Use of a Computerized Algorithm to Evaluate the Proportion and Causes of Potentially Preventable Readmissions Among Patients Discharged to Skilled Nursing Facilities

ObjectivesOlder patients discharged to skilled nursing facilities (SNFs) for post-acute care are at high risk for hospital readmission. Yet, as in the community setting, some readmissions may be preventable with optimal transitional care. This study examined the proportion of 30-day hospital readmissions from SNFs that could be considered potentially preventable readmissions (PPRs) and evaluated the reasons for these readmissions.DesignRetrospective cohort study.Setting and ParticipantsPost-acute practice of an integrated health care delivery system serving 11 SNFs in the US Midwest. Patients discharged from the hospital to an SNF and subsequently readmitted to the hospital within 30 days from January 1, 2009, through November 31, 2016.MethodsA computerized algorithm evaluated the relationship between initial and repeat hospitalizations to determine whether the repeat hospitalization was a PPR. We assessed for changes in PPR rates across the system over the study period and evaluated the readmission categories to identify the most prevalent PPR categories.ResultsOf 11,976 discharges to SNFs for post-acute care among 8041 patients over the study period, 16.6% resulted in rehospitalization within 30 days, and 64.8% of these rehospitalizations were considered PPRs. Annual proportion of PPRs ranged from 58.2% to 66.4% [mean (standard deviation) 0.65 (0.03); 95% confidence interval CI 0.63-0.67; P = .36], with no discernable trend. Nearly one-half (46.2%) of all 30-day readmissions were classified as potentially preventable medical readmissions related to recurrence or continuation of the reason for initial admission or to complications from the initial hospitalization.Conclusions and ImplicationsFor this cohort of patients discharged to SNFs, a computerized algorithm categorized a large proportion of 30-day hospital readmissions as potentially preventable, with nearly one-half of those linked to the reason for the initial hospitalization. These findings indicate the importance of improvement in postdischarge transitional care for patients discharged to SNFs.     

Functional Status Across Post-Acute Settings is Associated With 30-Day and 90-Day Hospital Readmissions

ObjectiveTo examine the association between cocalibrated functional scores across post-acute care settings and the subsequent risk of hospital readmission.DesignRetrospective cohort study.Setting and ParticipantsWe analyzed 781,021 fee-for-service Medicare beneficiaries discharged to either inpatient rehabilitation facilities (IRFs), skilled nursing facilities (SNFs), or home health agencies (HHA) after an acute hospital stay for stroke (N = 143,277), lower extremity joint replacements (512,577), and hip/femur fracture (125,167) between January 1, 2013, and August 31, 2014.MeasuresFunctional items from IRF-PAI, MDS, and OASIS were categorized into self-care and mobility domains. We cocalibrated admission functional scores across post-acute settings and divided scores into 4 functional levels using quartiles (Q1-Q4, with Q4 representing the most independent function). The primary outcomes were 30-day and 90-day hospital readmissions (yes/no) after an initial post-acute stay.ResultsPatients who were more dependent in self-care and mobility at the initial post-acute setting were significantly more likely to experience hospital readmission [eg, hazard ratios of 30-day readmission in stroke: 1.54 (95% confidence interval [CI] 1.47-1.61), 1.18 (95% CI 1.14-1.23), and 1.12 (95% CI 1.08-1.16) for Q1, Q2 and Q3, compared to Q4]. We found similar results for risk of 90-day hospital readmission across impairment conditions.Conclusions and ImplicationsPatients who were more functionally dependent at the initial post-acute setting had a higher risk to readmit to the hospitals after discharging from the post-acute setting for 30 and 90 days, compared with patients who were more functionally independent. This finding is consistent across impairment conditions and post-acute settings. Future research should determine effective strategies of maintaining and facilitating functional performance across post-acute settings to optimize long-term patient outcomes.     

