American Journal of Managed Care最新文献

{"title":"The feasibility and equity of text messaging to determine patient eligibility for lung cancer screening.","authors":"Ilona Fridman, Lisa Carter-Bawa, Christine M Neslund-Dudas, Jennifer Elston Lafata","doi":"10.37765/ajmc.2024.89602","DOIUrl":"https://doi.org/10.37765/ajmc.2024.89602","url":null,"abstract":"<p><strong>Objectives: </strong>Text messaging could be effective for determining patient eligibility for lung cancer screening (LCS). We explored people's willingness to share their tobacco use history via text message among diverse groups.</p><p><strong>Study design: </strong>Cross-sectional survey.</p><p><strong>Methods: </strong>In 2020, we conducted a cross-sectional survey asking respondents about cellular phone usage, smoking habits, sociodemographic characteristics, and the likelihood of responding to a text message from their health care provider's office about tobacco use. We used χ² and analysis of variance tests for comparisons.</p><p><strong>Results: </strong>Among 745 respondents, 90% used text messaging casually. Overall, 54% never smoked, 33% currently smoked, and 13% previously smoked. Six percent were LCS eligible, and 20% used both cigarettes and e-cigarettes (dual users). Current smokers were significantly younger, less likely to be female, and more likely to use text messaging. LCS-eligible respondents were older and less likely to have a high income. Dual users were younger, less likely to report female gender and live in rural areas, and more likely to have a college education and high income. Most respondents (83%) indicated they were likely to respond to text message inquiries regarding smoking status. Middle-aged respondents (mean age, 37 years) were significantly more willing to report smoking status than younger or older respondents (91% vs 84% and 84%, respectively). Respondents with no college education (83% vs 88%) or with a low income vs a middle or high income (81% vs 86% and 88%, respectively) were significantly less willing to report smoking status via text messages.</p><p><strong>Conclusions: </strong>Text messaging showed promise for evaluating smoking history and for simplifying the process of identifying LCS-eligible individuals. However, achieving equity in identifying eligibility for LCS requires the implementation of multimodal strategies.</p>","PeriodicalId":50808,"journal":{"name":"American Journal of Managed Care","volume":"30 9","pages":"440-444"},"PeriodicalIF":2.5,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142300108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The accelerated approval program for oncology drugs: celebrating more than 250,000 life-years gained and counting.","authors":"Julie Patterson, John Michael O'Brien, Jonathan D Campbell","doi":"10.37765/ajmc.2024.89590","DOIUrl":"https://doi.org/10.37765/ajmc.2024.89590","url":null,"abstract":"<p><p>This commentary explores how 2 recently published studies evaluating the clinical benefit of the FDA's accelerated approval program for oncology drugs came to different conclusions.</p>","PeriodicalId":50808,"journal":{"name":"American Journal of Managed Care","volume":"30 8","pages":"e223-e225"},"PeriodicalIF":2.5,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141989467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geographic variability of Medicaid acceptance among allergists in the US.","authors":"Frances O Ho, Chaoyi Zheng, Mech Frazier, Sai R Nimmagadda, Ruchi S Gupta, Lucy A Bilaver","doi":"10.37765/ajmc.2024.89588","DOIUrl":"https://doi.org/10.37765/ajmc.2024.89588","url":null,"abstract":"<p><strong>Objective: </strong>To determine the geographic variability of Medicaid acceptance among allergists in the US.</p><p><strong>Study design: </strong>Geospatial analysis predicted Medicaid acceptance across space, and a multivariable regression identified area-level population demographic variables associated with acceptance.</p><p><strong>Methods: </strong>We used the National Plan & Provider Enumeration System database to identify allergists. Medicaid acceptance was determined from lists or search engines from state Medicaid offices and calls to provider offices. Spatial analysis was performed using the empirical Bayesian kriging tool. Multivariate logistic regression was used to identify county-level characteristics associated with provider Medicaid acceptance.