List the common data sources used in QI reporting (EHR data, claims data, patient-reported outcomes) and discuss their strengths and limitations.

• A. EHR data timely but variable quality; claims data broad coverage but limited clinical detail; PROs capture patient perspective but require engagement; Combining sources improves validity but adds complexity.
• B. EHR data are always complete; claims data have clinical detail; PROs are not useful.
• C. PROs are easiest and require no engagement; EHR data are unreliable.
• D. Combining sources never improves validity.

Answer: (A)

In quality improvement reporting, using multiple data sources provides a more complete and trustworthy picture because each source sheds light on different aspects of care. EHR data can give timely reflections of what’s happening in care delivery, but the quality and completeness of that information can vary widely due to how clinicians document and code events. This means you get a current view, but with potential gaps or inconsistencies that can affect reliability.

Claims data cover a broad population and can show how services are used across settings, which is great for understanding coverage, utilization, and cost patterns over time. However, they usually lack detailed clinical information, so you may miss nuances about disease severity, exact symptoms, or clinical rationale behind decisions.

Patient-reported outcomes bring the patient’s voice into the picture, capturing symptoms, functional status, and quality of life that data alone can’t always reveal. The trade-off is that they require patient participation, and responses can be incomplete or biased if engagement is low or if respondents differ from non-respondents.

Combining these sources improves validity because you can cross-verify findings, fill gaps where one source falls short, and build a more robust picture of quality, safety, and value. It also supports triangulation, helping confirm that observed patterns aren’t artifacts of a single data stream. The trade-off is that integrating diverse data adds complexity: linking records across systems, harmonizing definitions and measures, addressing differing data quality and missingness, and managing the governance and resource demands of a multi-source approach.