Outcome And Assessment Information Set Research Articles

Trends in Post-Acute Care use in Medicare Advantage Versus Traditional Medicare: A Retrospective Cohort Analysis

ObjectivesWe sought to describe national trends in hospitalization and post-acute care utilization rates in skilled nursing facilities (SNFs) and home health (HH) for both Medicare Advantage (MA) and Traditional Medicare (TM) beneficiaries, reaching up to the COVID-19 pandemic (2015–2019). DesignRetrospective, observational using 100% sample of Medicare Provider Analysis and Review file (MedPAR), the Medicare Beneficiary Summary File, the Minimum Data Set (MDS), and the Outcome and Assessment Information Set (OASIS). Setting and ParticipantsMedicare beneficiaries aged 66 and older enrolled in MA or TM who were hospitalized and discharged alive. MethodsWe first calculated the proportions of MA and TM beneficiaries who were hospitalized and who used any post-acute care, as well as the total number of days of post-acute care used. We also calculated the size of the post-acute care network used by TM and MA beneficiaries within each hospital in our sample and the measured quality (star ratings) of the post-acute care providers used. ResultsWe found hospitalizations, SNF stays, and HH stays were all decreasing over time in both populations. Although similar proportions of MA and TM beneficiaries received SNF or HH care, MA beneficiaries received fewer days. The largest difference we found was in the number of post-acute care providers used in TM and MA, with MA using far fewer; however, quality ratings were similar among post-acute care providers used in each program. Conclusions and ImplicationsTogether, these results suggest MA beneficiaries have fewer days in post-acute care, receive care from fewer providers of similar measured quality to TM, but have a similar number of days outside the hospital or SNF in the first 100 days after hospital discharge.

Hospital-to-Home-Health Transition Quality (H3TQ) Index: Further Evidence on its Validity and Recommendations for Implementation.

We developed the Hospital-to-Home-Health Transition Quality (H3TQ) Index for skilled home healthcare (HH) agencies to identify threats to safe, high-quality care transitions in real time. Assess the validity of H3TQ in a large sample across diverse communities. A survey of recently hospitalized older adults referred for skilled HH services and their HH provider at two large HH agencies in Baltimore, MD, and New York, NY. There were five hundred eighty-seven participants (309 older adults, 141 informal caregivers, and 137 HH providers). Older adults, caregivers, and HH providers rated 747 unique transitions. Of these, 403 were rated by both the older adult/caregiver and their HH provider, whereas the remaining transitions were rated by either party. Construct, concurrent, and predictive validity were assessed via the overall H3TQ rating, correlation with the care transition measure (CTM), and the Medicare Outcome and Assessment Information Set (OASIS). Proportion of transitions with quality issues as identified by HH providers and older adults/caregivers, respectively; Baltimore 55%, 35%; NYC 43%, 32%. Older adults/caregivers across sites rated their transitions as higher quality than did providers (P<0.05). H3TQ summed scores showed construct validity with the CTM-3 and concurrent validity with OASIS measures. Summed H3TQ scores were not significantly correlated with 30-day ED visits or rehospitalization. The H3TQ identifies care transition quality issues in real-time and demonstrated construct and concurrent validity, but not predictive validity. Findings demonstrate value in collecting multiple perspectives to evaluate care transition quality. Implementing the H3TQ could help identify transition-quality intervention opportunities for HH patients.

Using Interpersonal Continuity of Care in Home Health Physical Therapy to Reduce Hospital Readmissions

This paper is an examination of the relationship between continuity of care with home health physical therapists following hospitalization and the likelihood of readmission. We conducted a retrospective cohort study. Using rehospitalization as the dependent variable, a continuity of care indicator variable was analyzed with a multivariable logistic regression. The indicator variable was created using the Bice-Boxerman Index to measure physical therapist continuity of care. The mean of the index (0.81) was used to separate between high continuity (0.81 or greater) of care and low continuity of care (lower than 0.81). The sample included 90,220 patients, with data coming from the linking of the Outcome Assessment and Information Set (OASIS) and an administrative dataset. All subjects lived in the NYC metro area. Inclusion criteria was a patient’s admission to their first home health care site following discharge occurring between 2010 and 2015, and individuals who identified as Male or Female. In comparison to low continuity of physical therapy, high continuity of physical therapy significantly decreased hospital readmissions (OR = 0.74, 95% CI 0.71-0.76, p ≤ .001, AME = −4.28%). Interpersonal continuity of physical therapy care has been identified as a key factor in decreasing readmissions from the home care setting. The research suggests an increased emphasis in preserving physical therapist continuity following hospitalization should be explored, with the potential to reduce hospital readmissions.