</p><p><strong>Results: </strong>Of 5694 allergists, 55.5% accepted Medicaid. Acceptance in each state ranged from 13% to 90%. Washington, Arizona, and the Northeast had lowest predicted proportion of both Medicaid acceptance and Medicaid acceptance per 10,000 enrollees. Overall, county-level characteristics were not associated with the likelihood of accepting Medicaid in multivariate analyses. Only the percentage of individuals living in poverty was associated with a higher likelihood of providers accepting Medicaid (OR, 1.245; 95% CI, 1.156-1.340; P < .001).</p><p><strong>Conclusions: </strong>A barrier to accessing allergy-related health care is finding a provider who accepts a patient's insurance, which is largely variable by state. Lack of access to allergy care likely affects health outcomes for children with prevalent atopic conditions such as food allergy.</p>","PeriodicalId":50808,"journal":{"name":"American Journal of Managed Care","volume":"30 8","pages":"374-379"},"PeriodicalIF":2.5,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141989463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adherence patterns 1 year after initiation of SGLT2 inhibitors: results of a national cohort study.","authors":"Hsiao-Ching Huang, Daniel R Touchette, Mina Tadrous, Glen T Schumock, Saria Awadalla, Todd A Lee","doi":"10.37765/ajmc.2024.89591","DOIUrl":"10.37765/ajmc.2024.89591","url":null,"abstract":"<p><strong>Objectives: </strong>Adherence to medications is important for the management of chronic diseases. Although the proportion of days covered (PDC) is a common metric for measuring adherence, it may be insufficient to distinguish relevant differences in medication-taking behavior. Group-based trajectory models (GBTMs) have been used to better represent adherence over time. This study aims to examine adherence patterns 1 year after initiation among users of sodium-glucose cotransporter 2 (SGLT2) inhibitors using GBTMs and evaluate the ability of baseline characteristics to predict adherence trajectory.</p><p><strong>Study design: </strong>SGLT2 inhibitor new-user cohort study from 2014 to 2018.</p><p><strong>Methods: </strong>We calculated 12-month PDC and categorized patients with PDC of 80% or greater as adherent. We performed multivariable logistic regression on adherence status controlling for baseline covariates. GBTMs were fit to identify adherence patterns 12 months following SGLT2 inhibitor initiation. Five multinomial logistic regression models including different subsets of predictors were used to predict adherence trajectory group assignment.</p><p><strong>Results: </strong>In a cohort of 228,363 SGLT2 inhibitor users, the mean PDC was 57%, with 36% of the cohort being adherent. Overall, women and patients with anxiety or depression were less likely to be adherent. Six patterns of SGLT2 inhibitor adherence were identified with GBTMs: 1 fill (PDC = 0.08), early discontinuation (PDC = 0.22), consistently low adherence (PDC = 0.35), moderate adherence (PDC = 0.48), high adherence (PDC = 0.79), and near-perfect adherence (PDC = 0.95). All prediction models showed poor predictive accuracy (0.35).</p><p><strong>Conclusions: </strong>We found wide variation in adherence patterns among SGLT2 inhibitor users in a national cohort. Predictors from a health care claims database were unable to accurately predict adherence trajectory.</p>","PeriodicalId":50808,"journal":{"name":"American Journal of Managed Care","volume":"30 8","pages":"e226-e232"},"PeriodicalIF":2.5,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141989459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-validation of insurer and hospital price transparency data.","authors":"Morgan A Henderson, Morgane C Mouslim","doi":"10.37765/ajmc.2024.89594","DOIUrl":"https://doi.org/10.37765/ajmc.2024.89594","url":null,"abstract":"<p><p>Given recent congressional interest in codifying price transparency regulations, it is important to understand the extent to which newly available price transparency data capture true underlying procedure-level prices. To that end, we compared the prices for maternity services negotiated between a large payer and 26 hospitals in Mississippi across 2 separate price transparency data sources: payer and hospital. The degree of file overlap is low, with only 16.3% of hospital-billing code observations appearing in both data sources. However, for the observations that overlap, pricing concordance is high: Corresponding prices have a correlation coefficient of 0.975, 77.4% match to the penny, and 84.4% are within 10%. Exact price matching rates are greater than 90% for 3 of the 4 service lines included in this study. Taken together, these results suggest that although administrative misalignment exists between payers and hospitals, there is a measure of signal amid the price transparency noise.