American Journal of Managed Care最新文献

Objective: To determine the association between telemedicine use and preventive care among a rural population.

Study design: Retrospective cohort study with administrative claims data.

Methods: We utilized propensity score matching and multivariate logistic regressions to match rural telemedicine users with rural telemedicine nonusers to determine the relationship between telemedicine use and the utilization of preventive services.

Results: The propensity score-matched sample consisted of 2,012,290 individuals residing in rural areas between January 2019 and December 2023. The sample consisted of 1,006,145 individuals who did not use telemedicine from 2019 to 2023, 535,418 individuals who utilized telemedicine in 2020, and 730,828 individuals who utilized telemedicine between 2021 and 2023. Telemedicine use in 2020 was associated with a higher likelihood of completing a preventive care visit or service in 2021 (adjusted OR [AOR], 2.01; 95% CI, 1.93-2.09), and telemedicine use between 2021 and 2023 was associated with a higher likelihood of completing a preventive care visit or service in 2023 (AOR, 1.88; 95% CI, 1.79-1.96). Telemedicine use in 2020 and between 2021 and 2023 were both evaluated to determine whether the results remained consistent after the initial surge in telemedicine use in 2020.

Conclusions: The use of telemedicine in rural areas was associated with a higher likelihood of utilizing preventive care. The magnitude of this relationship varied depending on underlying health conditions, sex, and region of residence.

Objectives: To evaluate the association between perceived and actual changes in physician documentation time (DocTime) following implementation of an artificial intelligence (AI) scribe and to determine whether physicians with higher baseline DocTime experience greater reductions in DocTime from AI scribe use.

Study design: Retrospective assessment of AI scribe use among 310 ambulatory physicians across specialties who chose to adopt a commercial tool at a large academic medical center. We utilized data from a postimplementation user feedback survey and electronic health record audit log measures of scribe use and DocTime.

Methods: We used an ordered logit model to assess adjusted associations between perceived and actual changes in DocTime in the 12 weeks after AI scribe adoption for the 252 physicians (81.3%) with survey data. Multivariate regression models assessed whether baseline DocTime modified the relationship between level of AI scribe use (percentage of weekly encounters) and DocTime.

Results: Although the majority of physicians perceived reductions in DocTime (86.5%) following AI scribe adoption, there was no overall association between perceived reductions and actual changes in DocTime (OR, 0.975; P  = .144). In multivariate models, higher levels of AI scribe use were associated with lower DocTime. For each additional 10% of encounters with AI scribe use, DocTime decreased by just over 30 seconds per scheduled hour (P < .001). This effect was modified by baseline DocTime, with less-efficient physicians realizing the majority of time savings.

Conclusions: Although most physicians perceived DocTime reductions from AI scribe use, those realizing the majority of actual time savings were those with higher relative baseline DocTime.

Value-based payment (VBP) models are increasingly adopted in oncology to promote high-quality, cost-effective, and patient-centered care. To identify essential characteristics of effective oncology VBP models, a modified Delphi process was conducted with 9 experts representing diverse oncology organizations. Panelists participated in 2 survey rounds and an in-person discussion, evaluating the importance and feasibility of key VBP model elements using Likert scales. Results revealed consensus on the need for patient-centered care, robust risk adjustment, and oncology-specific outcome measures. While pay-for-performance and enhanced monthly payment models were seen as feasible and widely used, they were also criticized for outdated metrics, insufficient reimbursement, and high administrative burden. High-impact models such as dual-sided risk and cost-containment approaches were viewed as promising but challenging to implement, particularly for small or rural practices. Panelists cited stakeholder misalignment, financial risk, and lack of standardized benchmarks as major barriers to effective implementation. The authors concluded that future VBP models must align incentives across stakeholders, accommodate clinical complexity, and evolve iteratively to support innovation, equity, and sustainability in oncology care.

Objectives: This analysis evaluated changes in glucose management indicator (GMI) at 12 months from baseline hemoglobin A1c (HbA1c)-which are both used interchangeably in the Healthcare Effectiveness Data and Information Set (HEDIS) quality measures for glycemic status-in individuals with type 1 diabetes (T1D) who switched from multiple daily insulin injections (MDI) to Control-IQ technology, an automated insulin delivery (AID) therapy.

