The identification of outcomes within EHRs may be easier when computable phenotypes have been created for the conditions of interest. As described in the Electronic Health Records–Based Phenotyping chapter of the Living Textbook, "a computable phenotype is a clinical condition, characteristic, or set of clinical features that can be determined solely from data in EHRs and ancillary data sources and does not require chart review or interpretation by a clinician.… For a phenotype definition to be valid, it must identify the condition for which it was developed and meet the desired degrees of sensitivity and specificity."

Watch the video module: Defining Outcomes With Electronic Health Record Data

The NIH Collaboratory’s Electronic Health Records Core, which includes representatives from the NIH Collaboratory Trials, has published an article describing approaches for using "phenotypes" data to identify clinically equivalent populations across multiple sites, includiong how to assess whether the data collected from healthcare systems are comparable, valid, and reliable (Richesson et al. 2013).

Some key questions and considerations related to outcomes within EHRs include the following:

• Is the outcome a medically significant such that a patient would seek care?
  • Will the endpoint be medically attended?
  • Does it require hospitalization?
  • Is the treatment for the outcome generally provided in inpatient or outpatient settings?
    • Outpatient events may include diagnoses that justify a specific test order, also called a "rule out," and rule-out diagnoses might not indicate true outcomes.
• What is the intensity of medical care?
  • If high, as with a myocardial infarction, then there will be a clear record in claims and/or EHR data.
  • If low, as with the gout example, there may or may not be a record of the event. A solution to this problem is to use a PRO, and reach out to the participant at specified intervals.
• Where would the signal show up?
  • EHR (laboratory values, treatments, etc.)
  • Claims data (does the event generate a bill?)
  • Both
• What sensitivity is required?
  • Conditions may not be consistently and reliably recorded.
• Will the data be structured or unstructured?
  • If structured, the data may be usable as is.
  • If unstructured, some work will need to be done to ensure the data are captured in a uniform way. (Prompts can be added to the EHR system, artificial intelligence and large language models can be used, clinicians can be given special training, etc.)
• Are there overlapping conditions (eg, chest pain and unstable angina)?
  • The data may need adjudication, especially if they are claims data.

An additional consideration, discussed in greater detail in the Using Electronic Health Record Data in Pragmatic Clinical Trials of the Living Textbook, is the ability to capture outcomes from a number of different sources over time. For example, in the Aspirin Dosing: A Patient-Centric Trial Assessing Benefits and Long-term Effectiveness (ADAPTABLE) trial, patients enroll through an online portal, which also provides online consent and randomization. Data collected during routine care are used in the study, however, to capture complete information about all outcomes—which include myocardial infarction, mortality, and hospitalizations—participants are asked to login to the portal and report hospitalizations. If the patient does not go back to the portal, the call center at the Duke Clinical Research Institute calls for follow up.

For more the Food and Drug Administration has issued a guidance that discusses selection of data sources, development and validation of definitions for study design elements, and data traceability and quality when using data from the EHR (FDA 2025). The EHR Core has also developed the following the Living Textbook Chapters:

• Using Electronic Health Record Data in Pragmatic Clinical Trials
• Acquiring Real-World Data
• Assessing Fitness-for-Use of Real-World Data