Medical Care最新文献

Background: Administrative data used to predict unplanned hospital readmissions often lack patient-reported symptoms and functional status. Integrating patient-reported outcome measures (PROMs) may improve risk prediction.

Objectives: To assess the incremental value of PROMs in predicting unplanned readmissions to inform postdischarge monitoring and ongoing care management.

Methods: This population-based retrospective cohort study used linked administrative and PROMs data from British Columbia, Canada. Adults discharged from acute care who provided response to the EQ-5D-5L and Veterans RAND 12-Item Health Survey (VR-12) within 60 days were included. Aggregated Cox proportional hazards models were fitted to estimate unplanned readmission risk across 30-, 180-, and 360-day horizons. The primary prediction horizons were 30 and 180 days. The 360-day horizon was a secondary focus. Model performance was assessed using the concordance statistics and calibration, with subgroup analysis for Ambulatory Care Sensitive Conditions (ACSC).

Results: Among 11,177 individuals, observed unplanned readmission rates within 30, 180, and 360 days of discharge were 5.6%, 18.4%, and 25.0%, respectively. Conditional on surviving to weekly landmarks (23-60 days postdischarge), PROMs modestly improved discrimination. For the 180-day horizon following landmarks, the C-index was 0.762 (95% CI, 0.761-0.763) using predictors from administrative data alone, increasing to 0.774 (95% CI, 0.773-0.774) with EQ-5D-5L and 0.782 (95% CI, 0.781-0.783) with VR-12. Similar gains in discrimination were observed at 30-day and 360-day horizons. All models showed adequate calibration. Among patients with ACSCs, including PROMs improved discrimination by 2.4%-3.0%.

Conclusions: PROMs added predictive value for unplanned hospital readmissions, particularly among patients with ACSCs.

Background: Veteran enrollees of the Veterans Health Administration (VA) have increasing options for where and how to access health care, including within VA in person or virtually and through VA-purchased community care. To promote Veterans' informed choice and streamline access to appointments, VA initiated referral coordination, which entails clinical review and conversations with Veterans before scheduling all specialty care referrals.

Objective: Identify how VA facilities implemented referral coordination and local contextual factors influencing the implementation.

Research design: Qualitative formative evaluation, using process maps to compare implementation approaches by hospital system and thematic analysis to identify contextual influences on implementation.

Subjects: Between March and August 2022, we interviewed VA referral coordinators (n=27) for acupuncture, cardiology, endocrinology, and hematology/oncology at 8 VA hospital systems in a geographic region with urban and rural settings.

Results: We identified 2 implementation approaches for referral coordination. Three facilities added clinical review to employees' existing responsibilities ("expanders"), and 5 created new roles dedicated to referral coordination ("creators"). "Expander" facilities relied minimally on VA-purchased care and received little implementation support from local leadership. "Creator" facilities relied heavily on VA-purchased care and local leadership was actively involved in implementation. The effort employees dedicated to referral coordination tasks varied according to other demands on their time.

Conclusions: This work provides an empirically grounded way to identify different implementation approaches (ie, expanders and creators), which we conceptualize as a framework for interpreting the outcomes of referral coordination on waiting times, utilization of different types of care, and Veterans' experiences seeking care.

Objectives: This study investigated intraoccupational income and wage-rate distributions across 8 health care professions: physicians and surgeons, dentists, physician assistants, optometrists, pharmacists, nurse practitioners and nurse midwives, physical therapists, and registered nurses.

Methods: The research was based on a sample of 142,527 U.S. practitioners from the 2019 to 2022 American Community Survey (ACS) and focused on 4 inequality indicators: the coefficient of variation, lower median share, 90-10 decile ratio, and Gini coefficient.

Results: Findings revealed substantial income dispersion, with dentists and physicians/surgeons displaying the highest levels of inequality, while pharmacists, registered nurses, physical therapists, and nurse practitioners and nurse midwives exhibited more even distributions. The occupations' degree of inequality was correlated with average annual income and wage-rate levels. Gender disparities were significant across all professions, with male practitioners consistently earning more than their female counterparts. The degree of inequality was greater for professions in which there were more male than female practitioners. Annual income and wage-rate inequality also was identified within genders.