Crosswalk Algorithms for Cognitive and Functional Outcomes Among 2013–2018 Medicare Beneficiaries With Dementia

ObjectiveBefore 2019, the Minimum Data Set (MDS) and Outcome and Assessment Information Set (OASIS) had incongruent response categories for rating cognitive impairment and activities of daily living (ADLs), hindering direct comparisons between nursing facilities and home health. We devised rule-based algorithms to compare cognitive impairment and ADL limitations between these 2 care settings among people with Alzheimer's disease and Alzheimer's disease–related dementias (ADRD). DesignA retrospective cohort study. Setting and ParticipantsIncluded fee-for-service Medicare beneficiaries (2013–2018) transitioning from nursing facilities to home health, with 1-year of continuous enrollment, aged ≥65 years, diagnosed ADRD, and with complete MDS discharge and OASIS admission assessments (N = 398,496). MethodsWe identified target phenotypes using the Cognitive Function Scale (CFS) and ADL items from the MDS discharge assessment as reference standards. We compared 6 OASIS-based algorithms for cognitive impairment and 1 for each ADL limitation by estimating sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). ResultsThe average age was 83.5 (SD = 7.5) years and 82.3% transitioned from nursing to home health within 3 days. In the MDS discharge assessment, 42.2% had moderate-to-severe cognitive impairment. ADL limitations ranged from 71.4% for feeding to 97.8% for bathing. Compared with the moderate-to-severe cognitive impairment (CFS ≥3) on the MDS, the OASIS cognitive assessment indicating “considerable assistance to total dependence in routine situations” had 24% sensitivity, 94% specificity, 75% PPV, and 63% NPV. The ADL limitation algorithms exhibited high sensitivities (>96%) and PPVs (>94%) except for feeding (Sensitivity: 82%; PPV: 74%). Despite the short time frame between the 2 assessments, the OASIS admission assessment showed a higher prevalence of ADL limitations than the MDS discharge assessment. Conclusions and ImplicationsWe highlighted differences in patient function between post-acute care settings. Our algorithms can help researchers, clinicians, and policymakers standardize patient-centered outcomes for comparative effectiveness research or quality initiatives.

Identifying Dementia Severity Among People Living With Dementia Using Administrative Claims Data

ObjectivesThere is currently no reliable tool for classifying dementia severity level based on administrative claims data. We aimed to develop a claims-based model to identify patients with severe dementia among a cohort of patients with dementia. DesignRetrospective cohort study. Setting and ParticipantsWe identified people living with dementia (PLWD) in US Medicare claims data linked with the Minimum Data Set (MDS) and Outcome and Assessment Information Set (OASIS). MethodsSevere dementia was defined based on cognitive and functional status data available in the MDS and OASIS. The dataset was randomly divided into training (70%) and validation (30%) sets, and a logistic regression model was developed to predict severe dementia using baseline (assessed in the prior year) features selected by generalized linear mixed models (GLMMs) with least absolute shrinkage and selection operator (LASSO) regression. We assessed model performance by area under the receiver operating characteristic curve (AUROC), area under precision-recall curve (AUPRC), and precision and recall at various cutoff points, including Youden Index. We compared the model performance with and without using Synthetic Minority Oversampling Technique (SMOTE) to reduce the imbalance of the dataset. ResultsOur study cohort included 254,410 PLWD with 17,907 (7.0%) classified as having severe dementia. The AUROC of our primary model, without SMOTE, was 0.81 in the training and 0.80 in the validation set. In the validation set at the optimized Youden Index, the model had a sensitivity of 0.77 and specificity of 0.70. Using a SMOTE-balanced validation set, the model had an AUROC of 0.83, AUPRC of 0.80, sensitivity of 0.79, specificity of 0.74, positive predictive value of 0.75, and negative predictive value of 0.78 when at the optimized Youden Index. Conclusions and ImplicationsOur claims-based algorithm to identify patients living with severe dementia can be useful for claims-based pharmacoepidemiologic and health services research.

Development of Improved Weighed Quantum Lion Optimization with Smooth Support Vector Machine for Alzheimer’s Disease

Accurate diagnosis of Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) was identified on an early stage is essential in the healthcare industry to stop degeneration. The Smooth Support Vector Machine (SSVM) model, Principal Component Analysis (PCA), feature extraction, and Magnetic Resonance Imaging (MRI) image prepossessing are the components for the diagnosis of AD is proposed in this research at early stage. To assist in the classifier's training, we proposed a novel Improved Weighed Quantum Lion Optimization (IWQLO). The SSVM parameters are specifically proposed to be optimized using a new Switching delayed Lion Optimization (SLO) algorithm. The IWQLO-SSVM approach was effectively used to classify AD and MCI utilizing MRI scans of the Alzheimer's disease Neuroimaging Initiative (ADNI) database and Outcome and Assessment Information Set (OASIS) database. For six example scenarios, the classification accuracy of our proposed method is acceptable. Testing show that the proposed approach improves the performance measures such as accuracy, precision, specificity, sensitivity and recall for detecting the early stage AD diagnosis.

