Background

Hospitals that have robust financial performance may have improved publicly reported outcomes.

Objectives

To assess the relationship between hospital financial performance and publicly reported outcomes of care, and to assess whether improved outcome metrics affect subsequent hospital financial performance.

Design

Observational cohort study.

Setting and patients

Hospital financial data from the Office of Statewide Health Planning and Development in California in 2008 and 2012 were linked to data from the Centers for Medicare and Medicaid Services Hospital Compare website.

Measurements

Hospital financial performance was measured by net revenue by operations, operating margin, and total margin. Outcomes were 30-day risk-standardized mortality and readmission rates for acute myocardial infarction (AMI), congestive heart failure (CHF), and pneumonia (PNA).

Results

Among 279 hospitals, there was no consistent relationship between measures of financial performance in 2008 and publicly reported outcomes from 2008 to 2011 for AMI and PNA. However, improved hospital financial performance (by any of the 3 measures) was associated with a modest increase in CHF mortality rates (ie, 0.26% increase in CHF mortality rate for every 10% increase in operating margin [95% confidence interval: 0.07%-0.45%]). Conversely, there were no significant associations between outcomes from 2008 to 2011 and subsequent financial performance in 2012 (P > 0.05 for all).

Conclusions

Robust financial performance is not associated with improved publicly reported outcomes for AMI, CHF, and PNA. Financial incentives in addition to public reporting, such as readmissions penalties, may help motivate hospitals with robust financial performance to further improve publicly reported outcomes. Reassuringly, improved mortality and readmission rates do not necessarily lead to loss of revenue. Journal of Hospital Medicine 2016;11:481-488. © 2016 Society of Hospital Medicine.

Background

Although hypothesized to be a hazard of hospitalization, it is unclear whether hospital-acquired anemia (HAA) is associated with increased adverse outcomes following discharge.

Objective

To examine the incidence, predictors, and postdischarge outcomes associated with HAA.

Design

Observational cohort study using electronic health record data.

Subjects

Consecutive medicine discharges between November 1, 2009 and October 30, 2010 from 6 Texas hospitals, including safety-net, teaching, and nonteaching sites. Patients with anemia on admission or missing hematocrit values at admission or discharge were excluded.

Measures

HAA was defined using the last hematocrit value prior to discharge and categorized by severity. The primary outcome was a composite of 30-day mortality and nonelective readmission.

Results

Among 11,309 patients, one-third developed HAA (21.6% with mild HAA; 10.1% with moderate HAA; and 1.4% with severe HAA). The 2 strongest potentially modifiable predictors of developing moderate or severe HAA were length of stay (adjusted odds ratio [OR], 1.26 per day; 95% confidence interval [CI], 1.23-1.29) and receipt of a major procedure (adjusted OR, 5.09; 95% CI, 3.79-6.82). Patients without HAA had a 9.7% incidence for the composite outcome versus 16.4% for those with severe HAA. Severe HAA was independently associated with a 39% increase in the odds for 30-day readmission or death (95% CI, 1.09-1.78). Most patients with severe HAA (85%) underwent a major procedure, had a discharge diagnosis of hemorrhage, and/or a discharge diagnosis of hemorrhagic disorder.

Conclusions

Severe HAA is associated with increased odds for 30-day mortality and readmission after discharge; however, it is uncertain whether severe HAA is preventable. Journal of Hospital Medicine 2017;12:317-322.

Background

Respiratory rate (RR) is an independent predictor of adverse outcomes and an integral component of many risk prediction scores for hospitalised adults. Yet, it is unclear if RR is recorded accurately. We sought to assess the potential accuracy of RR by analysing the distribution and variation as a proxy, since RR should be normally distributed if recorded accurately.

Methods

We conducted a descriptive observational study of electronic health record data from consecutive hospitalisations from 2009 to 2010 from six diverse hospitals. We assessed the distribution of the maximum RR on admission, using heart rate (HR) as a comparison since this is objectively measured. We assessed RR patterns among selected subgroups expected to have greater physiological variation using the coefficient of variation (CV=SD/mean).

Results

Among 36 966 hospitalisations, recorded RR was not normally distributed (p<0.001), but right skewed (skewness=3.99) with values clustered at 18 and 20 (kurtosis=23.9). In contrast, HR was relatively normally distributed. Patients with a cardiopulmonary diagnosis or hypoxia only had modestly greater variation (CV increase of 2%-6%). Among 1318 patients transferred from the ward to the intensive care unit (n=1318), RR variation the day preceding transfer was similar to that observed on admission (CV 0.24 vs 0.26), even for those transferred with respiratory failure (CV 0.25).

Conclusions

The observed patterns suggest that RR is inaccurately recorded, even among those with cardiopulmonary compromise, and represents a 'spot' estimate with values of 18 and 20 breaths per minute representing 'normal.' While spot estimates may potentially be adequate to indicate clinical stability, inaccurate RR may alternatively lead to misclassification of disease severity, potentially jeopardising patient safety. Thus, we recommend greater training for hospital personnel to accurately record RR.

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.

Objectives

To determine why non-mechanically ventilated hospitalized older adults are transferred to long-term acute care (LTAC) hospitals rather than remaining in the hospital.

Design

Observational cohort.

Setting

National Medicare data.

Participants

Non-mechanically ventilated hospitalized adults aged 65 and older with fee-for-service Medicare in 2012 who were transferred to an LTAC hospital (n=1,831) or had a prolonged hospitalization without transfer (average hospital length of stay or longer of those transferred to an LTAC hospital) and had one of the 50 most common hospital diagnoses leading to LTAC transfer (N=12,875).

Measurements

We assessed predictors of transfer using a multilevel model, adjusting for patient-, hospital-, and hospital referral region (HRR)-level factors. We estimated proportions of variance at each level and adjusted hospital- and HRR-specific LTAC transfer rates using sequential models.

Results

The strongest predictor of transfer was being hospitalized near an LTAC hospital (<1.4 vs > 33.6 miles, adjusted odds ratio=6.2, 95% confidence interval (CI)=4.2-9.1). After adjusting for case mix, differences between hospitals explained 15.4% of the variation in LTAC use and differences between regions explained 27.8%. Case mix-adjusted LTAC use was high in the South, where many HRRs had rates between 20.3% and 53.1%, whereas many HRRs were less than 5.4% in the Pacific Northwest, North, and New England. From our fully adjusted model, the median adjusted hospital LTAC transfer rate was 7.2% (interquartile range 2.8-17.5%), with substantial within-region variation (intraclass coefficient=0.25, 95% CI=0.21-0.30).

Conclusions

Nearly half of the variation in LTAC use is independent of illness severity and is explained by which hospital and what region the individual was hospitalized in. Because of the greater fragmentation of care and Medicare spending with LTAC transfers (because LTAC hospitals generate a separate bundled payment from the hospital), greater attention is needed to define the optimal role of LTAC hospitals in caring for older adults. J Am Geriatr Soc 66:2112-2119, 2018.