Hospital-Skilled Nursing Facility Integration and Participation in a Bundled Payment Initiative.

Importance: Reduced institutional postacute care has been associated with savings in alternative payment models. However, organizations may avoid voluntary participation if participation could threaten their own revenues. Objective: To characterize the association between hospital-skilled nursing facility (SNF) integration and participation in Medicare's Bundled Payments for Care Improvement Advanced (BPCI-A) program. Design, Setting, and Participants: This is a cross-sectional analysis of hospital participation in BPCI-A beginning with its launch in 2018. Each SNF-integrated hospital was matched with 2 nonintegrated hospitals for each of 4 episode-specific analyses. Fifteen hospital-level variables were used for matching: beds, case mix index, days, area SNF beds, metropolitan location, ownership, region, system membership, and teaching status. Hospitals were also matched on episode-specific volume, target price, and the interaction of target price and case mix. Episode-specific logistic models were estimated regressing hospital participation on integration and the previously listed variables. The marginal effect of integration on participation was then calculated. Analysis took place from August 2022 to May 2024. Exposure: Hospital-SNF integration, as defined by common ownership and referral patterns and identified using cost reports, Medicare claims, and Provider Enrollment, Chain, and Ownership System records. Additional sources included records of target prices and participation, the Area Health Resources File, and the Compendium of US Health Systems. Main Outcomes and Measures: Participation in BPCI-A. Results: In total, 1524 hospitals met criteria for inclusion in the hip and femur (HFP) analysis, 1825 were included in the major joint replacement of the lower extremity (MJRLE) analysis, 2018 were included in the sepsis analysis, and 1564, were included in the stroke-specific analysis. Across episodes, 191 HFP-eligible hospitals (12.5% of HFP-eligible hospitals), 302 MJRLE-eligible hospitals (16.5%), 327 sepsis-eligible hospitals (16.2%), and 185 sepsis-eligible hospitals (11.8%) were SNF integrated. In total, 79 hospitals (5.2%) participated in the HFP episode, 128 (7.0%) participated in the MJRLE episode, 204 (10.1%) participated in the sepsis episode, and 141 (9.0%) participated in the stroke episode. Integration was associated with a 4.7-percentage point decrease (95% CI, 2.4 to 6.9 percentage points) in participation in the MJRLE episode. There was no association between integration and participation for HFP (0.5-percentage point increase in participation moving from nonintegrated to integrated; 95% CI, -2.9 to 3.8 percentage points), sepsis (1.0-percentage point increase; 95% CI, -2.2 to 4.2 percentage points), and stroke (0.3-percentage point decrease; 95% CI, -3.1 to 3.8 percentage points). Conclusions and Relevance: In this cross-sectional study, there was an uneven association between hospital-SNF integration and participation in Medicare's BPCI-A program. Other factors may be more consistent determinants of selection into voluntary payment reform.

Cross-setting Comparison of Risk Factors for Pressure Injuries Acquired in Post-Acute Care.

GENERAL PURPOSE: To provide information on the association between risk factors and the development of new or worsened stage 2 to 4 pressure injuries (PIs) in patients in long-term care hospitals (LTCHs), inpatient rehabilitation facilities (IRFs), and skilled nursing facilities (SNFs). TARGET AUDIENCE: This continuing education activity is intended for physicians, physician assistants, nurse practitioners, and nurses with an interest in skin and wound care. LEARNING OBJECTIVES/OUTCOMES: After participating in this educational activity, the participant will:1. Compare the unadjusted PI incidence in SNF, IRF, and LTCH populations.2. Explain the extent to which the clinical risk factors of functional limitation (bed mobility), bowel incontinence, diabetes/peripheral vascular disease/peripheral arterial disease, and low body mass index are associated with new or worsened stage 2 to 4 PIs across the SNF, IRF, and LTCH populations.3. Compare the incidence of new or worsened stage 2 to 4 PI development in SNF, IRF, and LTCH populations associated with high body mass index, urinary incontinence, dual urinary and bowel incontinence, and advanced age.

Improving pressure injury risk assessment using real-world data from skilled nursing facilities: A cohort study.

