American Journal of Managed Care最新文献

Objective: Major depressive disorder (MDD) is linked to a 61% increased risk of emergency department (ED) visits and frequent ED usage. Collaborative care management (CoCM) models target MDD treatment in primary care, but how best to prioritize patients for CoCM to prevent frequent ED utilization remains unclear. This study aimed to develop and validate a risk identification model to proactively detect patients with MDD in CoCM at high risk of frequent (≥ 3) ED visits.

Study design: This retrospective cohort study utilized electronic health records from Mayo Clinic's primary care system to develop and validate a machine learning-based risk identification model. The model predicts the likelihood of frequent ED visits among patients with MDD within a 12-month period.

Methods: Data were collected from Mayo Clinic's primary care system between May 1, 2006, and December 19, 2018. Risk identification models were developed and validated using machine learning classifiers to estimate frequent ED visit risks over 12 months. The Shapley Additive Explanations model identified variables driving frequent ED visits.

Results: The patient population had a mean (SD) age of 39.78 (16.66) years, with 30.3% being male and 6.1% experiencing frequent ED visits. The best-performing algorithm (elastic-net logistic regression) achieved an area under the curve of 0.79 (95% CI, 0.74-0.84), a sensitivity of 0.71 (95% CI, 0.57-0.82), and a specificity of 0.76 (95% CI, 0.64-0.85) in the development data set. In the validation data set, the best-performing algorithm (random forest) achieved an area under the curve of 0.79, a sensitivity of 0.83, and a specificity of 0.61. Significant variables included male gender, prior frequent ED visits, high Patient Health Questionnaire-9 score, low education level, unemployment, and use of multiple medications.

Conclusions: The risk identification model has potential for clinical application in triaging primary care patients with MDD in CoCM, aiming to reduce future ED utilization.

This editorial discusses positions for academic medical centers to consider when designing and implementing artificial intelligence (AI) tools.

Objectives: To understand whether and how equity is considered in artificial intelligence/machine learning governance processes at academic medical centers.

Study design: Qualitative analysis of interview data.

Methods: We created a database of academic medical centers from the full list of Association of American Medical Colleges hospital and health system members in 2022. Stratifying by census region and restricting to nonfederal and nonspecialty centers, we recruited chief medical informatics officers and similarly positioned individuals from academic medical centers across the country. We created and piloted a semistructured interview guide focused on (1) how academic medical centers govern artificial intelligence and prediction and (2) to what extent equity is considered in these processes. A total of 17 individuals representing 13 institutions across 4 census regions of the US were interviewed.

Results: A minority of participants reported considering inequity, racism, or bias in governance. Most participants conceptualized these issues as characteristics of a tool, using frameworks such as algorithmic bias or fairness. Fewer participants conceptualized equity beyond the technology itself and asked broader questions about its implications for patients. Disparities in health information technology resources across health systems were repeatedly identified as a threat to health equity.

Conclusions: We found a lack of consistent equity consideration among academic medical centers as they develop their governance processes for predictive technologies despite considerable national attention to the ways these technologies can cause or reproduce inequities. Health systems and policy makers will need to specifically prioritize equity literacy among health system leadership, design oversight policies, and promote critical engagement with these tools and their implications to prevent the further entrenchment of inequities in digital health care.

Objectives: First, to analyze the relationship between value-based payment (VBP) program participation and documentation burden among office-based physicians. Second, to analyze the relationship between specific VBP programs (eg, accountable care organizations [ACOs]) and documentation burden.

Study design: Retrospective analyses of US office-based physicians in 2019 and 2021.

Methods: We used cross-sectional data from the National Electronic Health Records Survey to measure VBP program participation and our outcomes of reported electronic health record (EHR) documentation burden. We used ordinary least squares regression models adjusting for physician and practice characteristics to estimate the relationship between participation in any VBP program and EHR burden outcomes. We also estimated the relationship between participation in 6 distinct VBP programs and our outcomes to decompose the aggregate relationship into program-specific estimates.

