One-Year Recovery Among Survivors of Prolonged Severe COVID-19: A National Multicenter Cohort.

OBJECTIVES: Understanding the long-term effects of severe COVID-19 illness on survivors is essential for effective pandemic recovery planning. Therefore, we investigated impairments among hospitalized adults discharged to long-term acute care hospitals (LTACHs) for prolonged severe COVID-19 illness who survived 1 year. DESIGN: The Recovery After Transfer to an LTACH for COVID-19 (RAFT COVID) study was a national, multicenter, prospective longitudinal cohort study. SETTING AND PATIENTS: We included hospitalized English-speaking adults transferred to one of nine LTACHs in the United States between March 2020 and February 2021 and completed a survey. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Validated instruments for impairments and free response questions about recovering. Among 282 potentially eligible participants who provided permission to be contacted, 156 (55.3%) participated (median age, 65; 38.5% female; 61.3% in good prior health; median length of stay of 57 d; 77% mechanically ventilated for a median of 26 d; 42% had a tracheostomy). Approximately two-thirds (64%) had a persistent impairment, including physical (57%), respiratory (49%; 19% on supplemental oxygen), psychiatric (24%), and cognitive impairments (15%). Nearly half (47%) had two or more impairment types. Participants also experienced persistent debility from hospital-acquired complications, including mononeuropathies and pressure ulcers. Participants described protracted recovery, attributing improvements to exercise/rehabilitation, support, and time. While considered life-altering with 78.7% not returning to their usual health, participants expressed gratitude for recovering; 99% returned home and 60% of previously employed individuals returned to work. CONCLUSIONS: Nearly two-thirds of survivors of among the most prolonged severe COVID-19 illness had persistent impairments at 1 year that resembled post-intensive care syndrome after critical illness plus debility from hospital-acquired complications.

National Prevalence of Alcohol and Other Substance Use Disorders Among Emergency Department Visits and Hospitalizations: NHAMCS 2014-2018.

BACKGROUND: Acute healthcare utilization attributed to alcohol use disorders (AUD) and other substance use disorders (SUD) is rising. OBJECTIVE: To describe the prevalence and characteristics of emergency department (ED) visits and hospitalizations made by adults with AUD or SUD. DESIGN, SETTING, AND PARTICIPANTS: Observational study with retrospective analysis of the National Hospital Ambulatory Medical Care Survey (2014 to 2018), a nationally representative survey of acute care visits with information on the presence of AUD or SUD abstracted from the medical chart. MAIN MEASURES: Outcome measured as the presence of AUD or SUD. KEY RESULTS: From 2014 to 2018, the annual average prevalence of AUD or SUD was 9.4% of ED visits (9.3 million visits) and 11.9% hospitalizations (1.4 million hospitalizations). Both estimates increased over time (30% and 57% relative increase for ED visits and hospitalizations, respectively, from 2014 to 2018). ED visits and hospitalizations from individuals with AUD or SUD, compared to individuals with neither AUD nor SUD, had higher percentages of Medicaid insurance (ED visits: AUD: 33.1%, SUD: 35.0%, neither: 24.4%; hospitalizations: AUD: 30.7%, SUD: 36.3%, neither: 14.8%); homelessness (ED visits: AUD: 6.2%, SUD 4.4%, neither 0.4%; hospitalizations: AUD: 5.9%, SUD 7.3%, neither: 0.4%); coexisting depression (ED visits: AUD: 26.3%, SUD 24.7%, neither 10.5%; hospitalizations: AUD: 33.5%, SUD 35.3%, neither: 13.9%); and injury/trauma (ED visits: AUD: 51.3%, SUD 36.3%, neither: 26.4%; hospitalizations: AUD: 31.8%, SUD: 23.8%, neither: 15.0%). CONCLUSIONS: In this nationally representative study, 1 in 11 ED visits and 1 in 9 hospitalizations were made by adults with AUD or SUD, and both increased over time. These estimates are higher or similar than previous national estimates using claims data. This highlights the importance of identifying opportunities to address AUD and SUD in acute care settings in tandem with other medical concerns, particularly among visits presenting with injury, trauma, or coexisting depression.

Man vs. Machine: Comparing Physician vs. Electronic Health Record-Based Model Predictions for 30-Day Hospital Readmissions.

