Background: Predictive analytics in implementation research (PAIR) begins with integration of relevant, diversified data that provide comprehensive profiles of disease burden and community resources. Experiential data of requirements and impact of evidence-based interventions generated previously are applied to the targeted community-specific profiles. Forecasts of the resources needed and anticipated impact of adaptation of evidence-based interventions are generated. Predictive analytical forecasts may include: risk-factor burdens, resources needed to implement the intervention, projections of subsequent improvements in health care delivery and/or outcomes, assessment of wide-scale health policy changes, etc. Through the use of pertinent data and modelling, the critical details and challenges can be identified and addressed prior to implementation or conducted intermittently to refine deployed strategies. Some intervention research projects for evidence-based interventions to address heart, lung, blood, and sleep disorders (HLBS) are now being sub-optimally conducted. The use of PAIR may lead to less-disruptive, more cost-efficient and sustainable intervention strategies.
Executive Summary: PAIR’s ability to assess the complex interrelationships of a community’s state-of-health and state-of-resources, in order to forecast prospective impact of implementation research was well recognized. The term "environment-wide" was offered to describe the appropriate organization and integration of data representing the interrelationships of clinical, social, behavioral, and environmental risk and resource profiles, which are needed to give the full community picture in order to achieve better public health. This definition led to the refined concept that PAIR could be more accurately expressed as implementation research which is driven through the conduct of environment-wide associations (EWAS). Participants emphasized the timeliness of extending on-going advancements across the pre-workshop defined elements of PAIR: data science; artificial intelligence and machine learning; predictive modelling; data visualization; training and implementation research. Other areas which were explored include: data access; need for continual updating of data; ethical use, privacy, and security of the data; importance of shared values of the data and the analyses; need for information driving behavior be readily accessible and actionable; ability to explain EWAS to garner public support and use; critical need for development of EWAS/PAIR implementation frameworks to support hypothesis testing; and, importance for on-going stakeholder engagement.
Highlights of prospective elements of 21st Century Agenda for PAIR to address HLBS disorders include:
CTRIS Meeting Attendees
NHLBI Meeting Attendees
Panel: Dr. Thomas A. Pearson and Dr. Robert M. Califf
Moderator: LeShawndra N. Price, Ph.D.
At 11:20, Dr. Smith gives five-minute summary of highlights.
Intersection of data sources and methods in conducting public health informatics modelling
At 12:15, Dr. Green Parker gives five-minute summary of highlights.
Sparked by summaries of four use cases, discussants will join the conversation as everyone compares and contrasts their experience in use of predictive analytics for implementation research. We foresee a lively dialogue of practical insights on data sources, data linkages, strategies for stakeholder partnerships, analytical methods, and scope of prospective application in the conduct of implementation research, across the spectrum of heart, lung, blood and sleep conditions.
At 2:20, Drs. Brown and Papanicolaou give eight-minute summary of highlights.
Discussion of suggestions and questions collected over the course of the previous session, including obstacles and incentives for integration and use of existing data.
At 3:50, Drs. Campo and Croxton give eight-minute summary of highlights.