{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/90744"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/90744","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Impact of technology on patient discharge decision making","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Schreiner, James H"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Systems & Entrepreneurial Engr","degree_department":null,"school":null,"contributors":["Thurston, Deborah","Kirlik, Alex","Morrow, Daniel","Kesavadas, Thenkurussi"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-07-07T20:27:12Z","date_published":"2016-07-07T20:27:12Z","updated_at":"2026-07-22T22:26:34Z","subjects":["Patient Discharge Decision Making","Readmission Risk Technology","Technology Acceptance","Expert-Based Decision Making","Unplanned 30-day Re-admissions","Socio-Technical Systems","Accountable Care Organizations"],"languages":["en"],"rights":["Copyright 2016 by James H. 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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.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2018-05-01","The student, James Schreiner, accepted the attached license on 2016-04-11 at 08:36.","The student, James Schreiner, submitted this Dissertation for approval on 2016-04-11 at 09:08.","This Dissertation was approved for publication on 2016-04-13 at 14:58.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9183 on 2016-07-07 at 13:49:02","Made available in DSpace on 2016-07-07T20:27:12Z (GMT). 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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.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2018-05-01","The student, James Schreiner, accepted the attached license on 2016-04-11 at 08:36.","The student, James Schreiner, submitted this Dissertation for approval on 2016-04-11 at 09:08.","This Dissertation was approved for publication on 2016-04-13 at 14:58.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9183 on 2016-07-07 at 13:49:02","Made available in DSpace on 2016-07-07T20:27:12Z (GMT). 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