Health Services Research最新文献

Objective: To examine the impact of two interventions aimed at increasing the adoption of social risk screening in community health centers (CHCs).

Study setting and design: Intervention CHCs were in one of three groups, which received either: (1) tailored practice facilitation-focused social risk screening implementation supports; (2) financial incentives for screening; and (3) both practice facilitation and financial incentives in staggered order. A group of control clinics was identified through propensity score matching and a difference-in-difference analysis compared effects across groups.

Data sources and analytic sample: Using electronic health record data, we calculated monthly rates of social risk screening (per 100 adult patients) at 32 intervention clinics (19 practice facilitation supports only, 6 financial incentives only, 7 both financial incentives and practice facilitation supports), and 32 control clinics.

Principal findings: Compared to control clinics, clinics in any intervention group had a greater increase in average monthly social risk screenings from pre- to post-intervention that was maintained over the 24 months following intervention (difference-in-difference: 4.66, 95% CI: 0.89, 8.43). In the primary analysis, clinics engaged in both interventions increased screening rates when practice facilitation implementation supports were added to financial incentives (12 months 3.70, 95% CI: 0.34, 7.07; 24 months 4.18, 95% CI: -0.01, 8.87); adding financial incentives to practice facilitation supports resulted in increased screening rates but did not reach statistical significance.

Conclusions: This study is the first to compare different interventions intended to bolster CHCs' social risk screening activities. As social risk screening becomes increasingly tied to US policy and payment structures, it is critical to identify strategies that can support implementation in settings serving underserved populations. Our findings suggest modest impacts of both financial incentives and practice facilitation supports.

Objective: To assess the utility of using discrete primary care electronic health record (EHR) data to identify social risk referrals in a national network of community-based clinics.

Data sources and study setting: Primary data were abstracted from the OCHIN network EHR (June 2016 to February 2022) of 1459 community-based clinics across the United States.

Study design: Structured data elements included 913 commonly used smartphrases and 53 procedure codes that were considered potential indicators of social risk referrals. Using stratified purposive sampling, we compared these discrete data with clinical notes to assess concordance of social risk referral documentation, and of the prevalence, types, and outcomes of such referrals.

Data collection/extraction methods: Smartphrases were classified into three categories (likely, possible, or unlikely to indicate a social risk referral); 50 chart notes were sampled for each of the 25 most frequently used smartphrases in each category, and for 53 of the most frequently used procedure codes. A total of 6104 chart notes were reviewed.

Principal findings: In 59% of chart notes where discrete data suggested a social risk referral occurred, there was no documentation of this in the note. Primary domains addressed were food insecurity (38%), financial stress (18%) and housing needs (18%). Common referral activities included providing contact information (26%), help with assistance applications (17%), and direct provision of resources (16%). Documentation indicated the patient received resources in 29% of notes.

Conclusions: EHR documentation of social risk referrals in structured data fields is inconsistent. Further work should establish best practices, especially given emerging policies that tie payments to documentation of social risk screening and intervention provision. Community health centers may struggle to use data elements such as smartphrases and procedure codes to monitor and report on their social risk referrals until standardized coding practices are established and effectively implemented.

Objective: This study aimed to measure the changes in rural hospital bypass for 11 common elective surgeries following the implementation of the Pennsylvania Rural Health Model (PARHM), a global budget payment model.

Study setting and design: We leveraged a natural experiment arising from the phase-in of PHARM in Pennsylvania. We conducted a comparative interrupted time series analysis to assess changes in rural hospital bypass, comparing trends in rural hospital bypass among patients in hospital service areas (HSAs) with PARHM-participating hospitals to patients in control HSAs with hospitals eligible for but not participating in PARHM. Analyses accounted for staggered entry into PARHM and examined outcomes up to 4 years post-entry.

Data sources and analytic sample: We used Pennsylvania all-payer visit-level inpatient discharge data (2016-2022) to measure rural hospital bypass, encompassing 175,138 surgeries.

