American Journal of Managed Care最新文献

The 340B Drug Pricing Program entitles participating hospitals and clinics (called covered entities ) to purchase outpatient drugs at a substantial discount from manufacturers. Covered entities either distribute 340B drugs at reduced prices to patients or generate a subsidy by charging insured patients market prices for discounted drugs. Drugs purchased through 340B accounted for $66 billion in 2023, with approximately one-fifth of this sum coming from sales through unaffiliated community retail pharmacies, called contract pharmacies . We linked data on contract pharmacy locations to Managed Market Surveyor data to describe the markets in which 340B covered entities locate contract pharmacies. We found that the number of contract pharmacies has grown dramatically over the last decade: From 2010 to 2021, the mean share of pharmacies in a county that dispensed 340B drugs increased from 1.5% to 43.7%. Contract pharmacy growth was positively correlated with Medicare and Medicaid coverage and negatively correlated with uninsured and commercial coverage. Although contract pharmacies were positively associated with Medicaid coverage, this relationship was strongest in states that allowed covered entities to dispense 340B drugs to Medicaid patients through a contract pharmacy. Our findings are consistent with those of other studies that covered entities, particularly hospitals, tend to locate contract pharmacies in markets that maximize the 340B subsidy.

Objectives: Ambulatory cardiac monitors (ACMs) enable heart rhythm monitoring for various durations, including Holter monitors (0-48 hours), long-term continuous monitors (LTCMs; 3-14 days), and external ambulatory event monitors (AEMs; up to 30 days). These devices detect intermittent or asymptomatic arrhythmias that might go unnoticed with a standard electrocardiogram. Previous research has explored variations in ACM use among Medicare beneficiaries. This study assessed the incidence of clinical and economic outcomes among commercially insured patients who had never had an arrhythmia diagnosis and received their first ACM.

Study design: Retrospective cohort study using a large commercial claims database focused on patients without prior arrhythmia diagnoses who received their first ACM between 2016 and 2023.

Methods: Outcomes included new arrhythmia diagnoses, repeat ACM testing, cardiovascular (CV) events, and health care resource use and costs. Results were stratified by major ACM manufacturers using National Provider Identifiers. To minimize confounding, inverse probability of treatment weighting was used to balance covariates, and adjusted regression models were used to evaluate outcomes during follow-up.

Results: Of 428,707 patients meeting inclusion criteria, 36% used LTCMs, 36% used Holter monitors, and 27% used external AEMs. Adjusted analyses showed that a certain LTCM brand was associated with higher odds of a new arrhythmia diagnosis, fewer retests (except vs AEMs), lower odds of CV events, and less follow-up health care resource use and costs than other ACM types and manufacturers.

Conclusions: Clinical and economic outcomes can vary by ACM type among commercially insured patients. A specific LTCM manufacturer demonstrated superior performance, with greater diagnoses of arrhythmia, fewer repeat tests, and fewer CV events compared with other ACM types and manufacturers.

The originator of the phase 1 drug development program at Sarah Cannon Research Institute reflects on his career.

Objectives: Older adults with Alzheimer disease and related dementias (ADRD) experience hospital-to-skilled-nursing-facility (SNF) transitions at disproportionate rates, yet it is unclear whether investments in information sharing practices are equitably distributed across SNFs that care for more of these patients. The purpose of this study was to characterize and compare hospital-SNF dyads according to the proportion of patients they share who have diagnosed ADRD and to analyze whether specific motivating (ie, historical readmission rates) and/or enabling (ie, health information exchange [HIE], informal integration) factors are associated with higher-quality information sharing practices relative to the concentration of patients with ADRD.

Study design: Cross-sectional study linking pooled Medicare claims data (2016-2019) to a nationally representative survey (2019-2020) that collected detailed information on how hospitals share information to support postacute care transitions with SNF partners.

Methods: Multivariate linear regression.

Results: Hospital-SNF dyads sharing a high volume of patients with ADRD report information received during transfer to be of similar quality (ie, information is timely, complete, and usable) to low-ADRD dyads, although capacity for HIE still lags. Overall, hospital-SNF dyads that combined informal integration efforts with HIE capabilities fared better with respect to the quality of information shared.