</p>","PeriodicalId":50808,"journal":{"name":"American Journal of Managed Care","volume":"30 8","pages":"e247-e250"},"PeriodicalIF":2.5,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141989462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hospitals' strategies to reduce costs and improve quality: survey of hospital leaders.","authors":"Gmerice Hammond, Tierney Lanter, Fengxian Wang, R J Waken, Jie Zheng, Arnold M Epstein, E John Orav, Karen E Joynt Maddox","doi":"10.37765/ajmc.2024.89593","DOIUrl":"10.37765/ajmc.2024.89593","url":null,"abstract":"<p><strong>Objectives: </strong>Hospitals in the US operate under various value-based payment programs, but little is known regarding the strategies they use in this context to improve quality and reduce costs, overall or in voluntary programs including Bundled Payments for Care Improvement Advanced (BPCI-A).</p><p><strong>Study design: </strong>A survey was administered to hospital leaders at 588 randomly selected acute care hospitals, with oversampling of BPCI-A participants, from November 2020 to June 2021. Twenty strategies and 20 barriers were queried in 4 domains: inpatient, postacute, outpatient, and community resources for vulnerable patients.</p><p><strong>Methods: </strong>Summary statistics were tabulated, and responses were adjusted for sampling strategy and nonresponse.</p><p><strong>Results: </strong>There were 203 respondents (35%), of which 159 (78%) were BPCI-A participants and 44 (22%) were nonparticipants. On average, respondents reported implementing 89% of queried strategies in the inpatient domain, such as care pathways or predictive analytics; 65% of postacute strategies, such as forming partnerships with skilled nursing facilities; 84% of outpatient strategies, such as scheduling close follow-up to prevent emergency department visits/hospitalizations; and 82% of strategies aimed at high-risk populations, such as building connections with community resources. There were no differences between BPCI-A and non-BPCI-A hospitals in 19 of 20 care redesign strategies queried. However, 78.3% of BPCI-A-participating hospitals reported programs aimed at reducing utilization of skilled nursing and inpatient rehabilitation facilities compared with 37.6% of non-BPCI-A hospitals (P < .0001).</p><p><strong>Conclusions: </strong>Hospitals pursue a broad range of efforts to improve quality. BPCI-A hospitals have attempted to reduce use of postacute care, but otherwise the strategies they pursue are similar to other hospitals.</p>","PeriodicalId":50808,"journal":{"name":"American Journal of Managed Care","volume":"30 8","pages":"e240-e246"},"PeriodicalIF":2.5,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141989464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cancer drugs speed to accelerated approvals, then hit the brakes in timely, clinically beneficial confirmatory trials.","authors":"Terra Wonsettler","doi":"10.37765/ajmc.2024.89597","DOIUrl":"https://doi.org/10.37765/ajmc.2024.89597","url":null,"abstract":"","PeriodicalId":50808,"journal":{"name":"American Journal of Managed Care","volume":"30 9 Spec No.","pages":"SP722-SP724"},"PeriodicalIF":2.5,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142005744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Care management improves total cost of care for patients with dementia.","authors":"R John Sawyer, Carolina Pereira Osorio, Sakshi Sharma, Emily Brickell","doi":"10.37765/ajmc.2024.89559","DOIUrl":"https://doi.org/10.37765/ajmc.2024.89559","url":null,"abstract":"<p><strong>Objectives: </strong>To examine a 12-month dementia care management program's effect on health care cost, utilization, and overall return on investment in a Medicare managed care population.