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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. https://www.osti.gov/servlets/purl/1530117.
@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:

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Early sepsis detection in critical care patients using multiscale blood pressure and heart rate dynamics
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Early Goal-Directed Therapy in the Treatment of Severe Sepsis and Septic Shock
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Hospital Case Volume and Outcomes among Patients Hospitalized with Severe Sepsis
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Long-Term Mortality and Major Adverse Cardiovascular Events in Sepsis Survivors. A Nationwide Population-based Study
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    Works referencing / citing this record:

    Long-term Survival and Function After Suspected Gram-negative Sepsis
    journal, July 1995


    Readmission Diagnoses After Hospitalization for Severe Sepsis and Other Acute Medical Conditions
    journal, March 2015

    • Prescott, Hallie C.; Langa, Kenneth M.; Iwashyna, Theodore J.
    • JAMA, Vol. 313, Issue 10
    • DOI: 10.1001/jama.2015.1410

    Incidence and Trends of Sepsis in US Hospitals Using Clinical vs Claims Data, 2009-2014
    journal, October 2017


    Surviving Sepsis Campaign: International Guidelines for Management of Severe Sepsis and Septic Shock, 2012
    journal, January 2013

    • Dellinger, R. P.; Levy, Mitchell M.; Rhodes, Andrew
    • Intensive Care Medicine, Vol. 39, Issue 2
    • DOI: 10.1007/s00134-012-2769-8

    Long-term mortality and quality of life after septic shock: a follow-up observational study
    journal, January 2013

    • Nesseler, Nicolas; Defontaine, Anne; Launey, Yoann
    • Intensive Care Medicine, Vol. 39, Issue 5
    • DOI: 10.1007/s00134-013-2815-1

    EEG signals classification using the K-means clustering and a multilayer perceptron neural network model
    journal, September 2011


    Early sepsis detection in critical care patients using multiscale blood pressure and heart rate dynamics
    journal, November 2017


    Multilayer perceptron tumour diagnosis based on chromatography analysis of urinary nucleosides
    journal, July 2007


    Random Forest ensembles for detection and prediction of Alzheimer's disease with a good between-cohort robustness
    journal, January 2014


    A cost-benefit analysis of electronic medical records in primary care
    journal, April 2003


    Random Forests
    journal, January 2001


    Deep learning
    journal, May 2015

    • LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey
    • Nature, Vol. 521, Issue 7553
    • DOI: 10.1038/nature14539

    Early Goal-Directed Therapy in the Treatment of Severe Sepsis and Septic Shock
    journal, November 2001

    • Rivers, Emanuel; Nguyen, Bryant; Havstad, Suzanne
    • New England Journal of Medicine, Vol. 345, Issue 19
    • DOI: 10.1056/nejmoa010307

    Long-term survival after intensive care unit admission with sepsis
    journal, January 1995


    Epidemiology of severe sepsis in the United States: Analysis of incidence, outcome, and associated costs of care
    journal, January 2001


    966: Sepsis
    journal, January 2014


    Precision Medicine—Personalized, Problematic, and Promising
    journal, January 2015


    The Surviving Sepsis Campaign: Results of an international guideline-based performance improvement program targeting severe sepsis*
    journal, January 2010


    Implementation of a real-time computerized sepsis alert in nonintensive care unit patients*
    journal, January 2011


    Specific Etiologies Associated With the Multiple Organ Dysfunction Syndrome in Children: Part 1
    journal, January 2017


    How much data should we collect? A case study in sepsis detection using deep learning
    conference, November 2017

    • van Wyk, Franco; Khojandi, Anahita; Kamaleswaran, Rishikesan
    • 2017 IEEE Healthcare Innovations and Point-of-Care Technologies (HI-POCT), 2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT)
    • DOI: 10.1109/hic.2017.8227596

    A targeted real-time early warning score (TREWScore) for septic shock
    journal, August 2015

    • Henry, Katharine E.; Hager, David N.; Pronovost, Peter J.
    • Science Translational Medicine, Vol. 7, Issue 299
    • DOI: 10.1126/scitranslmed.aab3719

    Late mortality after sepsis: propensity matched cohort study
    journal, May 2016

    • Prescott, Hallie C.; Osterholzer, John J.; Langa, Kenneth M.
    • BMJ
    • DOI: 10.1136/bmj.i2375

    Error rates in a clinical data repository: lessons from the transition to electronic data transfer—a descriptive study
    journal, January 2013


    Optimizing deep learning hyper-parameters through an evolutionary algorithm
    conference, January 2015