Assessing the accuracy of race-and-ethnicity data in the Outcome and Assessment Information Set.

Limitations in the quality of race-and-ethnicity information in Medicare's data systems constrain efforts to assess disparities in care among older Americans. Using demographic information from standardized patient assessments may be an efficient way to enhance the accuracy and completeness of race-and-ethnicity information in Medicare's data systems, but it is critical to first establish the accuracy of these data as they may be prone to inaccurate observer-reported or third-party-based information. This study evaluates the accuracy of patient-level race-and-ethnicity information included in the Outcome and Assessment Information Set (OASIS) submitted by home health agencies. We compared 2017-2022 OASIS-D race-and-ethnicity data to gold-standard self-reported information from the Medicare Consumer Assessment of Healthcare Providers and Systems® survey in a matched sample of 304,804 people with Medicare coverage. We also compared OASIS data to indirect estimates of race-and-ethnicity generated using the Medicare Bayesian Improved Surname and Geocoding (MBISG) 2.1.1 method and to existing Centers for Medicare & Medicaid Services (CMS) administrative records. Compared with existing CMS administrative data, OASIS data are far more accurate for Hispanic, Asian American and Native Hawaiian or other Pacific Islander, and White race-and-ethnicity; slightly less accurate for American Indian or Alaska Native race-and-ethnicity; and similarly accurate for Black race-and-ethnicity. However, MBISG 2.1.1 accuracy exceeds that of both OASIS and CMS administrative data for every racial-and-ethnic category. Patterns of inconsistent reporting of racial-and-ethnic information among people for whom there were multiple observations in the OASIS and Consumer Assessment of Healthcare Providers and Systems (CAHPS) datasets suggest that some of the inaccuracies in OASIS data may result from observation-based reporting that lessens correspondence with self-reported data. When health record data on race-and-ethnicity includes observer-reported information, it can be less accurate than both true self-report and a high-performing imputation approach. Efforts are needed to encourage collection of true self-reported data and explicit record-level data on the source of race-and-ethnicity information.

Characterizing changes to older adults' care transition patterns from hospital to home care in the initial year of COVID-19.

Skilled home healthcare (HH) provided in-person care to older adults during the COVID-19 pandemic, yet little is known about the pandemic's impact on HH care transition patterns. We investigated pandemic impact on (1) HH service volume; (2) population characteristics; and (3) care transition patterns for older adults receiving HH services after hospital or skilled nursing facility (SNF) discharge. Retrospective, cohort, comparative study of recently hospitalized older adults (≥ 65 years) receiving HH services after hospital or SNF discharge at two large HH agencies in Baltimore and New York City (NYC) 1-year pre- and 1-year post-pandemic onset. We used the Outcome and Assessment Information Set (OASIS) and service use records to examine HH utilization, patient characteristics, visit timeliness, medication issues, and 30-day emergency department (ED) visit and rehospitalization. Across sites, admissions to HH declined by 23% in the pandemic's first year. Compared to the year prior, older adults receiving HH services during the first year of the pandemic were more likely to be younger, have worse mental, respiratory, and functional status in some areas, and be assessed by HH providers as having higher risk of rehospitalization. Thirty-day rehospitalization rates were lower during the first year of the pandemic. COVID-positive HH patients had lower odds of 30-day ED visit or rehospitalization. At the NYC site, extended duration between discharge and first HH visit was associated with reduced 30-day ED visit or rehospitalization. HH patient characteristics and utilization were distinct in Baltimore versus NYC in the initial year of the COVID-19 pandemic. Study findings suggest some older adults who needed HH may not have received it, since the decrease in HH services occurred as SNF use decreased nationally. Findings demonstrate the importance of understanding HH agency responsiveness during public health emergencies to ensure older adults' access to care.

Clinician Perception of Likelihood of Death in the Next Year Is Associated With 1-Year Mortality and Hospice Use Among Older Adults Receiving Home Health Care.