Results: In adjusted analyses, participation in any VBP program was associated with 10.5% greater probability of reporting more than 1 hour per day of after-hours documentation time (P = .01), which corresponded to an estimated additional 11 minutes per day (P = .03). Program-specific estimates illustrated that ACO participation drove the aggregate relationship, with ACO participants reporting greater after-hours documentation time (18 additional minutes per day; P < .001), more difficulty documenting (30.6% more likely; P < .001), and more inappropriateness of time spent documenting (21.7% more likely; P < .001).

Conclusions: Office-based physicians participating in ACOs report greater documentation burden across several measures; the same is not true for other VBP programs. Although many ACOs relax documentation requirements for reimbursement, documentation for quality reporting and risk adjustment may lead to a net increase in burden, especially for physicians exposed to numerous programs and payers.

In 2020, cancer claimed more than 600,000 lives in the US. Cancer is an unyielding public health crisis. Cancer treatments typically involve multidisciplinary approaches, including surgery, radiation therapy, chemotherapy, and oral medications. For patients, especially those in rural areas, obtaining multiple oral medications can be inconvenient. The adoption of delivering cancer medications from medically integrated pharmacies (MIPs) has become popular in recent years. On May 12, 2023, CMS introduced guidelines restricting MIPs from delivering cancer-specific medications by mail or to oncology satellite offices and also requiring patients themselves to pick up the medications in person. This regulatory change has had a devastating impact on patients with cancer in rural and underserved communities, exacerbating existing health care disparities. This commentary explores the negative impacts of the policy change by CMS in rural America.

Objective: To examine the association between missed CMS Star Ratings quality measures for medication adherence over 3 years for diabetes, hypertension, and hyperlipidemia medications (9 measures) and health care utilization and relative costs.

Study design: Retrospective cohort study.

Methods: The study examined eligible patients who qualified for the diabetes, statin, and renin-angiotensin system antagonist medication adherence measures in 2018, 2019, and 2020 and were continuously enrolled in a Medicare Advantage prescription drug plan from 2017 through 2021. A total of 103,900 patients were divided into 4 groups based on the number of adherence measures missed (3 medication classes over 3 years): (1) missed 0 measures, (2) missed 1 measure, (3) missed 2 or 3 measures, and (4) missed 4 or more measures. To achieve a quality measure, patients had to meet the Pharmacy Quality Alliance 80% threshold of proportion of days covered during the calendar year.

Results: The mean age of the cohort was 71.1 years, and 49.9% were female. Compared with patients who missed 0 of 9 adherence measures, those who missed 1 measure, 2 or 3 measures, and 4 or more measures experienced 12% to 26%, 22% to 42%, and 24% to 50% increased risks, respectively, of all-cause and diabetes-related inpatient stays and all-cause and diabetes-related emergency department visits (all  P  values < .01). Additionally, patients who missed 1, 2 or 3, and 4 or more adherence measures experienced 14%, 19%, and 20% higher monthly medical costs, respectively.

Conclusions: Missing Star Ratings quality measures for medication adherence was associated with an increased likelihood of health care resource utilization and increased costs for patients taking medications to treat diabetes, hypertension, and hyperlipidemia.

Objective: To examine patient and provider perspectives on privacy and security considerations in telemedicine during the COVID-19 pandemic.

Study design: Qualitative study with patients and providers from primary care practices in 3 National Patient-Centered Clinical Research Network sites in New York, New York; North Carolina; and Florida.

Methods: Semistructured interviews were conducted, audio recorded, transcribed verbatim, and coded using an inductive process. Data related to privacy and information security were analyzed.

Results: Sixty-five patients and 21 providers participated. Patients and providers faced technology-related security concerns as well as difficulties ensuring privacy in the transformed shared space of telemedicine. Patients expressed increased comfort doing telemedicine from home but often did not like their providers to offer virtual visits from outside an office setting. Providers initially struggled to find secure and Health Insurance Portability and Accountability Act-compliant platforms and devices to host the software. Whereas some patients preferred familiar platforms such as FaceTime, others recognized potential security concerns. Audio-only encounters sometimes raised patient concerns that they would not be able to confirm the identity of the provider.

