Incorporating patient generated health data into pharmacoepidemiological research

Abstract

Epidemiology and pharmacoepidemiology frequently employ Real-World Data (RWD) from healthcare teams to inform research. These data sources usually include signs, symptoms, tests, and treatments, but may lack important information such as the patient's diet or adherence or quality of life. By harnessing digital tools a new fount of evidence, Patient (or Citizen/Person) Generated Health Data (PGHD), is becoming more readily available. This review focusses on the advantages and considerations in using PGHD for pharmacoepidemiological research. New and corroborative types of data can be collected directly from patients using digital devices, both passively and actively. Practical issues such as patient engagement, data linking, validation, and analysis are among important considerations in the use of PGHD. In our ever increasingly patient-centric world, PGHD incorporated into more traditional Real-Word data sources offers innovative opportunities to expand our understanding of the complex factors involved in health and the safety and effectiveness of disease treatments. Pharmacoepidemiologists have a unique role in realizing the potential of PGHD by ensuring that robust methodology, governance, and analytical techniques underpin its use to generate meaningful research results. K E Y W O R D S big data, data privacy, digital epidemiology, mobile apps, mobile health, patient generated health data, patient reported outcomes, pharmacoepidemiology, real world data, real world evidence, social media This article reflects the personal views of the authors and should not be construed to represent FDA's views or policies.

Core concepts in pharmacoepidemiology: Validation of health outcomes of interest within real‐world healthcare databases

“…Additionally, the availability of patient-generated data from home monitoring devices (e.g., pulse oximeters, pacemakers, glucometers), wearables, mobile applications, social media, and other sources is increasing, and incorporating these data sources into case-identifying algorithms, and studies validating their use, will be important in the future. 34…”

Designing and conducting adaptive trials to evaluate interventions in health services and implementation research: practical considerations

“…Similarly, patient reported information from connected devices might also provide data appropriate as outcomes for adaptive trials in this field. 26 Examples include accelerometer data from smartphones about physical activity, home blood pressure or glucose monitors, especially if delivered via Bluetooth connections that require minimal patient manipulation and can be quickly acquired. Their increasing real time data availability could provide avenues for future adaptive trials.…”

Developing Patient-Centered Real-World Evidence: Emerging Methods Recommendations From a Consensus Process

“…11,12 A variety of initiatives to improve the data landscape are underway, including linking and curating "research-ready" data sets, sometimes including patient-generated health data or patient-reported outcomes. [13][14][15][16][17] These data are already advancing the quality of RWE by reducing missing data and enabling studies to measure patient-centered outcomes. 15 Nevertheless, even these more advanced data sets are prone to missing information and lack context regarding whether or why subjects delay treatment or are nonadherent to care plans or how other life factors, including social determinants of health, affect satisfaction, experiences, and outcomes.…”

“…[13][14][15][16][17] These data are already advancing the quality of RWE by reducing missing data and enabling studies to measure patient-centered outcomes. 15 Nevertheless, even these more advanced data sets are prone to missing information and lack context regarding whether or why subjects delay treatment or are nonadherent to care plans or how other life factors, including social determinants of health, affect satisfaction, experiences, and outcomes. 18 Context regarding patient experiences is still needed to explain data trends.…”