Time spent in prior hospital stay and outcomes for ventilator patients in long-term acute care hospitals.

BACKGROUND: Long-term acute care hospitals (LTACHs) treat mechanical ventilator patients who are difficult to wean and expected to be on mechanical ventilator for a prolonged period. However, there are varying views on who should be transferred to LTACHs and when they should be transferred. The purpose of this study is to assess the relationship between length of stay in a short-term acute care hospital (STACH) after endotracheal intubation (time to LTACH) and weaning success and mortality for ventilated patients discharged to an LTACH. METHODS: Using 2014-2015 Medicare claims and assessment data, we identified patients who had an endotracheal intubation in STACH and transferred to an LTACH with prolonged mechanical ventilation (defined as 96 or more consecutive hours on a ventilator). We controlled for age, gender, STACH stay procedures and diagnoses, Elixhauser comorbid conditions, and LTACH quality characteristics. We used instrumental variable estimation to account for unobserved patient and provider characteristics. RESULTS: The study cohort included 13,622 LTACH cases with median time to LTACH of 18 days. The unadjusted ventilator weaning rate at LTACH was 51.7%, and unadjusted 90-day mortality rate was 43.7%. An additional day spent in STACH after intubation is associated with 11.6% reduction in the odds of weaning, representing a 2.5 percentage point reduction in weaning rate at 18 days post endotracheal intubation. We found no statistically significant relationship between time to LTACH and the odds of 90-day mortality. CONCLUSIONS: Discharging ventilated patients earlier from STACH to LTACH is associated with higher weaning probability for LTACH patients on prolonged mechanical ventilation. Our findings suggest that delaying ventilated patients' discharge to LTACH may negatively influence the patients' chances of being weaned from the ventilator.

Determinants of Hip and Knee Replacement: The Role of Social Support and Family Dynamics.

The objective of this study was to examine variations in the determinants of joint replacement (JR) across gender and age, with emphasis on the role of social support and family dynamics. We analyzed data from the US Health and Retirement Study (1998-2010) on individuals aged 45 or older with no prior receipt of JR. We used logistic regression to analyze the probability of receiving knee or hip replacement by gender and age (<65, 65+). We estimated the effect of demographic, health needs, economic, and familial support variables on the rate of JR. We found that being married/partnered with a healthy spouse/partner is positively associated with JR utilization in both age groups (65+ group OR: 1.327 and <65 group OR: 1.476). While this finding holds for men, it is not statistically significant for women. Among women younger than 65, having children younger than 18 lowers the odds (OR: 0.201) and caring for grandchildren increases the odds (1.364) of having a JR. Finally, elderly women who report availability of household assistance from a child have higher odds of receiving a JR as compared with elderly women without a child who could assist (OR: 1.297). No effect of available support from children was observed for those below 65 years old and elderly men. Our results show that intrafamily dynamics and familial support are important determinants of JR; however, their effects vary by gender and age. Establishing appropriate support mechanisms could increase access to cost-effective JR among patients in need of surgery.

Hospital patients with severe wounds: early evidence on the impact of Medicare payment changes on treatment patterns and outcomes.

AIMS: This study examines the effects of recent changes in Medicare long-term care hospital (LTCH) payments on treatment patterns and outcomes for severe wound patients discharged from short-term acute care hospitals (STACHs). MATERIALS AND METHODS: The rolling implementation of a new Medicare payment policy was used to develop a difference-in-difference model. The study population consisted of Medicare beneficiaries subjected to the payment policy changes and hospitalized for stage 3, 4, or unstageable wounds; non-healing surgical wounds; and fistula. Using 2015-Q1-2017 Medicare claims data, changes in outcomes were examined for severe wound patients exposed to the new policy (treatment) and those that were not (comparison). All outcomes were modeled using linear regressions and adjusted for patient clinical characteristics. Analysis was conducted in a full sample and a sample with high-LTCH-use propensity. RESULTS: Severe wound patients exposed to the new policy experienced 4.1 and 7.5 percentage point (pp) reductions in LTCH use relative to the comparison group in the full sample and high-LTCH-propensity sample, respectively (p < .01 and p = .039). No statistically significant change was found in 60-day mortality or Medicare spending after the policy change in the treatment group as compared to the comparison group (p > .10). However, among severe wound patients who are exposed to the new policy in the high-LTCH-propensity sample, readmission and post-discharge sepsis rates increased after the policy change relative to the comparison group (readmission rate = 8.1 pp, p = .075; sepsis rate = 7.0 pp, p = .033). LIMITATIONS: The findings are based on data from a limited timeframe around the policy change and, thus, provide only early evidence on the effects of the new policy. CONCLUSION: The new LTCH payment policy is associated with no changes in Medicare spending and mortality, but higher readmissions and post-discharge sepsis rates among severe wound patients with a high likelihood to use an LTCH.

Identifying Poor-Performing Hospitals in the Medicare Hospital-Acquired Condition Reduction Program: An Assessment of Reliability.

Centers for Medicare and Medicaid Services (CMS) estimated that Medicare's Hospital-Acquired Condition Reduction Program (HAC-RP) would reduce hospital payments by $364 million in fiscal year 2016. Although observers have questioned the validity of certain HAC-RP measures, less attention has been paid to the determination of low-performing hospitals (bottom quartile) and the reliability of penalty assignment. This study used publicly available data from CMS's Hospital Compare to simulate the consistency of hospitals' scores and the assignment of penalties under repeated measurement with no change in each hospital's underlying quality. The simulation showed that 64.0% of all hospitals and 40.6% of hospitals subject to payment penalty are statistically significantly different from the penalty threshold at the 95% confidence level. The proportion of hospitals statistically different from the threshold showed significant variation by ownership status, teaching status, bed size, and other factors. The simulation further showed that due only to chance, 18.0% of penalized hospitals would escape penalty on repeated measurement. Policymakers should consider alterations to the HAC-RP to improve its reliability.

Complication Rates, Hospital Size, and Bias in the CMS Hospital-Acquired Condition Reduction Program.

In 2016, Medicare's Hospital-Acquired Condition Reduction Program (HAC-RP) will reduce hospital payments by $364 million. Although observers have questioned the validity of certain HAC-RP measures, less attention has been paid to the determination of low-performing hospitals (bottom quartile) and the assignment of penalties. This study investigated possible bias in the HAC-RP by simulating hospitals' likelihood of being in the worst-performing quartile for 8 patient safety measures, assuming identical expected complication rates across hospitals. Simulated likelihood of being a poor performer varied with hospital size. This relationship depended on the measure's complication rate. For 3 of 8 measures examined, the equal-quality simulation identified poor performers similarly to empirical data (c-statistic approximately 0.7 or higher) and explained most of the variation in empirical performance by size (Efron's R2 > 0.85). The Centers for Medicare & Medicaid Services could address potential bias in the HAC-RP by stratifying by hospital size or using a broader "all-harm" measure.