Faculty Bibliography

Background: Heart failure is among the most common reasons for admission to hospital and is associated with high rates of readmission. Studies have shown that the frequency of heart failure hospitalisation in temperate climates increases in winter months. While such findings suggest that the risk of hospitalisation is higher in winter as compared to summer months, there has been little evaluation of whether there is seasonal variation in the readmission risk of patients who are hospitalised for heart failure. Purpose: To examine seasonal variations in: 1) readmission risk for patients hospitalised with heart failure; and 2) frequency of heart failure hospitalisation for patients at different risk of readmission. Methods: We performed a retrospective study of United States Medicare beneficiaries age >=65 who were hospitalised for heart failure between January 1, 2009 and June 30, 2015. We used a predictive model for 30-day unplanned hospital readmission to assign each hospitalisation a predicted risk of readmission; this model adds demographic and prior utilization data to the hospital-wide readmission model used by the Centres for Medicare and Medicaid Services (CMS). Each hospitalisation was categorized as lowest 20%, middle 60%, and highest 20% of predicted readmission risk. We calculated rate of hospitalisations per calendar month, predicted readmission risk, and monthly rate of hospitalisations for each risk stratum in the study period; monthly hospitalisations were standardized to 30 days. When comparing risk strata, we divided the totals for the middle 60% stratum by three. Results: Among 2,661,837 heart failure hospitalisations, we observed the highest rates of hospitalisation in January through March (range 37,185-37,949 per month) and the lowest rates in July through September (range 29,901-30,603 per month). Conversely, predicted readmission rates were lowest in January and highest in August, with rates of 23.1% and 24.0%, respectively. The number of hospitalisations increased in winter months for patients in all three risk strata, with greatest variation in seasonal differences observed for patients in the lowest 20% of predicted risk (Figure). For example, hospitalisation rates for highest risk patients were 7058 per 30 days in January versus 6473 per 30 days in August; for lowest risk patients, these values were 7852 versus 5595, respectively. (Figure Presented) Conclusion: Readmission risk decreased in winter versus summer months for patients hospitalised for heart failure. Much of the seasonal variation in heart failure hospitalisations appears to be due to a large excess of hospitalisations of these low risk patients in winter months. Our results suggest that preventative measures, such as vaccinations or dietary education, that target lower risk patients in colder months may reduce overall utilisation

BACKGROUND: Therapeutic interchange of a same class medication for an outpatient medication is a widespread practice during hospitalization in response to limited hospital formularies. However, therapeutic interchange may increase risk of medication errors. The objective was to characterize the prevalence and safety of therapeutic interchange. METHODS AND FINDINGS: Secondary analysis of a transitions of care study. We included patients over age 64 admitted to a tertiary care hospital between 2009-2010 with heart failure, pneumonia, or acute coronary syndrome who were taking a medication in any of six commonly-interchanged classes on admission: proton pump inhibitors (PPIs), histamine H2-receptor antagonists (H2 blockers), hydroxymethylglutaryl CoA reductase inhibitors (statins), angiotensin-converting enzyme (ACE) inhibitors, angiotensin receptor blockers (ARBs), and inhaled corticosteroids (ICS). There was limited electronic medication reconciliation support available. Main measures were presence and accuracy of therapeutic interchange during hospitalization, and rate of medication reconciliation errors on discharge. We examined charts of 303 patients taking 555 medications at time of admission in the six medication classes of interest. A total of 244 (44.0%) of medications were therapeutically interchanged to an approved formulary drug at admission, affecting 64% of the study patients. Among the therapeutically interchanged drugs, we identified 78 (32.0%) suspected medication conversion errors. The discharge medication reconciliation error rate was 11.5% among the 244 therapeutically interchanged medications, compared with 4.2% among the 311 unchanged medications (relative risk [RR] 2.75, 95% confidence interval [CI] 1.45-5.19). CONCLUSIONS: Therapeutic interchange was prevalent among hospitalized patients in this study and elevates the risk for potential medication errors during and after hospitalization. Improved electronic systems for managing therapeutic interchange and medication reconciliation may be valuable.