This study aimed to improve the predictive accuracy of the Braden assessment for pressure injury risk in skilled nursing facilities (SNFs) by incorporating real-world data and training a survival model. A comprehensive analysis of 126 384 SNF stays and 62 253 in-house pressure injuries was conducted using a large calibrated wound database. This study employed a time-varying Cox Proportional Hazards model, focusing on variations in Braden scores, demographic data and the history of pressure injuries. Feature selection was executed through a forward-backward process to identify significant predictive factors. The study found that sensory and moisture Braden subscores were minimally contributive and were consequently discarded. The most significant predictors of increased pressure injury risk were identified as a recent (within 21 days) decrease in Braden score, low subscores in nutrition, friction and activity, and a history of pressure injuries. The model demonstrated a 10.4% increase in predictive accuracy compared with traditional Braden scores, indicating a significant improvement. The study suggests that disaggregating Braden scores and incorporating detailed wound histories and demographic data can substantially enhance the accuracy of pressure injury risk assessments in SNFs. This approach aligns with the evolving trend towards more personalized and detailed patient care. These findings propose a new direction in pressure injury risk assessment, potentially leading to more effective and individualized care strategies in SNFs. The study highlights the value of large-scale data in wound care, suggesting its potential to enhance quantitative approaches for pressure injury risk assessment and supporting more accurate, data-driven clinical decision-making.

Discharge to Skilled Nursing Facility Is a Risk Factor for Readmission: A Nationwide Propensity-Matched Study.

INTRODUCTION: General surgery procedures place stress on geriatric patients, and postdischarge care options should be evaluated. We compared the association of discharge to a skilled nursing facility (SNF) versus home on patient readmission. METHODS: We retrospectively reviewed the Nationwide Readmission Database (2016-2019) and included patients ≥65 y who underwent a general surgery procedure between January and September. Our primary outcome was 30-d readmissions. Our secondary outcome was predictors of readmission after discharge to an SNF. We performed a 1:1 propensity-matched analysis adjusting for patient demographics and hospital course to compare patients discharged to an SNF with patients discharged home. We performed a sensitivity analysis on patients undergoing emergency procedures and a stepwise regression to identify predictors of readmission. RESULTS: Among 140,056 included patients, 33,916 (24.2%) were discharged to an SNF. In the matched population of 19,763 pairs, 30-d readmission was higher in patients discharged to an SNF. The most common diagnosis at readmission was sepsis, and a greater proportion of patients discharged to an SNF were readmitted for sepsis. In the sensitivity analysis, emergency surgery patients discharged to an SNF had higher 30-d readmission. Higher illness severity during the index admission and living in a small or fringe county of a large metropolitan area were among the predictors of readmission in patients discharged to an SNF, while high household income was protective. CONCLUSIONS: Discharge to an SNF compared to patients discharged home was associated with a higher readmission. Future studies need to identify the patient and facility factors responsible for this disparity.

Healthcare utilization 9 months pre- and post- COVID-19 hospitalization among patients discharged alive.

BACKGROUND: Emerging evidence suggests that there is an increase in healthcare utilization (HCU) in patients due to Coronavirus Disease 2019 (COVID-19). We investigated the change in HCU pre and post hospitalization among patients discharged home from COVID-19 hospitalization for up to 9 months of follow up. STUDY DESIGN AND METHODS: This retrospective study from a United States cohort used Optum® de-identified Clinformatics Data Mart; it included adults discharged home post hospitalization with primary diagnosis of COVID-19 between April 2020 and March 2021. We evaluated HCU of patients 9 months pre and post -discharge from index hospitalization. We defined HCU as emergency department (ED), inpatient, outpatient (office), rehabilitation/skilled nursing facility (SNF), telemedicine visits, and length of stay, expressed as number of visits per 10,000 person-days. RESULTS: We identified 63,161 patients discharged home after COVID-19 hospitalization. The cohort of patients was mostly white (58.8%) and women (53.7%), with mean age 72.4 (SD± 12) years. These patients were significantly more likely to have increased HCU in the 9 months post hospitalization compared to the 9 months prior. Patients had a 47%, 67%, 65%, and 51% increased risk of ED (rate ratio 1.47; 95% CI 1.45-1.49; p < .0001), rehabilitation (rate ratio 1.67; 95% CI 1.61-1.73; p < .0001), office (rate ratio1.65; 95% CI 1.64-1.65; p < .0001), and telemedicine visits (rate ratio 1.5; 95% CI 1.48-1.54; p < .0001), respectively. We also found significantly different rates of HCU for women compared to men (women have higher risk of ED, rehabilitation, and telemedicine visits but a lower risk of inpatient visits, length of stay, and office visits than men) and for patients who received care in the intensive care unit (ICU) vs those who did not (ICU patients had increased risk of ED, inpatient, office, and telemedicine visits and longer length of stay but a lower risk of rehabilitation visits). Outpatient (office) visits were the highest healthcare service utilized post discharge (64.5% increase). Finally, the risk of having an outpatient visit to any of the specialties studied significantly increased post discharge. Interestingly, the risk of requiring a visit to pulmonary medicine was the highest amongst the specialties studied (rate ratio 3.35, 95% CI 3.26-3.45, p < .0001). CONCLUSION: HCU was higher after index hospitalization compared to 9 months prior among patients discharged home post-COVID-19 hospitalization. The increases in HCU may be driven by those patients who received care in the ICU.

