Pathways to Increasing Trust in Public Health Data

Abstract

Abstract Digital tools make it easier to collect data about patients closer to where they live, understand their health needs better, and treat them faster, thereby saving lives. Community Health Workers (CHWs) are increasingly collecting digital data through the course of care delivery. However, despite the widespread championing of digital health tools, the full impact of these technologies has not been realized because CHW-collected data is often considered low quality and unreliable for data-driven decision-making. At a systemic level, mistrust in the quality of the data (a lack of so-called ‘data trust’) limits the potential impact. The primary objective of this research program was to identify inconsistent or problematic data (IoP) occurring across a digitally enabled CHW health system in order to recommend changes in digital health tools and processes that might increase trust in CHW-collected data. In this exploratory study, data were analyzed using a variety of statistical and data science approaches including clustering algorithms, histograms, box plots, and Sankey diagrams. IoP were identified from a suite of 160 tests internally developed to identify IoP at the health platform level. As anticipated, data exhibited issues with accuracy, completeness, and timeliness across digital health forms. While each IoP issue identified could be individually remediated, recommendations provided are centered on a platform-wide (and tool agnostic) approach to data quality in community health.