Masoud Hosseini1, Anthony Faiola2, Josette Jones1, Daniel J Vreeman3,4, Huanmei Wu1, Brian E Dixon3,5. 1. Department of BioHealth Informatics, Indiana University school of Informatics and Computing, Indianapolis, Indiana, USA. 2. Department of Biomedical and Health Information Sciences, College of Applied Health Sciences, University of Illinois at Chicago, Chicago, IL, USA. 3. Center for Biomedical Informatics, Regenstrief Institute, Inc., Indianapolis, Indiana, USA. 4. Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA. 5. Department of Epidemiology, Richard M. Fairbanks School of Public Health, Indiana University, Indianapolis, USA.
Abstract
Background: Information reconciliation is a common yet complex and often time-consuming task performed by healthcare providers. While electronic health record systems can receive "outside information" about a patient in electronic documents, rarely does the computer automate reconciling information about a patient across all documents. Materials and Methods: Using a mixed methods design, we evaluated an information system designed to reconcile information across multiple electronic documents containing health records for a patient received from a health information exchange (HIE) network. Nine healthcare providers participated in scenario-based sessions in which they manually consolidated information across multiple documents. Accuracy of consolidation was measured along with the time spent completing 3 different reconciliation scenarios with and without support from the information system. Participants also attended an interview about their experience. Perceived workload was evaluated quantitatively using the NASA-TLX tool. Qualitative analysis focused on providers' impression of the system and the challenges faced when reconciling information in practice. Results: While 5 providers made mistakes when trying to manually reconcile information across multiple documents, no participants made a mistake when the system supported their work. Overall perceived workload decreased significantly for scenarios supported by the system (37.2% in referrals, 18.4% in medications, and 31.5% in problems scenarios, P < 0.001). Information reconciliation time was reduced significantly when the system supported provider tasks (58.8% in referrals, 38.1% in medications, and 65.1% in problem scenarios). Conclusion: Automating retrieval and reconciliation of information across multiple electronic documents shows promise for reducing healthcare providers' task complexity and workload.
Background: Information reconciliation is a common yet complex and often time-consuming task performed by healthcare providers. While electronic health record systems can receive "outside information" about a patient in electronic documents, rarely does the computer automate reconciling information about a patient across all documents. Materials and Methods: Using a mixed methods design, we evaluated an information system designed to reconcile information across multiple electronic documents containing health records for a patient received from a health information exchange (HIE) network. Nine healthcare providers participated in scenario-based sessions in which they manually consolidated information across multiple documents. Accuracy of consolidation was measured along with the time spent completing 3 different reconciliation scenarios with and without support from the information system. Participants also attended an interview about their experience. Perceived workload was evaluated quantitatively using the NASA-TLX tool. Qualitative analysis focused on providers' impression of the system and the challenges faced when reconciling information in practice. Results: While 5 providers made mistakes when trying to manually reconcile information across multiple documents, no participants made a mistake when the system supported their work. Overall perceived workload decreased significantly for scenarios supported by the system (37.2% in referrals, 18.4% in medications, and 31.5% in problems scenarios, P < 0.001). Information reconciliation time was reduced significantly when the system supported provider tasks (58.8% in referrals, 38.1% in medications, and 65.1% in problem scenarios). Conclusion: Automating retrieval and reconciliation of information across multiple electronic documents shows promise for reducing healthcare providers' task complexity and workload.
Authors: Kenneth S Boockvar; Susan L Santos; Andre Kushniruk; Christopher Johnson; Jonathan R Nebeker Journal: J Hosp Med Date: 2011 Jul-Aug Impact factor: 2.960
Authors: John W Showalter; Colleen M Rafferty; Nicole A Swallow; Kolapo O Dasilva; Cynthia H Chuang Journal: J Gen Intern Med Date: 2011-04-16 Impact factor: 5.128
Authors: Colene M Byrne; Lauren M Mercincavage; Omar Bouhaddou; Jamie R Bennett; Eric C Pan; Nathan E Botts; Lois M Olinger; Elaine Hunolt; Karl H Banty; Tim Cromwell Journal: Int J Med Inform Date: 2014-04-28 Impact factor: 4.046
Authors: Jeffrey L Schnipper; Claus Hamann; Chima D Ndumele; Catherine L Liang; Marcy G Carty; Andrew S Karson; Ishir Bhan; Christopher M Coley; Eric Poon; Alexander Turchin; Stephanie A Labonville; Ellen K Diedrichsen; Stuart Lipsitz; Carol A Broverman; Patricia McCarthy; Tejal K Gandhi Journal: Arch Intern Med Date: 2009-04-27