Clinical Outcomes After Admission of Patients With COVID-19 to Skilled Nursing Facilities.

Importance: During the COVID-19 pandemic, stabilized COVID-19-positive patients were discharged to skilled nursing facilities (SNFs) to alleviate hospital crowding. These discharges generated controversy due to fears of seeding outbreaks, but there is little empirical evidence to inform policy. Objective: To assess the association between the admission to SNFs of COVID-19-positive patients and subsequent COVID-19 cases and death rates among residents. Design, Setting, and Participants: This cohort study analyzed survey data from the National Healthcare Safety Network of the Centers for Disease Control and Prevention. The cohort included SNFs in the US from June 2020 to March 2021. Exposed facilities (ie, with initial admission of COVID-19-positive patients) were matched to control facilities (ie, without initial admission of COVID-19-positive patients) in the same county and with similar preadmission case counts. Data were analyzed from June 2023 to February 2024. Exposure: The week of the first observable admission of COVID-19-positive patients (defined as those previously diagnosed with COVID-19 and continued to require transmission-based precautions) during the study period. Main Outcomes and Measures: Weekly counts of new cases of COVID-19, COVID-19-related deaths, and all-cause deaths per 100 residents in the week prior to the initial admission. A stacked difference-in-differences approach was used to compare outcomes for 10 weeks before and 15 weeks after the first admission. Additional analyses examined whether outcomes differed in facilities with staff or personal protective equipment (PPE) shortages. Results: A matched group of 264 exposed facilities and 518 control facilities was identified. Over the 15-week follow-up period, exposed SNFs had a cumulative increase of 6.94 (95% CI, 2.91-10.98) additional COVID-19 cases per 100 residents compared with control SNFs, a 31.3% increase compared with the sample mean (SD) of 22.2 (26.4). Exposed facilities experienced 2.31 (95% CI, 1.39-3.24) additional cumulative COVID-19-related deaths per 100 residents compared with control facilities, representing a 72.4% increase compared with the sample mean (SD) of 3.19 (5.5). Exposed facilities experiencing potential staff shortage and PPE shortage had larger increases in COVID-19 cases per 100 residents (additional 10.97 [95% CI, 2.76-19.19] cases and additional 14.81 [95% CI, 2.38-27.25] cases, respectively) compared with those without such shortages. Conclusion: This cohort study suggests that admission of COVID-19-positive patients into SNFs early in the pandemic was associated with preventable COVID-19 cases and mortality among residents, particularly in facilities with potential staff and PPE shortages. The findings speak to the importance of equipping SNFs to adhere to infection-control best practices as they continue to face COVID-19 strains and other respiratory diseases.

Use of electronic health record data to examine administrations of pro re nata analgesics during hip fracture post-acute care.

PURPOSE: Medications prescribed to older adults in US skilled nursing facilities (SNF) and administrations of pro re nata (PRN) "as needed" medications are unobservable in Medicare insurance claims. There is an ongoing deficit in our understanding of medication use during post-acute care. Using SNF electronic health record (EHR) datasets, including medication orders and barcode medication administration records, we described patterns of PRN analgesic prescribing and administrations among SNF residents with hip fracture. METHODS: Eligible participants resided in SNFs owned by 11 chains, had a diagnosis of hip fracture between January 1, 2018 to August 2, 2021, and received at least one administration of an analgesic medication in the 100 days after the hip fracture. We described the scheduling of analgesics, the proportion of available PRN doses administered, and the proportion of days with at least one PRN analgesic administration. RESULTS: Among 24 038 residents, 57.3% had orders for PRN acetaminophen, 67.4% PRN opioids, 4.2% PRN non-steroidal anti-inflammatory drugs, and 18.6% PRN combination products. The median proportion of available PRN doses administered per drug was 3%-50% and the median proportion of days where one or more doses of an ordered PRN analgesic was administered was 25%-75%. Results differed by analgesic class and the number of administrations ordered per day. CONCLUSIONS: EHRs can be leveraged to ascertain precise analgesic exposures during SNF stays. Future pharmacoepidemiology studies should consider linking SNF EHRs to insurance claims to construct a longitudinal history of medication use and healthcare utilization prior to and during episodes of SNF care.

Bundled payments lead to quality improvements in hospitals' skilled nursing facility referral networks.

