Journal of the American Medical Directors Association最新文献

Objectives: To identify different approaches (unique domains) and indicators to evaluate quality in assisted living communities in the United States.

Design: Scoping review supplemented with key informant and technical panel guidance.

Setting and participants: Assisted living settings and residents.

Methods: We obtained feedback on our review protocol and search strategy from key informants with expertise in AL quality, including feedback on a supplementary gray literature search for relevant non-peer-reviewed literature (such as nonempirical policy briefs). We scanned MEDLINE and CINAHL databases for peer-reviewed literature published from 2009 through 2019 assessing quality in assisted living in the United States. We synthesized evidence using the Donabedian framework and presented our analyses in 2 expert panel discussions for additional insights.

Results: We screened 833 abstracts, of which 49 studies met our selection criteria. Gray literature search yielded an additional 45 non-peer-reviewed sources. Nine unique domains were assessed: (1) resident quality of life, (2) resident and family satisfaction, (3) staffing and staff-related outcomes, (4) resident safety, (5) resident health outcomes, (6) care planning and integration, (7) physical and social environment, (8) service availability, and (9) core values and philosophy. Resident quality of life and satisfaction were the most prioritized domains in published literature, key informant interviews, and expert panel discussions. Domains such as staffing, safety, resident health outcomes, care planning, and integration were identified as vitally important for the increasing clinical and sociodemographic heterogeneity in the resident population. Expert panels emphasized the importance of including residents' voice in the quality measures development process.

Conclusions and implications: Lack of standardized measurement of quality impedes provision of person-centered, value-based care in US-based assisted living settings. Our comprehensive list of domains and indicators should inform future concerted efforts to develop and incorporate standardized quality measurement as part of routine practice in assisted living communities in the United States.

Objectives: To assess the relationship between nursing home staffing levels and excess outpatient emergency department (ED) visits by residents.

Study design: A retrospective analysis of nursing home facility-level data.

Setting and participants: A total of 14,860 Medicare- and Medicaid-certified long-term care facilities in the United States.

Methods: Using publicly available data from the Centers for Medicare and Medicaid Services database for the period October 1, 2022, to September 30, 2023, we used linear regression analysis to assess the association between facility-level rates of excess outpatient emergency department (ED) visits by residents and staffing hours per resident per day for physical therapists, registered nurses, nurse aides, and licensed practical nurses, controlling for other facility characteristics.

Results: For long-stay residents, an increase in physical therapist (PT) hours per resident per day was negatively associated with excess ED utilization (-0.58 per 1000; 95% CI, -0.91 to -0.25; P < .001). Similarly, registered nurse (RN) hours per resident per day were significantly associated with a decrease in excess ED utilization (-0.27 per 1000; 95% CI, -0.35 to -0.19; P < .001). For short-stay residents, although RN hours showed a significant negative association (-0.69%; 95% CI, -1.03 to -0.35; P < .001), PT hours did not reach statistical significance (-0.88%; 95% CI, -2.36 to 0.60). Nurse aide and licensed practical nurse hours did not significantly correlate with excess visits for long- and short-stay residents.

Conclusions and implications: The findings underscore the significance of an optimal combination of nursing home staff to reduce excess outpatient ED visits, particularly for long-stay residents. They point to the need for policy measures that promote balanced staffing levels across different roles to effectively minimize excess outpatient ED visits by residents.

Objectives: The study aimed to develop a machine learning (ML) model to predict early postdischarge falls in older adults using data that are easy to collect in acute care hospitals. This may reduce the burden imposed by complex measures on patients and health care staff.

Design: This prospective multicenter study included patients admitted to and discharged from geriatric wards at 3 university hospitals and 1 national medical center in Japan between October 2019 and July 2023.

Setting and participants: The participants were individuals aged ≥65 years. Of the 1307 individuals enrolled during the study period, 684 were excluded, leaving 706 for inclusion in the analysis.

Methods: We extracted 19 variables from admission and discharge data, including physical, mental, psychological, and social aspects and in-hospital events, to assess the main outcome measure: falls occurring within 3 months postdischarge. We developed a prediction model using 4 major classifiers, Extra Trees, Bernoulli Naive Bayes, AdaBoost, and Random Forest, which were evaluated using a 5-fold cross-validation. The area under the receiver operating characteristic curve (AUC) was used to evaluate predictive performance.

Results: Among the 706 patients, 114 (16.1%) reported a fall within 3 months postdischarge. The Extra Trees classifier demonstrated the best predictive performance, with an AUC of 0.73 on the test data. Important features included the Lawton Instrumental Activities of Daily Living scale, Clinical Frailty Scale (≥4 points), presence of urinary incontinence, 15-item Geriatric Depression Scale (≥5 points), and preadmission residence, all assessed at admission.

Conclusions and implications: To our knowledge, this is the first study to develop an ML model for predicting early postdischarge falls among older patients in acute care hospitals. The findings suggest that this model could assist in developing fall-prevention strategies to ensure seamless transition of care from hospitals to communities.