Conclusions: SNFs experiencing high-ADRD referral flows may be working harder than most to manage transitional care without similar availability of resources that enable high-quality handoffs. Policy makers should consider systematic investments in postacute care data sharing standards and payment models that incentivize informal integration efforts to enhance the value of investments in information technology-supported information continuity.

Objectives: Using data from the 2020-2022 Medical Expenditure Panel Survey, we examined the relationship among food insecurity, access to a usual source of care, and telehealth utilization across 4 types of office-based and outpatient visits: general checkup, diagnosis or treatment, psychotherapy or mental health counseling, and follow-up or postoperative care.

Study design: Retrospective analysis of nationally representative data.

Methods: The study employed logistic regression models for access to care, Poisson models for annual visit counts, and 2-stage Heckman selection models for telehealth utilization and associated expenditures.

Results: Food-insecure individuals had a 7.2% lower rate of annual visits (in-person and telehealth combined) than their food-secure counterparts. Food insecurity was associated with a 1.7-percentage point increase in the share of telehealth visits. Among individuals with a usual source of care, food insecurity was linked to higher telehealth use as travel time increased: 2.6 percentage points higher with 15 to 30 minutes of travel time, and 4 percentage points higher for travel times exceeding 30 minutes. Additionally, each 1-percentage point increase in telehealth share corresponded to a $117.64 reduction in health care expenditures per visit.

Conclusions: These findings highlight significant disparities in health care utilization based on food security status in the US. Even after accounting for geographic access, food insecurity remains strongly associated with reduced health care use. It is important to test whether integrating food insecurity screening in health care settings and developing hybrid telehealth models (eg, mobile clinics) may help close gaps in access and improve outcomes for food-insecure populations.

Objectives: Electronic health record (EHR) systems are central to modern practice yet contribute to physician workload and burnout. Metrics such as documentation timeliness, efficiency, and after-hours "pajama time" are increasingly used to assess provider performance, but variation across procedural roles and specialties remains understudied.

Study design: Cross-sectional study.

Methods: We analyzed 23 months of provider-level EHR data from a single academic health system. Providers with more than 0 operative notes were classified as operators. Six standardized metrics-delayed visit closure, delayed discharge note signing, delayed cosign and verbal order completion, delayed review of high-priority results, mean pajama time, and an institutional proficiency score-were compared between operators and nonoperators using Welch t tests. Subgroup analyses were performed within medicine, obstetrics-gynecology (ob-gyn), and pediatrics. Additional operator comparisons across 6 procedural specialties used analysis of variance.

Results: Of 2516 providers, 724 (28.8%) were operators. Operators had higher rates of delayed cosign (9.5% vs 5.7%; P < .001) and verbal order completion (20.5% vs 17.1%; P = .003) but similar pajama time and proficiency compared with nonoperators. In medicine, operators had lower pajama time than nonoperators (21.8 vs 29.2 minutes; P = .006). In pediatrics, operators had fewer delayed discharge notes (P = .035). In ob-gyn, operators showed fewer verbal order and result review delays but higher proficiency and more delayed discharge notes (all P < .05). Among procedural specialties, ophthalmology operators had the highest proficiency yet greater delays across timeliness metrics (all P < .05).

Conclusions: EHR utilization varies by procedural status and specialty, underscoring the need for workflow-specific optimization rather than uniform performance benchmarks.

Objectives: Labeling and the use of model cards have been promoted as ways to increase transparency for multiple end users. This study aimed to identify key content for a health artificial intelligence (AI) tool label based on public perspectives and expectations.

Study design: We used a mixed-methods study design, combining public deliberation and pre-/post surveys to inform participants about AI in health care and gather input on key information for a health AI tool label.

Methods: In 2024, we conducted 5 virtual community deliberations across Michigan, engaging 159 participants in facilitated small-group discussions that were qualitatively coded. Participants completed a 20-minute survey before and after the deliberation to assess changes in knowledge, attitudes, and trust regarding AI in health care.