OBJECTIVES: To assess whether hospitals participating in Medicare's Bundled Payments for Care Improvement (BPCI) program for joint replacement changed their referral patterns to favor higher-quality skilled nursing facilities (SNFs). STUDY DESIGN: Retrospective observational study using 2009-2015 inpatient and outpatient claims from a 20% sample of Medicare beneficiaries undergoing joint replacement in US hospitals (N = 146,074) linked with data from Medicare's BPCI program and Nursing Home Compare. METHODS: We ran fixed effect regression models regressing BPCI participation on hospital-SNF referral patterns (number of SNF discharges, number of SNF partners, and SNF referral concentration) and SNF quality (facility inspection survey rating, patient outcome rating, staffing rating, and registered nurse staffing rating). RESULTS: We found that BPCI participation was associated with a decrease in the number of SNF referrals and no significant change in the number of SNF partners or concentration of SNF partners. BPCI participation was associated with discharge to SNFs with a higher patient outcome rating by 0.04 stars (95% CI, 0.04-0.26). BPCI participation was not associated with improvements in discharge to SNFs with a higher facility survey rating (95% CI, -0.03 to 0.11), staffing rating (95% CI, -0.07 to 0.04), or registered nurse staffing rating (95% CI, -0.09 to 0.02). CONCLUSIONS: BPCI participation was associated with lower volume of SNF referrals and small increases in the quality of SNFs to which patients were discharged, without narrowing hospital-SNF referral networks.

Geriatric conditions and healthcare utilization among older adults living in subsidized housing.

BACKGROUND: Nearly 2.9 million older Americans with lower incomes live in subsidized housing. While regional and single-site studies show that this group has higher rates of healthcare utilization compared to older adults in the general community, little is known about healthcare utilization nationally nor associated risk factors. METHODS: We conducted a retrospective cohort study of Medicare beneficiaries aged ≥65 enrolled in the National Health and Aging Trends Study in 2011, linked to Medicare claims data, including individuals living in subsidized housing and the general community. Participants were followed annually through 2020. Outcomes were hospitalization, short-term skilled nursing facility (SNF) utilization, long-term care utilization, and death. Fine-Gray competing risks regression analysis was used to assess the association of subsidized housing residence with hospitalization and nursing facility utilization, and Cox proportional hazards regression analysis was used to assess the association with death. RESULTS: Among 6294 participants (3600 women, 2694 men; mean age, 75.5 years [SD, 7.0]), 295 lived in subsidized housing at baseline and 5999 in the general community. Compared to older adults in the general community, those in subsidized housing had a higher adjusted subdistribution hazard ratio [sHR] of hospitalization (sHR 1.21; 95% CI, 1.03-1.43), short-term SNF utilization (sHR 1.49; 95% CI, 1.15-1.92), and long-term care utilization (sHR 2.72; 95% CI, 1.67-4.43), but similar hazard of death (HR, 0.86; 95% CI, 0.69-1.08). Individuals with functional impairment had a higher adjusted subdistribution hazard of hospitalization and short-term SNF utilization and individuals with dementia and functional impairment had a higher hazard of long-term care utilization. CONCLUSIONS: Older adults living in subsidized housing have higher hazards of hospitalization and nursing facility utilization compared to those in the general community. Housing-based interventions to optimize aging in place and mitigate risk of nursing facility utilization should consider risk factors including functional impairment and dementia.

Interdisciplinary videoconference model for identifying potential adverse transition of care events following hospital discharge to postacute care.

Discharge from hospitals to postacute care settings is a vulnerable time for many older adults, when they may be at increased risk for errors occurring in their care. We developed the Extension for Community Healthcare Outcomes-Care Transitions (ECHO-CT) programme in an effort to mitigate these risks through a mulitdisciplinary, educational, case-based teleconference between hospital and skilled nursing facility providers. The programme was implemented in both academic and community hospitals. Through weekly sessions, patients discharged from the hospital were discussed, clinical concerns addressed, errors in care identified and plans were made for remediation. A total of 1432 discussions occurred for 1326 patients. The aim of this study was to identify errors occurring in the postdischarge period and factors that predict an increased risk of experiencing an error. In 435 discussions, an issue was identified that required further discussion (known as a transition of care event), and the majority of these were related to medications. In 14.7% of all discussions, a medical error, defined as 'any preventable event that may cause or lead to inappropriate medical care or patient harm', was identified. We found that errors were more likely to occur for patients discharged from surgical services or the emergency department (as compared with medical services) and were less likely to occur for patients who were discharged in the morning. This study shows that a number of errors may be detected in the postdischarge period, and the ECHO-CT programme provides a mechanism for identifying and mitigating these events. Furthermore, it suggests that discharging service and time of day may be associated with risk of error in the discharge period, thereby suggesting potential areas of focus for future interventions.

Interventional analytics in skilled nursing facilities associated with reduced readmissions.