Conclusions: This research contributes to understanding income disparities within health care professions and suggests that further exploration is needed to identify the determinants of these inequalities and their long-term evolution.

Background: Although telehealth is increasingly being used for providing postpartum care, its role in ensuring timely postpartum care initiation in racial/ethnic minorities and rural residents is unknown.

Objectives: To compare attendance and timeliness of postpartum care initiation by pandemic exposure and telehealth uptake across race/ethnicity and rural-urban residences.

Research design: Retrospective cohort study.

Subjects: Medicaid-insured individuals who gave birth in South Carolina between January 1, 2018, and September 30, 2022, were aged 15-49 years, and were followed up until December 31, 2022.

Measures: Cox proportional hazards models examined associations between pandemic exposures, telehealth uptake, and racial/ethnic and rural/urban disparities in postpartum care initiation timeliness.

Results: Median time to postpartum care initiation was 25 days [interquartile range (IQR): 14-41 d] with variations across race/ethnicity and residence. Fully-exposed nontelehealth users had slower initiation [adjusted hazard ratio (aHR): 0.95; 95% CI: 0.91-1.00], while telehealth users had quicker initiation (aHR: 2.19; 95% CI: 1.93-2.48) compared with non- or partially-exposed individuals. Among minimal- or no-telehealth users, postpartum care initiation was slower for Hispanic and non-Hispanic Black individuals compared with their non-Hispanic White peers. There were no differences in timely care initiation by race or residence among fully-exposed telehealth users.

Conclusions: Telehealth may improve racial/ethnic disparities in timely postpartum care initiation. Rural-urban disparities in initiating timely postpartum care still warrant further investigation.

Background: Health care systems and insurers are expanding coverage for practitioner-delivered and self-care complementary and integrative health (CIH) therapies for chronic pain.

Objectives: To determine if combining practitioner-delivered and self-care CIH therapies (PD/SC-CIH) improves pain outcomes more than practitioner-delivered CIH (PD-CIH) therapies alone.

Research design: Pragmatic nonrandomized trial. Structural nudges and the availability of CIH therapies were used as a surrogate to randomization.

Subjects: Of 3306 veterans with chronic musculoskeletal pain at 18 medical centers in the Veterans Health Administration between March 2021 and March 2023.

Measures: PD-CIH therapies included acupuncture, chiropractic care, or massage therapy. Participants in the PD/SC-CIH arm also received yoga, mindfulness/meditation, and/or Tai Chi/Qigong. The primary outcome was the change in pain-related functional interference at 6 months.

Results: Pain interference improved in both arms (-0.62 and -0.70), with 39.5% and 41.1%, respectively, achieving clinically meaningful improvement with no difference between arms in improvement in pain interference: -0.12 (-0.28 to 0.05). At 6 months, more participants in the PD/SC-CIH arm reported their use of CIH therapies specifically led to perceived improvements across 4 global patient-centered measures: pain (11%; 5%-18%); fatigue (28%; 17%-40%); mental health (24%; 14%-35%); and overall well-being (27%; 18%-35%).

Conclusions: Both approaches to offering CIH therapies were equally associated with improvements in pain interference among this large cohort with real-world CIH therapy engagement. More patients in the PD/SC-CIH arm perceived that their use of CIH therapies improved multiple health dimensions. Patients with chronic musculoskeletal pain should be encouraged to add self-care CIH therapies and health care systems should expand their availability.

Study registration: ClinicalTrials.gov Identifier: NCT05097521.

Background: Artificial neural networks (ANNs) are increasingly applied in health care outcome prediction, yet their relative benefits compared with traditional methods in health services research remain unclear.

Objective: To examine health care utilization and costs among community-dwelling older adults using the Andersen Behavioral Model, and to compare the performance of logistic regression and ANN models.

