Consensus Statement on Electronic Health Predictive Analytics: A Guiding Framework to Address Challenges
| Autor: | Lynn Etheredge, I. Glenn Cohen, Anand Shahi, Suchi Saria, Ruben Amarasingham, Lisa M. Schilling, Lucila Ohno-Machado, Sudha Ram, Ewout W. Steyerberg, Walter F. Stewart, Bin Xie, David W. Bates, Anne-Marie J. Audet, Bernard Lo, Martin Entwistle, Gabriel J. Escobar, Vincent X. Liu |
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| Přispěvatelé: | Public Health |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
endocrine system
Informatics Knowledge management Health information technology Big data Certification 030204 cardiovascular system & hematology lcsh:Computer applications to medicine. Medical informatics Clinical decision support system 03 medical and health sciences 0302 clinical medicine big data Health care Medicine 030212 general & internal medicine Ethics business.industry Clinical decision support systems Health Information Technology Articles electronic predictive analytics Predictive analytics predictive models 3. Good health Data sharing Analytics lcsh:R858-859.7 business |
| Zdroj: | eGEMs (Generating Evidence & Methods to improve patient outcomes); Vol 4, No 1 (2016); 3 EGEMS, 1:1163 eGEMs eGEMs, Vol 4, Iss 1 (2016) |
| ISSN: | 2327-9214 |
| Popis: | Context: The recent explosion in available electronic health record (EHR) data is motivating a rapid expansion of electronic health care predictive analytic (e-HPA) applications, defined as the use of electronic algorithms that forecast clinical events in real time with the intent to improve patient outcomes and reduce costs. There is an urgent need for a systematic framework to guide the development and application of e-HPA to ensure that the field develops in a scientifically sound, ethical, and efficient manner.Objectives: Building upon earlier frameworks of model development and utilization, we identify the emerging opportunities and challenges of e-HPA, propose a framework that enables us to realize these opportunities, address these challenges, and motivate e-HPA stakeholders to both adopt and continuously refine the framework as the applications of e-HPA emerge.Methods: To achieve these objectives, 17 experts with diverse expertise including methodology, ethics, legal, regulation, and health care delivery systems were assembled to identify emerging opportunities and challenges of e-HPA and to propose a framework to guide the development and application of e-HPA.Findings: The framework proposed by the panel includes three key domains where e-HPA differs qualitatively from earlier generations of models and algorithms (Data Barriers, Transparency, and Ethics) and areas where current frameworks are insufficient to address the emerging opportunities and challenges of e-HPA (Regulation and Certification; and Education and Training). The following list of recommendations summarizes the key points of the framework:1. Data Barriers: Establish mechanisms within the scientific community to support data sharing for predictive model development and testing.2. Transparency: Set standards around e-HPA validation based on principles of scientific transparency and reproducibility.3. Ethics: Develop both individual-centered and society-centered risk-benefit approaches to evaluate e-HPA.4. Regulation and Certification: Construct a self-regulation and certification framework within e-HPA.5. Education and Training: Make significant changes to medical, nursing, and paraprofessional curricula by including training for understanding, evaluating, and utilizing predictive models. |
| Databáze: | OpenAIRE |
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