Over the last decade, the idea of person-generated health data (PGHD) and its value have become more widely recognized across clinical research and development, public health, and care delivery. In all these settings, the promise of PGHDis extremely enticing — generating rich insights for more rapid and efficient design of effective medical products, personalized care that mitigates some of the inequalities in our current health system, and providing much needed visibility to clinicians on their patients’ health and medical product use between visits. However, as stakeholders experiment with the use of PGHD, they have encountered challenges in collecting, analyzing and incorporating such novel data into care settings. Such challenges are not uncommon during the early adoption and integration phase of any novel data stream, and particularly so given the complex and heterogeneous healthcare system in the US.
In this report, we frame the value of PGHD in clinical settings in particular, lay out the common challenges that are barriers to its adoption, and ways in which these challenges can be mitigated to increase confidence in PGHD-derived insights. In turn, such robust insights can be used to improve clinical practice, implement better population health measures, and improve medical product design.