EHR data and systems can be used to support 5 major activities of pragmatic trials, which include:

• Preparation: Estimating the potential study population
  • Estimating numbers of eligible patients
  • Estimating rates of study outcomes among eligible patients
  • Estimating clustering (intraclass correlation) to inform sample size calculations
• Enrollment or recruitment: Identifying the study population, often in terms of phenotypes or clinical profiles including current health status or medical history
  • Identifying a cohort of patients for screening, recruitment, or enrollment.
  • Identifying clusters (clinicians, clinics, etc.) for cluster-randomized or stepped-wedge trials
• Assessing baseline prognostic characteristics of the research sample or cohort for a number of objective and subjective measures
  • In a cluster-randomized design, patient populations may be unbalanced due to facility differences in patient mix. Prognostic factors can be used to detect the balance (or imbalance) of specified independent variables. However, if the factors are used as part of the stratification scheme for cluster-randomization, then this type of balancing is not possible.
  • Example of an objective measure: definitive lab values for a specific disease, such as chronic kidney disease or HbA1C for diabetes control. (These are subject, of course, to the kinds of limitations noted above. In PCTs, investigators make do with whatever value is in the chart, typically without capturing any information about the clinical lab’s quality control values for these tests.)
  • Example of a subjective measure: clinical judgement about autism spectrum disorder or an observation by a clinician on skin discoloration or patient discomfort.
• Implementing and monitoring the delivery of an intervention (using EHR system functions, such as alerts and computerized provider order entry (CPOE) interfaces)
• Measuring the outcomes for both the intervention and control populations.
  • Longitudinal data linkage—following a cohort of patients over time may require aggregating data from multiple encounters, providers, and information systems. Linkage techniques to ensure that the correct data are being applied to each patient are critical for randomized PCTs, especially if the longitudinal data are being used to measure the effectiveness of the intervention.

In the following sections, we describe each of these uses for EHR data by PCT activity.