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

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. Lastly, we discuss how the analysis may help guide incentives/policies with regard to adopting automated data acquisition systems.

[1]  P. Pronovost,et al.  A targeted real-time early warning score (TREWScore) for septic shock , 2015, Science Translational Medicine.

[2]  Jack Mardekian,et al.  Identification of a potential fibromyalgia diagnosis using random forest modeling applied to electronic medical records. , 2015 .

[3]  C. Sprung,et al.  Surviving Sepsis Campaign: International Guidelines for Management of Severe Sepsis and Septic Shock 2012 , 2013, Critical care medicine.

[4]  N. Nesseler,et al.  Long-term mortality and quality of life after septic shock: a follow-up observational study , 2013, Intensive Care Medicine.

[5]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[6]  Shu-Chen Kuo,et al.  Long-Term Mortality and Major Adverse Cardiovascular Events in Sepsis Survivors. A Nationwide Population-based Study. , 2016, American journal of respiratory and critical care medicine.

[7]  Carolyn McGregor,et al.  Big Data in Neonatal Intensive Care , 2013, Computer.

[8]  C. Torio,et al.  National Inpatient Hospital Costs: The Most Expensive Conditions by Payer, 2011 , 2013 .

[9]  William E Encinosa,et al.  Will meaningful use electronic medical records reduce hospital costs? , 2013, The American journal of managed care.

[10]  Susan Gruber,et al.  Incidence and Trends of Sepsis in US Hospitals Using Clinical vs Claims Data, 2009-2014 , 2017, JAMA.

[11]  John P. Donnelly,et al.  Automated electronic medical record sepsis detection in the emergency department , 2014, PeerJ.

[12]  Jiaquan Xu,et al.  Deaths: Final Data for 2014. , 2016, National vital statistics reports : from the Centers for Disease Control and Prevention, National Center for Health Statistics, National Vital Statistics System.

[13]  Viju Raghupathi,et al.  Big data analytics in healthcare: promise and potential , 2014, Health Information Science and Systems.

[14]  C. Sprung,et al.  Surviving Sepsis Campaign: International Guidelines for Management of Severe Sepsis and Septic Shock, 2012 , 2013, Intensive Care Medicine.

[15]  Anne F. Kittler,et al.  A cost-benefit analysis of electronic medical records in primary care. , 2003, The American journal of medicine.

[16]  Mahmut Ozer,et al.  EEG signals classification using the K-means clustering and a multilayer perceptron neural network model , 2011, Expert Syst. Appl..

[17]  Jeffrey S Upperman,et al.  Specific Etiologies Associated With the Multiple Organ Dysfunction Syndrome in Children: Part 1 , 2017, Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies.

[18]  Charles Elkan,et al.  Learning to Diagnose with LSTM Recurrent Neural Networks , 2015, ICLR.

[19]  Michael E. Chernew,et al.  Willingness to Pay for a Quality-adjusted Life Year , 2000, Medical decision making : an international journal of the Society for Medical Decision Making.

[20]  D. Longo,et al.  Precision medicine--personalized, problematic, and promising. , 2015, The New England journal of medicine.

[21]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[22]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[23]  P. Mecocci,et al.  Random Forest ensembles for detection and prediction of Alzheimer's disease with a good between-cohort robustness , 2014, NeuroImage: Clinical.

[24]  Theodore J Iwashyna,et al.  Readmission diagnoses after hospitalization for severe sepsis and other acute medical conditions. , 2015, JAMA.

[25]  Renda Soylemez Wiener,et al.  Hospital case volume and outcomes among patients hospitalized with severe sepsis. , 2014, American journal of respiratory and critical care medicine.

[26]  Theodore J Iwashyna,et al.  Late mortality after sepsis: propensity matched cohort study , 2016, British Medical Journal.

[27]  P. Seidel,et al.  Multilayer perceptron tumour diagnosis based on chromatography analysis of urinary nucleosides , 2007, Neural Networks.

[28]  Milos Miljanovic,et al.  Comparative analysis of Recurrent and Finite Impulse Response Neural Networks in Time Series Prediction Author , 2012 .