Principal findings: The average bypass rate for elective surgeries was 59.9%, with an increasing trend observed during the study period. Overall, differential changes in bypass rates between PARHM-participating and control HSAs were not statistically significant, from a low of 0.53 percentage points (-8.17-9.22) among Cohort 2 HSAs and a high of 5.96 percentage points (-4.63-16.55) among Cohort 1 HSAs. However, among critical access hospitals, PARHM participation was associated with a significant relative increase in levels and trends in bypass rates compared to controls, from a low of 9.12 percentage points (2.45-15.79) among Cohort 1 HSAs and a high of 29.70 percentage points (12.54-46.86) among Cohort 2 HSAs. These relative increases were largely due to a stable rate in PARHM-participating HSAs and a marked decrease in control HSAs.

Conclusions: This study fills a gap in the relationship between global budgets and hospital bypass. Although PARHM did not broadly alter rural bypass rates overall, the differential increase in bypass among HSAs with CAHs participating in PARHM suggests meaningful effect heterogeneity, warranting further research and analysis of impacts on patient outcomes.

Objective: To assess the utility and challenges of using natural language processing (NLP) in electronic health records (EHRs) to ascertain health-related social needs (HRSNs) among older adults.

Study setting and design: We extracted HRSN information using the NLP system Clinical Text Analysis and Knowledge Extraction System (cTAKES), combined with Concept Unique Identifiers and Systematized Nomenclature for Medicine codes. We validated cTAKES performance, via manual chart review, on two HRSNs: food insecurity, which was included in the healthcare system's HRSN screening tool, and housing insecurity, which was not.

Data sources and analytic sample: De-identified EHRs in a large California healthcare system (January 2013 through October 2022) from 119,127 patients aged 55+ in primary and emergency care settings (n = 1,385,259 clinical notes).

Principal findings: Although cTAKES had a moderate positive predictive value (77.5%) for housing insecurity, housing challenges among older adults frequently did not align with the concepts the algorithm recognized. cTAKES performed poorly for food insecurity (positive predictive value: 18.5%) because this NLP system incorrectly flagged structured fields from the screening tool.

Conclusion: Unstandardized terminology and poor integration of HRSN screeners in EHR remain important barriers to identifying older adults' food and housing insecurity using cTAKES.

Objective: To assess how patient and caregiver factors influence caregiver readiness for hospital discharge in palliative care patients.

Study setting and design: This transitional care study uses cross-sectional data from a randomized controlled trial conducted from 2018 to 2023 testing an intervention for caregivers of hospitalized adult patients with a serious or life-limiting illness who received a palliative care consult prior to transitioning out of the hospital.

Data sources and analytical sample: Caregiver readiness was measured with the Family Readiness for Hospital Discharge Scale (n = 231). Caregiver demographic, intra- and interpersonal factors were self-reported. Patient demographic, comorbidity score, and risk score for complicated discharge planning were extracted from electronic health records. Stepwise regression models estimated variance explained (r²) in caregiver readiness for patient hospital discharge.

Principal findings: Patient demographics and complexity were not statistically associated with caregiver readiness for discharge. Caregiver depressive symptoms, poor caregiver-patient relationship quality, and fewer hours spent caregiving prior to hospitalization explained 29% of the variance in caregiver readiness.

Conclusions: Reliance on patient data may not be sufficient for explaining caregiver readiness for discharge. Assessing caregiver factors may be a better alternative for identifying caregivers at risk for low discharge readiness and those in need of additional support.

Trial registration: ClinicalTrials.gov on November 13, 2017, (No. NCT03339271).

Objectives: To explore how the Medicaid continuous coverage requirement and unwinding process was implemented in three states with diverse existing policy environments and implications for the implementation of post-emergency 12-month postpartum extensions.

Data sources: State data on unwinding performance and qualitative in-depth interviews with 48 stakeholders and patient-facing healthcare workers in Texas, New York and New Jersey.

Study design: State Medicaid stakeholders and patient-facing healthcare workers in each state were interviewed with the goal of gaining insights into: (1) How the continuous coverage requirement was implemented; (2) What effects continuous coverage had on access to care for postpartum mothers; (3) How states are implementing the pandemic unwinding and postpartum extensions.

Data collection/extraction: Interviews were recorded and took place over Zoom between September 2022 and March 2024. Interview transcripts were analyzed thematically using an open coding approach.