Machine-Learning Modeling to Predict Hospital Readmission Following Discharge to Post-Acute Care

ObjectivesPrimary purpose was to generate a model to identify key factors relevant to acute care hospital readmission within 90 days from 3 types of post-acute care (PAC) sites: home with home care services (HC), skilled nursing facility (SNF), and inpatient rehabilitation facility (IRF). Specific aims were to (1) examine demographic characteristics of adults discharged to 3 types of PAC sites and (2) compare 90-day acute hospital readmission rate across PAC sites and risk levels.DesignRetrospective, secondary analysis design was used to examine hospital readmissions within 90 days for persons discharged from hospital to SNF, IRF, or HC.Settings and ParticipantsCohort sample was composed of 2015 assessment data from 3,592,995 Medicare beneficiaries, including 1,536,908 from SNFs, 306,878 from IRFs, and 1,749,209 patients receiving HC services.MeasuresInitial level of analysis created multiple patient profiles based on predictive patient characteristics. Second level of analysis consisted of multiple logistic regressions within each profile to create predictive algorithms for likelihood of readmission within 90 days, based on risk profile and PAC site.ResultsTotal sample 90-day hospital readmission rate was 27.48%. Patients discharged to IRF had the lowest readmission rate (23.34%); those receiving HC services had the highest rate (31.33%). Creation of model risk subgroups, however, revealed alternative outcomes. Patients seem to do best (i.e., lowest readmission rates) when discharged to SNF with one exception, those in the very high risk group. Among all patients in the low-, intermediate-, and high-risk groups, the lowest readmission rates occurred among SNF patients.Conclusions and ImplicationsThe proposed model has potential use to stratify patients’ potential risk for readmission as well as optimal PAC destination. Machine-learning modeling with large data sets is a useful strategy to increase the precision accuracy in predicting outcomes among patients who have nonhome discharges from the hospital.     

Risk of 30-Day Hospital Readmission Among Patients Discharged to Skilled Nursing Facilities: Development and Validation of a Risk-Prediction Model

Objectives

Patients discharged to a skilled nursing facility (SNF) for post-acute care have a high risk of hospital readmission. We aimed to develop and validate a risk-prediction model to prospectively quantify the risk of 30-day hospital readmission at the time of discharge to a SNF.

Functional Recovery Rate: A Feasible Method for Evaluating and Comparing Rehabilitation Outcomes Between Skilled Nursing Facilities

ObjectivesThe recovery of patients' physical function and the rate at which this occurs are important parameters for evaluating value in post-acute care (PAC). However, no metrics are presently used to compare skilled nursing facilities (SNFs) based on the functional recovery rates (FRRs) for patients in their care. The objectives of this study were to examine whether the average FRR differed significantly among SNFs and to compare the FRR to other measures currently used to assess care quality in SNFs.DesignRetrospective observational study.Setting and Participants3913 patients discharged from hospitals in one health system to one of 10 partner SNFs between January 2017 and September 2019.MethodsThe FRR—the difference in Activity Measure for Post-Acute Care 6-Clicks basic mobility score from SNF admission to discharge relative to the SNF length of stay (in days)—was the primary outcome. Secondary outcomes included metrics from the SNF Quality Reporting Program (functional recovery alone, discharge to the community, and 30-day hospital readmission). Differences in patients' outcomes between SNFs were tested using multiple regression in order to adjust for patient characteristics.ResultsAcross the 10 SNFs, the highest adjusted mean FRR was 0.70 [95% confidence interval (CI): 0.55, 0.90] and the lowest was 0.39 (95% CI: 0.33, 0.46) points per day. Two SNFs had an adjusted mean FRR statistically higher, and 2 had an FRR statistically lower, than the sample mean (0.50, 95% CI: 0.48-0.52). SNF rankings varied by metric.Conclusions and ImplicationsIndividual SNFs vary in their mean FRR for patients making it a potentially useful measure of value for comparing SNFs. Standardized measurement and reporting of FRR could be beneficial to patients and their families as they consider specific SNFs for necessary post-acute rehabilitation and to hospital systems seeking to identify high-value PAC providers with whom to partner in collaborative care models.     

