A Method for Understanding Some Consequences of Bringing Patient-Generated Data into Health Care Delivery
Objective. The consequences of personal health record (PHR) phenomena on the health care system are poorly understood. This research measures one aspect of the phenomena—the time-cost impact of patient-generated data (PGD) using discrete event model (DEM) simulation. Background/Significance. Little...
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Veröffentlicht in: | Medical decision making 2010-07, Vol.30 (4), p.E1-E13 |
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description | Objective. The consequences of personal health record (PHR) phenomena on the health care system are poorly understood. This research measures one aspect of the phenomena—the time-cost impact of patient-generated data (PGD) using discrete event model (DEM) simulation. Background/Significance. Little has been written about the temporal and cognitive burden associated with new workflows that include PGD. This pilot study reports the results for time-cost and resource utilization of a ‘‘typical’’ ambulatory clinic under varying conditions of PGD burden. Methods. PGD effects are modeled with DEM simulation reflecting the sequential relationships, temporal coupling, and impact assumptions within a virtual clinic. Three simulation scenarios of ever-increasing PGD impact are compared to a baseline case of no PGD use. Results. Introduction of PGD resulted in expected increases in cost and resource utilization along with a few key exceptions and unanticipated consequences. Direct and indirect impacts were observed with notable nonlinear, nonadditive, disproportionate, heterogeneous aspects and interactions among consequent labor cost, visit length, workday length, and resource utilization. The middle-impact simulations showed a 29% increase in daily labor costs and 28% shrinkage of the margin between revenues and labor costs. Lengths of both workday and patient visit were extended and less predictable with PGD use. Utilization rates of most staff positions rose. Nurse utilization rates showed greatest increases. Physicians’ utilization rates paradoxically stayed relatively unchanged. Conclusion. This analysis contributes to an understanding of the effects of PGD on time and cognitive burdens of physicians, staff, and physical resources. It illustrates the usefulness of DEM simulation for the purpose. Avoidable consequences are exposed quantifiably for both the patient and the clinic. More realistic ways to respond to PGD impact are needed. |
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The consequences of personal health record (PHR) phenomena on the health care system are poorly understood. This research measures one aspect of the phenomena—the time-cost impact of patient-generated data (PGD) using discrete event model (DEM) simulation. Background/Significance. Little has been written about the temporal and cognitive burden associated with new workflows that include PGD. This pilot study reports the results for time-cost and resource utilization of a ‘‘typical’’ ambulatory clinic under varying conditions of PGD burden. Methods. PGD effects are modeled with DEM simulation reflecting the sequential relationships, temporal coupling, and impact assumptions within a virtual clinic. Three simulation scenarios of ever-increasing PGD impact are compared to a baseline case of no PGD use. Results. Introduction of PGD resulted in expected increases in cost and resource utilization along with a few key exceptions and unanticipated consequences. Direct and indirect impacts were observed with notable nonlinear, nonadditive, disproportionate, heterogeneous aspects and interactions among consequent labor cost, visit length, workday length, and resource utilization. The middle-impact simulations showed a 29% increase in daily labor costs and 28% shrinkage of the margin between revenues and labor costs. Lengths of both workday and patient visit were extended and less predictable with PGD use. Utilization rates of most staff positions rose. Nurse utilization rates showed greatest increases. Physicians’ utilization rates paradoxically stayed relatively unchanged. Conclusion. This analysis contributes to an understanding of the effects of PGD on time and cognitive burdens of physicians, staff, and physical resources. It illustrates the usefulness of DEM simulation for the purpose. Avoidable consequences are exposed quantifiably for both the patient and the clinic. More realistic ways to respond to PGD impact are needed.</description><identifier>ISSN: 0272-989X</identifier><identifier>EISSN: 1552-681X</identifier><identifier>DOI: 10.1177/0272989X10371829</identifier><identifier>PMID: 20634543</identifier><language>eng</language><publisher>Los Angeles, CA: SAGE Publications</publisher><subject>Delivery of Health Care ; Medical Records - economics ; Pilot Projects</subject><ispartof>Medical decision making, 2010-07, Vol.30 (4), p.E1-E13</ispartof><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c336t-9a7e03d60e1663434f48e6789a76e9fa3ce75c8229972ff2d4d1e7a2c6fbee923</citedby><cites>FETCH-LOGICAL-c336t-9a7e03d60e1663434f48e6789a76e9fa3ce75c8229972ff2d4d1e7a2c6fbee923</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://journals.sagepub.com/doi/pdf/10.1177/0272989X10371829$$EPDF$$P50$$Gsage$$H</linktopdf><linktohtml>$$Uhttps://journals.sagepub.com/doi/10.1177/0272989X10371829$$EHTML$$P50$$Gsage$$H</linktohtml><link.rule.ids>314,780,784,21819,27924,27925,43621,43622</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/20634543$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Steward, Duane A.