Principal findings: The study found that low awareness of the continuous coverage requirement blunted potential benefits: little changed in practice in recommended patient care or follow-up procedures. Differences in state unwinding approaches reflected differential state capacity to minimize procedural disenrollment and political incentives to either rapidly reduce or maintain Medicaid rolls. Based on these findings, we describe how political will, state capacity and policy legacies interact to either increase or decrease administrative burdens associated with program enrollment/re-enrollment.

Conclusions: While the continuous coverage requirement in theory applies equally across US states, its implementation was inconsistent and influenced by state capacity and political objectives creating differential experiences across states. To ensure that postpartum Medicaid coverage extensions have maximal impact on improving maternal health, states should develop improved communication strategies to ensure that providers and community-based organizations are aware of coverage changes and leverage available flexibilities to increase use of administrative renewal and ensure smooth coverage transitions.

Objective: To understand how Medicare Advantage (MA) networks impact utilization patterns and plan choices, using the 2019 discontinuation of MA 1876 Cost plans as a natural experiment.

Study setting and design: We study 1876 Cost plans, MA plans for which out-of-network care is covered through traditional Medicare (TM) and many of which CMS discontinued in 2019. We characterize the proportion of Cost plan enrollees who utilized out-of-network care in 2018 from different types of medical specialties. We then study how enrollees in discontinued plans selected into new plans in 2019. We use regression analysis to characterize whether higher risk enrollees selected into TM at higher rates.

Data sources and analytic sample: We identify discontinued plans using public MA plan data. We employ administrative Medicare enrollment and TM claims data to identify 2018 enrollees of discontinued plans, their 2018 out-of-network utilization, and their subsequent 2019 enrollment decisions.

Principal findings: Among Cost plan enrollees, 69% utilized non-emergency room related care out of network in 2018. Out-of-network utilization was distributed across several types of specialties: 43% of Cost plan enrollees had at least one out-of-network claim with a primary care physician and over 20% had a claim with a medical specialist, surgical specialist, or nurse practitioner. We find evidence of adverse selection among enrollees of discontinued Cost plans in 2019. Conditional on one's 2018 Cost plan and county of residence, a standard deviation increase in risk score was on average associated with a 26.35% (95% CI, 25.57%-27.12%) increased likelihood of enrolling in TM.

Conclusion: The high rate of out-of-network utilization suggests that MA enrollees value access to care outside of standard MA networks. Subsequent selection patterns indicate that preferences for broader networks and subsequent enrollment in TM is highest among higher risk enrollees, suggesting limited networks may induce extensive margin selection.

Objective: To estimate associations between Wisconsin Medicaid's Prenatal Care Coordination (PNCC) program and infant mortality.

Data sources and study setting: We analyzed birth records, Medicaid claims, and infant death records for all resident and in-state Medicaid-paid live deliveries during 2010-2018.

Study design: We measured PNCC exposure during pregnancy dichotomously (none; any) and categorically (none; assessment/care plan only; service receipt). Our outcome was infant mortality (death at age < 365 days). Adjusted binary logit regressions and propensity score weighted regressions tested associations between PNCC receipt and infant mortality, and we estimated probabilities and average marginal effects of infant mortality. We also executed regressions with interactions on maternal race/ethnicity to determine if associations varied across Black non-Hispanic (NH), Hispanic, and White NH births.

Data collection/extraction methods: Our sample consisted of 231,540 Medicaid-paid births during 2010-2018. PNCC is only available to pregnant Medicaid beneficiaries.

Principal findings: Infant mortality was lower among PNCC assessment/care plan only births (5.0 deaths/1000 births) and PNCC service receipt births (6.1 deaths/1000 births) relative to non-PNCC births (6.8 deaths/1000 births). This pattern was consistent in Black NH and Hispanic subgroups, but infant mortality did not vary by PNCC among White NH deliveries. Overall, adjusted binary logit regressions indicated that the probabilities of infant mortality were 0.70% for no PNCC and 0.53% for any PNCC, yielding an average marginal effect of -0.17 percentage points (95% confidence interval -0.22 percentage points, -0.11 percentage points). This association did not vary by PNCC exposure level. PNCC-infant mortality associations were significantly stronger for Black NH births relative to White NH births. Results were consistent in propensity score weighted regressions.

Conclusions: PNCC during pregnancy is associated with a lower probability of infant mortality, particularly in Black NH families. The benefit of PNCC on infant mortality may not depend on receiving services beyond care planning.