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Title:Impact of technology on patient discharge decision making
Author(s):Schreiner, James H
Director of Research:Thurston, Deborah
Doctoral Committee Chair(s):Thurston, Deborah
Doctoral Committee Member(s):Kirlik, Alex; Morrow, Daniel; Kesavadas, Thenkurussi
Department / Program:Industrial&Enterprise Sys Eng
Discipline:Systems & Entrepreneurial Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Patient Discharge Decision Making
Readmission Risk Technology
Technology Acceptance
Expert-Based Decision Making
Unplanned 30-day Re-admissions
Socio-Technical Systems
Accountable Care Organizations
Abstract:Approximately 20 to 25 percent of patients discharged from primary healthcare facilities are readmitted within 30 days at a cost of roughly $42 billion dollars per year to insurance providers. Accountable Care Organizations (ACOs) create a network of healthcare providers aimed at improving the quality of patient care within a new 'pay for performance' business model. The Affordable Care Act (ACA) of 2010 directed the ACOs to establish new accounting practices including financial penalties for unplanned 30-day readmissions. Some unplanned patient readmissions can be caused by inappropriate interventions and in others, patients were unable to comply due to numerous complex social and technical complications. Incentives within the ACA for adoption of electronic health records (EHR) has motivated the rapid creation and adoption of new complementary predictive risk and decision technologies aimed at enhancing discharge decision processes. At least 26 unique risk prediction technologies of varying predictive nature have been created. New technologies are often proposed without methods to guide their design or implementation. The impacts of inserting a new patient discharge risk technology into an expert heuristic-based decision process are not well defined, nor are the acceptance levels of that technology in a highly trained group of healthcare professionals. Research conducted on heuristics and cognitive biases within the healthcare industry is not particular to patient discharge care management, and has not been assessed since the ACA was implemented. This research will present new knowledge about risk technology impacts on expert heuristics and cognitive biases while examining the acceptance of these technologies. Simultaneously, the research presents a methodology rooted in cognitive task analysis methods to analyze current discharge systems and guide training design strategies for health care professionals towards enhancing the quality of patient discharge care.
Issue Date:2016-04-13
Rights Information:Copyright 2016 by James H. Schreiner
Date Available in IDEALS:2016-07-07
Date Deposited:2016-05

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