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 in | Health Affairs Vol. 33; no. 7; pp. 1139 - 1147 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
United States
The People to People Health Foundation, Inc., Project HOPE
01.07.2014
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Subjects | |
Online Access | Get full text |
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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. |
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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 |
Author_xml | – sequence: 1 givenname: I. Glenn surname: Cohen fullname: Cohen, I. Glenn organization: I. Glenn Cohen is a professor of law and director of the Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics, both at Harvard Law School, in Cambridge, Massachusetts – sequence: 2 givenname: Ruben surname: Amarasingham fullname: Amarasingham, Ruben organization: Ruben Amarasingham is president and CEO of PCCI, a nonprofit research and development corporation, and an associate professor in the Departments of Internal Medicine and of Clinical Sciences at the University of Texas Southwestern Medical Center, both in Dallas – sequence: 3 givenname: Anand surname: Shah fullname: Shah, Anand organization: Anand Shah is vice president of clinical services at PCCI – sequence: 4 givenname: Bin surname: Xie fullname: Xie, Bin organization: Bin Xie is a health services manager at PCCI – sequence: 5 givenname: Bernard surname: Lo fullname: Lo, Bernard 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 |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25006139$$D View this record in MEDLINE/PubMed |
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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 |
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