Derivation and Validation of a Model to Predict 30-Day Readmission in Surgical Patients Discharged to Skilled Nursing Facility

ObjectivesTo identify factors associated with 30-day all-cause readmission rates in surgical patients discharged to skilled nursing facilities (SNFs), and derive and validate a risk score.DesignRetrospective cohort.Setting and participantsPatients admitted to 1 tertiary hospital's surgical services between January 1, 2011, and December 31, 2014 and subsequently discharged to 110 SNFs within a 25-mile radius of the hospital. The first 2 years were used for the derivation set and the last 2 for validation.MethodsData were collected on 30-day all cause readmissions, patient demographics, procedure and surgical service, comorbidities, laboratory tests, and prior health care utilization. Multivariate regression was used to identify risk factors for readmission.ResultsDuring the study period, 2405 surgical patients were discharged to 110 SNFs, and 519 (21.6%) of these patients experienced readmission within 30 days. In a multivariable regression model, hospital length of stay [odds ratio (OR) per day: 1.03, 95% confidence interval (CI) 1.02-1.04], number of hospitalizations in past year (OR 1.24 per hospitalization, 95% CI 1.18-1.31), nonelective surgery (OR 1.33, 95% CI 1.18-1.65), low-risk service (orthopedic/spine service) (OR 0.32, 95% CI 0.25-0.42), and intermediate-risk service (cardiothoracic surgery/urology/gynecology/ear, nose, throat) (OR 0.69, 95% CI 0.53-0.88) were associated with all-cause readmissions. The model had a C index of 0.71 in the validation set. Using the following risk score [0.8 × (hospital length of stay) + 7 × (number of hospitalizations in past year) +10 for nonelective surgery, +36 for high-risk surgery, and +20 for intermediate-risk surgery], a score of >40 identified patients at high risk of 30-day readmission (35.8% vs 12.6%, P < .001).Conclusions/ImplicationsAmong surgical patients discharged to an SNF, a simple risk score with 4 parameters can accurately predict the risk of 30-day readmission.     

Reducing Hospital Readmissions Through a Skilled Nursing Facility Discharge Intervention: A Pragmatic Trial

ObjectiveTo determine if implementation of Project Re-Engineered Discharge (RED), designed for hospitals but adapted for skilled nursing facilities (SNFs), reduces hospital readmissions after SNF discharge to the community in residents admitted to the SNF following an index hospitalization.DesignA pragmatic trial.Setting and participantsSNFs in southeastern Massachusetts, and residents discharged to the community.MethodsWe compared SNFs that deployed an adapted RED intervention to a matched control group from the same region. The primary outcome was hospital readmission within 30 days after SNF discharge, among residents who had been admitted to the SNF following an index hospitalization and then discharged home. January 2016 through March 2017 was the baseline period; April 2017 through June 2018 was the follow-up period (after implementation of the intervention). We used a difference-in-differences analysis to compare the intervention SNFs to the control group, using generalized estimating equation regression and controlling for facility characteristics.ResultsAfter implementation of RED, readmission rates were lower across all 4 measures in the intervention group; control facilities’ readmission rates remained stable or increased. The relative decrease was 0.9% for the primary outcome of hospital readmission within 30 days after SNF discharge and 1.7% for readmission within 30 days of the index hospitalization discharge date (P ≤ .001 for both comparisons).Conclusions and ImplicationsWe found that a systematic discharge process developed for the hospital can be adapted to the SNF environment and can reduce readmissions back to the hospital, perhaps through improved self-management skills and better engagement with community services. This work is particularly timely because of Medicare's new Value-Based Purchasing Program, in which nursing homes can receive incentive payments if their hospital readmission rates are low relative to their peers. To verify its scalability and broad potential, RED should be validated across a broader diversity of SNFs nationally.     

