1, 2020. The timeframe of home health payments changed from a 60-day episode to a 30-day period. To evaluate the agencies, the model uses data from several sources. The inclusion of Outcome and Assessment Information Set (OASIS) dataset derived from the patient home care episodes coupled with the shortening of the home care episode period place a burden on the admission visit. Systems engineering research can help to address the associated data collection and documentation burden. Given that home care and other post-acute care settings were omitted from Meaningful Use developments, their progress in supporting smooth information transfer and applications of decision support and data science lag behind acute care. Thus research can identify whether some data could be acquired as part of the referral into home care. Finally research is required to ensure that the quality and outcome measures are differentiating the agencies in ways that improve patient care. This talk will discuss a four-year multidisciplinary collaborative research project addressing standards for health information technology to support the homecare admission process. It will address the characterization of information requirements, decision-making, and workflow for admitting nurses based on focus groups, observations, and document review of 3 agencies (serving rural, suburban, urban populations) using 3 different HIT systems. The analysis will focus on three critical clinical decisions (medication self-management capability, problems to put on the care plan, next visit timing and frequency of future visits).
Ellen J. Bass is Interim Associate Dean for Research and Professor in the Department of Information Science in the Drexel University’s College of Computing and Informatics. She is a Professor and Chair of the Department of Health Systems and Sciences Research in the College of Nursing and Health Professions. She also holds affiliate status in Drexel University’s School of Biomedical Engineering, Science and Health Systems. She is also Adjunct Professor of Anesthesiology and Critical Care at the University of Pennsylvania’s School of Medicine.
Bass has over 30 years of human-centered systems engineering research and design experience in multiple domains. The focus of her research is to develop theories of human performance, quantitative modeling methodologies, measures, and associated experimental designs that can be used to evaluate human-automation interaction and human-human coordination in the context of total system performance. She has published over 150 peer-reviewed publications. Her research program is currently funded by the FAA, NIH, PCORI, and the VA.
Bass is a fellow of the Human Factors and Ergonomics Society and a senior member of the IEEE and of the American Institute of Aeronautics and Astronautics. Dr. Bass is the incoming Secretary-Treasurer Elect of the Human Factors and Ergonomics Society. She is a member of the editorial board for three journals: Human Factors, IIE Transaction on Occupational Ergonomics and Human Factors and the Journal of Cognitive Engineering and Decision Making. She was the inaugural editor of the IEEE Trans. on Human-Machine Systems. She is a peer reviewer for several international research programs.
Bass holds a Ph.D. in Industrial and Systems Engineering from the Georgia Institute of Technology, an M.S. in Advanced Technology from the State University of New York at Binghamton, a B.S.Eng. in Bioengineering from the University of Pennsylvania, and a B.S.Econ. in Finance from the University of Pennsylvania.