OBJECTIVE: To assess differences in longitudinal profiles for 30-day risk-adjusted readmission rates in skilled nursing facilities (SNFs) associated with Penn Medicine's Lancaster General Hospital (LGH) that implemented an interventional analytics (IA) platform vs other LGH facilities lacking IA vs other SNFs in Pennsylvania vs facilities in all other states. STUDY DESIGN: Retrospective longitudinal analysis of CMS readmissions data from 2017 through 2022, and cross-sectional analysis using CMS quality metrics data. METHODS: CMS SNF quality performance data were aggregated and compared with risk-adjusted readmissions by facility and time period. Each SNF was assigned to a cohort based on location, referral relationship with LGH, and whether it had implemented IA. Multivariable mixed effects modeling was used to compare readmissions by cohort, whereas quality measures from the fourth quarter of 2022 were compared descriptively. RESULTS: LGH profiles differed significantly from both state and national profiles, with LGH facilities leveraging IA demonstrating an even greater divergence. In the most recent 12 months ending in the fourth quarter of 2022, LGH SNFs with IA had estimated readmission rates that were 15.24, 12.30, and 13.06 percentage points lower than the LGH SNFs without IA, Pennsylvania, and national cohorts, respectively (all pairwise P < .0001). SNFs with IA also demonstrated superior CMS claims-based quality metric outcomes for the 12 months ending in the fourth quarter of 2022. CONCLUSIONS: SNFs implementing the studied IA platform demonstrated statistically and clinically significant superior risk-adjusted readmission rate profiles compared with peers nationally, statewide, and within the same SNF referral network (P < .0001). A more detailed study on the use of IA in this setting is warranted.

The effect of dementia on patterns of healthcare use in older adults with diabetes.

BACKGROUND: For persons with diabetes, incidence of dementia has been associated with increased hospitalization; however, little is known about healthcare use preceding and following incident dementia. We describe healthcare utilization in the 3 years pre- and post-incident dementia among older adults with diabetes. METHODS: We used the National Health and Aging Trends Study (NHATS) linked to Medicare fee-for-service claims from 2011 to 2018. We included community-dwelling adults ≥65 years who had diabetes without dementia. We matched older adults with dementia (identified with validated NHATS algorithm) at the year of incident dementia to controls using coarsened exact matching. We examined annual outpatient visits, emergency department (ED) visits, hospitalization, and post-acute skilled nursing facility (SNF) use 3 years preceding and 3 years following dementia onset. RESULTS: We included 195 older adults with diabetes with incident dementia and 1107 controls. Groups had a similar age (81.6 vs 81.7 years) and were 56.4% female. Persons with dementia were more likely to be of minority racial and ethnic groups (26.7% vs 21.3% Black, non-Hispanic, 15.3% vs 6.7% other race or Hispanic). We observed a larger decrease in outpatient visits among persons with dementia, primarily due to decreasing specialty visits (mean outpatient visits: 3 years pre-dementia/matching 6.8 (SD 2.6) dementia vs 6.4 (SD 2.6) controls, p < 0.01 to 3 years post-dementia/matching 4.6 (SD 2.3) dementia vs 5.5 (SD 2.7) controls, p < 0.01). Hospitalization, ED visits, and post-acute SNF use were higher for persons with dementia and rose in both groups (e.g., ED visits 3 years pre-dementia/matching 3.9 (SD 5.4) dementia vs 2.2 (SD 4.8) controls, p < 0.001; 3 years post-dementia/matching 4.5 (SD 4.7) dementia vs 3.5 (SD 6.1) controls, p = 0.04). CONCLUSIONS: Older adults with diabetes with incident dementia have higher rates of acute and post-acute care use, but decreasing outpatient use over time, primarily due to a decrease in specialty visits.

Discharge to a Skilled Nursing Facility After Hip Fracture Results in Higher Rates of Periprosthetic Joint Infection.

BACKGROUND: Femoral neck fractures (FNFs) in elderly patients are associated with major morbidity and mortality. The influence of postoperative discharge location on recovery and outcomes after arthroplasty for hip fractures is not well understood. METHODS: A multisite retrospective cohort from 9 academic centers identified patients who had FNF treated with hemiarthroplasty or total hip arthroplasty between 2010 and 2019. Patients who had diagnoses of dementia, stroke, age > 80 years, or high energy fracture were excluded. Discharge location was identified, including home-based health services (HHS), inpatient rehabilitation (IPR), or a skilled nursing facility (SNF). Rates of reoperation, periprosthetic joint infection (PJI), and mortality were compared between cohorts. Multivariate logistic regressions were performed, adjusting for age, American Society of Anesthesiologists (ASA) score, body mass index, sex, and tobacco use. Statistical significance was defined as P < .05. RESULTS: A total of 672 patients (315 HHS, 144 IPR, and 213 SNF) were included in this study. The average follow-up was 30 months. The SNF cohort was significantly older (P < .0001) with higher ASA scores (P < .0001) than the HHS cohort. In a logistic regression model adjusting for age, ASA score, and body mass index, the SNF cohort had higher mortality rates than the HHS cohort (P = .0296) and were more likely to have PJI within 90 days (odds ratio = 4.55, 95% confidence interval = 1.40, 4.74) and within 1 year (odds ratio = 3.08, 95% confidence interval = 1.08, 8.78). Time to PJI was significantly shorter in the SNF cohort (SNF 38 versus HHS 231 days, P = .0155). No differences were seen in dislocation or reoperation rates between the SNF and HHS cohorts. No differences were seen in complication rates between the IPR and HHS cohorts. CONCLUSIONS: Discharge to a SNF after arthroplasty for FNF is associated with increased mortality and higher rates of PJI. Hip fracture care pathways that uniformly discharge patients to SNFs may need to be re-evaluated, and surgeons should consider discharge to home with HHS when possible.