Background: Given the complex care needs of older adults receiving home health care (HHC), it is important for HHC clinicians to identify those with limited prognosis who may benefit from a transition to hospice care. Objectives: To assess the association between HHC clinician-identified likelihood of death and (1) 1-year mortality, and (2) hospice use. Methods: Prospective cohort study from the National Health and Aging Trends Study (NHATS) waves 2011-2018, linked to the Outcomes and Assessment Information Set (OASIS) HHC assessment and Medicare data among 915 community-dwelling NHATS respondents. HHC clinician-identified likelihood of death/decline was determined using OASIS item M1034. Multivariable logistic regression was used to assess the association between clinician-identified likelihood of death/decline and 1-year mortality and hospice use. Results: HHC clinicians identified 42% of the sample as at increased risk of decline or death. One year mortality was 22.3% (n = 548), and 15.88% (n = 303) used hospice within 12 months of HHC. HHC clinician-perceived likelihood of death/decline was associated with greater odds of 1-year mortality (odds ratio [OR], 6.57; confidence interval (95% CI), 2.56-16.90) and was associated with greater likelihood of hospice use (OR, 1.61; 95% CI, 1.00-2.62). Conclusion: HHC clinician perception of patients' risk of death or decline is associated with 1-year mortality. A better understanding of HHC patients at high risk for mortality can facilitate improved care planning and identification of homebound older adults who may benefit from hospice.

HARMONIZATION OF CHRONIC CONDITIONS WAREHOUSE DATA FOR OUTCOMES RESEARCH ON GERIATRIC SYNDROMES

Abstract Geriatric syndromes such as cognitive impairment, falls, depression, urinary incontinence, pressure ulcers, and polypharmacy are common conditions and syndromes that affect older adults. Studying geriatric syndromes in Medicare beneficiaries may provide important insights for improving health outcomes and quality of life. As a result of the Improving Medicare Post-Acute Care Transformation Act of 2014, it is now easier to compare standardized geriatric assessment data (including information about geriatric syndromes) collected in post-acute and long-term care settings with corresponding data from claims, encounter, Part D, and durable medical equipment files. However, to our knowledge, methods to link and harmonize post-acute and long-term care assessment data (available for 75% of decedents) are yet to be utilized in the health services research community. The goal of this project is to develop harmonized datasets that can be used to identify geriatric syndromes and study their prevalence, cost, health outcomes, and service delivery factors over time. This work will allow our team and other researchers to fully leverage these data for use in predictive algorithms and artificial intelligence and machine learning (AI/ML) research. The project uses the five most recent years of data available from the Centers for Medicare and Medicaid Services (CMS) Chronic Conditions Data Warehouse. Data sources include Medicare and Medicaid claims, encounter, MedPAR, Hospice claims, Minimum Data Set (MDS), MDS-Swing Bed (SB), Outcome and Assessment Information Set (OASIS), Inpatient Rehabilitation Facility Patient Assessment Instrument (IRF-PAI), Pharmacy (Part D), and Durable Medical Equipment (DME).

Association Between Documented Severe Pain and Cognitive Impairment in Home Health Care Patients: Results from the National Outcome and Assessment Information Set Data.

Background: Despite the growing importance of home health care (HHC) in the care of older adults with cognitive impairment, limited evidence exists about factors associated with documented severe pain among older adults receiving HHC. Methods: This secondary data analysis used a 5% random national sample of the 2017 national Outcome and Assessment Information Set (OASIS) data. Multivariable Poisson regression model was used to examine the association between documented severe pain, cognitive impairment, and a range of sociodemographic, clinical, and cognitive factors. Results: HHC patients (n = 183,038) were mean age 79.7 years, 61.7% female, and 78.6% non-Hispanic White. In multivariable models, cognitive impairment was associated with lower likelihood of documented severe pain (prevalence ratio [PR] = 0.96, confidence interval [CI] = [0.94-0.98]). Other factors independently associated with less documented severe pain included Hispanic ethnicity (PR = 0.92, CI = [0.89-0.95]), the oldest (≥85 years) groups (PR = 0.65, CI = [0.63-0.66]), male patients (PR = 0.83, CI = [0.82-0.85]), those with a formal diagnosis of Alzheimer's Disease Related Dementias (PR = 0.67, CI = [0.65-0.69]), and patients with verbal- and speech-related difficulty (PR = 0.83, CI = [0.80-0.86]). Patients with history of falls (PR = 1.18, CI = [1.16-1.20]), positive screen on the Patient Health Questionnaire-2 (PR = 1.36, CI = [1.31-1.41]), shortness of breath (PR = 1.14, CI = [1.12-1.14]), anxiety daily (PR = 1.16, CI = [1.14-1.18]), and anxiety daily or more often (PR = 1.40, CI = [1.37-1.43]) were more likely to have documented severe pain. Conclusions: HHC patients with cognitive impairment were less likely to have documented severe pain even with a range of sociodemographic, clinical, functional, and cognitive characteristics were considered. These findings may reflect a link between cognitive impairment missed opportunities for clinicians to provide pain management. Tailored interventions are needed to better assess and manage pain in this vulnerable group of HHC patients.

Identifying Medicare beneficiaries with Alzheimer's disease and related dementia using home health OASIS assessments.

