Literature DB >> 20634543

A method for understanding some consequences of bringing patient-generated data into health care delivery.

Duane A Steward1, Richard A Hofler, Carey Thaldorf, David E Milov.   

Abstract

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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Year:  2010        PMID: 20634543     DOI: 10.1177/0272989X10371829

Source DB:  PubMed          Journal:  Med Decis Making        ISSN: 0272-989X            Impact factor:   2.583


  6 in total

1.  Designing patient-centered personal health records (PHRs): health care professionals' perspective on patient-generated data.

Authors:  Nicholas Huba; Yan Zhang
Journal:  J Med Syst       Date:  2012-05-30       Impact factor: 4.460

2.  Patient generated health data use in clinical practice: A systematic review.

Authors:  George Demiris; Sarah J Iribarren; Katherine Sward; Solim Lee; Rumei Yang
Journal:  Nurs Outlook       Date:  2019-04-26       Impact factor: 3.250

3.  Integrating Patient-Generated Health Data Into Clinical Care Settings or Clinical Decision-Making: Lessons Learned From Project HealthDesign.

Authors:  Deborah J Cohen; Sara R Keller; Gillian R Hayes; David A Dorr; Joan S Ash; Dean F Sittig
Journal:  JMIR Hum Factors       Date:  2016-10-19

4.  Information Quality Challenges of Patient-Generated Data in Clinical Practice.

Authors:  Peter West; Max Van Kleek; Richard Giordano; Mark Weal; Nigel Shadbolt
Journal:  Front Public Health       Date:  2017-11-01

5.  Use of patient-generated health data across healthcare settings: implications for health systems.

Authors:  Elizabeth Austin; Jenney R Lee; Dagmar Amtmann; Rich Bloch; Sarah O Lawrence; Debbe McCall; Sean Munson; Danielle C Lavallee
Journal:  JAMIA Open       Date:  2019-11-29

Review 6.  Patient generated health data and electronic health record integration in oncologic surgery: A call for artificial intelligence and machine learning.

Authors:  Laleh G Melstrom; Andrei S Rodin; Lorenzo A Rossi; Paul Fu; Yuman Fong; Virginia Sun
Journal:  J Surg Oncol       Date:  2020-09-24       Impact factor: 3.454

  6 in total

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