Readmission Patterns Over 90-Day Episodes of Care Among Medicare Fee-for-Service Beneficiaries Discharged to Post-acute Care

Objective

Examine readmission patterns over 90-day episodes of care in persons discharged from hospitals to post-acute settings.

Prioritizing Partners Across the Continuum

With the advent of accountable care organizations, bundled payments, value-based purchasing, and penalties for preventable hospital readmission, tight connections and collaboration across the care continuum will become critical to achieve successful patient outcomes and to reduce the cost of care delivery.Lehigh Valley Health Network (LVHN), the largest provider of health services in eastern Pennsylvania, set out on a journey to build collaborative relationships with skilled nursing facilities (SNFs) in their eastern Pennsylvania community. LVHN desired SNF partners with mutual interests in improving quality of care and lowering costs of delivery where possible.Recognizing that not all SNFs are alike, LVHN developed a Collaborative Partner Prioritization Tool to assess and prioritize skilled nursing facilities in an effort to determine those that would make the best collaborators. SNFs were reviewed based on their volume of mutual patients, quality of care delivery, and their perceived willingness to align with LVHN. Six variables were used to assess these facilities, including (1) patient discharge destination volume by SNF; (2) 30-day all-cause readmission rate to an LVHN hospital; (3) Medicare’s Nursing Home Compare 5-Star Overall Rating; (4) the health network affiliation of the SNF’s medical director; (5) the level of LVHN-employed or -affiliated physician presence at the SNF; and (6) the SNF’s current participation in LVHN-sponsored programs and meetings.Through use of the Collaborative Partner Prioritization Tool, it was discovered that roughly 70% of LVHN patients who required skilled nursing care following their inpatient stay received care at 1 of 20 SNFs. Of these, 5 facilities performed well on the 6-variable assessment, deeming them the “Tier 1 Facilities” to initially focus collaborative efforts.LVHN has strategically deployed physician resources and has increased physician presence at these “Tier 1 SNFs.” These facilities have also gained remote read-only access to LVHN’s inpatient electronic medical record and have had opportunity to participate in LVHN-sponsored programs. Special projects have been co-developed with several SNFs, including a telemedicine-based Parkinson’s disease program to increase patient access to a neurologist specially trained in movement disorders.The Collaborative Partner Prioritization Tool has become a powerful tool when used for prioritization of relationships and allocation of LVHN physicians and resources. Collaboration with strong SNF partners has offered a shared opportunity to improve quality of care, reduce costs, and prepare for the many policies affecting the health care industry.Future outcomes of this work will include quality metrics, such as readmissions, patient satisfaction with care, time for decision to admit, and overall costs of care. The data and metrics used to define the prioritization tool will continue to be adapted as the post-acute market and hospital-SNF relationships continue to evolve.     

Passing the SNF Test: A Secondary Analysis of a Sepsis Transition Intervention Trial Among Patients Discharged to Post-Acute Care

