American Journal of Managed Care最新文献

A letter from the guest editor highlights how the findings in this special issue touch on timely themes in health technology research and yield real-world considerations for practice.

Objectives: To investigate demographic disparities in failed episodes of telemedicine utilization. The primary hypothesis was that certain demographic groups, including older adults and specific racial or ethnic groups, would experience disparate amounts of failed video visits.

Study design: A retrospective review was conducted using electronic health record-integrated scheduled telehealth video visit telemetry data gathered for all video visits at a California academic health center from September 1, 2020, to November 30, 2020. For each visit, we collected demographics including age, sex, ethnicity, primary language, and race.

Methods: Outcomes were categorized as successful or failed based on review of telemetry data. Successful visits were defined as simultaneous connections and completion of video visit, whereas failed visits were defined as provider-reported failure or lack of simultaneous connections for the telemedicine visit. Binomial generalized logistic regression using a generalized estimating equation approach was used to assess the impact of demographic factors on video visit success. Of 47,065 scheduled telemedicine video visits, telemetry data were available for 30,996; the 16,069 visits excluded from the study were due to no-shows, cancellations, or a nonintegrated solution being utilized.

Results: Of 30,996 visits included in the study, 27,273 were successfully completed. Analysis of the 3723 failed visits revealed that older adults and African American/Black patients were more likely to experience failed video visits, with ORs of 2.02 and 1.56, respectively.

Conclusions: This study highlights the significant demographic disparities in failed video visit occurrence caused by technical failure as demonstrated by telemetry data. These findings highlight the need for targeted interventions and opportunity for improved outcomes.

Objectives: To describe a regional health system's experience with medically tailored groceries (MTG), focusing on program reach and effectiveness as determined by observed within-person changes in cardiometabolic measures.

Study design: Case study including individuals aged 18 to 79 years referred by an ambulatory health care provider to a single regional health system's MTG program from April 2020 through September 2023.

Methods: Demographics, clinical characteristics, and cardiometabolic measures (blood pressure [BP], weight, body mass index [BMI], and hemoglobin A1c [HbA1c]) were abstracted from electronic health records. Descriptive and bivariate analyses evaluated differences in demographics and comorbid conditions among those who ever vs never used the Food for Life Market. Weighted linear mixed-effect models evaluated the expected change in outcomes from baseline to recent measure, accounting for demographics, time between measures, and attributed market location.

Results: A total of 2259 adults received referrals to the MTG program (median, 1 referral; range, 1-7; 3184 total referrals). Of those referred, 1397 (61.8%) ever attended; MTG users were significantly older than nonusers (median age, 52.9 vs 38.3 years; P < .001). MTG program attendance was associated with favorable changes in market attendees vs nonusers in diastolic BP (-0.54 vs -0.51 mm Hg; P = .04) and BMI (0.20 vs 0.23; P = .02) after 3 years from baseline, after accounting for confounders. No significant differences were observed in systolic BP, HbA1c, or weight.

Conclusions: An unincentivized MTG intervention demonstrated modest impacts on key cardiometabolic measures. Future efforts to colocate MTG sites with clinical settings may enhance program uptake and impact on cardiometabolic measures.

Objective: To evaluate trends in telemedicine utilization overall and across clinical specialties, providing insights into its evolving role in health care delivery.

Study design: This retrospective cross-sectional study analyzed 1.9 million telemedicine video visits from a large academic health care system in New York City between 2020 and 2023. The data, collected from the health care system's electronic health records, included telemedicine encounters across more than 500 ambulatory locations.

Methods: We used descriptive statistics to outline telemedicine usage trends and compared telemedicine utilization rates and evaluation and management characteristics across clinical specialties.

Results: Telemedicine utilization peaked during the COVID-19 pandemic, then declined and stabilized. Despite an overall decline, 2 non-primary care specialties (behavioral health and psychiatry) experienced continued growth in telemedicine visits. Primary care and urgent care visits were mainly characterized by low-complexity visits, whereas non-primary care specialties witnessed a rise in moderate- and high-complexity visits, with the number of moderate-level visits surpassing those of low complexity.

Conclusions: The findings highlight a dynamic shift in telemedicine utilization, with non-primary care settings witnessing an increase in the complexity of cases. To address future demands from increasingly complex medical cases managed through telemedicine in non-primary care, appropriate resource allocation is essential.

To mark the 30th anniversary of The American Journal of Managed Care, each issue in 2025 includes reflections from a thought leader on what has changed over the past 3 decades and what's next for managed care. The March issue, which is our annual health information technology (IT) theme issue, features a conversation with Julia Adler-Milstein, PhD, professor of medicine at the University of California, San Francisco, and guest editor of the 2014 health IT issue.

Objectives: To implement a technology-based, systemwide readmission reduction initiative in a safety-net health system and evaluate clinical, care equity, and financial outcomes.

Study design: Retrospective interrupted time series analysis between October 2015 and January 2023.