</p><p><strong>Study design: </strong>Pre-post analysis of participants (n = 121) enrolled in Ochsner's Care Ecosystem program from 2019 through 2021 compared with propensity-matched controls (n = 121). The primary outcome comparison was total cost of care. Secondary outcomes included components of total cost of care (eg, inpatient, outpatient, emergency department [ED] costs), health care utilization (eg, number of ED visits), and differences in Hierarchical Condition Category (HCC) risk scores.</p><p><strong>Methods: </strong>Difference-in-differences analyses were conducted from baseline through 12 months comparing various financial metrics and utilization between groups.</p><p><strong>Results: </strong>Care Ecosystem participants had significantly lower total cost of care at 12 months, mean savings of $475.80 per member per month compared with controls. Care Ecosystem participants had fewer ED, outpatient, and professional visits. HCC risk scores were also better relative to matched controls.</p><p><strong>Conclusions: </strong>A collaborative dementia care program demonstrated significant financial benefit in a managed Medicare population.</p>","PeriodicalId":50808,"journal":{"name":"American Journal of Managed Care","volume":"30 8","pages":"353-358"},"PeriodicalIF":2.5,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141989461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Biosimilars and employers: strategies for success.","authors":"Kathy Oubre","doi":"10.37765/ajmc.2024.89596","DOIUrl":"https://doi.org/10.37765/ajmc.2024.89596","url":null,"abstract":"","PeriodicalId":50808,"journal":{"name":"American Journal of Managed Care","volume":"30 9 Spec No.","pages":"SP720-SP722"},"PeriodicalIF":2.5,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142005743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A machine learning technology for addressing medication-related risk in older, multimorbid patients.","authors":"Diane L Seger, Mary G Amato, Michelle Frits, Christine Iannaccone, Aqsa Mugal, Frank Chang, Julie Fiskio, Lynn A Volk, Lisa S Rotenstein","doi":"10.37765/ajmc.2024.89592","DOIUrl":"https://doi.org/10.37765/ajmc.2024.89592","url":null,"abstract":"<p><strong>Objectives: </strong>To evaluate the FeelBetter machine learning system's ability to accurately identify older patients with multimorbidity at Brigham and Women's Hospital at highest risk of medication-associated emergency department (ED) visits and hospitalizations, and to assess the system's ability to provide accurate medication recommendations for these patients.</p><p><strong>Study design: </strong>Retrospective cohort study.</p><p><strong>Methods: </strong>The system uses medications, demographics, diagnoses, laboratory results, health care utilization patterns, and costs to stratify patients' risk of ED visits and hospitalizations. Patients were assigned 1 of 22 risk levels based on their system-generated risk percentile of either ED visits or hospitalizations. Logistic regression models were used to estimate the odds of ED visits and hospitalizations associated with each successive risk level compared with the 45th to 50th percentiles. After stratification, 100 high-risk (95th-100th percentiles) and 100 medium-risk (45th-55th percentiles) patients were randomly selected for generation of medication recommendations. Two clinical pharmacists reviewed the system-generated medication recommendations for these patients.</p><p><strong>Results: </strong>Logistic regression models predicting 3-month utilization showed that compared with the 45th to 50th percentiles, patients in the top 1% risk percentile had ORs of 7.9 and 17.3 for ED visits and hospitalizations, respectively. The first 5 high-priority medications on each patient's medication list were associated with a mean (SD) of 6.65 (4.09) warnings. Of 1290 warnings reviewed, 1151 (89.2%) were assessed as correct.</p><p><strong>Conclusions: </strong>The FeelBetter system effectively stratifies older patients with multimorbidity at risk of ED use and hospitalizations. Medication recommendations provided by the system are largely accurate and can potentially be beneficial for patient care.</p>","PeriodicalId":50808,"journal":{"name":"American Journal of Managed Care","volume":"30 8","pages":"e233-e239"},"PeriodicalIF":2.5,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141989458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}