Home health services are an important site of care following hospitalization among Medicare beneficiaries, providing health assessments that can be leveraged to detect diagnoses that are not available in other data sources. In this work, we aimed to develop a parsimonious and accurate algorithm using home health outcome and assessment information set (OASIS) measures to identify Medicare beneficiaries with a diagnosis of Alzheimer's disease and related dementia (ADRD). We conducted a retrospective cohort study of Medicare beneficiaries with a complete OASIS start of care assessment in 2014, 2016, 2018, or 2019 to determine how well the items from various versions could identify those with an ADRD diagnosis by the assessment date. The prediction model was developed iteratively, comparing the performance of different models in terms of sensitivity, specificity, and accuracy of prediction, from a multivariable logistic regression model using clinically relevant variables, to regression models with all available variables and predictive modeling techniques, to estimate the best performing parsimonious model. The most important predictors of having a diagnosis of ADRD by the start of care OASIS assessment were a prior discharge diagnosis of ADRD among those admitted from an inpatient setting, and frequently exhibiting symptoms of confusion. Results from the parsimonious model were consistent across the four annual cohorts and OASIS versions with high specificity (above 96%), but poor sensitivity (below 58%). The positive predictive value was high, over 87% across study years. The proposed algorithm has high accuracy, requires a single OASIS assessment, is easy to implement without sophisticated statistical models, and can be used across four OASIS versions and in situations where claims are not available to identify individuals with a diagnosis of ADRD, including the growing population of Medicare Advantage beneficiaries.

Functional status and therapy receipt among older adults with multiple myeloma.

e18770 Background: Multiple myeloma (MM) disproportionately affects older adults, among whom aging-related impairments in activities of daily living (ADLs) are common. We aimed to evaluate the association between functional disability and receipt of myeloma therapy in a population of older MM patients who had received home health (HH) services in the year prior to MM diagnosis. Methods: We identified a cohort of adults age ≥66 diagnosed with MM from 2010-2017 who received HH services prior to diagnosis from the Surveillance, Epidemiology, and End Results (SEER) database linked to Medicare claims and the Outcome and Assessment Information Set (OASIS). OASIS captures functional assessments for Medicare beneficiaries receiving HH. Continuous Medicare enrollment for 12 months before and after cancer diagnosis was required. Primary exposure was disability, a composite score of OASIS ADL items, categorized by distributional breakpoints as mild (0-9), moderate (10-25) or severe (26-40). Primary outcome was receipt of MM therapy in the year following diagnosis. Secondary outcomes were treatment type (triplet therapy, stem cell transplant) and healthcare utilization (emergency department [ED] visits, hospitalizations). Associations between disability and outcomes were assessed via hazard ratios, adjusted for age, sex, race, comorbidity, and SEER region with death as a competing risk. For utilization outcomes, we examined differences in mean cumulative counts (MCC) for each event between disability strata. Results: Among 4,108 adults, those with severe disability were less likely to receive MM therapy (vs mild, HR = 0.70 (95% CI: 0.56-0.88); vs. moderate, HR = 0.63 (0.51-0.79). Individuals with moderate disability were more likely to receive treatment than those with mild disability (HR = 1.10 (1.02, 1.20)). Individuals with severe disability were less likely to receive triplet therapy (vs. mild, HR = 0.57 (0.35, 0.93)). Rates of triplet therapy did not differ significantly between moderate and mild disability groups (HR = 1.11 (0.94, 1.32)). Transplant was rare in all groups ( &lt; 5%). Individuals with mild disability had fewer ED visits (MCC difference vs. moderate = -0.34 (-0.50, -0.18); vs. severe = -0.57 (-1.05, -0.10)) and fewer hospitalizations (MCC difference vs. moderate = -0.42 (-0.54, -0.30); vs. severe = -0.59 (-0.93, -0.26)) in the year following diagnosis. Both measures did not differ significantly between moderate and severe groups. Conclusions: In this large, nationwide study of older adults with MM and HH use, we identified differences in treatment receipt and healthcare utilization by degree of pre-diagnosis functional disability. Individuals with moderate disability had comparable or more treatment than those with mild disability, though they were similar to the severe group in healthcare utilization. Individuals with moderate disability receiving HH may need supportive care resources when initiating therapy.