Stagnation in hospitals' adoption of data integration functionalities coupled with reduction in the number of operational health information exchanges could become a significant impediment to hospitals' adoption of 3 critical capabilities: electronic health information exchange, interoperability, and medication reconciliation, in which electronic systems are used to assist with resolving medication discrepancies and improving patient safety. Against this backdrop, we assessed the relationships between the 3 capabilities.We conducted an observational study applying partial least squares-structural equation modeling technique to 27 variables obtained from the 2013 American Hospital Association annual survey Information Technology (IT) supplement, which describes health IT capabilities.We included 1330 hospitals. In confirmatory factor analysis, out of the 27 variables, 15 achieved loading values greater than 0.548 at P < .001, as such were validated as the building blocks of the 3 capabilities. Subsequent path analysis showed a significant, positive, and cyclic relationship between the capabilities, in that decreases in the hospitals' adoption of one would lead to decreases in the adoption of the others.These results show that capability for high quality medication reconciliation may be impeded by lagging adoption of interoperability and health information exchange capabilities. Policies focused on improving one or more of these capabilities may have ancillary benefits.

Background To isolate hospital effects on risk-standardized hospital-readmission rates, we examined readmission outcomes among patients who had multiple admissions for a similar diagnosis at more than one hospital within a given year. Methods We divided the Centers for Medicare and Medicaid Services hospital-wide readmission measure cohort from July 2014 through June 2015 into two random samples. All the patients in the cohort were Medicare recipients who were at least 65 years of age. We used the first sample to calculate the risk-standardized readmission rate within 30 days for each hospital, and we classified hospitals into performance quartiles, with a lower readmission rate indicating better performance (performance-classification sample). The study sample (identified from the second sample) included patients who had two admissions for similar diagnoses at different hospitals that occurred more than 1 month and less than 1 year apart, and we compared the observed readmission rates among patients who had been admitted to hospitals in different performance quartiles. Results In the performance-classification sample, the median risk-standardized readmission rate was 15.5% (interquartile range, 15.3 to 15.8). The study sample included 37,508 patients who had two admissions for similar diagnoses at a total of 4272 different hospitals. The observed readmission rate was consistently higher among patients admitted to hospitals in a worse-performing quartile than among those admitted to hospitals in a better-performing quartile, but the only significant difference was observed when the patients were admitted to hospitals in which one was in the best-performing quartile and the other was in the worst-performing quartile (absolute difference in readmission rate, 2.0 percentage points; 95% confidence interval, 0.4 to 3.5; P=0.001). Conclusions When the same patients were admitted with similar diagnoses to hospitals in the best-performing quartile as compared with the worst-performing quartile of hospital readmission performance, there was a significant difference in rates of readmission within 30 days. The findings suggest that hospital quality contributes in part to readmission rates independent of factors involving patients. (Funded by Yale-New Haven Hospital Center for Outcomes Research and Evaluation and others.).

INTRODUCTION: Orthopaedic care is shifting to alternative payment models. We examined whether New York University Langone Medical Center achieved savings under the Centers for Medicare and Medicaid Services Bundled Payments for Care Improvement initiative. METHODS: This study was a difference-in-differences study of Medicare fee-for-service patients hospitalized from April 2011 to June 2012 and October 2013 to December 2014 for lower extremity joint arthroplasty, cardiac valve procedures, or spine surgery (intervention groups), or for congestive heart failure, major bowel procedures, medical peripheral vascular disorders, medical noninfectious orthopaedic care, or stroke (control group). We examined total episode costs and costs by service category. RESULTS: We included 2,940 intervention episodes and 1,474 control episodes. Relative to the trend in the control group, lower extremity joint arthroplasty episodes achieved the greatest savings: adjusted average episode cost during the intervention period decreased by $3,017 (95% confidence interval [CI], -$6,066 to $31). For cardiac procedures, the adjusted average episode cost decreased by $2,999 (95% CI, -$8,103 to $2,105), and for spinal fusion, it increased by $8,291 (95% CI, $2,879 to $13,703). Savings were driven predominantly by shifting postdischarge care from inpatient rehabilitation facilities to home. Spinal fusion index admission costs increased because of changes in surgical technique. DISCUSSION: Under bundled payment, New York University Langone Medical Center decreased total episode costs in patients undergoing lower extremity joint arthroplasty. For patients undergoing cardiac valve procedures, evidence of savings was not as strong, and for patients undergoing spinal fusion, total episode costs increased. For all three conditions, the proportion of patients referred to inpatient rehabilitation facilities upon discharge decreased. These changes were not associated with an increase in index hospital length of stay or readmission rate. CONCLUSION: Opportunities for savings under bundled payment may be greater for lower extremity joint arthroplasty than for other conditions.