</creatorcontrib><creatorcontrib>Hofler, Richard A.</creatorcontrib><creatorcontrib>Thaldorf, Carey</creatorcontrib><creatorcontrib>Milov, David E.</creatorcontrib><title>A Method for Understanding Some Consequences of Bringing Patient-Generated Data into Health Care Delivery</title><title>Medical decision making</title><addtitle>Med Decis Making</addtitle><description>Objective. The consequences of personal health record (PHR) phenomena on the health care system are poorly understood. This research measures one aspect of the phenomena—the time-cost impact of patient-generated data (PGD) using discrete event model (DEM) simulation. Background/Significance. Little has been written about the temporal and cognitive burden associated with new workflows that include PGD. This pilot study reports the results for time-cost and resource utilization of a ‘‘typical’’ ambulatory clinic under varying conditions of PGD burden. Methods. PGD effects are modeled with DEM simulation reflecting the sequential relationships, temporal coupling, and impact assumptions within a virtual clinic. Three simulation scenarios of ever-increasing PGD impact are compared to a baseline case of no PGD use. Results. Introduction of PGD resulted in expected increases in cost and resource utilization along with a few key exceptions and unanticipated consequences. Direct and indirect impacts were observed with notable nonlinear, nonadditive, disproportionate, heterogeneous aspects and interactions among consequent labor cost, visit length, workday length, and resource utilization. The middle-impact simulations showed a 29% increase in daily labor costs and 28% shrinkage of the margin between revenues and labor costs. Lengths of both workday and patient visit were extended and less predictable with PGD use. Utilization rates of most staff positions rose. Nurse utilization rates showed greatest increases. Physicians’ utilization rates paradoxically stayed relatively unchanged. Conclusion. This analysis contributes to an understanding of the effects of PGD on time and cognitive burdens of physicians, staff, and physical resources. It illustrates the usefulness of DEM simulation for the purpose. Avoidable consequences are exposed quantifiably for both the patient and the clinic. 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The consequences of personal health record (PHR) phenomena on the health care system are poorly understood. This research measures one aspect of the phenomena—the time-cost impact of patient-generated data (PGD) using discrete event model (DEM) simulation. Background/Significance. Little has been written about the temporal and cognitive burden associated with new workflows that include PGD. This pilot study reports the results for time-cost and resource utilization of a ‘‘typical’’ ambulatory clinic under varying conditions of PGD burden. Methods. PGD effects are modeled with DEM simulation reflecting the sequential relationships, temporal coupling, and impact assumptions within a virtual clinic. Three simulation scenarios of ever-increasing PGD impact are compared to a baseline case of no PGD use. Results. Introduction of PGD resulted in expected increases in cost and resource utilization along with a few key exceptions and unanticipated consequences. Direct and indirect impacts were observed with notable nonlinear, nonadditive, disproportionate, heterogeneous aspects and interactions among consequent labor cost, visit length, workday length, and resource utilization. The middle-impact simulations showed a 29% increase in daily labor costs and 28% shrinkage of the margin between revenues and labor costs. Lengths of both workday and patient visit were extended and less predictable with PGD use. Utilization rates of most staff positions rose. Nurse utilization rates showed greatest increases. Physicians’ utilization rates paradoxically stayed relatively unchanged. Conclusion. This analysis contributes to an understanding of the effects of PGD on time and cognitive burdens of physicians, staff, and physical resources. It illustrates the usefulness of DEM simulation for the purpose. Avoidable consequences are exposed quantifiably for both the patient and the clinic. More realistic ways to respond to PGD impact are needed.</abstract><cop>Los Angeles, CA</cop><pub>SAGE Publications</pub><pmid>20634543</pmid><doi>10.1177/0272989X10371829</doi></addata></record> |
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title | A Method for Understanding Some Consequences of Bringing Patient-Generated Data into Health Care Delivery |
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