MEDICATIONS ASSOCIATED WITH GERIATRIC SYNDROMES AND HOSPITALIZATION RISK IN HOME HEALTHCARE PATIENTS

Abstract Polypharmacy is common in the home health care (HHC) setting. This study examined the prevalence, predictors, and impact of medications associated with geriatric syndromes (MAGS) use on subsequent hospitalization in HHC patients. Data from the HHC electronic medical records, the Outcome and Assessment Information Set (OASIS), and Medicare HHC claims of 6,882 adults ≥ 65 years old receiving HHC from a large, non-profit HH agency in New York State in CY 2019 were used. MAGS use was identified from active medications reconciled during HHC visits and defined as total count and in quartiles. Hospitalization during the HHC episode was operationalized as a time-to-event variable. Regression analyses were conducted to identify predictors of MAGS use, and survival analyses were conducted to examine the association between MAGS use and hospitalization. Nearly all (98%) of the HHC patients used at least one MAGS and 41% of all active medications were MAGS. A higher MAGS count was found among HHC patients who were non-white, community-referred, with more comorbidities, depressive symptoms, and functional limitations. A higher MAGS count was also related to increased risk for hospitalization. Moreover, higher quartiles of MAGS use combined with having ≥10 diagnoses predicted a 2.5-fold increase in hospitalization risk, relative to the lowest quartile of MAGS use and having &amp;lt; 10 diagnoses. In conclusion, over 40% of medications taken by HHC patients are MAGS, which, along with multimorbidity, increased hospitalization risk. HHC clinicians should identify MAGS use to inform deprescribing discussion with patients and their prescribers.

Home Health Unplanned Readmission Across Metro and Rural Areas following Stroke and Joint Replacement.

<h3>Research Objectives</h3> Explore national geographic differences in unplanned readmission after accounting for patient-level admission factors and area-level post-acute care availability for individuals receiving home health services following stroke or lower-extremity joint replacement (JR). <h3>Design</h3> Retrospective cross-sectional analysis of Centers for Medicare & Medicaid Services (CMS) home health claims records and the Outcome and Assessment Information Set (OASIS). Using Rural-Urban Continuum Codes (RUCC) we classified all US counties into metro, metro-adjacent rural, and non-metro-adjacent rural areas and examined the availability of other post-acute care (PAC) provider types (long-term care hospitals, skilled nursing (SNF), and inpatient rehabilitation facilities) in each. We conducted multilevel logistic regressions to show the association between county classification and the odds of an unplanned readmission. <h3>Setting</h3> We examined acute care discharges to home health agencies. <h3>Participants</h3> The population included 100% Medicare records from 2013–2015 fee-for-service beneficiaries >65 years. The cohort included 21,395 stroke records and 160,287 JR records. <h3>Interventions</h3> N/A. <h3>Main Outcome Measures</h3> Odds of an unplanned readmission for individuals who received home health services following stroke or JR. <h3>Results</h3> Overall, 11.0% of stroke and 3.4% of JR had an unplanned readmission. For those >65 with JR, unplanned readmissions increased as age (range 2.6–5.4%) and length of stay (2.9–13%) increased. PAC availability other than home health varied across county groups. For metro counties, >50% were SNF-only; only 20% had all PAC provider types, whereas 80% of metro-adjacent rural counties were SNF-only with < 2% with all PAC provider types. Compared to metro areas, residing in a rural area was protective of readmission for those with stroke (metro-adjacent rural OR=0.887 (95CI 0.751–1.047), metro non-adjacent rural OR=0.789 (0.634–0.982). There was no significant effect of rural area on JR unplanned readmission. <h3>Conclusions</h3> Residing in rural counties is protective against unplanned readmission following home health services for stroke care. Further research should characterize social and contextual factors in rural areas that contribute to community-dwelling individuals' health. <h3>Author(s) Disclosures</h3> This work was supported by the Agency for Healthcare Research and Quality (AHRQ) R01-HS024711. Authors declare no conflicts.