Importance: The Affordable Care Act has led to US national reductions in hospital 30-day readmission rates for heart failure (HF), acute myocardial infarction (AMI), and pneumonia. Whether readmission reductions have had the unintended consequence of increasing mortality after hospitalization is unknown. Objective: To examine the correlation of paired trends in hospital 30-day readmission rates and hospital 30-day mortality rates after discharge. Design, Setting, and Participants: Retrospective study of Medicare fee-for-service beneficiaries aged 65 years or older hospitalized with HF, AMI, or pneumonia from January 1, 2008, through December 31, 2014. Exposure: Thirty-day risk-adjusted readmission rate (RARR). Main Outcomes and Measures: Thirty-day RARRs and 30-day risk-adjusted mortality rates (RAMRs) after discharge were calculated for each condition in each month at each hospital in 2008 through 2014. Monthly trends in each hospital's 30-day RARRs and 30-day RAMRs after discharge were examined for each condition. The weighted Pearson correlation coefficient was calculated for hospitals' paired monthly trends in 30-day RARRs and 30-day RAMRs after discharge for each condition. Results: In 2008 through 2014, 2962554 hospitalizations for HF, 1229939 for AMI, and 2544530 for pneumonia were identified at 5016, 4772, and 5057 hospitals, respectively. In January 2008, mean hospital 30-day RARRs and 30-day RAMRs after discharge were 24.6% and 8.4% for HF, 19.3% and 7.6% for AMI, and 18.3% and 8.5% for pneumonia. Hospital 30-day RARRs declined in the aggregate across hospitals from 2008 through 2014; monthly changes in RARRs were -0.053% (95% CI, -0.055% to -0.051%) for HF, -0.044% (95% CI, -0.047% to -0.041%) for AMI, and -0.033% (95% CI, -0.035% to -0.031%) for pneumonia. In contrast, monthly aggregate changes across hospitals in hospital 30-day RAMRs after discharge varied by condition: HF, 0.008% (95% CI, 0.007% to 0.010%); AMI, -0.003% (95% CI, -0.005% to -0.001%); and pneumonia, 0.001% (95% CI, -0.001% to 0.003%). However, correlation coefficients in hospitals' paired monthly changes in 30-day RARRs and 30-day RAMRs after discharge were weakly positive: HF, 0.066 (95% CI, 0.036 to 0.096); AMI, 0.067 (95% CI, 0.027 to 0.106); and pneumonia, 0.108 (95% CI, 0.079 to 0.137). Findings were similar in secondary analyses, including with alternate definitions of hospital mortality. Conclusions and Relevance: Among Medicare fee-for-service beneficiaries hospitalized for heart failure, acute myocardial infarction, or pneumonia, reductions in hospital 30-day readmission rates were weakly but significantly correlated with reductions in hospital 30-day mortality rates after discharge. These findings do not support increasing postdischarge mortality related to reducing hospital readmissions.

OBJECTIVE: To compare trends in readmission rates among safety net and non-safety net hospitals under the US Hospital Readmission Reduction Program (HRRP). DESIGN: A retrospective time series analysis using Medicare administrative claims data from January 2008 to June 2015. SETTING: We examined 3254 US hospitals eligible for penalties under the HRRP, categorised as safety net or non-safety net hospitals based on the hospital's proportion of patients with low socioeconomic status. PARTICIPANTS: Admissions for Medicare fee-for-service patients, age >/=65 years, discharged alive, who had a valid five-digit zip code and did not have a principal discharge diagnosis of cancer or psychiatric illness were included, for a total of 52 516 213 index admissions. PRIMARY AND SECONDARY OUTCOME MEASURES: Mean hospital-level, all-condition, 30-day risk-adjusted standardised unplanned readmission rate, measured quarterly, along with quarterly rate of change, and an interrupted time series examining: April-June 2010, after HRRP was passed, and October-December 2012, after HRRP penalties were implemented. RESULTS: 58.0% (SD 15.3) of safety net hospitals and 17.1% (SD 10.4) of non-safety net hospitals' patients were in the lowest quartile of socioeconomic status. The mean safety net hospital standardised readmission rate declined from 17.0% (SD 3.7) to 13.6% (SD 3.6), whereas the mean non-safety net hospital declined from 15.4% (SD 3.0) to 12.7% (SD 2.5). The absolute difference in rates between safety net and non-safety net hospitals declined from 1.6% (95% CI 1.3 to 1.9) to 0.9% (0.7 to 1.2). The quarterly decline in standardised readmission rates was 0.03 percentage points (95% CI 0.03 to 0.02, p<0.001) greater among safety net hospitals over the entire study period, and no differential change among safety net and non-safety net hospitals was found after either HRRP was passed or penalties enacted. CONCLUSIONS: Since HRRP was passed and penalties implemented, readmission rates for safety net hospitals have decreased more rapidly than those for non-safety net hospitals.