Methods: The readmission reduction initiative standardized inpatient care for patients through a novel, electronic health record-integrated, digitally automated point-of-care decision-support tool. A predictive artificial intelligence algorithm was utilized to identify patients at the highest risk of readmission in both the inpatient and outpatient settings, allowing a population health team to perform proactive outpatient management in medical and social domains to avoid readmission.

Results: Readmission rates declined from 27.9% in the preimplementation period to 23.9% in the postimplementation period ( P  < .004) by the end of 2023. A significant gap in readmission rates between Black/African American patients and the general population was eliminated over the course of the evaluation period. Survival analysis demonstrated a reduction in all-cause mortality in the postimplementation period (HR, 0.82; 95% CI, 0.68-0.99; P  = .037). Improvement in readmission rates allowed the health system to retain $7.2 million of at-risk pay-for-performance funding.

Conclusions: This technology-based readmission reduction initiative demonstrated efficacy in reducing readmission rates, closing equity gaps, improving survival, and leading to a positive financial impact in a safety-net health system. This approach could be an effective model of technology-based, value-based care for other resource-limited health systems to meet pay-for-performance metrics and retain at-risk funding while improving clinical and equity outcomes.

This commentary presents a summary of 8 major regulations and guidelines that have direct implications for the equitable design, implementation, and maintenance of health care-focused large language models (LLMs) deployed in the US. We grouped key equity issues for LLMs into 3 domains: (1) linguistic and cultural bias, (2) accessibility and trust, and (3) oversight and quality control. Solutions shared by these regulations and guidelines are to (1) ensure diverse representation in training data and in teams that develop artificial intelligence (AI) tools, (2) develop techniques to evaluate AI-enabled health care tool performance against real-world data, (3) ensure that AI used in health care is free of discrimination and integrates equity principles, (4) take meaningful steps to ensure access for patients with limited English proficiency, (5) apply AI tools to make workplaces more efficient and reduce administrative burdens, (6) require human oversight of AI tools used in health care delivery, and (7) ensure AI tools are safe, accessible, and beneficial while respecting privacy. There is an opportunity to prevent further embedding of existing disparities and issues in the health care system by enhancing health equity through thoughtfully designed and deployed LLMs.

Tobacco use rates remain high in many subpopulations (eg, low-income individuals) who experience several addressable health inequities. Community clinics are ideal sites to address these inequities because of their traditional service populations, commitment to prevention, and links to their communities. We present a case study of one such clinic's strategies to improve system-based tobacco cessation and discuss observed gains in relevant quality improvement metrics.

Objectives: Previous research has demonstrated that having patients complete an optional preappointment survey can increase their likelihood of attending their appointment. However, there is no literature examining how requiring preappointment engagement affects outcomes. The current study aimed to investigate the impact of mandatory preappointment surveys on patient show rates and wait-list times and provide guidance for implementing data-driven policy change.

Study design: This study examined show rates and wait-list times during the 1 year before and 1 year following a policy change requiring new patients to complete preappointment surveys before they are scheduled. The χ2 test of homogeneity was used to determine changes between pre- and post-policy change show rates, and an independent t test was used to examine changes in wait-list time.

Methods: This study examined the medical records of 275 youth with intake appointments at an interdisciplinary chronic pain management clinic at a large hospital. A retrospective chart review was conducted to determine changes in patient show rates and wait-list times.

Results: Findings demonstrated that patient show rates increased from 78.8% to 86.1% after the policy change, and average wait-list time decreased by 55.2% from the year before the policy change.

Conclusions: This study's findings provide evidence that requiring patients to complete a preappointment survey before being scheduled significantly improved show rates and decreased wait-list times in a pediatric pain clinic. Providers should balance benefits with potential limitations, such as restricting access to care, when implementing such a policy change. This study also offers practical guidance for implementing data-driven policy change in health care settings.

Hemophilia A is a bleeding disorder caused by a deficiency in clotting factor VIII (FVIII), leading to recurrent joint bleeds, musculoskeletal damage, and chronic pain. The World Federation of Hemophilia (WFH) recommends prophylactic FVIII replacement therapy to reduce bleeding risk, yet joint deterioration and pain persist. Maintaining high FVIII levels provides clinical benefits but requires awareness of best practices and managed care considerations. This publication examines the clinical and economic impact of hemophilia A, treatment goals, FDA-approved therapies, and managed care factors. People with hemophilia experience lower bone mineral density, increased osteoporosis risk, and significant effects on mental health, mobility, and quality of life. Treatment options-including standard and extended half-life FVIII therapies, non-factor therapies, and gene therapy-vary in sustaining FVIII levels and preventing bleeds. The high cost of care burdens patients and health systems, though prophylaxis reduces emergency visits and hospitalizations. Adherence challenges arise as patients transition to self-infusion, and insurance restrictions often limit access to comprehensive care. The WFH supports individualized, patient-centered management with pharmacokinetic-guided dosing, multidisciplinary care, and shared decision-making. Maximizing FVIII levels, rather than maintaining minimal thresholds, may improve long-term health. A holistic approach-combining early intervention, personalized prophylaxis, and strategies to address treatment barriers-is essential to better outcomes and achieving the WFH goal of zero bleeds.