Quality measurement has been evolving to become more patient-focused and more meaningful in supporting quality improvement. Recent advancements in digital data and measurement standards have made this evolution possible, but this move to digital measurement presents several challenges despite its many benefits. Digital quality measures (dQMs) substantially reduce the computational burden of generating “quality” knowledge and improve the reliability of the measure scores they generate, however they rely on a very specific presentation of the electronic data to achieve the aforementioned benefits. Newer dQMs based on patient-reported outcomes (PROs) measured using patient-reported outcome measures (PROMs) have been gaining attention as they generate valuable insight into a person’s perception of their own health status. Reliably capturing these insights is challenging however, as the information does not often exist in a format that can be processed by a dQM. This lack of standardization has resulted in the formation of clinical data repositories (CDRs) for the explicit purpose of extracting, transforming, and loading (ETL) PROM data from patients’ medical records into a format that can support digital quality measurement.
These ETL processes are subject to rigorous evaluation to ensure that, as the information is being transformed, the integrity of the original information is being preserved. These evaluations inform decisions regarding data fitness for the specific purpose of using the data to measure quality of care. These “fit for purpose” decisions are not guided by a uniform set of expectations or requirements to assure consistency in decision-making, rather they frequently rely upon a variety of statistical and operational test results that can often present seemingly inconsistent information that requires substantial expertise to interpret and reconcile. A uniform, well-defined list of data quality concepts pertinent to using patient-reported outcome measures for the purpose of quality measurement would provide much needed guidance and enhance the consistency and reliability of data fitness decision-making.
This research confirmed the scarcity of access to effective guidance for assessing fitness of PROM data and that there is a desire for a standard PROM-based data quality assessment (DQA) model to support decision making