Medicare Advantage growth and skilled nursing facility finances.

OBJECTIVE: To examine the relationship between growth in Medicare Advantage (MA) enrollment and changes in finances at skilled nursing facilities (SNFs). DATA SOURCES: Medicare SNF cost reports, LTCFocus.org data, and county MA penetration rates. STUDY DESIGN: We used ordinary least squares regression with SNF and year fixed effects. Our primary outcomes were SNF revenues, expenses, profits, and occupancy. Our primary independent variable was the yearly county Medicare Advantage penetration. DATA COLLECTION/EXTRACTION: We linked facility-year data from 2012 to 2019 obtained from cost reports and LTCFocus.org to county-year MA penetration. PRINCIPAL FINDINGS: A 10 percentage point increase in county MA enrollment was associated with a $213,883.89 (95% Confidence Interval [CI]: -296,869.08, -130,898.71) decrease in revenue, a $132,456.19 (95% CI: -203,852.28, -61,060.10) decrease in expenses, and a 0.59 percentage point (95% CI: -0.97, -0.21) decrease in profit margin. A 10 percentage point increase in county MA enrollment was associated with a decline (-318.93; 95% CI: -468.84, -169.02) in the number of resident-days (a measure of occupancy) as well as a decline in the revenue per resident day ($4.50; 95% CI: -6.81, -2.20), potentially because of lower prices in MA. There was also a decline in expenses per patient day (-2.35; 95% CI: -4.76, 0.05), though this was only statistically significant at the 10% level. While increased MA enrollment was associated with a substantial decline in the number of Medicare resident days (487.53; 95% CI: -588.70, -386.37), this was partially offset by an increase in other payer (e.g., private pay) resident days (285.91; 95% CI: 128.18, 443.63). Increased MA enrollment was not associated with changes in the number of Medicaid resident days or a decrease in staffing per resident day. CONCLUSION: SNFs in counties with more MA growth had substantially greater relative declines in revenue, expenses, and profit margins. The continued growth of MA may result in significant changes in the SNF industry.

The association between opioid use disorder and skilled nursing facility acceptances: A multicenter retrospective cohort study.

BACKGROUND: Prior single-hospital studies have documented barriers to acceptance that hospitalized patients with opioid use disorder (OUD) face when referred to skilled nursing facilities (SNFs). OBJECTIVE: To examine the impact of OUD on the number of SNF referrals and the proportion of referrals accepted. DESIGN, SETTINGS, AND PARTICIPANTS: A retrospective cohort study of hospitalizations with SNF referrals in 2019 at two academic hospitals in Baltimore, MD. EXPOSURE: OUD status was determined by receipt of medications for OUD during admission, upon discharge, or the presence of a diagnosis code for OUD. KEY RESULTS: The cohort included 6043 hospitalizations (5440 hospitalizations of patients without OUD and 603 hospitalizations of patients with OUD). Hospitalizations of patients with OUD had more SNF referrals sent (8.9 vs. 5.6, p < .001), had a lower proportion of SNF referrals accepted (31.3% vs. 46.9%, p < .001), and were less likely to be discharged to an SNF (65.6% vs. 70.3%, p = .003). The effect of OUD status on the number of SNF referrals and the proportion of referrals accepted remained significant in multivariable analyses. Our subanalysis showed that reduced acceptances were driven by the hospitalizations of patients discharged without medications for OUD and those receiving methadone. Hospitalizations of patients discharged on buprenorphine were accepted at the same rates as hospitalizations of patients without OUD. CONCLUSIONS: This multicenter retrospective cohort study found that hospitalizations of patients with OUD had more SNF referrals sent and fewer referrals accepted. Further work is needed to address the limited discharge options for patients with OUD.

Post-acute care transitions during COVID-19: Racial, ethnic, and socioeconomic differences in older adults with Alzheimer's disease and related dementia.