BACKGROUND: Electronic health record (EHR)-based readmission risk prediction models can be automated in real-time but have modest discrimination and may be missing important readmission risk factors. Clinician predictions of readmissions may incorporate information unavailable in the EHR, but the comparative usefulness is unknown. We sought to compare clinicians versus a validated EHR-based prediction model in predicting 30-day hospital readmissions. METHODS: We conducted a prospective survey of internal medicine clinicians in an urban safety-net hospital. Clinicians prospectively predicted patients' 30-day readmission risk on 5-point Likert scales, subsequently dichotomized into low- vs. high-risk. We compared human with machine predictions using discrimination, net reclassification, and diagnostic test characteristics. Observed readmissions were ascertained from a regional hospitalization database. We also developed and assessed a "human-plus-machine" logistic regression model incorporating both human and machine predictions. RESULTS: We included 1183 hospitalizations from 106 clinicians, with a readmission rate of 20.8%. Both clinicians and the EHR model had similar discrimination (C-statistic 0.66 vs. 0.66, p = 0.91). Clinicians had higher specificity (79.0% vs. 48.9%, p < 0.001) but lower sensitivity (43.9 vs. 75.2%, p < 0.001) than EHR model predictions. Compared with machine, human was better at reclassifying non-readmissions (non-event NRI + 30.1%) but worse at reclassifying readmissions (event NRI - 31.3%). A human-plus-machine approach best optimized discrimination (C-statistic 0.70, 95% CI 0.67-0.74), sensitivity (65.5%), and specificity (66.7%). CONCLUSION: Clinicians had similar discrimination but higher specificity and lower sensitivity than EHR model predictions. Human-plus-machine was better than either alone. Readmission risk prediction strategies should incorporate clinician assessments to optimize the accuracy of readmission predictions.

Comparative effectiveness of long-term acute care hospital versus skilled nursing facility transfer.

BACKGROUND: Long-term acute care hospital (LTACH) use varies considerably across the U.S., which may reflect uncertainty about the effectiveness of LTACHs vs. skilled nursing facilities (SNF), the principal post-acute care alternative. Given that LTACHs provide more intensive care and thus receive over triple the reimbursement of SNFs for comparable diagnoses, we sought to compare outcomes and spending between LTACH versus SNF transfer. METHODS: Using Medicare claims linked to electronic health record (EHR) data from six Texas Hospitals between 2009 and 2010, we conducted a retrospective cohort study of patients hospitalized on a medicine service in a high-LTACH use region and discharged to either an LTACH or SNF and followed for one year. The primary outcomes included mortality, 60-day recovery without inpatient care, days at home, and healthcare spending RESULTS: Of 3503 patients, 18% were transferred to an LTACH. Patients transferred to LTACHs were younger (median 71 vs. 82 years), less likely to be female (50.5 vs 66.6%) and white (69.0 vs. 84.1%), but were sicker (24.3 vs. 14.2% for prolonged intensive care unit stay; median diagnosis resource intensity weight of 2.03 vs. 1.38). In unadjusted analyses, patients transferred to an LTACH vs. SNF were less likely to survive (59.1 vs. 65.0%) or recover (62.5 vs 66.0%), and spent fewer days at home (186 vs. 200). Adjusting for demographic and clinical confounders available in Medicare claims and EHR data, LTACH transfer was not significantly associated with differences in mortality (HR, 1.12, 95% CI, 0.94-1.33), recovery (SHR, 1.07, 0.93-1.23), and days spent at home (IRR, 0.96, 0.83-1.10), but was associated with greater Medicare spending ($16,689 for one year, 95% CI, $12,216-$21,162). CONCLUSION: LTACH transfer for Medicare beneficiaries is associated with similar clinical outcomes but with higher healthcare spending compared to SNF transfer. LTACH use should be reserved for patients who require complex inpatient care and cannot be cared for in SNFs.

Can we predict early 7-day readmissions using a standard 30-day hospital readmission risk prediction model?

