Faculty Bibliography

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: 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.

BACKGROUND: Educational and computerized interventions have been shown to reduce red blood cell (RBC) transfusion rates, yet controversy remains surrounding the optimal strategy needed to achieve sustained reductions in liberal transfusions. STUDY DESIGN AND METHODS: The purpose of this study was to assess the impact of clinician decision support (CDS) along with targeted education on liberal RBC utilization to four high-utilizing service lines compared with no education to control service lines across an academic medical center. Clinical data along with associated hemoglobin levels at the time of all transfusion orders between April 2014 and December 2015 were obtained via retrospective chart review. The primary outcome was the change in the rate of liberal RBC transfusion orders (defined as any RBC transfusion when the hemoglobin level is >7.0 g/dL). Secondary outcomes included the annual projected reduction in the number of transfusions and the associated decrease in cost due to these changes as well as length of stay (LOS) and death index. These measures were compared between the 12 months prior to the initiative and the 9-month postintervention period. RESULTS: Liberal RBC utilization decreased from 13.4 to 10.0 units per 100 patient discharges (p = 0.002) across the institution, resulting in a projected 12-month savings of $720,360. The mean LOS and the death index did not differ significantly in the postintervention period. CONCLUSION: Targeted education combined with the incorporation of CDS at the time of order entry resulted in significant reductions in the incidence of liberal RBC utilization without adversely impacting inpatient care, whereas control service lines exposed only to CDS had no change in transfusion habits.

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: Many hospitalized patients have at least 1 chronic disease that is not optimally controlled. The purpose of this study was to explore inpatient provider attitudes about chronic disease management and, in particular, barriers and facilitators of chronic disease management in the hospital. METHODS: We conducted a qualitative study of semi-structured interviews of 31 inpatient providers from an academic medical center. We interviewed attending physicians, resident physicians, physician assistants, and nurse practitioners from various specialties about attitudes, experiences with, and barriers and facilitators towards chronic disease management in the hospital. Qualitative data were analyzed using constant comparative analysis. RESULTS: Providers perceived that hospitalizations offer an opportunity to improve chronic disease management, as patients are evaluated by a new care team and observed in a controlled environment. Providers perceived clinical benefits to in-hospital chronic care, including improvements in readmission and length of stay, but expressed concerns for risks related to adverse events and distraction from the acute problem. Barriers included provider lack of comfort with managing chronic diseases, poor communication between inpatient and outpatient providers, and hospital-system focus on patient discharge. A strong relationship with the outpatient provider and involvement of specialists were facilitators of inpatient chronic disease management. CONCLUSIONS: Providers perceived benefits to in-hospital chronic disease management for both processes of care and clinical outcomes. Efforts to increase inpatient chronic disease management will need to overcome barriers in multiple domains. Journal of Hospital Medicine 2017;12:162-167.

OBJECTIVE: To determine the clinical significance of tachycardia in the postoperative period. PATIENTS AND METHODS: Individuals 18 years or older undergoing hip and knee arthroplasty were included in the study. Two data sets were collected from different time periods: development data set from January 1, 2011, through December 31, 2011, and validation data set from December 1, 2012, through September 1, 2014. We used the development data set to identify the optimal definition of tachycardia with the strongest association with the vascular composite outcome (pulmonary embolism and myocardial necrosis and infarction). The predictive value of this definition was assessed in the validation data set for each outcome of interest, pulmonary embolism, myocardial necrosis and infarction, and infection using multiple logistic regression to control for known risk factors. RESULTS: In 1755 patients in the development data set, a maximum heart rate (HR) greater than 110 beats/min was found to be the best cutoff as a correlate of the composite vascular outcome. Of the 4621 patients who underwent arthroplasty in the validation data set, 40 (0.9%) had pulmonary embolism. The maximum HR greater than 110 beats/min had an odds ratio (OR) of 9.39 (95% CI, 4.67-18.87; sensitivity, 72.5%; specificity, 78.0%; positive predictive value, 2.8%; negative predictive value, 99.7%) for pulmonary embolism. Ninety-seven patients (2.1%) had myocardial necrosis (elevated troponin). The maximum HR greater than 110 beats/min had an OR of 4.71 (95% CI, 3.06-7.24; sensitivity, 47.4%; specificity, 78.1%; positive predictive value, 4.4%; negative predictive value, 98.6%) for this outcome. Thirteen (.3%) patients had myocardial infarction according to our predetermined definition, and the maximum HR greater than 110 beats/min had an OR of 1.72 (95% CI, 0.47-6.27). CONCLUSION: Postoperative tachycardia within the first 4 days of surgery should not be dismissed as a postoperative variation in HR, but may precede clinically significant adverse outcomes.

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.