Testing an Adapted Modified Delphi Method: Synthesizing Multiple Stakeholder Ratings of Health Care Service Effectiveness.

BACKGROUND: The Affordable Care Act incentivizes health systems for better meeting patient needs, but often guidance about patient preferences for particular health services is limited. All too often vulnerable patient populations are excluded from these decision-making settings. A community-based participatory approach harnesses the in-depth knowledge of those experiencing barriers to health care. METHOD: We made three modifications to the RAND-UCLA appropriateness method, a modified Delphi approach, involving patients, adding an advisory council group to characterize existing knowledge in this little studied area, and using effectiveness rather than "appropriateness" as the basis for rating. As a proof of concept, we tested this method by examining the broadly delivered but understudied nonmedical services that community health centers provide. RESULTS: This method created discrete, new knowledge about these services by defining 6 categories and 112 unique services and by prioritizing among these services based on effectiveness using a 9-point scale. Consistent with the appropriateness method, we found statistical convergence of ratings among the panelists. DISCUSSION: Challenges include time commitment and adherence to a clear definition of effectiveness of services. This diverse stakeholder engagement method efficiently addresses gaps in knowledge about the effectiveness of health care services to inform population health management.

Adjusting Clinical Plans Based on Social Context.

BACKGROUND: Social risk data collection is expanding in community health centers (CHCs). We explored clinicians' practices of adjusting medical care based on their awareness of patients' social risk factors-that is, changes they make to care plans to mitigate the potential impacts of social risk factors on their patients' care and health outcomes-in a set of Texas CHCs. METHODS: Convergent mixed methods. Surveys/interviews explored clinician perspectives on adjusting medical care based on patient social risk factors. Survey data were analyzed with descriptive statistics; interviews were analyzed using thematic analysis and inductive coding. RESULTS: Across 4 CHCs, we conducted 15 clinician interviews and collected 97 surveys. Interviews and surveys overall indicated support for adjustment activities. Two main themes emerged: 1) clinicians reported making frequent adjustments to patient care plans based on their awareness of patients' social contexts, while simultaneously expressing concerns about adjustment; and 2) awareness of patients' social risk factors, and clinician time, training, and experience all influenced clinician adjustments. CONCLUSIONS: Clinicians at participating CHCs described routinely adjusting patient care plans based on their patients' social contexts. These adjustments were being made without specific guidelines or training. Standardization of adjustments may facilitate the contextualization of patient care through shared decision making to improve outcomes.

"Wanting the Best for Our Folks"-A Mixed Methods Analysis of Community Health Center Social Risk Screening Initiatives.

BACKGROUND: Many community health centers (CHC) are scaling social risk screening in response to growing awareness of the influence of social adversity on health outcomes and concurrent incentives for social risk data collection. We studied the implementation of social risk screening in Texas CHCs to inform best practices and understand equity implications. METHODS: Convergent mixed methods of 3 data sources. Using interviews and surveys with CHC providers and staff, we explored social risk screening practices to identify barriers and facilitators; we used electronic health record (EHR) data to assess screening reach and disparities in screening. RESULTS: Across 4 urban/suburban Texas CHCs, we conducted 27 interviews (15 providers/12 staff) and collected 97 provider surveys; 2 CHCs provided EHR data on 18,672 patients screened during the study period. Data revealed 2 cross-cutting themes: 1) there was broad support for social risk screening/care integration that was rooted in CHCs' mission and positionalities, and 2) barriers to social risk screening efforts were largely a result of limited time and staffing. Though EHR data showed screens per month and screens/encounters increased peri-pandemic (4.1% of encounters in 8/2019 to 46.1% in 2/2021), there were significant differences in screening rates by patient race/ethnicity and preferred language (P < .001). In surveys, 90.0% of surveyed providers reported incorporating social risk screening into patient conversations; 28.6% were unaware their clinic had an embedded screening tool. CONCLUSIONS: Study CHCs were in the early stages of standardizing social risk screening. Differences in screening reach by patient demographics raise concerns that social screening initiatives, which often serve as a path to resource/service connection, might exacerbate disparities. Overcoming barriers to reach, sustainability, and equity requires supports targeted to program design/development, workforce capacity, and quality improvement.

Impact of Social Determinants of Health on Patients with Complex Diabetes Who Are Served by National Safety-Net Health Centers.

OBJECTIVE: Recent research demonstrates an increased need to understand the contribution of social determinants of health (SDHs) in shaping an individual's health status and outcomes. We studied patients with diabetes in safety-net centers and evaluated associations of their disease complexity, demographic characteristics, comorbidities, insurance status, and primary language with their HbA1c level over time. METHODS: Adult patients with diabetes with at least 3 distinct primary care visits between January 1, 2006, and December 31, 2013, were identified in the CHARN data warehouse. These patients were categorized into 4 groups: those without a diagnosis of cardiovascular disease (CVD) or depression; those with CVD but not depression; those with depression but not CVD; and those with CVD and depression. Charlson score; demographic characteristics such as age, sex, and race/ethnicity; and SDHs such as primary language and insurance status were used as predictors. The outcome measure was HbA1c. Hypothesis testing was conducted using 3-level hierarchical linear models. RESULTS: Baseline HbA1c differed significantly across the 4 diabetes groups and by race/ethnicity. The amount of HbA1c change over time differed by insurance status. Patients who were continuously insured tended to have lower baseline HbA1c and a smaller increase. Chinese-speaking patients tended to have lower baseline HbA1c but a larger increase over time compared with English speakers. There were various unexpected associations: compared with the diabetes-only group, mean HbA1c tended to be lower among the other more complex groups at baseline; women tended to have lower measures at baseline; older age and higher Charlson scores were associated with lower HbA1c. CONCLUSIONS: There is still unexplained variability relating to both baseline HbA1c values and change over time in the model. SDHs, such as insurance status and primary language, are associated with HbA1c, and results suggest that these relationships vary with disease status among patients with diabetes in safety-net centers. It is important to recognize that there are complex relationships among demographic and SDH measures in complex patients, and there is work to be done in correctly modeling and understanding these relationships. We also recommend prioritizing the collection of SDH and enabling services data for safety-net patients that would be instrumental in conducting a more comprehensive study.