BACKGROUND: Despite focus on preventing 30-day readmissions, early readmissions (within 7 days of discharge) may be more preventable than later readmissions (8-30 days). We assessed how well a previously validated 30-day EHR-based readmission prediction model predicts 7-day readmissions and compared differences in strength of predictors. METHODS: We conducted an observational study on adult hospitalizations from 6 diverse hospitals in North Texas using a 50-50 split-sample derivation and validation approach. We re-derived model coefficients for the same predictors as in the original 30-day model to optimize prediction of 7-day readmissions. We then compared the discrimination and calibration of the 7-day model to the 30-day model to assess model performance. To examine the changes in the point estimates between the two models, we evaluated the percent changes in coefficients. RESULTS: Of 32,922 index hospitalizations among unique patients, 4.4% had a 7-day admission and 12.7% had a 30-day readmission. Our original 30-day model had modestly lower discrimination for predicting 7-day vs. any 30-day readmission (C-statistic of 0.66 vs. 0.69, p ≤ 0.001). Our re-derived 7-day model had similar discrimination (C-statistic of 0.66, p = 0.38), but improved calibration. For the re-derived 7-day model, discharge day factors were more predictive of early readmissions, while baseline characteristics were less predictive. CONCLUSION: A previously validated 30-day readmission model can also be used as a stopgap to predict 7-day readmissions as model performance did not substantially change. However, strength of predictors differed between the 7-day and 30-day model; characteristics at discharge were more predictive of 7-day readmissions, while baseline characteristics were less predictive. Improvements in predicting early 7-day readmissions will likely require new risk factors proximal to day of discharge.

An Evidence-Based Medicine Approach to Antihyperglycemic Therapy in Diabetes Mellitus to Overcome Overtreatment.

Overtreatment is pervasive in medicine and leads to potential patient harms and excessive costs in health care. Although evidence-based medicine is often derided as practice by rote algorithmic medicine, the appropriate application of key evidence-based medicine principles in clinical decision making is fundamental to preventing overtreatment and promoting high-value, individualized patient-centered care. Specifically, this article discusses the importance of (1) using absolute rather than relative estimates of benefits to inform treatment decisions; (2) considering the time horizon to benefit of treatments; (3) balancing potential harms and benefits; and (4) using shared decision making by physicians to incorporate the patient's values and preferences into treatment decisions. Here, we illustrate the application of these principles to considering the decision of whether or not to recommend intensive glycemic control to patients to minimize microvascular and cardiovascular complications in type 2 diabetes mellitus. Through this lens, this example will illustrate how an evidence-based medicine approach can be used to individualize glycemic goals and prevent overtreatment, and can serve as a template for applying evidence-based medicine to inform treatment decisions for other conditions to optimize health and individualize patient care.

Vital Signs Are Still Vital: Instability on Discharge and the Risk of Post-Discharge Adverse Outcomes.

BACKGROUND: Vital sign instability on discharge could be a clinically objective means of assessing readiness and safety for discharge; however, the association between vital sign instability on discharge and post-hospital outcomes is unclear. OBJECTIVE: To assess the association between vital sign instability at hospital discharge and post-discharge adverse outcomes. DESIGN: Multi-center observational cohort study using electronic health record data. Abnormalities in temperature, heart rate, blood pressure, respiratory rate, and oxygen saturation were assessed within 24 hours of discharge. We used logistic regression adjusted for predictors of 30-day death and readmission. PARTICIPANTS: Adults (≥18 years) with a hospitalization to any medicine service in 2009-2010 at six hospitals (safety-net, community, teaching, and non-teaching) in north Texas. MAIN MEASURES: Death or non-elective readmission within 30 days after discharge. KEY RESULTS: Of 32,835 individuals, 18.7 % were discharged with one or more vital sign instabilities. Overall, 12.8 % of individuals with no instabilities on discharge died or were readmitted, compared to 16.9 % with one instability, 21.2 % with two instabilities, and 26.0 % with three or more instabilities (p < 0.001). The presence of any (≥1) instability was associated with higher risk-adjusted odds of either death or readmission (AOR 1.36, 95 % CI 1.26-1.48), and was more strongly associated with death (AOR 2.31, 95 % CI 1.91-2.79). Individuals with three or more instabilities had nearly fourfold increased odds of death (AOR 3.91, 95 % CI 1.69-9.06) and increased odds of 30-day readmission (AOR 1.36, 95 % 0.81-2.30) compared to individuals with no instabilities. Having two or more vital sign instabilities at discharge had a positive predictive value of 22 % and positive likelihood ratio of 1.8 for 30-day death or readmission. CONCLUSIONS: Vital sign instability on discharge is associated with increased risk-adjusted rates of 30-day mortality and readmission. These simple vital sign criteria could be used to assess safety for discharge, and to reduce 30-day mortality and readmissions.

Factors associated with primary vein graft occlusion in a multicenter trial with mandated ultrasound surveillance.

