Kathryn H Bowles1, Alexandra Hanlon, Diane Holland, Sheryl L Potashnik, Maxim Topaz. 1. Kathryn H. Bowles, PhD, RN, FAAN, FACMI, is a Professor and Director of the Center for Integrative Science in Aging at the University of Pennsylvania School of Nursing. Dr. Bowles has more than 20 years of experience and sustained NIH funding in transitional care and informatics research. Her program of research focuses on using information technology to improve care for older adults. Alexandra Hanlon, PhD, is a Research Associate Professor of Biostatistics at the University of Pennsylvania School of Nursing. Her methodological research focus is in longitudinal data analysis, while her collaborative endeavors can be found in the design and analysis of research studies involving oncology, women's and infant health, risk perceptions of various diseases, adolescent obesity and mental disorders, telemedicine, and bilingual language development. Diane Holland, PhD, RN, is an Assistant Professor and Clinical Nurse Researcher at the Mayo Clinic in Rochester, MN. Dr. Holland has more than 15 years of hospital discharge planning experience and more than 10 years of experience in discharge planning and transitional care research. Her program of research focuses on improving the experience of care for patients transitioning from the hospital to home. Sheryl L. Potashnik, PhD, is a Project Manager at the University of Pennsylvania School of Nursing. Dr. Potashnik has 28 years of experience in project management of NIH-funded R01 and P01 grants in the areas of gerontology, cancer epidemiology, nursing, and genetic-related research. Maxim Topaz, MA, is a doctoral student at the University of Pennsylvania School of Nursing. Mr. Topaz is a Fulbright Scholar from Israel. His research focuses on informatics, specifically on using clinical decision support tools to support transitions from hospitals to post-acute care settings.
Abstract
PURPOSE OF THE STUDY: Hospital clinicians are overwhelmed with the volume of patients churning through the health care systems. The study purpose was to determine whether alerting case managers about high-risk patients by supplying decision support results in better discharge plans as evidenced by time to first hospital readmission. PRIMARY PRACTICE SETTING: Four medical units at one urban, university medical center. METHODOLOGY AND SAMPLE: A quasi-experimental study including a usual care and experimental phase with hospitalized English-speaking patients aged 55 years and older. The intervention included using an evidence-based screening tool, the Discharge Decision Support System (D2S2), that supports clinicians' discharge referral decision making by identifying high-risk patients upon admission who need a referral for post-acute care. The usual care phase included collection of the D2S2 information, but not sharing the information with case managers. The experimental phase included data collection and then sharing the results with the case managers. The study compared time to readmission between index discharge date and 30 and 60 days in patients in both groups (usual care vs. experimental). RESULTS: After sharing the D2S2 results, the percentage of referral or high-risk patients readmitted by 30 and 60 days decreased by 6% and 9%, respectively, representing a 26% relative reduction in readmissions for both periods. IMPLICATIONS FOR CASE MANAGEMENT PRACTICE: Supplying decision support to identify high-risk patients recommended for postacute referral is associated with better discharge plans as evidenced by an increase in time to first hospital readmission. The tool supplies standardized information upon admission allowing more time to work with high-risk admissions.
PURPOSE OF THE STUDY: Hospital clinicians are overwhelmed with the volume of patients churning through the health care systems. The study purpose was to determine whether alerting case managers about high-risk patients by supplying decision support results in better discharge plans as evidenced by time to first hospital readmission. PRIMARY PRACTICE SETTING: Four medical units at one urban, university medical center. METHODOLOGY AND SAMPLE: A quasi-experimental study including a usual care and experimental phase with hospitalized English-speaking patients aged 55 years and older. The intervention included using an evidence-based screening tool, the Discharge Decision Support System (D2S2), that supports clinicians' discharge referral decision making by identifying high-risk patients upon admission who need a referral for post-acute care. The usual care phase included collection of the D2S2 information, but not sharing the information with case managers. The experimental phase included data collection and then sharing the results with the case managers. The study compared time to readmission between index discharge date and 30 and 60 days in patients in both groups (usual care vs. experimental). RESULTS: After sharing the D2S2 results, the percentage of referral or high-risk patients readmitted by 30 and 60 days decreased by 6% and 9%, respectively, representing a 26% relative reduction in readmissions for both periods. IMPLICATIONS FOR CASE MANAGEMENT PRACTICE: Supplying decision support to identify high-risk patients recommended for postacute referral is associated with better discharge plans as evidenced by an increase in time to first hospital readmission. The tool supplies standardized information upon admission allowing more time to work with high-risk admissions.
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Authors: Kathryn H Bowles; Sarah J Ratcliffe; Mary D Naylor; John H Holmes; Susan K Keim; Emilia J Flores Journal: AMIA Annu Symp Proc Date: 2018-04-16
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Authors: Kathryn H Bowles; Jesse Chittams; Eric Heil; Maxim Topaz; Kathy Rickard; Mrinal Bhasker; Matt Tanzer; Maryam Behta; Alexandra L Hanlon Journal: Res Nurs Health Date: 2015-01-25 Impact factor: 2.228
Authors: Kathryn H Bowles; Sarah J Ratcliffe; John H Holmes; Sue Keim; Sheryl Potashnik; Emilia Flores; Diane Humbrecht; Christina R Whitehouse; Mary D Naylor Journal: J Am Med Dir Assoc Date: 2018-11-08 Impact factor: 4.669