Research design: Cross-sectional study utilizing linked data from CMS Medicare fee-for-service (FFS) claims and Consumer Assessment of Healthcare Providers and Systems (CAHPS) surveys (2018-2022). The sample included 254,748 Medicare beneficiaries aged 65 and older. Outcomes were high Medicare costs (top 25%), 30-day readmissions, and preventable hospitalizations (PQIs). Predictors included socioeconomic factors, chronic conditions, and patient-reported measures. Model performance was assessed using the area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and Brier scores.

Results: Chronic conditions, including heart disease and depression, significantly predicted higher Medicare costs. Poor self-rated health, functional limitations, dual eligibility, and lower educational attainment correlated strongly with readmissions and preventable hospitalizations. ANN and logistic regression models demonstrated comparable performance across outcomes, with similar AUC, sensitivity, specificity, PPV, NPV, and Brier scores.

Conclusions: Both logistic regression and ANN models effectively predict health care utilization and high-risk outcomes among older adults using structured Medicare data. Logistic regression offers interpretability and robust predictive power, whereas ANN models may provide additional value as healthcare datasets grow increasingly complex and comprehensive.

Background: Stakeholders have called for measures of how much time patients spend at home and in the community to incentivize coordinated, timely, and primary care-based services and reduce unnecessary acute care.

Objectives: We developed a claims-based "Days at Home" Accountable Care Organization (ACO) measure for Medicare beneficiaries, intended to assess the number of days in a calendar year that adults with complex chronic disease spend at outside acute or postacute inpatient facilities.

Methods: Testing used 2017 and 2018 Medicare claims for fee-for-service patients enrolled in 610 Shared Savings Program ACOs. We used a split-half method for reliability testing, and a survey of technical experts and comparison to related measures for validity. The measure adjusts for 51 clinical and demographic variables and one social determinant of health (Medicaid dual-eligibility). To avoid unintended consequences of discouraging medically appropriate care and excessive reliance on nursing homes, the measure adjusts for mortality and new admissions to long-term nursing home care.

Results: The cohort (N=1,154,779) was predominantly White (84.9%) and aged 65 years or older (84.0%), and 22.8% were Medicaid dual-eligible. Mean ACO-level adjusted days at home was 330.4, ranging from 291.0 to 345.9 days (interquartile range: 329.1-332.1). Split-sample reliability was 0.833. External experts rated the measure as having face validity; in assessing external validity the measure generally correlated with related measures.

Conclusions: The Days at Home ACO measure is a reliable and valid measure that can be used to promote coordinated and prevention-focused home-based and community-based care.

Background: Federally qualified health centers and look-alikes are outpatient care settings that provide primary care and other health services in health professional shortage areas and to their populations. Compiling and disseminating data from these centers that is accessible for patient-centered outcomes research (PCOR) is critical to understanding health care provided at these settings.

Objective: To describe the National Ambulatory Medical Care Survey Health Center (NAMCS HC) Component, and how this redesigned survey can be utilized to understand health care provided at health centers, improve data capacity, and facilitate PCOR.

Methods: Beginning in 2021, the NAMCS HC Component began collecting visit data through electronic health record (EHR) submission from a nationally representative sample of FQHCs and FQHC look-alikes. Resulting datasets are made available for researchers to analyze, used to produce readily available interactive data visualizations, and linked to external datasets.

Results: The NAMCS HC Component and its resulting data resources are described. Availability of restricted and public datafiles is highlighted, with an example of how these can be used to study visits across different patient characteristics. Interactive dashboards are presented, including how researchers, health centers, and patients can view biannual preliminary visit rates/counts. Finally, linkages between the NAMCS HC Component and external data sources are highlighted, including how these linkages can be used to study health outcomes among different populations.

Conclusions: EHR data collected from FQHCs and look-alikes through the redesigned NAMCS HC Component fills a gap to improve PCOR capacity at these unique settings.