Study design: US-based retrospective analysis that used data from the Tandem Diabetes Care, Inc t:connect web application and customer relationship management database.

Methods: Inclusion criteria were having had prior treatment with MDI, having started Control-IQ at least 1 year prior to the study end date, having a most recent recorded baseline HbA1c measurement within 6 months prior to AID initiation, and having at least 70% continuous glucose monitoring use during the 12-month postinitiation period. The primary outcome was the change in number and proportion of individuals who met the HEDIS quality measure (HbA1c or GMI of < 8% [control] or > 9.0% [poor control]) and American Diabetes Association (ADA) glycemic target (< 7%) according to payer type (commercial, Medicaid, Medicare) during the 12-month observation.

Results: The analysis included 12,522 individuals with T1D. Following AID initiation, the number and proportion of individuals who met the HEDIS quality standard increased from 6205 (49.6%) to 11,632 (92.9%) at 12 months (∆ = 87.5%). Similar improvements were observed among those who achieved less than 7% GMI. Within all payer groups, the number of patients with baseline HbA 1c levels greater than 9% decreased from 3431 to 15 (∆ = -99.6%).

Conclusions: A greater proportion of individuals can achieve the HEDIS and ADA target goals for glycemic status with Control-IQ use compared with MDI use across all payer types.

US prescription drug costs remain the highest globally, prompting proposals such as the most favored nation (MFN) pricing model, which benchmarks US drug prices to those of peer nations. In contrast, domestic market-based reforms focus on internal negotiation, competition, and transparency. Although MFN promises rapid cost reductions, it risks innovation disincentives and access delays. This commentary argues for a hybrid policy approach that combines the efficiency of MFN with domestic reforms, guided by equity-focused valuation frameworks such as Generalized Risk-Adjusted Cost-Effectiveness and severity-adjusted willingness-to-pay. A balanced model can achieve affordability without undermining innovation or equity.

The availability of person-centered data is critical to more robustly characterize populations, which facilitates solutions to address unmet medical needs. The goal of RAISE (Real-World Accelerator to Improve the Standard of Collection and Curation of Race and Ethnicity Data in Healthcare) is to curate existing efforts to improve data collection, share the data with leaders in health care, and provide an enduring resource to support organizations in transforming their data systems to support healthy communities. We developed 11 virtual workshops to share solutions and address common barriers in reporting, collecting, curating, and sharing demographic data with experts from health care delivery systems, payers, data technology companies, government agencies, research settings, and local communities. We summarized workshop proceedings into thematic areas and, through community polling, developed a multidimensional action framework to translate our learnings into actionable steps to address the most pressing gaps in the collection of person-centered data, using race and ethnicity data as an initial use case. Community partnership is central to cocreate data systems that curate information necessary to produce reliable data that support health care and healthy communities. Doing so requires respect, intentionality, standards, education, and collaboration with partners across the health care ecosystem, including the communities themselves.

The recent endorsement of glucagon-like peptide-1 (GLP-1) receptor agonists, such as semaglutide (Wegovy), by the American College of Cardiology as first-line therapy for weight management marks a paradigm shift in cardiometabolic care. Semaglutide offers significant benefits, including improved glycemic control and substantial weight loss, with emerging data demonstrating its impact even in individuals without diabetes. However, GLP-1 receptor agonists' growing popularity raises important concerns regarding long-term safety, access equity, and health care priorities. Although common adverse effects are gastrointestinal, less frequent but serious risks such as gallbladder disease, pancreatitis, and anesthesia-related complications deserve attention. Obesity, a global epidemic, has traditionally been managed through lifestyle interventions. The increasing reliance on pharmacologic options must not overshadow the foundational role of diet, physical activity, and education. Although GLP-1 receptor agonists represent a powerful advancement in obesity and cardiovascular risk management, its widespread adoption demands a balanced, evidence-based approach that integrates it into a broader, patient-centered strategy. There is a pressing need for comprehensive care models that address both the physiological and behavioral aspects of obesity. As health care systems navigate this therapeutic shift, they must ensure ethical use, cost-effectiveness, and long-term safety. GLP-1 receptor agonists may indeed be a blessing, but only if applied judiciously within the context of holistic obesity management.

