Predictive Analytics: Helping Guide the Implementation Research Agenda at the National Heart, Lung, and Blood Institute

摘要

Global biomedical research investments have resulted in important discoveries and many effective preventive and therapeutic interventions for heart, lung, blood, and sleep (HLBS) disorders—the focus areas for the National Heart, Lung, and Blood Institute (NHLBI) at the National Institutes of Health (NIH). In most high-income countries, extensive elforts have synthesized this knowledge through systematic reviews and developed evidence-based guidelines for intervention delivery within health care systems and community-based settings [1—7]. In low- and middle-income countries, the World Economic Forum and World Health Organization have studied the economic toll of noncommunicable diseases and the cost of scaling up a set of proven-effective interventions, so-called best buys (e.g., providing drug therapy and counseling for eligible persons at high risk to prevent heart attacks and strokes) [8]. Thus, effective interventions are now available and recommended for implementation across the globe. However, adaptable and sustainable implementation strategies for HLBS interventions are lacking and return on investment for this vast knowledge base is diminished. This has led many to call for a more developed implementation research agenda—including leadership from institutions such as the World Health Organization [9], World Bank [10], academia [11], US Agency for International Development [12], and NIH [13-19]. Implementation research studies intervention delivery in real-world contexts of health care delivery systems and community public health systems, with the goal of delivering interventions optimally and sustainably across the entire socioecological context [20]. Several major challenges for implementation research include understanding key barriers and facilitators within the socioecological context, various health and community policies, delivery strategies within health systems (e.g., physical infrastructure, staffing and their skill levels, availability of diagnostics and therapeutics, geospatial issues), and community contexts (e.g., disease burden, community resources, social deprivation, and economic opportunities). Elements at each of these levels can impact intervention implementation and thus enhance or impair intervention delivery and outcomes.