The Legal And Ethical Concerns That Arise From Using Complex Predictive Analytics In Health Care

Predictive analytics, or the use of electronic algorithms to forecast future events in real time, makes it possible to harness the power of big data to improve the health of patients and lower the cost of health care. However, this opportunity raises policy, ethical, and legal challenges. In this ar...

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Published inHealth Affairs Vol. 33; no. 7; pp. 1139 - 1147
Main Authors Cohen, I. Glenn, Amarasingham, Ruben, Shah, Anand, Xie, Bin, Lo, Bernard
Format Journal Article
LanguageEnglish
Published United States The People to People Health Foundation, Inc., Project HOPE 01.07.2014
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Abstract Predictive analytics, or the use of electronic algorithms to forecast future events in real time, makes it possible to harness the power of big data to improve the health of patients and lower the cost of health care. However, this opportunity raises policy, ethical, and legal challenges. In this article we analyze the major challenges to implementing predictive analytics in health care settings and make broad recommendations for overcoming challenges raised in the four phases of the life cycle of a predictive analytics model: acquiring data to build the model, building and validating it, testing it in real-world settings, and disseminating and using it more broadly. For instance, we recommend that model developers implement governance structures that include patients and other stakeholders starting in the earliest phases of development. In addition, developers should be allowed to use already collected patient data without explicit consent, provided that they comply with federal regulations regarding research on human subjects and the privacy of health information.
AbstractList Predictive analytics, or the use of electronic algorithms to forecast future events in real time, makes it possible to harness the power of big data to improve the health of patients and lower the cost of health care. However, this opportunity raises policy, ethical, and legal challenges. In this article we analyze the major challenges to implementing predictive analytics in health care settings and make broad recommendations for overcoming challenges raised in the four phases of the life cycle of a predictive analytics model: acquiring data to build the model, building and validating it, testing it in real-world settings, and disseminating and using it more broadly. For instance, we recommend that model developers implement governance structures that include patients and other stakeholders starting in the earliest phases of development. In addition, developers should be allowed to use already collected patient data without explicit consent, provided that they comply with federal regulations regarding research on human subjects and the privacy of health information. Adapted from the source document.
Predictive analytics, or the use of electronic algorithms to forecast future events in real time, makes it possible to harness the power of big data to improve the health of patients and lower the cost of health care. However, this opportunity raises policy, ethical, and legal challenges. In this article we analyze the major challenges to implementing predictive analytics in health care settings and make broad recommendations for overcoming challenges raised in the four phases of the life cycle of a predictive analytics model: acquiring data to build the model, building and validating it, testing it in real-world settings, and disseminating and using it more broadly. For instance, we recommend that model developers implement governance structures that include patients and other stakeholders starting in the earliest phases of development. In addition, developers should be allowed to use already collected patient data without explicit consent, provided that they comply with federal regulations regarding research on human subjects and the privacy of health information.Predictive analytics, or the use of electronic algorithms to forecast future events in real time, makes it possible to harness the power of big data to improve the health of patients and lower the cost of health care. However, this opportunity raises policy, ethical, and legal challenges. In this article we analyze the major challenges to implementing predictive analytics in health care settings and make broad recommendations for overcoming challenges raised in the four phases of the life cycle of a predictive analytics model: acquiring data to build the model, building and validating it, testing it in real-world settings, and disseminating and using it more broadly. For instance, we recommend that model developers implement governance structures that include patients and other stakeholders starting in the earliest phases of development. In addition, developers should be allowed to use already collected patient data without explicit consent, provided that they comply with federal regulations regarding research on human subjects and the privacy of health information.
Predictive analytics, or the use of electronic algorithms to forecast future events in real time, makes it possible to harness the power of big data to improve the health of patients and lower the cost of health care. However, this opportunity raises policy, ethical, and legal challenges. In this article we analyze the major challenges to implementing predictive analytics in health care settings and make broad recommendations for overcoming challenges raised in the four phases of the life cycle of a predictive analytics model: acquiring data to build the model, building and validating it, testing it in real-world settings, and disseminating and using it more broadly. For instance, we recommend that model developers implement governance structures that include patients and other stakeholders starting in the earliest phases of development. In addition, developers should be allowed to use already collected patient data without explicit consent, provided that they comply with federal regulations regarding research on human subjects and the privacy of health information.
Author Lo, Bernard
Shah, Anand
Cohen, I. Glenn
Amarasingham, Ruben
Xie, Bin
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  surname: Amarasingham
  fullname: Amarasingham, Ruben
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  organization: Bernard Lo is president of the Greenwall Foundation and professor emeritus of medicine and director emeritus of the Program in Medical Ethics, both at the University of California, San Francisco
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PublicationTitle Health Affairs
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Snippet Predictive analytics, or the use of electronic algorithms to forecast future events in real time, makes it possible to harness the power of big data to improve...
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StartPage 1139
SubjectTerms Algorithms
Big Data
Biomedical Research - ethics
Consent
Cost
Data collection
Data Interpretation, Statistical
Data Mining - ethics
Data Mining - legislation & jurisprudence
Datasets
Datasets as Topic - ethics
Datasets as Topic - legislation & jurisprudence
Delivery of Health Care - ethics
Delivery of Health Care - legislation & jurisprudence
Ethics
Federal regulation
Government and politics
Health care
Health care access
Health care expenditures
Health care industry
Health care policy
Hospitals
Human subjects
Humans
Medical service
Models, Statistical
Patients
Physicians
Predictive analytics
Privacy
Real time
Regulation
Studies
Technology adoption
Title The Legal And Ethical Concerns That Arise From Using Complex Predictive Analytics In Health Care
URI https://www.ncbi.nlm.nih.gov/pubmed/25006139
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