Conclusions: Telemedicine led to novel concerns about privacy because patients and providers were often at home or in public spaces, and they shared concerns about software and hardware security. In addition to technological safeguards, our study emphasizes the critical role of physical infrastructure in ensuring privacy and security. As telemedicine continues to evolve, it is important to address and mitigate concerns around privacy and security to ensure high-quality and safe delivery of care to patients in remote settings.

Numerous advances in the standard of care for metastatic colorectal cancer (mCRC), including the approval of several new treatments indicated for treatment in the third line or later (3L+), have been made, yet data and appropriate guidance on the optimal sequencing and treatment strategies for these lines of therapy are lacking. Four treatments-regorafenib, trifluridine/tipiracil alone or with bevacizumab, and fruquintinib-are FDA-approved and recommended by the NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) for the treatment of mCRC in the 3L+. When considering sequencing of treatment options for patients in the 3L+, the goal of treatment is to improve survival, but also maintain quality of life, a goal that requires consideration of relative efficacy and cumulative toxicity such as persistent myelosuppression.

Objectives: Patients undergoing cardiac surgery are considered at high risk for developing drug-related problems (DRPs) due to comorbidities and complexity of drug treatment. This study aimed to identify DRPs in patients undergoing cardiac surgery and to develop and implement a framework to reduce potential risks associated with drug treatment.

Study design: Prospectively designed quasi-experimental study.

Methods: This study consisted of observational (risk assessment and framework development) and interventional (framework implementation) periods and was conducted at a department of cardiovascular surgery in a university hospital. An expert panel evaluated the causes of DRPs. Then a framework was developed in consensus to identify safeguards to be implemented during the interventional period.

Results: A total of 200 patients (100 patients per study period) were included. During the observational period, a total of 275 DRPs and 487 causes were identified; 74.5% of DRPs were not solved. For the risk analysis, 487 causes were evaluated and only 32.6% were considered acceptable risk. By implementing the framework in the interventional period, 215 DRPs and 304 causes were identified and 386 interventions were recommended by a clinical pharmacist. A total of 342 (88.6%) interventions were accepted by a health care team, and 128 (59.5%) DRPs were completely solved. For the risk analysis, 304 causes were evaluated and 84.9% were considered acceptable risk ( P  < .001 compared with the observational period).

Conclusions: It is possible to reduce risk levels or prevent occurrence of DRPs by implementing a framework for risk management developed by a multidisciplinary care team in areas such as cardiac surgery where time is limited.

Objectives: In 2018, CMS established reimbursement for the first Medicare-covered artificial intelligence (AI)-enabled clinical software: CT fractional flow reserve (FFRCT) to assist in the diagnosis of coronary artery disease. This study quantified Medicare utilization of and spending on FFRCT from 2018 through 2022 and characterized adopting hospitals, clinicians, and patients.

Study design: Analysis, using 100% Medicare fee-for-service claims data, of the hospitals, clinicians, and patients who performed or received coronary CT angiography with or without FFRCT.

Methods: We measured annual trends in utilization of and spending on FFRCT among hospitals and clinicians from 2018 through 2022. Characteristics of FFRCT-adopting and nonadopting hospitals and clinicians were compared, as well as the characteristics of patients who received FFRCT vs those who did not.

Results: From 2018 to 2022, FFRCT billing volume in Medicare increased more than 11-fold (from 1083 to 12,363 claims). Compared with nonbilling hospitals, FFRCT-billing hospitals were more likely to be larger, part of a health system, nonprofit, and financially profitable. FFRCT-billing clinicians worked in larger group practices and were more likely to be cardiac specialists. FFRCT-receiving patients were more likely to be male and White and less likely to be dually enrolled in Medicaid or receiving disability benefits.

Conclusions: In the initial 5 years of Medicare reimbursement for FFRCT, growth was concentrated among well-resourced hospitals and clinicians. As Medicare begins to reimburse clinicians for the use of AI-enabled clinical software such as FFRCT, it is crucial to monitor the diffusion of these services to ensure equal access.