ObjectivesSepsis survivors discharged to post-acute care facilities experience high rates of mortality and hospital readmission. This study compared the effects of a Sepsis Transition and Recovery (STAR) program vs usual care (UC) on 30-day mortality and hospital readmission among sepsis survivors discharged to post-acute care.DesignSecondary analysis of a multisite pragmatic randomized clinical trial.Setting and ParticipantsSepsis survivors discharged to post-acute care.MethodsWe conducted a secondary analysis of patients from the IMPACTS (Improving Morbidity During Post-Acute Care Transitions for Sepsis) randomized clinical trial who were discharged to post-acute care. IMPACTS evaluated the effectiveness of STAR, a nurse-navigator-led program to deliver best practice post-sepsis care. Subjects were randomized to receive either STAR or UC. The primary outcome was 30-day readmission and mortality. We also evaluated hospital-free days alive as a secondary outcome.ResultsOf 691 patients enrolled in IMPACTS, 175 (25%) were discharged to post-acute care [143 (82%) to skilled nursing facilities, 12 (7%) to long-term acute care hospitals, and 20 (11%) to inpatient rehabilitation]. Of these, 87 received UC and 88 received the STAR intervention. The composite 30-day all-cause mortality and readmission endpoint occurred in 26 (29.9%) patients in the UC group vs 18 (20.5%) in the STAR group [risk difference −9.4% (95% CI −22.2 to 3.4); adjusted odds ratio 0.58 (95% CI 0.28 to 1.17)]. Separately, 30-day all-cause mortality was 8.1% in the UC group compared with 5.7% in the STAR group [risk difference −2.4% (95% CI −9.9 to 5.1)] and 30-day all-cause readmission was 26.4% in the UC group compared with 17.1% in the STAR program [risk difference −9.4% (95% CI −21.5 to 2.8)].Conclusions and ImplicationsThere are few proven interventions to reduce readmission among patients discharged to post-acute care facilities. These results suggest the STAR program may reduce 30-day mortality and readmission rates among sepsis survivors discharged to post-acute care facilities.     

Risk Prediction Model for 6-Month Mortality for Patients Discharged to Skilled Nursing Facilities

ObjectiveHospitalized patients discharged to skilled nursing facilities (SNFs) for post-acute care are at high risk for adverse outcomes. Yet, absence of effective prognostic tools hinders optimal care planning and decision making. Our objective was to develop and validate a risk prediction model for 6-month all-cause death among hospitalized patients discharged to SNFs.DesignRetrospective cohort study.Setting and ParticipantsPatients discharged from 1 of 2 hospitals to 1 of 10 SNFs for post-acute care in an integrated health care delivery system between January 1, 2009, and December 31, 2016.MethodsGradient-boosting machine modeling was used to predict all-cause death within 180 days of hospital discharge with use of patient demographic characteristics, comorbidities, pattern of prior health care use, and clinical parameters from the index hospitalization. Area under the receiver operating characteristic curve (AUC) was assessed for out-of-sample observations under 10-fold cross-validation.ResultsWe identified 9803 unique patients with 11,647 hospital-to-SNF discharges [mean (SD) age, 80.72 (9.71) years; female sex, 61.4%]. These discharges involved 9803 patients alive at 180 days and 1844 patients who died between day 1 and day 180 of discharge. Age, comorbid burden, health care use in prior 6 months, abnormal laboratory parameters, and mobility status during hospital stay were the most important predictors of 6-month death (model AUC, 0.82).Conclusion and ImplicationsWe derived a robust prediction model with parameters available at discharge to SNFs to calculate risk of death within 6 months. This work may be useful to guide other clinicians wishing to develop mortality prediction instruments specific to their post-acute SNF populations.     

Quality of Post-Acute Care in Skilled Nursing Facilities That Disproportionately Serve Hispanics With Dementia

ObjectivesAs the number of Hispanics with dementia continues to increase, greater use of post-acute care in nursing home settings will be required. Little is known about the quality of skilled nursing facilities (SNFs) that disproportionately serve Hispanic patients with dementia and whether the quality of SNF care varies by the concentration of Medicare Advantage (MA) patients with dementia admitted to these SNFs.DesignCross-sectional study using 2016 data from Medicare certified providers.Setting and ParticipantsOur cohort included 177,396 beneficiaries with probable dementia from 8884 SNFs.MethodsWe examined facility-level quality of care among facilities with high and low proportions of Hispanic beneficiaries with probable dementia enrolled in MA and fee-for-service (FFS) using data from Medicare-certified providers. Three facility-level measures were used to assess quality of care: (1) 30-day rehospitalization rate; (2) successful discharge from the facility to the community; and (3) Medicare 5-star quality ratings.ResultsAbout 20% of residents were admitted to 1615 facilities with a resident population that was more than 15% Hispanic. Facilities with a higher share of Hispanic residents had a lower proportion of 4- or 5-star facilities by an average of 14% to 15% compared with facilities with little to no Hispanics. In addition, these facilities had a 1% higher readmission rate. There were also some differences in the quality of facilities with high (>26.5%) and low (<26.5%) proportions of MA beneficiaries. On average, SNFs with a high concentration of MA patients have lower readmission rates and higher successful discharge, but lower star ratings.Conclusions and ImplicationsAchieving better quality of care for people with dementia may require efforts to improve the quality of care among facilities with a high concentration of Hispanic residents.     

