Health Services Research最新文献

Objective: To explore how aspects of patient-centered communication (PCC) may directly or indirectly predict patients' preferences for artificial intelligences (AIs) versus human medical professionals, based on the stimulus-organism-response model.

Study setting and design: As AI gains popularity and researchers explore its application in the medical context, it is important to understand how current patient-provider dynamics involving high technology (e.g., telehealth communication) may shape patients' perceptions of future use of AI, especially in the context of cancer care where patient satisfaction and sense of care continuity are important. Participants were recruited from an online panel in China (June 2024). Structural equation modeling analyzed the relationships among variables, including six PCC dimensions (i.e., exchanging information, fostering healing relationships, making decisions, managing uncertainty, responding to emotions, and enabling patient self-management), communication outcomes (i.e., patient satisfaction, sense of care continuity), and patients' preference of AIs vs. human medical professionals.

Data sources and analytic sample: Primary data were collected from an online panel of 495 Chinese cancer patients in China, representative of the gender and age distribution of the overall Chinese population due to quota sampling.

Principal findings: Direct predictors of preference for replacing human medical professionals with AIs included lower patient satisfaction (β = -11, p < 0.05), lower ease of use (β = -0.1, p < 0.05), better care continuity (β = 0.15, p < 0.01), providers' attending to emotions (β = 0.17, p < 0.05), and less enablement in self-management (β = -0.17, p < 0.01). Patient satisfaction, ease of use, and care continuity mediated the relationships between different PCC dimensions and patients' preferences for AI use.

Conclusions: PCC and communication outcomes are associated with cancer patients' preferences in future AI use. Our study sheds light on how clinicians may improve their communication to educate patients on navigating the cancer care continuum using AI technology.

Objective: To understand the impact of the Balancing Incentive Program (BIP) and Community First Choice State Plan Option (CFC) on LTSS rebalancing as measured by the size of and balance between the community and institutional LTSS workforces.

Study setting and design: Using a stacked difference-in-difference design, this paper evaluates the impact of BIP and CFC on the number of LTSS workers per 1000 persons 65+, the number of community LTSS workers per 1000 persons 65+, the number of institutional LTSS workers per 1000 persons 65+, and the proportion of all LTSS workers employed in community-based settings. We also test the impact of BIP's performance targets by separately estimating program effects for states that had yet to meet BIP rebalancing targets upon entering the program.

Data sources and analytical sample: Workforce and population data from the American Community Survey from 2005 to 2021.

Principal findings: This study finds that BIP resulted in a 13.24% (95% CI: 1.14%, 25.34%) increase in the size of the HCBS workforce in participating states, while finding no statistically significant effect for CFC (1.51%, 95% CI: -12.77%, 15.79%). The point estimate for growth in the HCBS workforce caused by BIP is twice as large in states bound by performance targets embedded within the BIP program (16.18%, 95% CI: 4.01%, 28.35%) as it is in states that are not (8.25%, 95% CI: -9.77%, 26.27%), suggesting that additional federal funding may be more effective when tied to performance targets for states. Neither program had a statistically significant effect on the size of the institutional workforce (BIP: 5.04%, 95% CI: -2.38%, 12.44%; CFC: 0.24%, 95% CI: -6.52%, 7.00%).

Conclusion: Federal policymakers seeking to increase investment in HCBS should ensure that additional funds are tied to measurable performance targets, incentivizing states to undertake expansions in HCBS that would not otherwise have taken place.

Objective: To explore the satisfaction of limited English proficiency (LEP) Farsi- and Dari-speaking patients with the U.S. healthcare system using a qualitative approach.

Study setting and design: We employed a grounded theory approach to analyze qualitative data collected from five focus groups involving 25 Farsi- and Dari-speaking immigrants with LEP.

Data source and analytical sample: A total of 25 individuals with LEP participated in the focus group sessions, which were transcribed and analyzed using grounded theory methodology.

Principal findings: Two primary categories influencing satisfaction with care emerged: systemic factors and individual factors. Individual factors encompassed cultural beliefs, cross-contextual comparisons, experiences of misdiagnosis, and language barriers. Systemic factors, including discrimination, the high cost of care, the complexity of the U.S. healthcare system, and a pharmaco-centric approach to care, were found to negatively impact satisfaction among immigrants with LEP.

Conclusions: Although many Farsi- and Dari-speaking individuals with LEP expressed satisfaction with the structured aspects of the U.S. healthcare system, dissatisfaction with healthcare providers and interpersonal interactions persisted. Addressing these issues will require targeted interventions to enhance trust, communication, and cultural competency in healthcare delivery.

Objective: To examine the association between state Home- and Community-Based Services (HCBS) caregiver payment policies and timely follow-up visits (in-person and telehealth) within 14 days of hospital discharge among Medicare-Medicaid dual-eligible older adults with dementia.