Objectives: To explore the influenza and COVID-19 vaccination status among Dutch nursing home (NH) health care workers (HCWs), factors associated with vaccination including the influence of the pandemic, and the facilitators and barriers to vaccination willingness.

Design: An explanatory sequential mixed methods study.

Setting and participants: HCWs providing direct care to residents in Dutch NHs.

Methods: An online questionnaire (September 2022) assessed vaccination status, barriers to vaccination, and the influence of the pandemic on influenza vaccination willingness. Facilitators and barriers to vaccination willingness were identified in 10 semistructured interviews and 1 focus group (February and September 2023).

Results: A total of 298 HCWs completed the questionnaire (87.5% women, 43.5% aged > 50 years). Nearly all HCWs (94.0%) received the primary COVID-19 vaccination(s). Influenza vaccination coverage was 41.7% prepandemic, 56.4% in 2021-2022, and 54.7% of the HCWs intended to receive an influenza vaccination in 2022-2023. Perceived urgency was identified as an important reason for the increased influenza vaccination coverage during the pandemic and for the difference between COVID-19 and influenza vaccination willingness. We identified knowledge gaps and (dis-)beliefs regarding vaccine efficacy, the role of preventive measures, and the role of the own immune system. Facilitators to vaccination willingness included addressing practical issues (eg, flexible scheduling, on-site vaccine provision).

Conclusions and implications: The influence of the pandemic on influenza vaccination willingness among Dutch NH HCWs appears to be temporary, underscoring the importance of addressing practical barriers to vaccine access. Concerns about the added value of vaccinations relative to trust in one's immune system or health and other preventive measures should be addressed in strategies aimed at optimizing vaccination uptake. To achieve this, a foundation of trust must first be created by neutral and factual communication and education.

Objective: Over the past decade, the role of nurse practitioners (NPs) in long-term care home (LTCH) settings has significantly expanded. Despite this trend, gaps have been identified in the description of collaborative practices between NPs and physicians in the LTCH sector. This study aimed to characterize the elements of collaboration between NPs and physicians in LTCH settings by applying the "Structured Collaborative Practice Core Model."

Design: A secondary analysis of a scoping review that focuses on literature where NPs and physicians collaboratively provided care in LTCH settings.

Methods: The initial scoping review followed the Joanna Briggs Institute methodology and PRISMA-ScR guidelines and included 60 peer-reviewed articles. Data relevant to the 7 core elements of the Structured Collaborative Practice Core Model-responsibility and accountability, coordination, communication, cooperation, assertiveness, autonomy, and mutual trust and respect-were extracted and analyzed. We included articles that described at least 1 element in the analysis.

Results: Twenty-nine articles were included in the secondary analysis. The analysis revealed that coordination (n = 25) and communication (n = 23) were the most frequently reported elements. Coordination was often highlighted through descriptions of care delivery organization and decision-making processes, where NPs provided continuous oversight and referred complex cases to physicians. Effective communication pathways, such as joint rounding and face-to-face meetings, were essential for successful collaboration. In contrast, assertiveness (n = 3) was the least frequently discussed element.

Conclusion and implications: Applying the Structured Collaborative Practice Core Model to the existing literature on NP and physician collaboration in LTCH settings underscores the importance of effective coordination and communication. Future work needs to investigate the historical and hierarchical dynamics influencing the relationship. Understanding these elements will inform strategies to optimize collaborative efforts, ultimately improving patient care outcomes in LTCH settings. The unique dynamics of NP and physician care models need to be considered.

Objectives: To examine how risk of hospitalization among assisted living (AL) residents differs by dual enrollment in Medicare and Medicaid and by the percent of dually enrolled individuals in an AL community.

Design: Retrospective cohort study.

Setting and participants: We used Medicare data from 2008 to 2018 and a national directory of licensed AL communities to identify Medicare beneficiaries with a change in their ZIP+4 code suggesting a new residence in an AL.

Methods: We estimated linear regression models of hospitalization onto interactions of residents' dual enrollment status and categories of the AL community's percentage of dually enrolled residents. In the models, we adjusted for person-level clinical and demographic characteristics, year-fixed effects, and fixed effects for the AL residents' prior ZIP code.

Results: Among 620,542 Medicare beneficiaries who moved to an AL community, the 1-year risk of hospitalization was higher for dually enrolled residents compared with Medicare-only residents. In adjusted models, dually enrolled residents in high-dual AL communities (>50% dually enrolled) had an 7.4% higher risk of hospital admission compared with dually enrolled residents in low-dual AL communities. Medicare-only beneficiaries in high-dual AL communities had a 9.4% higher risk of hospitalization than Medicare-only beneficiaries in low-dual ALs.