Hospital Readmissions Among Post-acute Nursing Home Residents: Does Obesity Matter?

ObjectivesTo explore profiles of obese residents who receive post-acute care in nursing homes (NHs) and to assess the relationship between obesity and hospital readmissions and how it is modified by individual comorbidities, age, and type of index hospitalizations.DesignRetrospective cohort study.Setting and participantsMedicare fee-for-service beneficiaries who were newly admitted to free-standing US NHs after an acute inpatient episode between 2011 and 2014 (N = 2,323,019).MeasuresThe Minimum Data Set 3.0 were linked with Medicare data. The outcome variable was 30-day hospital readmission from an NH. Residents were categorized into 3 groups based on their body mass index (BMI): nonobese, mildly obese, moderate-to-severely obese. We tested the relationship between obesity and 30-day readmissions by fixed-effects logit models and stratified analyses by the type of index hospitalization and residents' age.ResultsForty percent of the identified residents were admitted after a surgical episode, and the rest were admitted after a medical episode. The overall relationship between obesity and readmissions suggested that obesity was associated with higher risks of readmission among the oldest old (≥85 years) residents but with lower risks of readmission among the youngest group (65-74 years). After accounting for individual co-covariates, the association between obesity and readmissions among the oldest old residents became weaker; the adjusted odds ratio was 1.061 (P = .049) and 1.004 (P = .829) for moderate-to-severely obese patients with surgical and medical index hospitalizations, respectively. The protective effect of obesity among younger residents reduced after adjusting for covariates.Conclusions/RelevanceThe relationship between obesity and hospital readmission among post-acute residents could be affected by comorbidities, age, and the type of index hospitalization. Further studies are also warranted to understand how to effectively measure NH quality outcomes, including hospital readmissions, so that policies targeting at quality improvement can successfully achieve their goals without unintended consequences.     

Transitions From Skilled Nursing Facility to Home: The Relationship of Early Outpatient Care to Hospital Readmission

Background

Many adults are discharged to skilled nursing facilities (SNFs) prior to returning home from the hospital. Patient characteristics and factors that can help to prevent postdischarge adverse outcomes are poorly understood.

Trends in Post-Acute Care Utilization During the COVID-19 Pandemic

ObjectiveTo examine the effect of the COVID-19 pandemic on post-acute care utilization and spending.DesignWe used a large national multipayer claims data set from January 2019 through October 2020 to examine trends in posthospital discharge location and spending.Setting and participantsWe identified and included 975,179 hospital discharges who were aged ≥65 years.MethodsWe summarized postdischarge utilization and spending in each month of the study: (1) the percentage of patients discharged from the hospital to home for self-care and to the 3 common post-acute care locations: home with home health, skilled nursing facility (SNF), and inpatient rehabilitation; (2) the rate of discharge to each location per 100,000 insured members in our cohort; (3) the total amount spent per month in each post-acute care location; and (4) the percentage of spending in each post-acute care location out of the total spending across the 3 post-acute care settings.ResultsThe percentage of patients discharged from the hospital to home or to inpatient rehabilitation did not meaningfully change during the pandemic whereas the percentage discharged to SNF declined from 19% of discharges in 2019 to 14% by October 2020. Total monthly spending declined in each of the 3 post-acute care locations, with the largest relative decline in SNFs of 55%, from an average of $42 million per month in 2019 to $19 million in October 2020. Declines in total monthly spending were smaller in home health (a 41% decline) and inpatient rehabilitation (a 32% decline). As a percentage of all post-acute care spending, spending on SNFs declined from 39% to 31%, whereas the percentage of post-acute care spending on home health and inpatient rehabilitation both increased.Conclusions and ImplicationsChanges in posthospital discharge location of care represent a significant shift in post-acute care utilization, which persisted 9 months into the pandemic. These shifts could have profound implications on the future of post-acute care.     

