2.50
Hdl Handle:
http://hdl.handle.net/10755/158083
Type:
Presentation
Title:
Capturing Health Care's Complexity Via Computational Modeling
Abstract:
Capturing Health Care's Complexity Via Computational Modeling
Conference Sponsor:Western Institute of Nursing
Conference Year:2004
Author:Garcia-Smith, Dianna, MS, RN
P.I. Institution Name:University of Arizona, College of Nursing
Contact Address:Unviversity of Arizona, Tucson, AZ, USA
Co-Authors:Anita Patil, BS; Judith Effken, PhD, RN
Introduction and Study Purpose: Health care organizations are complex dynamic systems in which patient outcomes depend critically on the nature of the work (patient characteristics such as acuity, age, co-morbidities), organizational characteristics (culture, economic environment, and physical environment), and unit characteristics (culture, workflow, staffing, and physical environment). Given the complex, dynamic nature of the organization, traditional experimental methods that rely on snapshots at specific points in time are inadequate for understanding the dynamic interactions that occur over time. For that reason, we are using OrgAhead, a theoretically-based computational modeling program designed specifically for exploring dynamic, complex systems. In our initial study, we created 16 virtual patient care units based on the data from 16 actual units from four hospitals in southern Arizona. To calibrate the computational model, we compared the medication errors and falls for the 16 actual units with their corresponding accuracy measures in OrgAhead. We then rank ordered the virtual and actual performance measured and compared them using correlation statistics. The resulting correlation was .83, which exceeded our preassigned criterion of r = .80. In this presentation, we describe how we tested and further refined the model using data from 16 additional patient care units from a second set of six hospitals. Conceptual Framework: The research described in this study uses as its conceptual framework the Systems Research Organizing Model. The framework contains four constructs: patient characteristics, organizational characteristics, unit characteristics, and patient outcomes. All constructs interact with each other. Methods: We created virtual units based on data from 16 additional units from six different hospitals. We are currently comparing performance for the virtual and actual units in terms of accuracy (OrgAhead's correlate for safety outcomes) and completion ratio (the correlate for quality outcomes). For both dependent measures, we will then rank order the virtual and actual performance and compare them using correlation statistics. Results: We will report the results of this analysis. Implications: Computational modeling is a theoretically-motivated analysis methodology that, although not developed specifically for health care, offers researchers a promising new way to analyze the complexities of the healthcare system and develop predictive models that patient care managers can use to help in their decision making. In future research, we will use OrgAhead to generate hypotheses about the kinds of changes nurses might make to improve patient outcomes on their units, help nurses use these hypotheses to identify and implement changes on their units, then measure the impact of those changes on patient care outcomes. Acknowledgement. This paper was supported by AHRQ, 1 HS11973, 2001-2002.
Repository Posting Date:
26-Oct-2011
Date of Publication:
17-Oct-2011
Sponsors:
Western Institute of Nursing

Full metadata record

DC FieldValue Language
dc.typePresentationen_GB
dc.titleCapturing Health Care's Complexity Via Computational Modelingen_GB
dc.identifier.urihttp://hdl.handle.net/10755/158083-
dc.description.abstract<table><tr><td colspan="2" class="item-title">Capturing Health Care's Complexity Via Computational Modeling </td></tr><tr class="item-sponsor"><td class="label">Conference Sponsor:</td><td class="value">Western Institute of Nursing</td></tr><tr class="item-year"><td class="label">Conference Year:</td><td class="value">2004</td></tr><tr class="item-author"><td class="label">Author:</td><td class="value">Garcia-Smith, Dianna, MS, RN</td></tr><tr class="item-institute"><td class="label">P.I. Institution Name:</td><td class="value">University of Arizona, College of Nursing</td></tr><tr class="item-address"><td class="label">Contact Address:</td><td class="value">Unviversity of Arizona, Tucson, AZ, USA</td></tr><tr class="item-co-authors"><td class="label">Co-Authors:</td><td class="value">Anita Patil, BS; Judith Effken, PhD, RN </td></tr><tr><td colspan="2" class="item-abstract">Introduction and Study Purpose: Health care organizations are complex dynamic systems in which patient outcomes depend critically on the nature of the work (patient characteristics such as acuity, age, co-morbidities), organizational characteristics (culture, economic environment, and physical environment), and unit characteristics (culture, workflow, staffing, and physical environment). Given the complex, dynamic nature of the organization, traditional experimental methods that rely on snapshots at specific points in time are inadequate for understanding the dynamic interactions that occur over time. For that reason, we are using OrgAhead, a theoretically-based computational modeling program designed specifically for exploring dynamic, complex systems. In our initial study, we created 16 virtual patient care units based on the data from 16 actual units from four hospitals in southern Arizona. To calibrate the computational model, we compared the medication errors and falls for the 16 actual units with their corresponding accuracy measures in OrgAhead. We then rank ordered the virtual and actual performance measured and compared them using correlation statistics. The resulting correlation was .83, which exceeded our preassigned criterion of r = .80. In this presentation, we describe how we tested and further refined the model using data from 16 additional patient care units from a second set of six hospitals. Conceptual Framework: The research described in this study uses as its conceptual framework the Systems Research Organizing Model. The framework contains four constructs: patient characteristics, organizational characteristics, unit characteristics, and patient outcomes. All constructs interact with each other. Methods: We created virtual units based on data from 16 additional units from six different hospitals. We are currently comparing performance for the virtual and actual units in terms of accuracy (OrgAhead's correlate for safety outcomes) and completion ratio (the correlate for quality outcomes). For both dependent measures, we will then rank order the virtual and actual performance and compare them using correlation statistics. Results: We will report the results of this analysis. Implications: Computational modeling is a theoretically-motivated analysis methodology that, although not developed specifically for health care, offers researchers a promising new way to analyze the complexities of the healthcare system and develop predictive models that patient care managers can use to help in their decision making. In future research, we will use OrgAhead to generate hypotheses about the kinds of changes nurses might make to improve patient outcomes on their units, help nurses use these hypotheses to identify and implement changes on their units, then measure the impact of those changes on patient care outcomes. Acknowledgement. This paper was supported by AHRQ, 1 HS11973, 2001-2002. </td></tr></table>en_GB
dc.date.available2011-10-26T20:29:31Z-
dc.date.issued2011-10-17en_GB
dc.date.accessioned2011-10-26T20:29:31Z-
dc.description.sponsorshipWestern Institute of Nursingen_GB
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