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Title: A Cost-Benefit Analysis of Automated Physiological Data Acquisition Systems Using Data-Driven Modeling

Abstract

Precision medicine and the continuous analysis of “Big data” promises to improve patient outcomes dramatically in the near future. Very recently, healthcare facilities have started to explore automatic collection of patient-specific physiological data with the aim of reducing nursing workload and decreasing manual data entry errors. In addition to those purposes, continuous physiological data can be used for the early detection and prevention of common, and possibly fatal, diseases. For instance, poor patient outcomes from sepsis, a leading cause of mortality in healthcare facilities and a major driver of hospital costs in the USA, can be mitigated when detected early using screening tools that monitor the changing dynamics of physiological data. However, the potential cost of collecting continuous physiological data remains a barrier to the widespread adoption of automated high-frequency data collection systems. In this paper, we perform cost-benefit analysis (CBA) of machine learning applied to various types of acquisition systems (with different collection intervals) to determine if the benefits of such systems will outweigh their implementation costs. Although such systems can be used in the detection of various complications, in order to showcase the immediate benefits, we focus on the early detection of sepsis, one of the major challengesmore » of hospital systems. We present a general approach to conduct such analysis for a wide range of hospitals and highlight its applicability using a case study for a small hospital with 150 beds and 3000 annual patients where the acquisition system would collect data at 1-min intervals. Finally, we discuss how the analysis may help guide incentives/policies with regard to adopting automated data acquisition systems.« less

Authors:
 [1]; ORCiD logo [1];  [2];  [2];  [3]; ORCiD logo [4];  [3]
  1. Univ. of Tennessee, Knoxville, TN (United States)
  2. Methodist Le Bonheur Healthcare, Memphis, TN (United States)
  3. Univ. of Tennessee Health Science Center, Memphis, TN (United States)
  4. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1530117
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Healthcare Informatics Research
Additional Journal Information:
Journal Volume: 3; Journal Issue: 2; Journal ID: ISSN 2509-4971
Publisher:
Springer
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS; Automated physiological data acquisition; Sepsis detection; Cost-benefit analysis (CBA); Random forest

Citation Formats

van Wyk, Franco, Khojandi, Anahita, Williams, Brian, MacMillan, Don, Davis, Robert L., Jacobson, Daniel A., and Kamaleswaran, Rishikesan. A Cost-Benefit Analysis of Automated Physiological Data Acquisition Systems Using Data-Driven Modeling. United States: N. p., 2018. Web. doi:10.1007/s41666-018-0040-y.
van Wyk, Franco, Khojandi, Anahita, Williams, Brian, MacMillan, Don, Davis, Robert L., Jacobson, Daniel A., & Kamaleswaran, Rishikesan. A Cost-Benefit Analysis of Automated Physiological Data Acquisition Systems Using Data-Driven Modeling. United States. doi:10.1007/s41666-018-0040-y.
van Wyk, Franco, Khojandi, Anahita, Williams, Brian, MacMillan, Don, Davis, Robert L., Jacobson, Daniel A., and Kamaleswaran, Rishikesan. Tue . "A Cost-Benefit Analysis of Automated Physiological Data Acquisition Systems Using Data-Driven Modeling". United States. doi:10.1007/s41666-018-0040-y.
@article{osti_1530117,
title = {A Cost-Benefit Analysis of Automated Physiological Data Acquisition Systems Using Data-Driven Modeling},
author = {van Wyk, Franco and Khojandi, Anahita and Williams, Brian and MacMillan, Don and Davis, Robert L. and Jacobson, Daniel A. and Kamaleswaran, Rishikesan},
abstractNote = {Precision medicine and the continuous analysis of “Big data” promises to improve patient outcomes dramatically in the near future. Very recently, healthcare facilities have started to explore automatic collection of patient-specific physiological data with the aim of reducing nursing workload and decreasing manual data entry errors. In addition to those purposes, continuous physiological data can be used for the early detection and prevention of common, and possibly fatal, diseases. For instance, poor patient outcomes from sepsis, a leading cause of mortality in healthcare facilities and a major driver of hospital costs in the USA, can be mitigated when detected early using screening tools that monitor the changing dynamics of physiological data. However, the potential cost of collecting continuous physiological data remains a barrier to the widespread adoption of automated high-frequency data collection systems. In this paper, we perform cost-benefit analysis (CBA) of machine learning applied to various types of acquisition systems (with different collection intervals) to determine if the benefits of such systems will outweigh their implementation costs. Although such systems can be used in the detection of various complications, in order to showcase the immediate benefits, we focus on the early detection of sepsis, one of the major challenges of hospital systems. We present a general approach to conduct such analysis for a wide range of hospitals and highlight its applicability using a case study for a small hospital with 150 beds and 3000 annual patients where the acquisition system would collect data at 1-min intervals. Finally, we discuss how the analysis may help guide incentives/policies with regard to adopting automated data acquisition systems.},
doi = {10.1007/s41666-018-0040-y},
journal = {Journal of Healthcare Informatics Research},
number = 2,
volume = 3,
place = {United States},
year = {2018},
month = {11}
}

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Works referenced in this record:

Random Forests
journal, January 2001