Objectives: To evaluate changes in mental health visits and specialties among beneficiaries with at least 1 mental health visit before and after switching from Medicare Advantage (MA) to traditional Medicare (TM).

Study design: This study examines Medicare beneficiaries with mental health diagnoses who switched from MA to TM in 2018, analyzing their mental health utilization 12 months before and after the switch using MA encounter and TM claims data.

Methods: A longitudinal design was used, comparing mental health visits before and after the switch. We applied Wilcoxon signed rank tests to compare the total number of visits and McNemar tests for specific provider specialties used. Statistical significance was defined as a P value less than .05.

Results: Of the 32,710 beneficiaries who switched from MA to TM in 2018, 1184 beneficiaries (11,015 claims) were included in our sample because they had at least 1 health care visit attributed to a mental health condition both before and after switching. We found a statistically significant increase in the number of mental health visits after switching (P = .014). For the top 5 most prevalent specialties used for mental health care, we found no change in the use of psychiatrists (P = .607) or family medicine specialists (P = .696). However, we found increased use of nurse practitioners (P < .001) alongside decreased use of internal medicine (P = .003) and emergency medicine specialists (P = .001) for mental health care after switching.

Conclusions: Among beneficiaries with continued mental health care utilization, switching from MA to TM was associated with increased mental health visits and a shift in provider composition, which suggests potential care gaps or unmet needs in MA.

Objectives: Screening for health-related social needs (HRSNs) has increased in importance, but screening large patient populations comes with a cost and potential burden for care delivery organizations. This study evaluated the performance of 2-stage HRSN screening that used residence in a high-poverty area to determine which patients were administered screening questions.

Study design: Screening evaluation.

Methods: Adult primary care patients in Indiana and Florida completed HRSN screening questions included in an electronic health record (EHR) system and a set of additional questionnaires that served as the gold standard for assessing HRSN presence. Responses were linked to patients' residential zip code (n = 1351). The first stage of screening applied residence in a high-poverty zip code, and the second stage was the EHR-based HRSN screening questions. Using the response to the gold-standard questions, we calculated sensitivity, specificity, positive and negative predictive values, and area under the curve (AUC) for each HRSN.

Results: The highest AUC value was for food insecurity (80%), which was largely driven by the strong performance of the EHR-based HRSN screening questions. The remaining HRSNs had lower AUC values, which were driven by the overall low sensitivities of the screening questions and the overall low performance of the first-stage area-level screen. Positive predictive values were high.

Conclusions: Two-stage HRSN screening based on geography is suboptimal. Although a 2-stage approach based on area-level socioeconomic measures can reduce the number of patients requiring individual-level HRSN screening, large percentages of patients in need would go unidentified.

Objective: The demand and the landscape of options for dementia care are growing. Standardization of care for persons with Alzheimer disease and related dementias (ADRD) lacks infrastructure across episodes of care. Use of electronic health records (EHRs) in practice settings yields valuable information that can enhance continuity of patient care. The objective of this study was to use EHR-derived variables to identify risk factors for 30-day readmissions in the ADRD population across episodes of care.

Study design: Cross-sectional, retrospective study of older adults (aged ≥ 65 years) with ADRD discharged from a large urban academic medical center between October 1, 2018, and March 31, 2022.

Methods: Data extracted across episodes of care from the EHR included demographic characteristics, medical variables, and encounter variables.

Results: A total of 14,101 patients diagnosed with ADRD were included in the study. Factors associated with patients being more likely to experience 30-day hospital readmissions included advanced age, male sex, being a non-English speaker, having more severe comorbidities, staying in the hospital for more than 5 days, having had more than 1 surgical procedure in the prior 6 months, having had 3 or more inpatient admissions in the 6 months prior to index admission, having had more than 3 physician consultations in the prior 6 months, and having been discharged to settings other than home (all P < .05).

Conclusions: By utilizing the EHR to connect medical and encounter data across episodes of care, health care providers and administrators can gain valuable insight into identifying factors contributing to readmissions, which could be used to improve continuity of care for patients and caregivers, ultimately leading to better outcomes and reduced health care costs.