Results Summary

What was the project about?

Quality measures assess how well healthcare providers deliver care. Examples of these measures include patient health outcomes like blood sugar control or how often patients go to the emergency room, or ER. These measures usually take into account how sick patients are by looking at how many health conditions a patient has and how severe they are.

Social risks also affect patient health outcomes. These risks may include unstable housing, income, or food. But quality measures don’t currently account for social risks.

In this study, the research team wanted to learn how health problems and level of social risk affect quality measures. To do this, the team combined patients’ health data with social risk data about their neighborhoods.

What did the research team do?

The research team used patient health data from community health centers. The data included health records and insurance claims for 73,328 patients with diabetes and 988,106 patients with Medicaid. Using these data, the team looked at the number and severity of patients’ health problems; they also looked at blood sugar levels and ER visits.

Then the research team linked the patient health data to data about social risks in the patients’ neighborhoods. Using data on seven social risks, such as unemployment, poverty, and overcrowded housing, the team calculated a social deprivation index, or SDI, score. Patients who lived in an area with more social risks had higher SDI scores.

Next, the research team looked at how patients’ health problems and SDI affected their blood sugar control and number of ER visits.

Clinicians, healthcare staff, and patients helped design the study.

What were the results?

Patients who had multiple or severe health problems were likely to have more ER visits but not poor blood sugar control.

After accounting for the number and severity of health problems, patients who lived in neighborhoods with more social risks were more likely to have poor blood sugar control. They were also more likely to have ER visits.

What were the limits of the project?

Patient health data came from community health centers. Social risk data were from the neighborhoods where patients lived and not from individual patients. Results may differ with other types of data.

Future studies could look at how including patients’ own social risk affects assessments of quality of care.

How can people use the results?

Health systems can include data on social risks to improve how well measures assess quality of care.

How this project fits under PCORI’s Research Priorities
The PCORnet® Study reported in this results summary was conducted using PCORnet®, the National Patient-Centered Clinical Research Network. PCORnet® is intended to improve the nation’s capacity to conduct health research, particularly comparative effectiveness research (CER), efficiently by creating a large, highly representative network for conducting clinical outcomes research. PCORnet® has been developed with funding from the Patient-Centered Outcomes Research Institute® (PCORI®).

Final Research Report

View this project's final research report.

Peer-Review Summary

Peer review of PCORI-funded research helps make sure the report presents complete, balanced, and useful information about the research. It also assesses how the project addressed PCORI’s Methodology Standards. During peer review, experts read a draft report of the research and provide comments about the report. These experts may include a scientist focused on the research topic, a specialist in research methods, a patient or caregiver, and a healthcare professional. These reviewers cannot have conflicts of interest with the study.

The peer reviewers point out where the draft report may need revision. For example, they may suggest ways to improve descriptions of the conduct of the study or to clarify the connection between results and conclusions. Sometimes, awardees revise their draft reports twice or more to address all of the reviewers’ comments.  

Peer reviewers commented and the researchers made changes or provided responses. Those comments and responses included the following:

  • The reviewers noted that when the researchers assessed the effect of social determinants of health (SDH), there were different effects whether the analytic model used community or individual SDH. The reviewers said that it was clear that if the analytic model already included individual SDH, the addition of community SDH did not have much effect. The reviewers asked for the researchers to add similar information about whether individual SDH had an effect on the results of the analytic model even when community SDH were already included in the analyses. The researchers added language to clarify that this was the case and agreed with the reviewers that individual SDH may possess a greater ability to provide actionable information to clinicians treating those patients.
  • The reviewers asked the researchers to explain how they operationalized number of emergency department visits: whether that was calculated as one or more visits, or number of visits. The researchers acknowledged that the measurement of emergency department visits was not consistent, mainly because the data were collected differently at the different sites and across different phases of the study.
  • One reviewer asked the researchers to clarify in the report that their research focused on one type of provider, community health centers. Therefore, the reviewer noted, the researchers’ advocacy for system-wide changes to ratings of provider performance metrics could lead to more resources for higher-income patients rather than resulting in more system resources going to patients with greater social complexity that could interfere with their health. The researchers agreed and clarified in the report their recommendations that system-wide reimbursement changes should not only take into account which providers had the best clinical outcomes, but also patients’ SDH, which were found to have a greater effect on outcomes than clinical complexity. 

Conflict of Interest Disclosures

Project Information

Abigail Sears, MBA, MHA and Erika Cottrell, PhD, MPP^
Oregon Community Health Information Network, Inc. (OCHIN)
The Impact of Patient Complexity on Healthcare Utilization

Key Dates

June 2016
March 2021

Study Registration Information

^The original principal investigator for this project was Scott Fields, MD, MHA.


Has Results
Health Conditions Health Conditions These are the broad terms we use to categorize our funded research studies; specific diseases or conditions are included within the appropriate larger category. Note: not all of our funded projects focus on a single disease or condition; some touch on multiple diseases or conditions, research methods, or broader health system interventions. Such projects won’t be listed by a primary disease/condition and so won’t appear if you use this filter tool to find them. View Glossary
Populations Populations PCORI is interested in research that seeks to better understand how different clinical and health system options work for different people. These populations are frequently studied in our portfolio or identified as being of interest by our stakeholders. View Glossary
State State The state where the project originates, or where the primary institution or organization is located. View Glossary
Last updated: April 12, 2024