OBJECTIVE: Even in the setting of duplex ultrasound (DUS) surveillance, a significant number of lower extremity vein bypass grafts (LEVBGs) become occluded as a first event. We sought to identify factors that may contribute to these primary occlusions. METHODS: This was a retrospective analysis of the Project of Ex Vivo Graft Engineering via Transfection III (PREVENT III) multicenter randomized clinical trial, in which 1404 patients with critical limb ischemia (CLI) underwent LEVBG with 1-year follow-up. Subjects were to undergo DUS at regular intervals (1, 3, 6, and 12 months), with reintervention based on prespecified DUS criteria. Patients who had nontechnical graft occlusion as the initial graft-related event were identified, and multivariate analysis was used to determine factors associated with primary graft occlusion. RESULTS: Primary vein graft occlusion occurred in 200 subjects and accounted for 36% of all primary patency events and 64% of all graft occlusions in the trial. Primary occlusion events were evenly distributed throughout the first postoperative year. Rates of recurrent CLI, loss of secondary patency, and major amputation in those with primary occlusion were 55%, 79%, and 22% respectively as compared to 18%, 10%, and 10% for subjects without primary occlusion (P < .001). On multivariate analysis, African-American race (subdistribution hazard ratio [SHR], 1.50; 95% confidence interval [CI], 1.06-2.12), a graft diameter <3 mm (SHR, 2.31; 95% CI, 1.33-4.01), and nonadherence with ultrasound surveillance (SHR, 1.58; 95% CI, 1.10-2.27) were independently associated with primary graft occlusion. Of the 123 subjects who received their last scheduled surveillance DUS prior to a primary occlusion event, 39 had a critical ultrasound abnormality identified but failed to undergo graft revision, while 84 had no critical ultrasound abnormality identified. Among these 84 subjects, female gender (SHR, 1.65; 95% CI, 1.07-2.54), and graft diameter <3 mm (SHR, 2.12; 95% CI, 1.03-4.37) were independent factors associated with unheralded graft occlusion. CONCLUSIONS: Among patients undergoing LEVBG for CLI, almost half of primary patency events are occlusions even in the setting of a DUS surveillance protocol. African Americans, patients with smaller-diameter grafts, and those who are nonadherent with surveillance ultrasound are at increased risk. Failure to intervene on critical findings, and lack of sensitivity of DUS threshold criteria to predict thrombosis, are also important contributors. These findings suggest that prevention of vein graft thrombosis requires further improvements in risk stratification, surveillance, and the timing of reinterventions.

Survival, Function, and Cognition After Hospitalization in Long-Term Acute Care Hospitals.

Importance: More than 70 000 Medicare beneficiaries receive care in long-term acute care hospitals (LTCHs) annually for prolonged acute illness. However, little is known about long-term functional and cognitive outcomes of middle-aged and older adults after hospitalization in an LTCH. Objective: To describe survival, functional, and cognitive status after LTCH hospitalization and to identify factors associated with an adverse outcome. Design, Setting, and Participants: This retrospective cohort study included middle-aged and older adults enrolled in the Health and Retirement Study (HRS) with linked fee-for-service Medicare claims. Included participants were aged 50 years or older with an LTCH admission between January 1, 2003, and December 31, 2016, with HRS interviews available before admission. Data were analyzed between November 1, 2021, and June 30, 2023. Main Outcomes and Measures: Function and cognition were ascertained from HRS interviews conducted every 2 years. The primary outcome was death or severe impairment in the 2.5 years after LTCH hospitalization, defined as dependencies in 2 or more activities of daily living (ADLs) or dementia. Multivariable logistic regression was performed to evaluate associations with a priori selected risk factors including pre-LTCH survival prognosis (Lee index score), pre-LTCH impairment status, and illness severity characterized by receipt of mechanical ventilation and prolonged intensive care unit stay of 3 days or longer. Results: This study included 396 participants, with a median age of 75 (IQR, 68-82) years. Of the participants, 201 (51%) were women, 125 (28%) had severe impairment, and 318 (80%) died or survived with severe impairment (functional, cognitive, or both) within 2.5 years of LTCH hospitalization. After accounting for acute illness characteristics, prehospitalization survival prognosis as determined by the Lee index score and severe baseline impairment (functional, cognitive, or both) were associated with an increased likelihood of death or severe impairment in the 2.5 years after LTCH hospitalization (adjusted odds ratio [AOR], 3.2 [95% CI, 1.7 to 6.0] for a 5-point increase in Lee index score; and AOR, 4.5 [95% CI, 1.3 to 15.4] for severe vs no impairment). Conclusions and Relevance: In this cohort study, 4 of 5 middle-aged and older adults died or survived with severe impairment within 2.5 years of LTCH hospitalization. Better preadmission survival prognosis and functional and cognitive status were associated with lower risk of an adverse outcome, and these findings should inform decision-making for older adults with prolonged acute illness.