BACKGROUND: Interventions to reduce readmission following hospitalization for acute decompensated heart failure (ADHF) require early identification of patients. Electronic health record (EHR)-based approaches to identify patients can range in complexity from simple algorithms based on few data elements to machine learning algorithms using big data. The purpose of this study was to compare performance of a range of algorithms to identify patients with ADHF. Given the emphasis that hospitals currently place on patients with ADHF, we developed models with high sensitivity to avoid missed opportunities for care improvement; this approach assumed secondary chart review would be necessary to confirm a diagnosis. We therefore estimated the time needed for secondary review by providers following initial screening with each algorithm. METHODS: We performed a retrospective study of hospitalizations for patients age > 18 at an academicmedical center in 2013-2015. Using a random 75% development set, we developed four algorithms to identify hospitalizations with a principal diagnosis of ADHF using EHR data through the second midnight of hospitalization: 1) one of three characteristics: heart failure on the problem list, loop diuretic use, or brain natriuretic peptide > 500 pg/ml; 2) logistic regression of 31 clinically-relevant data elements; 3) machine learning approach using L1-regularization logistic regression and unstructured data, including provider notes and radiology reports; 4) machine learning with both structured and unstructured data. We assessed performance of each algorithm in the 25% validation set. We also conducted a brief survey of providers who perform chart review for ADHF to estimate time needed for secondary screening following primary screening with each algorithm. RESULTS: We included 37,229 hospitalizations in the study, of which 1,294 (3.5%) carried a principal diagnosis of ADHF. Algorithm 1 had a sensitivity of 0.98 and positive predictive value (PPV) of 0.14 for ADHF. Algorithm 2 had an area under the receiver operating characteristic curve (AUC) of 0.96 and a PPV of 0.15 when setting the sensitivity at 0.98. Both machine learning algorithms had AUCs of 0.99; with a sensitivity of 0.98, algorithms 3 and 4 had PPVs of 0.30 and 0.34, respectively. Based on survey of three providers, we estimated providers spent 8.6 min per chart review; using this this parameter, providers would spend 61.4, 57.3, 28.7, and 25.3 min on secondary chart review for each case of ADHF if initial screening was done with algorithms 1, 2, 3, and 4, respectively. CONCLUSIONS: Traditional approaches with structured data can accurately identify nearly all patients with ADHF but are limited by a large number of false positives. Machine learning algorithms with unstructured notes and radiology reports can retain this sensitivity while significantly reducing false positives, thereby improving provider efficiency for delivery of quality improvement interventions

BACKGROUND: Safety-net and teaching hospitals are somewhat more likely to be penalized for excess readmissions, but the association of other hospital characteristics with readmission rates is uncertain and may have relevance for hospital-centered interventions. OBJECTIVE: To examine the independent association of 8 hospital characteristics with hospital-wide 30-day risk-standardized readmission rate (RSRR). DESIGN: This is a retrospective cross-sectional multivariable analysis. SUBJECTS: US hospitals. MEASURES: Center for Medicare and Medicaid Services specification of hospital-wide RSRR from July 1, 2013 through June 30, 2014 with race and Medicaid dual-eligibility added. RESULTS: We included 6,789,839 admissions to 4474 hospitals of Medicare fee-for-service beneficiaries aged over 64 years. In multivariable analyses, there was regional variation: hospitals in the mid-Atlantic region had the highest RSRRs [0.98 percentage points higher than hospitals in the Mountain region; 95% confidence interval (CI), 0.84-1.12]. For-profit hospitals had an average RSRR 0.38 percentage points (95% CI, 0.24-0.53) higher than public hospitals. Both urban and rural hospitals had higher RSRRs than those in medium metropolitan areas. Hospitals without advanced cardiac surgery capability had an average RSRR 0.27 percentage points (95% CI, 0.18-0.36) higher than those with. The ratio of registered nurses per hospital bed was not associated with RSRR. Variability in RSRRs among hospitals of similar type was much larger than aggregate differences between types of hospitals. CONCLUSIONS: Overall, larger, urban, academic facilities had modestly higher RSRRs than smaller, suburban, community hospitals, although there was a wide range of performance. The strong regional effect suggests that local practice patterns are an important influence. Disproportionately high readmission rates at for-profit hospitals may highlight the role of financial incentives favoring utilization.