[29]  Richard Beale,et al.  The Surviving Sepsis Campaign: Results of an international guideline-based performance improvement program targeting severe sepsis* , 2010, Critical care medicine.

[30]  Shamim Nemati,et al.  How much data should we collect? A case study in sepsis detection using deep learning , 2017, 2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT).

[31]  G. Clermont,et al.  Epidemiology of severe sepsis in the United States: Analysis of incidence, outcome, and associated costs of care , 2001, Critical care medicine.

[32]  A. Subasi,et al.  Diagnosis of Chronic Kidney Disease by Using Random Forest , 2017 .

[33]  J. Belperio,et al.  Subsequent Infections in Survivors of Sepsis , 2014, Journal of intensive care medicine.

[34]  Brenda Gannon,et al.  A systematic review of the cost of data collection for performance monitoring in hospitals , 2015, Systematic Reviews.

[35]  Lucien Le Cam,et al.  Probability Models and Cancer. , 1984 .

[36]  Steven R. Young,et al.  Optimizing deep learning hyper-parameters through an evolutionary algorithm , 2015, MLHPC@SC.

[37]  J. Vincent,et al.  Clinical review: Scoring systems in the critically ill , 2010, Critical care.

[38]  E Nauenberg,et al.  Long-term survival after intensive care unit admission with sepsis. , 1995, Critical care medicine.

[39]  Vivian Christensen,et al.  Technology Assessment: Early Sense for Monitoring Vital Signs in Hospitalized Patients , 2016 .

[40]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[41]  Joseph E. Aldy,et al.  The Value of a Statistical Life: A Critical Review of Market Estimates Throughout the World , 2003 .

[42]  Theodore J Iwashyna,et al.  Increased 1-year healthcare use in survivors of severe sepsis. , 2014, American journal of respiratory and critical care medicine.

[43]  P. Marik,et al.  SIRS, qSOFA and new sepsis definition. , 2017, Journal of thoracic disease.

[44]  Robin C. Meili,et al.  Can electronic medical record systems transform health care? Potential health benefits, savings, and costs. , 2005, Health affairs.

[45]  Jong Soo Choi,et al.  Cost-Benefit Analysis of Electronic Medical Record System at a Tertiary Care Hospital , 2013, Healthcare informatics research.

[46]  CM Torio,et al.  National Inpatient Hospital Costs: The Most Expensive Conditions by Payer, 2013: Statistical Brief #204 , 2006 .

[47]  Kevin M. Heard,et al.  Implementation of a real-time computerized sepsis alert in nonintensive care unit patients* , 2011, Critical care medicine.

[48]  R. Wenzel,et al.  Long-term survival and function after suspected gram-negative sepsis. , 1995, JAMA.

[49]  M. J. Hall,et al.  Inpatient care for septicemia or sepsis: a challenge for patients and hospitals. , 2011, NCHS data brief.

[50]  E. Ivers,et al.  Early Goal-Directed Therapy in the Treatment of Severe Sepsis and Septic Shock , 2001 .

[51]  Farhad Kaffashi,et al.  Information Technology in Critical Care: Review of Monitoring and Data Acquisition Systems for Patient Care and Research , 2015, TheScientificWorldJournal.

[52]  A. Costello,et al.  Error rates in a clinical data repository: lessons from the transition to electronic data transfer—a descriptive study , 2013, BMJ Open.

[53]  Lise Tuset Gustad,et al.  Early identification of sepsis in hospital inpatients by ward nurses increases 30-day survival , 2016, Critical Care.

[54]  M.H. Hassoun,et al.  Fundamentals of Artificial Neural Networks , 1996, Proceedings of the IEEE.

[55]  Dylan S. Small,et al.  Post-Acute Care Use and Hospital Readmission after Sepsis. , 2015, Annals of the American Thoracic Society.

[56]  S. Rafnsson,et al.  Assessing available information on the burden of sepsis: global estimates of incidence, prevalence and mortality , 2012, Journal of global health.

[57]  Matthew D. Stanley,et al.  Early sepsis detection in critical care patients using multiscale blood pressure and heart rate dynamics. , 2017, Journal of electrocardiology.