Conclusions and implications: The proportion of residents in an AL community who were dually enrolled was associated with residents' risk of hospitalization, regardless of their dual enrollment status. Additional research is needed to understand whether differences observed in residents' risk of hospitalization are due to differences in the types of services provided, unmeasured resident acuity, or the quality of care delivered in these settings.

Objectives: The aim of this systematic review was to assess the diagnostic test accuracy of muscle ultrasound for identifying older patients with sarcopenia and to investigate its association with frailty.

Design: Systematic review and meta-analysis of observational studies. Comprehensive searches were conducted in PubMed, MEDLINE, Cochrane Library, Scopus, and Embase through October 2024.

Setting and participants: Clinical and community settings across 7 countries, with 2537 adults aged ≥65 years.

Methods: Two reviewers assessed study quality using QUADAS-2. Data on participant characteristics, ultrasound methods, and diagnostic outcomes were extracted. Pooled sensitivity, specificity, diagnostic odds ratio (DOR), and summary receiver operating characteristic (SROC) curve were calculated with a random-effects model. Sensitivity analyses ensured robustness.

Results: Pooled sensitivity was 0.85 (95% CI, 0.78-0.93), specificity was 0.74 (95% CI, 0.65-0.81), DOR was 16.65 (95% CI, 4.90-96.67), and SROC-area under the curve was 0.87, indicating moderate to high diagnostic accuracy. Association with frailty yielded an odds ratio of 7.91 (95% CI, 6.15-10.17). Most studies received an "unclear" rating in several QUADAS-2 domains, especially in patient selection and reference standards, indicating limitations in study design that may impact the generalizability of results.

Conclusions and implications: Ultrasound is a reliable, noninvasive, and cost-effective tool for diagnosing sarcopenia in older patients. Further research should standardize cutoffs and explore integration with other methods.

Objectives: This study aimed to evaluate the association between home-based primary care (HBPC) and postdischarge outcomes in Korean older adults.

Design: HBPC was a service that provided medical care by visiting the homes of older adults with limited mobility. In this study, data from the National Health Insurance Service (NHIS) were used, and groups with and without HBPC services were matched 1:1 through propensity score matching. Cox proportional hazards model and linear regression analysis were performed to compare the HBPC group with the control group.

Setting and participants: Overall, 1580 older adults in Korea who used HBPC services after discharge from hospital and 1580 propensity score-matched older adults who did not use HBPC services were included.

Methods: For 30-day readmission, hospitalizations, and admission to long-term care hospitals or facilities after discharge, this study used a Cox proportional hazards regression model. And linear regression analysis was performed considering that the dependent variable was a continuous variable to examine the average total medical costs after discharge from acute hospital.

Results: Older adults who used HBPC services exhibited a lower risk of readmission for the same disease (risk reduction of 0.66, 95% CI 0.50, 0.87) and hospitalization (risk reduction of 0.58, 95% CI 0.46, 0.73) compared with those who did not use HBPC services. The annual average total medical cost was $4764 lower for older adults who used HBPC services than for those who did not (95% CI -6469.49, -3057.52). There was no significant difference in the risk of admission to long-term care hospitals or facilities.

Conclusions and implications: HBPC services had a positive effect on reducing 30-day readmission due to the same disease, hospitalization, and annual average total medical costs among older adults after discharge.

Objectives: Home health care (HHC) serves more than 5 million older adults annually in the United States, aiming to prevent unnecessary hospitalizations and emergency department (ED) visits. Despite efforts, up to 25% of patients in HHC experience these adverse events. The underutilization of clinical notes, aggregated data approaches, and potential demographic biases have limited previous HHC risk prediction models. This study aimed to develop a time-series risk model to predict hospitalizations and ED visits in patients in HHC, examine model performance over various prediction windows, identify top predictive variables and map them to data standards, and assess model fairness across demographic subgroups.

Setting and participants: A total of 27,222 HHC episodes between 2015 and 2017.

Methods: The study used health care process modeling of electronic health records, including clinical notes processed with natural language processing techniques and Medicare claims data. A Light Gradient Boosting Machine algorithm was used to develop the risk prediction model, with performance evaluated using 5-fold cross-validation. Model fairness was assessed across gender, race/ethnicity, and socioeconomic subgroups.

Results: The model achieved high predictive performance, with an F1 score of 0.84 for a 5-day prediction window. Twenty top predictive variables were identified, including novel indicators such as the length of nurse-patient visits and visit frequency. Eighty-five percent of these variables mapped completely to the US Core Data for Interoperability standard. Fairness assessment revealed performance disparities across demographic and socioeconomic groups, with lower model effectiveness for more historically underserved populations.

Conclusions and implications: This study developed a robust time-series risk model for predicting adverse events in patients in HHC, incorporating diverse data types and demonstrating high predictive accuracy. The findings highlight the importance of considering established and novel risk factors in HHC. Importantly, the observed performance disparities across subgroups emphasize the need for fairness adjustments to ensure equitable risk prediction across all patient populations.