BACKGROUND: Differences in the post-acute care (PAC) destinations among racial, ethnic, and socioeconomic groups have been documented before the COVID-19 pandemic. Yet, the pandemic's impact on these differences remains unknown. We examined the impact of the COVID-19 pandemic on PAC destinations and its variation by individual race, ethnicity, and socioeconomic status among community-dwelling older adults with Alzheimer's disease and related dementia (ADRD). METHODS: We linked 2019-2021 national data (Medicare claims, Minimum Data Set, Master Beneficiary Summary File) and several publicly available datasets, including Provider of Services File, Area Deprivation Index, Area Health Resource File, and COVID-19 infection data. PAC discharge destinations included skilled nursing facilities (SNFs), home health agencies (HHA), and homes without services. Key variables of interest included individual race, ethnicity, and Medicare-Medicaid dual status. The analytic cohort included 830,656 community-dwelling Medicare fee-for-service beneficiaries with ADRD who were hospitalized between 2019 and 2021. Regression models with hospital random effects and state-fixed effects were estimated, stratified by the time periods, and adjusted for the individual, hospital, and county-level covariates. RESULTS: SNF discharges decreased while home and HHA discharges increased during the pandemic. The trend was more prominent among racial and ethnic minoritized groups and even more so among dual-eligible beneficiaries. For instance, the reduction in the probabilities of SNF admissions between the pre-pandemic period and the 2nd year of COVID was 4.6 (White non-duals), 18.5 (White duals), 8.7 (Black non-duals), and 20.1 (Black duals) percentage-point, respectively. We also found that non-duals were more likely to replace SNF with HHA services, while duals were more likely to be discharged home without HHA. CONCLUSIONS: The COVID-19 pandemic significantly impacted PAC destinations for individuals with ADRD, especially among socioeconomically disadvantaged and racial and ethnic minoritized populations. Future research is needed to understand if and how these transitions may have affected health outcomes.

Longitudinal Care Network Changes and Associated Healthcare Utilization Among Care Recipients.

This study examines caregiver networks, including size, composition, and stability, and their associations with the likelihood of hospitalization and skilled-nursing facility (SNF) admissions. Data from the National Health and Aging Trends Study linked to Center for Medicare and Medicaid Services data were analyzed for 3855 older adults across five survey waves. Generalized estimating equation models assessed the associations. The findings indicate each additional paid caregiver was associated with higher adjusted risk ratios (aRR) for hospitalization (aRR = 1.24, 95% CI 1.10-1.41) and SNF admission (aRR = 1.28, 95% CI 1.06-1.54) among care recipients, a pattern that is also observed with the addition of unpaid caregivers (hospitalization: aRR = 1.13, 95% CI 1.06-1.20; SNF: aRR = 1.12, 95% CI 1.02-1.23). These results suggest that policies and approaches to enhance the quality and coordination of caregivers may be warranted to support improved outcomes for care recipients.

High Risk of Readmission After THA Regardless of Functional Status in Patients Discharged to Skilled Nursing Facility.