Functional Status and Mortality Among Older Adults with Multiple Myeloma

Introduction Multiple myeloma (MM) is disproportionately a disease of older adults, with a median age at diagnosis of 69. Prior single-center studies and therapeutic trials have demonstrated that aging-related functional impairments in activities of daily living (ADLs - e.g., bathing, toileting) are common among this population and predict treatment intolerance, healthcare utilization, and mortality. However, the prevalence and impact of functional impairments among broader, more generalizable populations of older adults with MM is less well understood. Methods We utilized cancer registry data from the Surveillance, Epidemiology, and End Results (SEER) program linked with Medicare enrollment and claims data from 2008-2019 and the Outcome and Assessment Information Set (OASIS) from 2008-2018 to construct a retrospective cohort of adults aged ≥66 diagnosed with MM between 2009 and 2017. OASIS data capture functional assessments for Medicare beneficiaries who are home health care recipients (HHRs). Individuals were deemed home health recipients if they had at least one OASIS assessment in the 12 months prior to MM diagnosis (inclusive of month of diagnosis). The primary exposures were (1) receipt of home health services prior to MM diagnosis and (2), degree of functional disability, a composite score derived from individual items related to ADLs contained in the OASIS data for HHRs and categorized by tertile as mild (composite score 0-11), moderate (12-17) or severe disability (18-40). The primary outcome was overall mortality within 5 years from diagnosis. Associations between primary exposures and outcomes were assessed via cumulative risk ratios (RRs) at 6, 12, and 36 months from diagnosis, with bootstrapped 95% confidence intervals. The association between receipt of any home health services and mortality was unadjusted; the association between degree of functional disability and mortality among those receiving home health services was adjusted for age, sex, race, and SEER region via inverse probability weighting. Results Among 41,635 beneficiaries with MM, 6,850 (16.4%) received home health services and had an OASIS assessment in the year prior to diagnosis. When compared to non-recipients, HHRs were older (38.6% in highest quartile vs 22.5%), more likely to be non-Hispanic Black (19.9%% vs 17.2%), and more likely to live in counties with greater rates of poverty (25.2% in highest quartile vs 21.7%). HHRs had similar rates of rural residence (10.5% vs 10.8%). Among HHRs, 79% required assistance with at least one ADL. Median number of activities with which HHRs required assistance was 3 (range 0-7). HHRs had higher all-cause mortality at all assessed time points compared to non-HHRs with MM (Figure 1); RRs were 1.91 (95% CI 1.83-1.99) at 6 months from diagnosis, 1.80 (1.74-1.86) at 12 months, and 1.50 at 36 months (1.47-1.53). Among HHRs, mortality was higher among those with severe functional disabilities compared to those with mild disability; RR for the severe versus mild disability groups was 1.38 (1.17-1.62) at 6 months, 1.29 (1.17-1.43) at 12 months, and 1.13 (1.08-1.19) at 36 months. In contrast, those with mild and moderate degrees of disability had similar survival (Figure 2), with non-significant differences in mortality at all assessed time points. Conclusions In this large nationwide study of older adults with MM, we describe the association between physical disability/activity dependence and 5-year mortality. These findings corroborate results of prior, smaller studies based on data from academic centers or therapeutic clinical trials. Our study is novel for its use of a large, diverse, and generalizable data source and the recent linkage with OASIS records. In this cohort, home health recipients had higher 5-year mortality compared to non-home health recipients. Additionally, mortality was most profoundly impacted among those with severe disabilities. In contrast, those with moderate degrees of disability had mortality rates that did not differ significantly from those individuals with mild disability. This moderate disability group, who had a median survival measured in years, may be at risk for undertreatment if their prognosis is underestimated. Future research will characterize the impact of physical disability on MM therapy receipt among this population. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Medications Associated With Geriatric Syndromes (MAGS) and Hospitalization Risk in Home Health Care Patients.

Polypharmacy is common in home health care (HHC). This study examined the prevalence of medications associated with geriatric syndromes (MAGS), its predictors, and association with subsequent hospitalization in HHC. Analysis of HHC electronic medical records, the Outcome and Assessment Information Set (OASIS), and Medicare HHC claims. A total of 6882 adults ≥65years old receiving HHC in 2019 from a large, not-for-profit home health agency serving multiple counties in New York State. MAGS use was identified from active medications reconciled during HHC visits (HHC electronic medical records). MAGS use was operationalized as count and in quartiles. Hospitalization during the HHC episode was operationalized as a time-to-event variable (ie, number of days from HHC admission to hospitalization). We used regression analyses to identify predictors of MAGS use, and survival analyses to examine the association between MAGS and hospitalization. Nearly all (98%) of the HHC patients used at least 1 MAGS and 41% of all active medications used by the sample were MAGS. More MAGS use was found in HHC patients who were community-referred, taking more medications, and having more diagnoses, depressive symptoms, and functional limitations. Adjusted for covariates, higher MAGS quartiles were not independently associated with the risk of hospitalization, but higher MAGS quartiles combined with multimorbidity (ie, having ≥10 diagnoses) were associated with a 2.3-fold increase in hospitalization risk (hazard ratio 2.24; 95% confidence interval: 1.61-3.13; P < .001), relative to the lowest quartile of MAGS use and having <10 diagnoses. More than 40% of medications taken by HHC patients are MAGS. Multimorbidity and MAGS use collectively increased the risk of hospitalization by up to 2.3 times. HHC clinicians should carefully review patients' medications and use information about MAGS to facilitate discussion about deprescribing with patients and their prescribers.

Clinical notes: An untapped opportunity for improving risk prediction for hospitalization and emergency department visit during home health care.