    • Young, Steven R.; Rose, Derek C.; Karnowski, Thomas P.
    • Proceedings of the Workshop on Machine Learning in High-Performance Computing Environments - MLHPC '15
    • DOI: 10.1145/2834892.2834896

    Information Technology in Critical Care: Review of Monitoring and Data Acquisition Systems for Patient Care and Research
    journal, January 2015

    • De Georgia, Michael A.; Kaffashi, Farhad; Jacono, Frank J.
    • The Scientific World Journal, Vol. 2015
    • DOI: 10.1155/2015/727694

    Hospital Case Volume and Outcomes among Patients Hospitalized with Severe Sepsis
    journal, March 2014

    • Walkey, Allan J.; Wiener, Renda Soylemez
    • American Journal of Respiratory and Critical Care Medicine, Vol. 189, Issue 5
    • DOI: 10.1164/rccm.201311-1967oc

    Increased 1-Year Healthcare Use in Survivors of Severe Sepsis
    journal, July 2014

    • Prescott, Hallie C.; Langa, Kenneth M.; Liu, Vincent
    • American Journal of Respiratory and Critical Care Medicine, Vol. 190, Issue 1
    • DOI: 10.1164/rccm.201403-0471oc

    Long-Term Mortality and Major Adverse Cardiovascular Events in Sepsis Survivors. A Nationwide Population-based Study
    journal, July 2016

    • Ou, Shuo-Ming; Chu, Hsi; Chao, Pei-Wen
    • American Journal of Respiratory and Critical Care Medicine, Vol. 194, Issue 2
    • DOI: 10.1164/rccm.201510-2023oc

    Willingness to Pay for a Quality-adjusted Life Year: In Search of a Standard
    journal, July 2000

    • Hirth, Richard A.; Chernew, Michael E.; Miller, Edward
    • Medical Decision Making, Vol. 20, Issue 3
    • DOI: 10.1177/0272989x0002000310

    Subsequent Infections in Survivors of Sepsis: Epidemiology and Outcomes
    journal, June 2012

    • Wang, Tisha; Derhovanessian, Ariss; De Cruz, Sharon
    • Journal of Intensive Care Medicine, Vol. 29, Issue 2
    • DOI: 10.1177/0885066612467162

    Big data analytics in healthcare: promise and potential
    journal, February 2014

    • Raghupathi, Wullianallur; Raghupathi, Viju
    • Health Information Science and Systems, Vol. 2, Issue 1
    • DOI: 10.1186/2047-2501-2-3

    Mortality reduction in patients with severe sepsis and septic shock through a comprehensive sepsis initiative
    journal, April 2014

    • Nguyen, K.; Cook, L.; Greenlee, Ep
    • Critical Care, Vol. 18, Issue S2
    • DOI: 10.1186/cc14031

    Clinical review: Scoring systems in the critically ill
    journal, January 2010

    • Vincent, Jean-Louis; Moreno, Rui
    • Critical Care, Vol. 14, Issue 2
    • DOI: 10.1186/cc8204

    Early identification of sepsis in hospital inpatients by ward nurses increases 30-day survival
    journal, August 2016


    A systematic review of the cost of data collection for performance monitoring in hospitals
    journal, April 2015


    Can Electronic Medical Record Systems Transform Health Care? Potential Health Benefits, Savings, And Costs
    journal, September 2005


    Post–Acute Care Use and Hospital Readmission after Sepsis
    journal, June 2015


    Classification and Regression Trees.
    journal, September 1984

    • Gordon, A. D.; Breiman, L.; Friedman, J. H.
    • Biometrics, Vol. 40, Issue 3
    • DOI: 10.2307/2530946

    Cost-Benefit Analysis of Electronic Medical Record System at a Tertiary Care Hospital
    journal, January 2013

    • Choi, Jong Soo; Lee, Woo Baik; Rhee, Poong-Lyul
    • Healthcare Informatics Research, Vol. 19, Issue 3
    • DOI: 10.4258/hir.2013.19.3.205

    Assessing available information on the burden of sepsis: global estimates of incidence, prevalence and mortality
    journal, June 2012

    • Jawad, Issrah; Lukšić, Ivana; Rafnsson, Snorri Bjorn
    • Journal of Global Health, Vol. 2, Issue 1
    • DOI: 10.7189/jogh.01.010404

    Automated electronic medical record sepsis detection in the emergency department
    journal, January 2014

    • Nguyen, Su Q.; Mwakalindile, Edwin; Booth, James S.
    • PeerJ, Vol. 2
    • DOI: 10.7717/peerj.343