BACKGROUND: The postoperative period and subsequent discharge planning are critical in our continued efforts to decrease the risk of complications after THA. Patients discharged to skilled nursing facilities (SNFs) have consistently exhibited higher readmission rates compared with those discharged to home healthcare. This elevated risk has been attributed to several factors but whether readmission is associated with patient functional status is not known. QUESTIONS/PURPOSES: After controlling for relevant confounding variables (functional status, age, gender, caregiver support available at home, diagnosis [osteoarthritis (OA) versus non-OA], Charlson comorbidity index [CCI], the Area Deprivation Index [ADI], and insurance), are the odds of 30- and 90-day hospital readmission greater among patients initially discharged to SNFs than among those treated with home healthcare after THA? METHODS: This was a retrospective, comparative study of patients undergoing THA at any of 11 hospitals in a single, large, academic healthcare system between 2017 and 2022 who were discharged to an SNF or home healthcare. During this period, 13,262 patients were included. Patients discharged to SNFs were older (73 ± 11 years versus 65 ± 11 years; p < 0.001), less independent at hospital discharge (6-click score: 16 ± 3.2 versus 22 ± 2.3; p < 0.001), more were women (71% [1279 of 1796] versus 56% [6447 of 11,466]; p < 0.001), insured by Medicare (83% [1497 of 1796] versus 52% [5974 of 11,466]; p < 0.001), living in areas with greater deprivation (30% [533 of 1796] versus 19% [2229 of 11,466]; p < 0.001), and had less assistance available from at-home caregivers (29% [527 of 1796] versus 57% [6484 of 11,466]; p < 0.001). The primary outcomes assessed in this study were 30- and 90-day hospital readmissions. Although the system automatically flags readmissions occurring within 90 days at the various facilities in the overall healthcare system, readmissions occurring outside the system would not be captured. Therefore, we were not able to account for potential differential rates of readmission to external healthcare systems between the groups. However, given the large size and broad geographic coverage of the healthcare system analyzed, we expect the readmissions data captured to be representative of the study population. The focus on a single healthcare system also ensures consistency in readmission identification and reporting across subjects. We evaluated the association between discharge disposition (home healthcare versus SNF) and readmission. Covariates evaluated included age, gender, primary payer, primary diagnosis, CCI, ADI, the availability of at-home caregivers for the patient, and the Activity Measure for Post-Acute Care (AM-PAC) 6-clicks basic mobility score in the hospital. The adjusted relative risk (ARR) of readmission within 30 and 90 days of discharge to SNF (versus home healthcare) was estimated using modified Poisson regression models. RESULTS: After adjusting for the 6-clicks mobility score, age, gender, ADI, OA versus non-OA, living environment, CCI, and insurance, patients discharged to an SNF were more likely to be readmitted within 30 and 90 days compared with home healthcare after THA (ARR 1.46 [95% CI 1.01 to 2.13]; p= 0.046 and ARR 1.57 [95% CI 1.23 to 2.01]; p < 0.001, respectively). CONCLUSION: Patients discharged to SNFs after THA had a slightly higher likelihood of hospital readmission within 30 and 90 days compared with those discharged with home healthcare. This difference persisted even after adjusting for relevant factors like functional status, home support, and social determinants of health. These results indicate that for suitable patients, direct home discharge may be a safer and more cost-effective option than SNFs. Clinicians should carefully consider these risks and benefits when making postoperative discharge plans. Policymakers could consider incentives and reforms to improve care transitions and coordination across settings. Further research using robust methods is needed to clarify the reasons for higher SNF readmission rates. Detailed analysis of patient complexity, care processes, and causes of readmission in SNFs versus home health could identify areas for quality improvement. Prospective cohorts or randomized trials would allow stronger conclusions about cause-and-effect. Importantly, no patients should be unfairly "cherry-picked" or "lemon-dropped" based only on readmission risk scores. With proper support and care coordination, even complex patients can have good outcomes. The goal should be providing excellent rehabilitation for all, while continuously improving quality, safety, and value across settings. LEVEL OF EVIDENCE: Level III, therapeutic study.

A Risk Prediction Model for Physical Restraints Among Older Chinese Adults in Long-term Care Facilities: Machine Learning Study.

BACKGROUND: Numerous studies have identified risk factors for physical restraint (PR) use in older adults in long-term care facilities. Nevertheless, there is a lack of predictive tools to identify high-risk individuals. OBJECTIVE: We aimed to develop machine learning (ML)-based models to predict the risk of PR in older adults. METHODS: This study conducted a cross-sectional secondary data analysis based on 1026 older adults from 6 long-term care facilities in Chongqing, China, from July 2019 to November 2019. The primary outcome was the use of PR (yes or no), identified by 2 collectors' direct observation. A total of 15 candidate predictors (older adults' demographic and clinical factors) that could be commonly and easily collected from clinical practice were used to build 9 independent ML models: Gaussian Naïve Bayesian (GNB), k-nearest neighbor (KNN), decision tree (DT), logistic regression (LR), support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), extreme gradient boosting (XGBoost), and light gradient boosting machine (Lightgbm), as well as stacking ensemble ML. Performance was evaluated using accuracy, precision, recall, an F score, a comprehensive evaluation indicator (CEI) weighed by the above indicators, and the area under the receiver operating characteristic curve (AUC). A net benefit approach using the decision curve analysis (DCA) was performed to evaluate the clinical utility of the best model. Models were tested via 10-fold cross-validation. Feature importance was interpreted using Shapley Additive Explanations (SHAP). RESULTS: A total of 1026 older adults (mean 83.5, SD 7.6 years; n=586, 57.1% male older adults) and 265 restrained older adults were included in the study. All ML models performed well, with an AUC above 0.905 and an F score above 0.900. The 2 best independent models are RF (AUC 0.938, 95% CI 0.914-0.947) and SVM (AUC 0.949, 95% CI 0.911-0.953). The DCA demonstrated that the RF model displayed better clinical utility than other models. The stacking model combined with SVM, RF, and MLP performed best with AUC (0.950) and CEI (0.943) values, as well as the DCA curve indicated the best clinical utility. The SHAP plots demonstrated that the significant contributors to model performance were related to cognitive impairment, care dependency, mobility decline, physical agitation, and an indwelling tube. CONCLUSIONS: The RF and stacking models had high performance and clinical utility. ML prediction models for predicting the probability of PR in older adults could offer clinical screening and decision support, which could help medical staff in the early identification and PR management of older adults.