Between 10 and 25% patients are hospitalized or visit emergency department (ED) during home healthcare (HHC). Given that up to 40% of these negative clinical outcomes are preventable, early and accurate prediction of hospitalization risk can be one strategy to prevent them. In recent years, machine learning-based predictive modeling has become widely used for building risk models. This study aimed to compare the predictive performance of four risk models built with various data sources for hospitalization and ED visits in HHC. Four risk models were built using different variables from two data sources: structured data (i.e., Outcome and Assessment Information Set (OASIS) and other assessment items from the electronic health record (EHR)) and unstructured narrative-free text clinical notes for patients who received HHC services from the largest non-profit HHC organization in New York between 2015 and 2017. Then, five machine learning algorithms (logistic regression, Random Forest, Bayesian network, support vector machine (SVM), and Naïve Bayes) were used on each risk model. Risk model performance was evaluated using the F-score and Precision-Recall Curve (PRC) area metrics. During the study period, 8373/86,823 (9.6%) HHC episodes resulted in hospitalization or ED visits. Among five machine learning algorithms on each model, the SVM showed the highest F-score (0.82), while the Random Forest showed the highest PRC area (0.864). Adding information extracted from clinical notes significantly improved the risk prediction ability by up to 16.6% in F-score and 17.8% in PRC. All models showed relatively good hospitalization or ED visit risk predictive performance in HHC. Information from clinical notes integrated with the structured data improved the ability to identify patients at risk for these emergent care events.

Symptom Burden and Activity of Daily Living (ADL) Dependency Among Home Health care Patients Discharged to Home Hospice.

Background: We sought to examine sociodemographic and clinical characteristics present on admission to HHC associated with discharge to hospice. Methods: We used a 5% random sample of 2017 national Outcome and Assessment Information Set (OASIS) data. A Cox proportional hazards regression model was estimated for the primary outcome (discharge to hospice) to examine the associations with sociodemographic and clinical characteristics of HHC patients. Results: Among 489, 230 HHC patients, 4268 were discharged to hospice. The median (interquartile range) length of HHC stay for patients discharged to hospice care was 33 (14-78) days. Compared to White patients, Black, Hispanic, and other race, (hazard ratio [HR] = .50 [95% confidence interval, CI = .44-.57]), (HR = .53 [95% CI = .46-.62]), and (HR = .49 [95% CI = .40-.61], respectively) was associated with shorter time to discharge to hospice care. Clinical characteristics including severe dependence in activities of daily (ADL) (HR = 1.68 [95% CI = 1.01-2.78]), cognitive impairment (HR = 1.10 [95% CI = 1.01-1.20]), disruptive behavior daily (HR = 1.11 [95% CI = 1.02-1.22]), and inability to feed oneself (HR = 4.78, 95% CI = 4.30, 5.31) was associated with shorter time to discharge to hospice. Symptoms of anxiety daily (HR = 1.55 [95% CI = 1.43-1.68]), and pain daily or all the time (HR = 1.54 [95% CI = 1.43-1.64]) were associated with shorter time to discharge to hospice. Conclusions: High symptom burden, ADL dependency, and cognitive impairment on admission to HHC services was associated with greater likelihood of discharge to hospice.

Frailty and Functional Status improvement after Skilled Nursing Facility based Post-Acute Care

People admitted to a skilled nursing facility (SNF) for post-acute care undergo comprehensive evaluation and rehabilitation, potentially enabling prediction of future functional recovery. We identified the first SNF admission per beneficiary (n=250,159) between 07/01/2014 – 06/30/2016 in a 5% Medicare sample, using the Minimum Data Set (MDS) and the Outcome and Assessment Information Set (OASIS). Episodes were excluded for non-community discharge (n=43,397) or no OASIS admission assessment within 14 days of SNF discharge (n=77,989). A deficit accumulation Frailty Index (FI) was measured on admission MDS assessment and categorized into robust (MDS-FI<0.15), pre-frailty (MDS-FI0.15-0.24), mild frailty (MDS-FI0.25-0.34), and moderate or worse frailty (MDS-FI≥0.35). Outcomes were functional decline obtained from OASIS, readmission, or death after initiation of home care. Functional status was measured by activities of daily living from OASIS assessments. A total of 135,310 SNF episodes were matched to OASIS episodes. Of these, there were 6,472 (4.8%) robust patients, 38,923 (28.8%) pre-frail, 63,727 (47.1%) mildly frail and 26,053 (19.3%) moderately frail or worse. In a logistic regression after adjustment for OASIS admission function, compared to robust status, frailty was associated with hospital readmission or death within 30 days of OASIS admission, (mild frailty OR1.33 [95%CI 1.23-1.45] and moderate or worse OR1.81 [95%CI 1.66-1.97]). Frailty was also associated with functional decline at OASIS discharge, after adjustment for OASIS admission function (mild frailty OR1.50 [95%CI 1.38-1.63] and moderate or worse OR2.30 [95%CI 2.11-2.50]). Among those discharged from SNF with home services, a SNF-based MDS-FI is associated with increased likelihood of poor community outcomes.