Results: Participants prioritized information regarding privacy and security, health equity, and safety and effectiveness of AI tools for inclusion on a health AI tool label. An AI label is, therefore, a familiar and transparent mechanism to build trust and address patients' desire for notification.

Conclusions: The findings highlight ethical gaps in using AI in health care settings and the value of publicly informed, patient-centered solutions. There is strong demand for clear, accessible information on how AI tools are used and their risks and benefits. A patient-informed label may address these ethical challenges and improve transparency, trust, and patient-centered communication as AI reshapes health care.

A letter from the guest editor introduces this year's Health Information Technology issue, which explores 4 key themes driving health care's digital transformation from basic automation to human-centered integration.

Objectives:  To estimate the prevalence of ambient artificial intelligence (AI) documentation tool adoption among US hospitals using Epic electronic health record (EHR) systems and to identify hospital characteristics associated with adoption.​ Study Design: Cross-sectional observational study of US hospitals using Epic.​ Methods: Among a national sample of US hospitals using Epic, we assessed ambient AI adoption using Epic Showroom (June 2025) to identify eligible ambient applications and health systems that had implemented or were implementing these applications. We linked adoption data to hospital characteristics from the American Hospital Association Annual Survey (2012-2023; most recent response per hospital) and estimated multivariable logistic regression models with robust SEs clustered at the domain level, reporting adjusted predicted probabilities (margins).​ Results: Among 6561 US hospitals, 2784 (42.4%) were Epic users. Among Epic hospitals, 62.6% adopted ambient AI. In adjusted analyses, adoption was higher across workload quartiles (61.7% in quartile [Q] 1 vs 73.1% in Q4; P = .003) and among hospitals in the top operating margin quartiles (58.0% in Q1 vs 67.6% in Q4; P = .001 vs Q1). Adoption was higher among metropolitan hospitals (64.7% vs 54.3% in nonmetropolitan hospitals; P = .012) and nonprofit hospitals (70.2% vs 28.8% in for-profit hospitals; P < .001).​ Conclusions: Ambient AI documentation tools were widely adopted among US hospitals using Epic EHR systems, with adoption associated with workload, financial performance, ownership, and select structural characteristics. These patterns suggest potential for uneven diffusion across hospitals and underscore the need for research on impacts on clinician outcomes, care quality, and equity.

Objective: To evaluate the impact of a centralized pharmacist-led telehealth model utilizing home blood pressure monitoring (HBPM) readings on hypertension quality measure pass rates and blood pressure reduction at 3 and 6 months.

Study design: Retrospective observational cohort study.

Methods: Adults with uncontrolled hypertension (≥ 140/90 mm Hg) were identified through a centralized outreach model. Patients who completed a visit with a pharmacist comprised the intervention group; those who were not reached or who declined were included in the usual care group. Pharmacists met with patients via telehealth appointments and sent recommendations to their providers.

Results: A total of 1776 patients were included: 179 patients in the intervention group and 1597 patients in usual care. The mean age was 56 years (95% CI, 55.7-57.0 years), with approximately half identifying as female (47.6%-53.1%). The intervention group had a higher proportion of Black patients (53.1% vs 37.3%; P = .0002) and a higher mean Elixhauser Comorbidity Index score (2.93 vs 2.54; P = .0016). Patients in the pharmacist intervention group were more likely to pass the Healthcare Effectiveness Data and Information Set Controlling Blood Pressure measure (2022: OR, 1.78; P = .014; 2023: OR, 1.75; P = .014). Greater systolic blood pressure reductions in the intervention group vs usual care were observed at both 3 months (-12.69 vs -5.69 mm Hg; P < .0001) and 6 months (-13.87 vs -9.05 mm Hg; P = .001).

Conclusions: Participating in telehealth visits integrating HBPM readings with a pharmacist between primary care visits significantly improves blood pressure control. Pharmacists play a critical role in actively engaging patients and optimizing medication therapy in team-based care for chronic hypertension management.