Identifying patients with diabetes and the earliest date of diagnosis in real time: an electronic health record case-finding algorithm.

BACKGROUND: Effective population management of patients with diabetes requires timely recognition. Current case-finding algorithms can accurately detect patients with diabetes, but lack real-time identification. We sought to develop and validate an automated, real-time diabetes case-finding algorithm to identify patients with diabetes at the earliest possible date. METHODS: The source population included 160,872 unique patients from a large public hospital system between January 2009 and April 2011. A diabetes case-finding algorithm was iteratively derived using chart review and subsequently validated (n = 343) in a stratified random sample of patients, using data extracted from the electronic health records (EHR). A point-based algorithm using encounter diagnoses, clinical history, pharmacy data, and laboratory results was used to identify diabetes cases. The date when accumulated points reached a specified threshold equated to the diagnosis date. Physician chart review served as the gold standard. RESULTS: The electronic model had a sensitivity of 97%, specificity of 90%, positive predictive value of 90%, and negative predictive value of 96% for the identification of patients with diabetes. The kappa score for agreement between the model and physician for the diagnosis date allowing for a 3-month delay was 0.97, where 78.4% of cases had exact agreement on the precise date. CONCLUSIONS: A diabetes case-finding algorithm using data exclusively extracted from a comprehensive EHR can accurately identify patients with diabetes at the earliest possible date within a healthcare system. The real-time capability may enable proactive disease management.

Use and satisfaction with key functions of a common commercial electronic health record: a survey of primary care providers.

BACKGROUND: Despite considerable financial incentives for adoption, there is little evidence available about providers' use and satisfaction with key functions of electronic health records (EHRs) that meet "meaningful use" criteria. METHODS: We surveyed primary care providers (PCPs) in 11 general internal medicine and family medicine practices affiliated with 3 health systems in Texas about their use and satisfaction with performing common tasks (documentation, medication prescribing, preventive services, problem list) in the Epic EHR, a common commercial system. Most practices had greater than 5 years of experience with the Epic EHR. We used multivariate logistic regression to model predictors of being a structured documenter, defined as using electronic templates or prepopulated dot phrases to document at least two of the three note sections (history, physical, assessment and plan). RESULTS: 146 PCPs responded (70%). The majority used free text to document the history (51%) and assessment and plan (54%) and electronic templates to document the physical exam (57%). Half of PCPs were structured documenters (55%) with family medicine specialty (adjusted OR 3.3, 95% CI, 1.4-7.8) and years since graduation (nonlinear relationship with youngest and oldest having lowest probabilities) being significant predictors. Nearly half (43%) reported spending at least one extra hour beyond each scheduled half-day clinic completing EHR documentation. Three-quarters were satisfied with documenting completion of pneumococcal vaccinations and half were satisfied with documenting cancer screening (57% for breast, 45% for colorectal, and 46% for cervical). Fewer were satisfied with reminders for overdue pneumococcal vaccination (48%) and cancer screening (38% for breast, 37% for colorectal, and 31% for cervical). While most believed the problem list was helpful (70%) and kept an up-to-date list for their patients (68%), half thought they were unreliable and inaccurate (51%). CONCLUSIONS: Dissatisfaction with and suboptimal use of key functions of the EHR may mitigate the potential for EHR use to improve preventive health and chronic disease management. Future work should optimize use of key functions and improve providers' time efficiency.

The effectiveness and value of AMX0035 and oral edaravone for amyotrophic lateral sclerosis: A summary from the Institute for Clinical and Economic Review's Midwest Comparative Effectiveness Public Advisory Council.

