As health systems increasingly rely on patient-reported outcomes (PROs) to inform care delivery, many are asking an important question: how do socioeconomic and demographic factors shape the patient experience before treatment even begins?
A nationally recognized academic health system recently explored this question through a short-term research initiative focused on PRO completion, symptom reporting, and demographic context in orthopedic care. Using PatientIQ as the underlying data and workflow platform, the team was able to integrate research seamlessly into routine clinical operations and uncover insights that would have been difficult to surface otherwise.
Studying Equity, Access, and Data Integrity
The research effort was led by an Associate Professor of Orthopaedic Surgery who sought to examine how patient demographics influenced both engagement with PROs and reported symptoms at the initial point of care.
Specifically, the study explored relationships between PROs and variables such as race, ethnicity, age, sex, and residential setting. The team was also interested in whether patients from urban, suburban, or rural environments differed in their likelihood to complete PRO surveys, as well as how they reported pain and symptoms.
Collecting and analyzing this type of data at scale is often challenging. Demographic information may live in multiple systems, PRO workflows can vary by clinic, and incomplete data can introduce bias into research findings.
To move forward, the team needed a way to embed PRO collection directly into clinical workflows, while maintaining consistency across practice locations.
Embedding Research Into the EHR Workflow
Using PatientIQ, the health system integrated PRO data collection directly into the electronic health record (EHR). This allowed patients to complete required PRO questionnaires prior to their initial evaluations, while ensuring that demographic and outcome data were captured in a structured, analyzable format.
Because the platform centralized data across clinics and locations, the research team could analyze task completion rates and symptom reporting patterns without relying on manual data pulls or fragmented reports.
This approach made it possible to examine trends across patient populations while minimizing operational burden for clinicians and staff.
What Short-Term Data Can Reveal
During a six-month pilot period, the health system collected thousands of patient-reported outcome measures, achieving a high completion rate across assigned tasks. Even within this relatively short timeframe, meaningful patterns began to emerge.
The analysis highlighted differences in PRO completion rates across practice locations, as well as variation in reported pain and symptom severity based on residential setting. Importantly, when data was analyzed through multiple lenses, some assumed correlations did not hold up, reinforcing the need for careful interpretation and contextual awareness when working with pooled datasets.
These findings helped the research team identify potential sources of bias and workflow friction, particularly in urban clinic settings, and informed future research questions around baseline differences in PRO scores.
Why This Work Matters
This spotlight underscores the value of short-term research conducted within everyday clinical practice. By pairing PROs with demographic and socioeconomic data, health systems can better understand not only what patients report, but why those reports may differ across populations.
Just as importantly, the study demonstrates that this type of research does not require separate infrastructure or resource-intensive workflows. With the right platform, health systems can explore equity, access, and bias questions while continuing to deliver care at scale.
What This Spotlight Shows
This short-term research effort highlights how PROs can surface meaningful differences in engagement, symptom reporting, and workflow performance across patient populations, even over a relatively brief timeframe.
By integrating PRO collection into the EHR and analyzing data through multiple demographic lenses, the health system was able to identify patterns that might otherwise be overlooked, including areas where workflow design and data interpretation warrant closer attention.
The full case study expands on how this approach supported short-term research, helped mitigate bias in pooled datasets, and informed future investigation into socioeconomic drivers of PROs.
Download the case study to see how PatientIQ supported this research and what other health systems can learn from the findings.
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