Consensus Statement on Electronic Health Predictive Analytics: A Guiding Framework to Address Challenges

Abstract

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.

Machine learning approaches to personalize early prediction of asthma exacerbations

“…3 The broad introduction of electronic algorithms predicting clinical events in real time with the intent to improve patient care and decrease costs has been successfully facilitated by Big Data analytics, 4 and the recent explosion in availability of electronic health data is motivating a rapid expansion of healthcare predictive analytics applications. 5,6 One of the potentially fruitful areas for further expansion of Big Data analytics is chronic disease management. Chronic health conditions afflict a significant proportion of the population and are particularly prevalent in older adults.…”

Path From Predictive Analytics to Improved Patient Outcomes

“…The Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) standards and the recent Consensus Statement on E-HPA elaborate these points. 2,3 Unfortunately, a disappointing number of publically available predictive models meet these criteria. 4 However, developing good predictive models that meet these criteria is just the beginning of successful implementation and perhaps easier than the downstream challenges to which we now turn.…”

What Are Best Practices for Ethical Use of Nanosensors for Worker Surveillance?

“…14 As nano-enabled mHealth devices increase the potential power and intrusiveness of worker monitoring programs, it is critical that employers implement such programs in cooperation with workers as that is the only way to realize in practice the significant benefits to employers and employees that are possible from such efforts. The following best practices, derived from an extensive literature on bioethics, employee management, technology acceptance, risk management, and practical experience with worker surveillance programs, 2,14,[16][17][18][19][20][21][22] can best ensure that nano-enabled mHealth applications can be a win-win for both workers and their employers.…”