Study setting and design: We categorized state HCBS caregiver payment policies into three groups: no caregiver payment, payment eligible for other friends/family, and payment eligible for two caregiver types (legally responsible relatives or other friends/family). The primary outcome was the mode of follow-up visit within 14 days post-hospital discharge (in-person, telehealth, or no visit). We used multinomial logistic regression with hospital random effects, adjusting for individual- and area-level and HCBS factors. Marginal effects were estimated.

Data sources and analytic sample: We analyzed 2021 Medicare claims data linked with publicly available datasets. The analytic cohort comprised 51,633 dual-eligible Medicare beneficiaries with dementia who were hospitalized and discharged to the community in 2021.

Principal findings: State HCBS caregiver payment policies were significantly associated with the mode of timely follow-up visits. Compared to states without providing caregiver payments, states providing payments to two caregiver types had a 6.8 percentage point higher probability (p < 0.01) of timely in-person visits but a 3.2 percentage point lower probability (p < 0.01) of timely telehealth visits. Similar, though smaller, significant differences were observed between states that provided payments to only other family or friends and those with no caregiver payments. Other HCBS generosity measures, as well as racial, ethnic, and geographic locations, were also associated with the mode of post-discharge visits.

Conclusion: Providing financial support to family caregivers through state HCBS policies may increase the rate of timely post-discharge visits, primarily driven by an increase in in-person visits. The effects were particularly prominent among states that allow payments to both types of caregivers.

Objective: To examine changes in colorectal cancer (CRC) screening rates over time and determine organizational-level factors influencing these shifts.

Study settign and design: This longitudinal study used mixed effects models to analyze data from Federally Qualified Health Centers (FQHCs) in the United States (US). Key organizational-level factors included Patient-Centered Medical Home (PCMH) recognition and duration, hypertension and diabetes management, and center-level characteristics such as racial composition, location, and center volume/size.

Data sources and analytic sample: This study used Uniform Data System (UDS) data from 2017 to 2022 for US-based FQHCs receiving full Public Health Service Section 330 grants and reporting CRC screening measures, excluding school-based centers, US territories, and look-alike centers.

Principal findings: Among the 1282 FQHCs analyzed, CRC screening rates were increasing before the COVID-19 pandemic but declined during and remain below pre-pandemic levels. FQHCs with consistent PCMH recognition reported significantly higher screening rates (β = 8.50, p < 0.001). Screening rates were also positively associated with a higher rate of controlled hypertension (β = 0.354, p < 0.0001) but lower in FQHCs with larger Black patient populations, Southern locations, and smaller center volume/size.

Conclusions: Consistent PCMH recognition and chronic disease management are essential for improving CRC screening rates in FQHCs. By integrating these population health management strategies, FQHCs can proactively address screening disparities. Prioritizing these organizational-level approaches may strengthen healthcare equity and expand CRC screening for historically marginalized communities.

Objective: To determine the effectiveness and cost-effectiveness of multi-gene panel sequencing compared to single-gene KRAS testing for metastatic colorectal cancer (mCRC).

Study setting and design: British Columbia, Canada (BC) is a provincial single-payer public healthcare system, and it was the first province to publicly reimburse multi-gene sequencing for mCRC. Panels expand treatment de-escalation by expanding RAS testing for more precise targeting of anti-EGFR therapies. Reimbursement of panels remains unequal across healthcare systems given uncertain clinical and economic impacts. Our quasi-experimental study design followed the target trial emulation approach, emulating random treatment assignment with two different methods to examine the sensitivity of estimates: inverse probability of treatment weighting estimated with super learning (SL-IPTW) and 1:1 genetic algorithm-based matching, a machine learning approach. We then estimated mean three-year survival time and costs (public healthcare payer perspective; 2021CAD) and calculated the incremental net monetary benefit (INMB) for life-years gained (LYG) at $50,000/LYG using weighted linear regression and nonparametric bootstrapping, also accounting for inverse probability of censoring weights. Our sensitivity analysis estimated LYG using targeted minimum-based loss estimation (TMLE), a doubly robust approach that also uses super learning.

Data sources and analytical sample: Patient-level linked administrative health databases capturing cancer and non-cancer care for all BC adults with a metastatic colorectal cancer between 2016 and 2019.

Principal findings: Our study included 892 patients (84.3%) receiving multi-gene panels and 166 (15.7%) receiving single-gene testing. INMB estimates were similar for SL-IPTW ($20,397; 95% CI: $9317, $34,862) and matching ($19,569; 95% CI: $8509, $31,447), with 99.3% and 98.8% probabilities, respectively, of panels being cost-effective. We found statistically significant survival benefits with LYG of 0.31 (SL-IPTW; 95% CI: 0.04, 0.54), 0.25 (matching; 95% CI: 0.03, 0.47) and 0.19 (TMLE; 95% CI: 0.02, 0.37).

Conclusions: Survival impacts were robust to super learning approaches. Real-world evidence demonstrates that reimbursing multi-gene sequencing for more precise targeting of mCRC treatments provides value for healthcare systems and clinically important benefits to patients.