Urgent readmission rates can be used to infer differences in avoidable readmission rates between hospitals

ObjectiveMost of the urgent readmissions are unavoidable. This study developed a method that used observed urgent readmission rates to compare the latent avoidable readmission rates between the two hospitals.Study Design and SettingTo compare two hospitals, we identified all proportions of urgent readmissions deemed avoidable at each hospital making their avoidable readmission rates significantly different. We then calculated the probability that any of these conditions occurred. We applied this method to 25 randomly selected Ontario acute-care hospitals in 2008.ResultsThe hospitals had a median 30-day urgent readmission rate of 10.8% (interquartile [IQR] 9.7–12.8%). The median P-value of the 300 hospital–hospital comparisons for 30-day urgent readmission rate was 0.05 (interquartile range [IQR] 0.0005–0.31). In contrast, the median probability that hospitals with the lower urgent 30-day readmission rate outperformed their comparator hospital with respect to avoidable readmissions was only 0.161 (IQR 0.079–0.274).ConclusionUrgent readmission rates can be used to estimate the probability that avoidable readmission rates differ significantly between the two hospitals. The probability that avoidable readmission rates differ significantly between hospitals is small even when significant differences in urgent 30-day readmission rates exist. Our results show that 30-day urgent readmission rates should be used very cautiously to compare hospital quality of care.     

Incidence and Outcomes of SARS-CoV-2 in Post-Acute Skilled Nursing Facility Care

ObjectivesTo examine the risk of contracting SARS-CoV-2 during a post-acute skilled nursing facility (SNF) stay and the associated risk of death.DesignCohort study using Minimum Data Set and electronic health record data from a large multistate long-term care provider. Primary outcomes included testing positive for SARS-CoV-2 during the post-acute SNF stay, and death among those who tested positive.Setting and ParticipantsThe sample included all new admissions to the provider's 286 SNFs between January 1 and December 31, 2020. Patients known to be infected with SARS-CoV-2 at the time of admission were excluded.MethodsSARS-CoV-2 infection and mortality rates were measured in time intervals by month of admission. A parametric survival model with SNF random effects was used to measure the association of patient demographic factors, clinical characteristics, and month of admission, with testing positive for SARS-CoV-2.ResultsThe sample included 45,094 post-acute SNF admissions. Overall, 5.7% of patients tested positive for SARS-CoV-2 within 100 days of admission, with 1.0% testing positive within 1-14 days, 1.4% within 15-30 days, and 3.4% within 31-100 days. Of all newly admitted patients, 0.8% contracted SARS-CoV-2 and died, whereas 6.7% died without known infection. Infection rates and subsequent risk of death were highest for patients admitted during the first and third US pandemic waves. Patients with greater cognitive and functional impairment had a 1.45 to 1.92 times higher risk of contracting SARS-CoV-2 than patients with less impairment.Conclusions and ImplicationsThe absolute risk of SARS-CoV-2 infection and death during a post-acute SNF admission was 0.8%. Those who did contract SARS-CoV-2 during their SNF stay had nearly double the rate of death as those who were not infected. Findings from this study provide context for people requiring post-acute care, and their support systems, in navigating decisions around SNF admission during the SARS-CoV-2 pandemic.     

