Informatics, Health Information Technology, Ethics, Clinical decision support systems, electronic predictive analytics, predictive models, big data
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:
- Data Barriers: Establish mechanisms within the scientific community to support data sharing for predictive model development and testing.
- Transparency: Set standards around e-HPA validation based on principles of scientific transparency and reproducibility.
- Ethics: Develop both individual-centered and society-centered risk-benefit approaches to evaluate e-HPA.
- Regulation and Certification: Construct a self-regulation and certification framework within e-HPA.
- Education and Training: Make significant changes to medical, nursing, and paraprofessional curricula by including training for understanding, evaluating, and utilizing predictive models.
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Amarasingham, Ruben; Audet, Anne-Marie J.; Bates, David W.; Cohen, I. Glenn; Entwistle, Martin; Escobar, G J.; Liu, Vincent; Etheredge, Lynn; Lo, Bernard; Ohno-Machado, Lucila; Ram, Sudha; Saria, Suchi; Schilling, Lisa M.; Shah, Anand; Stewart, Walter F.; Steyerberg, Ewout W.; and Xie, Bin
"Consensus Statement on Electronic Health Predictive Analytics: A Guiding Framework to Address Challenges,"
eGEMs (Generating Evidence & Methods to improve patient outcomes):
1, Article 3.
Available at: http://repository.edm-forum.org/egems/vol4/iss1/3