Objective: To examine which hospital-payer contracts include Diagnosis Related Group (DRG) codes and whether they set prices as a consistent multiple of hospital list prices or Medicare's DRG fee schedule.

Study setting and design: We study the cash rates and negotiated contracts (including commercial group, Medicare Advantage, Medicaid Managed Care, and individual market health plans) of US general and surgical acute care hospitals. We develop bunching and regression-based methods to classify the pricing bases of DRGs within contracts. We show the unadjusted and regression-adjusted variation in DRG inclusion and pricing across hospital and insurer characteristics.

Data sources and analytic sample: Hospital price transparency data from Turquoise Health (May 2024) is joined with hospital characteristics from the American Hospital Association, insurer market concentration from Clarivate, and Medicare DRG rates. We observe 4033 hospitals with 157,313 hospital-health plan contracts and 3902 sets of cash rates.

Principal findings: About 17% of hospitals do not include DRGs in any of their negotiated contracts or cash rates, while 54% include them in some, but not all contracts. Nearly half (48%) of hospitals exclude DRGs from their cash rates. Among commercial group contracts with DRGs, 25%-27% benchmark their DRG prices to hospital list prices, while 32%-36% are based on Medicare's fee schedule. Medicare Advantage contracts are more likely to be benchmarked to Medicare (64%), while most hospitals base their cash rates on list prices (85%). Hospitals facing less competition had lower rates of DRG contracting but were observed to be more likely to negotiate prices based on list prices conditional on including DRGs.

Conclusions: Our findings suggest that hospital market power may influence hospital-health plan negotiations beyond the average price levels. Policies aimed at standardizing these contracts must account for the wide variation in payment and pricing bases currently used in the private market.

Objective: To examine changes in children's Medicaid and CHIP enrollment during the Families First Coronavirus Response Act unwinding and assess whether CHIP enrollment offsets Medicaid declines.

Study setting and design: We used linear probability models with monthly indicators to estimate changes in enrollment from April 2023 to September 2024 overall and by CHIP structure type.

Data sources and analytic sample: We used monthly children's enrollment data from the U.S. Centers for Medicare & Medicaid Services for 32 states and the District of Columbia.

Principal findings: During the unwinding, Medicaid enrollment declined from 48.1% to 41.2% of children, while CHIP enrollment remained stable (8.7% to 8.6%). We found average declines of 62,032 (95% confidence interval [CI]: -108,018 to -16,045) Medicaid-enrolled children per state (6.5 percentage points [pp], 95% CI: -8.1 to -5.0). Medicaid declines were larger in states with combination CHIP (-8.7 pp, 95% CI: -10.3 to -7.2) than Medicaid expansion CHIP (-4.5 pp, 95% CI: -6.0 to -3.1). We found no evidence of significant changes in CHIP enrollment overall or by CHIP structure.

Conclusions: Children's Medicaid enrollment fell sharply without offsetting CHIP gains during the unwinding, underscoring the need for policies that prevent administrative disenrollment and ensure seamless coverage transitions.

Objective: To develop a novel method for enumerating the oncology specialist workforce triangulating taxonomy codes, board certification data, and clinical diagnosis codes in Medicaid claims, and to describe oncology specialists' Medicaid participation, their patient panels, and ascertain the concentration of types of cancers they treated.

Study setting and design: We identified oncology specialists using multiple data sources and conducted an exploratory analysis of their patient panels using multi-state Medicaid claims data. We used cluster analysis of diagnosis code patterns in claims to accurately determine the concentration of cancers by site in oncologists' panels.

Data sources and analytic sample: We used data from 2016 to 2020 Transformed Medicaid Statistical Information System (T-MSIS) and physician certification data. We included board-certified oncology physicians specialized in medical and radiation oncology, hematology, hematology-oncology, gynecologic oncology, and pediatric hematology-oncology. To identify surgical oncologists, we combined board certification and Medicare Provider Enrollment, Chain, and Ownership System (PECOS) data. We identified Medicaid beneficiaries with malignant neoplasms by cancer site using ICD-10-CM codes.

Principal findings: In 2016, about 89% of oncology specialists participated in Medicaid; this proportion decreased slightly to 86% in 2020. The trends in Medicaid participation and the mean number of beneficiaries differed by oncology specialty. Panels of pediatric hematologist-oncologists had a higher proportion of Hispanic Medicaid beneficiaries with cancer (26%) relative to other specialists. Cluster analysis identified 565 out of 5395 medical oncologists that had high concentration (at least 58%) of breast cancer patients in their panels. Among 6970 hematologist-oncologists, 269 had high concentrations in breast cancer (more than 60%), and 944 in hematological cancer (more than 59%).

Conclusions: Our study offers a pragmatic approach to understand the oncology specialist workforce available to Medicaid beneficiaries. The findings provide baseline estimates to track this workforce and provide policymakers with an opportunity to develop targeted strategies to improve access to cancer care.