Functional Status Is Associated With 30-Day Potentially Preventable Readmissions Following Skilled Nursing Facility Discharge Among Medicare Beneficiaries

Objectives

The objectives of this study were to determine the association between patients’ functional status at discharge from skilled nursing facility (SNF) care and 30-day potentially preventable hospital readmissions, and to examine common reasons for potentially preventable readmissions.

How Context Influences Hospital Readmissions from Skilled Nursing Facilities: A Rapid Ethnographic Study

IntroductionImproving hospital discharge processes and reducing adverse outcomes after hospital discharge to skilled nursing facilities (SNFs) are gaining national recognition. However, little is known about how the social-contextual factors of hospitals and their affiliated SNFs may influence the discharge process and drive variations in patient outcomes. We sought to categorize contextual drivers that vary between high- and low-performing hospitals in older adult transition from hospitals to SNFs.DesignTo identify contextual drivers, we used a rapid ethnographic approach with interviews and direct observations of hospital and SNF clinicians involved in discharging patients. We conducted thematic analysis to categorize contextual factors and compare differences in high- and low-performing sites.Setting and ParticipantsWe stratified hospitals on 30-day hospital readmission rates from SNFs and used convenience sampling to identify high- and low-performing sites and associated SNFs. The final sample included 4 hospitals (n = 2 high performing, n = 2 low performing) and affiliated SNFs (n = 5) with 148 hours of observations.MeasuresCentral themes related to how contextual factors influence variations in high- and low-performing hospitals.ResultsWe identified 3 main contextual factors that differed across high- and low-performing hospitals and SNFs: team dynamics, patient characteristics, and organizational context. First, we observed high-quality communication, situational awareness, and shared mental models among team members in high-performing sites. Second, the types of patients cared for at high-performing hospitals had better insurance coverage that made it feasible for clinicians to place patients based on their needs instead of financial abilities. Third, at high-performing hospitals a more engaged staff in the transition process and building rapport with SNFs characterized smooth transitions from hospitals to SNFs.Conclusions and ImplicationsContextual factors distinguish high- and low-performing hospitals in transitions to SNF and can be used to develop interventions to reduce adverse outcomes in transitions.     

Identifying Patient Readmissions: Are Our Data Sources Misleading?

BackgroundThe accuracy of data is vital to identifying hospitalization outcomes for clinical trials. Patient attrition and recall bias affects the validity of patient-reported outcomes, and the growing prevalence of Medicare Advantage (MA) could mean Fee-for-Service (FFS) claims are less reliable for ascertaining hospital utilization. Statewide health information exchanges (HIEs) may be a more complete data source but have not been frequently used for research.DesignSecondary analysis comparing identification of readmissions using 3 different acquisition approaches.SettingRandomized controlled trial of heart failure (HF) disease management in 37 skilled nursing facilities (SNFs).ParticipantsPatients with HF discharged from the hospital to SNF.MeasuresReadmissions up to 60 days post-SNF admission collected by patient self-report, recorded by nursing home (NH) staff during the SNF stay, or recorded in the state HIE.ResultsAmong 657 participants (mean age 79 ± 10 years, 49% with FFS), 295 unique readmissions within 60 days of SNF admission were identified. These readmissions occurred among 221 patients. Twenty percent of all readmissions were found using only patient self-report, 28% were only recorded by NH staff during the SNF stay, and 52% were identified only using the HIE. The readmission rate (first readmission only) based only on patient self-report and direct observation was 18% rather than 34% with the addition of the enhanced HIE method.Conclusions and implicationsMore than one-quarter (34%) of HF patients were rehospitalized within 60 days post SNF admission. Use of a statewide HIE resulted in identifying an additional 153 admissions, 52% of all the readmissions seen in this study. Without use of an HIE, nearly half of readmissions would have been missed as a result of incomplete patient self-report or loss to follow-up. Thus, HIEs serve as an important resource for researchers to ensure accurate outcomes data.