The objective was to measure specialty provider networks in Medicare Advantage (MA) and examine associations with market factors.
We relied on traditional Medicare (TM) and MA prescription drug event data from 2011 to 2017 for all Medicare beneficiaries in the United States as well as data from the Area Health Resources File.
Relying on a recently developed and validated prediction model, we calculated the provider network restrictiveness of MA contracts for nine high-prescribing specialties. We characterized network restrictiveness through an observed-to-expected ratio, calculated as the number of unique providers seen by MA beneficiaries divided by the number expected based on the prediction model. We assessed the relationship between network restrictiveness and market factors across specialties with multivariable linear regression.
Prescription drug event data for a 20% random sample of beneficiaries enrolled in prescription drug coverage from 2011 to 2017.
Provider networks in MA varied in restrictiveness. OB-Gynecology was the most restrictive with enrollees seeing 34.5% (95% CI: 34.3%–34.7%) as many providers as they would absent network restrictions; cardiology was the least restrictive with enrollees seeing 58.6% (95% CI: 58.4%–58.8%) as many providers as they otherwise would. Factors associated with less restrictive networks included the county-level TM average hierarchical condition category score (0.06; 95% CI: 0.04–0.07), the county-level number of doctors per 1000 population (0.04; 95% CI: 0.02–0.05), the natural log of local median household income (0.03; 95% CI: 0.007–0.05), and the parent company's market share in the county (0.16; 95% CI: 0.13–0.18). Rurality was a major predictor of more restrictive networks (−0.28; 95% CI: −0.32 to −0.24).
Our findings suggest that rural beneficiaries may face disproportionately reduced access in these networks and that efforts to improve access should vary by specialty.
To evaluate universal suicide risk screening and evaluation processes among newly homeless Veterans.
Not applicable.
Examination of Veterans Health Administration (VHA) using newly homeless patients' health record data in Calendar Year 2021.
Not applicable.
Health record data.
Most patients received suicide risk screening and/or evaluation in the year prior to and/or following homeless identification (n = 49,505; 87.4%). Smaller percentages of patients were screened and/or evaluated in close proximity to identification (n = 7358; 16.0%), 1–30 days prior to identification (n = 12,840; 39.6%), or 1–30 days following identification (n = 14,263; 34.3%). Common settings for screening included primary care, emergency and urgent care, and mental health services. Of positive screens (i.e., potentially elevated risk for suicide), 72.6% had a Comprehensive Suicide Risk Evaluation (CSRE) completed in a timely manner (i.e., same day or within 24 h). Age, race, and sex were largely unrelated to screening and/or evaluation.
Although many newly identified homeless patients were screened and/or evaluated for suicide risk, approximately 13% were not screened; and 27% of positive screens did not receive a timely CSRE. Continued efforts are warranted to facilitate suicide risk identification to ensure homeless patients have access to evidence-based interventions.
To improve the performance of International Classification of Disease (ICD) code rule-based algorithms for identifying low acuity Emergency Department (ED) visits by using machine learning methods and additional covariates.
We used secondary data on ED visits from the National Hospital Ambulatory Medical Survey (NHAMCS), from 2016 to 2020.
We established baseline performance metrics with seven published algorithms consisting of International Classification of Disease, Tenth Revision codes used to identify low acuity ED visits. We then trained logistic regression, random forest, and gradient boosting (XGBoost) models to predict low acuity ED visits. Each model was trained on five different covariate sets of demographic and clinical data. Model performance was compared using a separate validation dataset. The primary performance metric was the probability that a visit identified by an algorithm as low acuity did not experience significant testing, treatment, or disposition (positive predictive value, PPV). Subgroup analyses assessed model performance across age, sex, and race/ethnicity.
We used 2016–2019 NHAMCS data as the training set and 2020 NHAMCS data for validation.
The training and validation data consisted of 53,074 and 9542 observations, respectively. Among seven rule-based algorithms, the highest-performing had a PPV of 0.35 (95% CI [0.33, 0.36]). All model-based algorithms outperformed existing algorithms, with the least effective—random forest using only age and sex—improving PPV by 26% (up to 0.44; 95% CI [0.40, 0.48]). Logistic regression and XGBoost trained on all variables improved PPV by 83% (to 0.64; 95% CI [0.62, 0.66]). Multivariable models also demonstrated higher PPV across all three demographic subgroups.
Machine learning models substantially outperform existing algorithms based on ICD codes in predicting low acuity ED visits. Variations in model performance across demographic groups highlight the need for further research to ensure their applicability and fairness across diverse populations.