DISCLOSURES: Funding for this summary was contributed by Blue Cross Blue Shield of MA, California Healthcare Foundation, The Patrick and Catherine Weldon Donaghue Medical Research Foundation, Arnold Ventures, and Kaiser Foundation Health Plan Inc., to the Institute for Clinical and Economic Review (ICER), an independent organization that evaluates the evidence on the value of health care interventions. ICER's annual policy summit is supported by dues from Aetna, America's Health Insurance Plans, AbbVie, Alnylam, AstraZeneca, Biogen, Blue Shield of CA, Boehringer Ingelheim, Cambia Health Services, CVS, Editas, Evolve Pharmacy Solutions, Express Scripts, Genentech/ Roche, GlaxoSmithKline, Harvard Pilgrim, Health Care Service Corporation, Health First, Health Partners, Humana, Johnson & Johnson (Janssen), Kaiser Permanente, LEO Pharma, Mallinckrodt, Merck, Novartis, National Pharmaceutical Council, Pfizer. Premera, Prime Therapeutics, Regeneron, Sanofi, Spark Therapeutics, Sun Life Financial, uniQure, and United Healthcare. Mr Nikitin, Ms McKenna, Ms Richardson, and Drs Rind and Pearson are employed by ICER. Through their affiliated institutions, Drs Makam, Carlson, and Suh received funding from ICER for the work described in this summary.

Characterizing patients hospitalized without an acute care indication: A retrospective cohort study.

BACKGROUND: Hospitalizations by patients who do not meet acute inpatient criteria are common and overburden healthcare systems. Studies have characterized these alternate levels of care (ALC) but have not delineated prolonged (pALC) versus short ALC (sALC) stays. OBJECTIVE: To descriptively compare pALC and sALC hospitalizations-groups we hypothesize have unique needs. DESIGNS, SETTINGS, AND PARTICIPANTS: A retrospective study of hospitalizations from March-April 2018 at an academic safety-net hospital. MAIN OUTCOME AND MEASURES: Levels of care for pALC (>3 days) and sALC (1-3 days) were determined using InterQual©, an industry standard utilization review tool for determining the clinical appropriateness of hospitalization. We examined sociodemographic and clinical characteristics. RESULTS: Of 2365 hospitalizations, 215 (9.1%) were pALC, 277 (11.7%) were sALC, and 1873 (79.2%) had no ALC days. There were 17,683 hospital days included, and 28.3% (n = 5006) were considered ALC. Compared to patients with sALC, those with pALC were older and more likely to be publicly insured, experience homelessness, and have substance use or psychiatric comorbidities. Patients with pALC were more likely to be admitted for care meeting inpatient criteria (89.3% vs. 66.8%, p < .001), had significantly more ALC days (median 8 vs. 1 day, p < .001), and were less likely to be discharged to the community (p < .001). CONCLUSIONS: Patients with prolonged ALC stays were more likely to be admitted for acute care, had greater psychosocial complexity, significantly longer lengths of stay, and unique discharge needs. Given the complexity and needs for hospitalizations with pALC days, intensive interdisciplinary coordination and resource mobilization are necessary.

Establishing Medicaid incentives for liberating nursing home patients from ventilators.

BACKGROUND: Chronic ventilator use in Tennessee nursing homes surged following 2010 increases in respiratory care payment rates. Tennessee's Medicaid program implemented multiple policies between 2014 and 2017 to promote ventilator liberation in 11 nursing homes, including quality reporting, on-site monitoring, and pay-for-performance incentives. METHODS: Using repeated cross-sectional analysis of Medicare and Medicaid nursing home claims (2011-2017), hospital discharge records (2010-2017), and nursing home quality reports (2015-2017), we examined how service use changed as Tennessee implemented policies designed to promote ventilator liberation in nursing homes. We measured the annual number of nursing home patients with ventilator-related service use; discharge destination of ventilated inpatients and percent of nursing home patients liberated from ventilators. RESULTS: Between 2011 and 2014, the number of Medicare SNF and Medicaid nursing home patients with ventilator use increased more than sixfold. Among inpatients with prolonged mechanical ventilation, discharges to home decreased as discharges to nursing homes increased. As Tennessee implemented policy changes, ventilator-related service use moderately declined in nursing homes from a peak of 198 ventilated Medicare SNF patients in 2014 to 125 in 2017 and from 182 Medicaid patients with chronic ventilator use in 2014 to 145 patients in 2017. Nursing home weaning rates peaked at 49%-52% in 2015 and 2016, but declined to 26% by late 2017. Median number of days from admission to wean declined from 81 to 37 days. CONCLUSIONS: This value-based approach demonstrates the importance of designing payment models that target key patient outcomes like ventilator liberation.