To investigate whether the Veterans Health Administration's (VA) 2019 Referral Coordination Initiative (RCI) was associated with changes in the proportion of VA specialty referrals completed by community-based care (CC) providers and mean appointment waiting times for VA and CC providers.
Monthly facility level VA data for 3,097,366 specialty care referrals for eight high-volume specialties (cardiology, dermatology, gastroenterology, neurology, ophthalmology, orthopedics, physical therapy, and podiatry) from October 1, 2019 to May 30, 2022.
We employed a staggered difference-in-differences approach to evaluate RCI's effects on referral patterns and wait times. Our unit of analysis was facility-month. We dichotomized facilities into high and low RCI use based on the proportion of total referrals for a specialty. We stratified our analysis by specialty and the staffing model that high RCI users adopted: centralized, decentralized, and hybrid.
Administrative data on referrals and waiting times were extracted from the VA's corporate data warehouse. Data on staffing models were provided by the VA's Office of Integrated Veteran Care.
We did not reject the null hypotheses that high RCI use do not change CC referral rates or waiting times in any of the care settings for most specialties. For example, high RCI use for physical therapy—the highest volume specialty studied—was associated with −0.054 (95% confidence interval [CI]: −0.114 to 0.006) and 2.0 days (95% CI: −4.8 to 8.8) change in CC referral rate and waiting time at CC providers, respectively, among centralized staffing model adopters.
In the initial years of the RCI program, RCI does not have a measurable effect on waiting times or CC referral rates. Our findings do not support concerns that RCI might be impeding Veterans' access to CC providers. Future evaluations should examine whether RCI facilitates Veterans' ability to receive care in their preferred setting.
To examine whether hospitals' experience in a prior payment model incentivizing care coordination is associated with their decision to adopt a new payment program for a care delivery innovation.
Data were sourced from Medicare fee-for-service claims in 2017, the list of participants in Bundled Payment for Care Improvement initiatives (BPCI and BPCI-Advanced), the list of hospitals approved for Acute Hospital Care at Home (AHCaH) between November 2020 and August 2022, and the American Hospital Association Survey.
Retrospective cohort study. Hospitals' adoption of AHCaH was measured as a function of hospitals' BPCI experiences. Hospitals' BPCI experiences were categorized into five mutually exclusive groups: (1) direct BPCI participation, (2) indirect participation through physician group practices (PGPs) after dropout, (3) indirect participation through PGPs only, (4) dropout only, and (5) no BPCI exposure.
All data are derived from pre-existing sources. General acute hospitals eligible for both BPCI initiatives and AHCaH are included.
Of 3248 hospitals included in the sample, 7% adopted AHCaH as of August 2022. Hospitals with direct BPCI experience had the highest adoption rate (17.7%), followed by those with indirect participation through BPCI physicians after dropout (11.8%), while those with no exposure to BPCI were least likely to participate (3.2%). Hospitals that adopted AHCaH were more likely to be located in communities where more peer hospitals participated in the program (median 10.8% vs. 0%). After controlling for covariates, the association of the adoption of AHCaH with indirect participation through physicians after dropout was as strong as with early BPCI adopter hospitals (average marginal effect: 5.9 vs. 6.2 pp, p < 0.05), but the other categories were not.
Hospitals that participated in the bundled payment model either directly or indirectly PGPs were more likely to adopt a care delivery innovation requiring similar competence in the next period.
The objective was to describe characteristics of emergency department visits to Texas satellite and independent freestanding emergency departments (FrEDs) relative to hospital emergency departments (EDs).
The study used all 2021–2022 hospital and FrED discharges from the publicly available Texas Emergency Department Public Use Data Files (PUDF).
We conducted a descriptive analysis, comparing patient and visit characteristics at satellite and independent FrEDs and hospital EDs using chi-square tests. We characterized the top 20 diagnoses and procedures ranked by volume, treatment intensity, and potentially avoidable ED use.
Discharge data from 2021 to 2022 were combined for the analysis, and ED data at critical access hospitals were excluded.
Our sample consisted of 21,605,421 ED visits, 76% occurring at hospitals, 12% at satellite FrEDs, and 12% at independent FrEDs. Compared with hospitals and satellite FrEDs, patients to independent FrEDs were younger, healthier, more likely covered by private insurance, and less likely to be identified as non-Hispanic Black or Hispanic. Visits at satellite and independent FrEDs were more likely to be of moderate and low intensity and potentially avoidable.
Our results underscore the need to address potentially avoidable utilization of emergency services.
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