Faculty Bibliography

BACKGROUND:The Medicare Hospital Readmissions Reduction Program has led to fewer readmissions following hospitalizations with a principal diagnosis of heart failure (HF). Patients with HF are frequently hospitalized for other causes. OBJECTIVES/OBJECTIVE:This study sought to compare trends in Medicare risk-adjusted, 30-day readmissions following principal HF hospitalizations and other hospitalizations with HF. METHODS:This was a retrospective study of 12,973,853 Medicare hospitalizations with a principal or secondary diagnosis of HF between January 2008 and June 2015. Hospitalizations were categorized as follows: principal HF hospitalizations; principal acute myocardial infarction or pneumonia hospitalizations with secondary HF; and other hospitalizations with secondary HF. The study examined trends in risk-adjusted, 30-day, all-cause readmission rates for each cohort and trends in differences in readmission rates among cohorts by using linear spline regression models. RESULTS:Before passage of the Affordable Care Act in March 2010, risk-adjusted, 30-day readmission rates were stable for all 3 cohorts, with mean monthly rates of 26.1%, 24.9%, and 24.4%, respectively. Risk-adjusted readmission rates started declining after passage of the Affordable Care Act by 1.09% (95% confidence interval [CI]: 0.51% to 1.68%), 1.24% (95% CI: 0.92% to 1.57%), and 1.05% (95% CI: 0.52% to 1.58%) per year, respectively, until implementation of the Hospital Readmissions Reduction Program in October 2012 and then stabilized for all 3 cohorts. CONCLUSIONS:Patients with HF are often hospitalized for other causes, and these hospitalizations have high readmission rates. Policy changes led to decreases in readmission rates for both principal and secondary HF hospitalizations. Readmission rates in both groups remain high, suggesting that initiatives targeting all hospitalized patients with HF continue to be warranted.

BACKGROUND:Temporal changes in the readmission rates for patient groups and conditions that were not directly under the purview of Hospital Readmissions Reduction Program (HRRP) can help assess whether efforts to lower readmissions extended beyond targeted patients and conditions. METHODS:Using Nationwide Readmissions Database (2010-2015), we assessed trends in all-cause readmission rates for one of the 3 HRRP conditions (acute myocardial infarction, heart failure, pneumonia) or conditions not targeted by HRRP in 6 age-insurance groups defined by age-groups (≥65 or <65 years) and payer (Medicare, Medicaid, or private insurance). RESULTS:In the ≥65-year age-group, readmission rates for those covered by Medicare, private-insurance, and Medicaid decreased annually for acute myocardial infarction (risk-adjusted odds ratio, OR [95%CI], Medicare 0.94 [0.94-0.95], private-insurance 0.95 [0.93-0.97], and Medicaid 0.93 [0.90-0.97]), heart failure (ORs, 0.96 [0.96-0.97], 0.97 [0.96-0.99], and 0.96 [0.94-0.98], for the 3 payers, respectively), and pneumonia (ORs, 0.96 [0.96-0.97), 0.96 [0.95-0.97], and 0.94 [0.92-0.96], respectively). In the <65-year age-group, there was a similar decline in 30-day readmission rates for acute myocardial infarction (risk-adjusted ORs for yearly decrease, Medicare 0.97 [0.96-0.98], private-insurance 0.93 [0.92-0.94], and Medicaid 0.94 [0.92-0.95]), heart failure (ORs, 0.98 [0.97-0.98], 0.97 [0.95-0.98], and 0.96 [0.96-0.97], for the 3 payers, respectively), and pneumonia (ORs, 0.98 [0.97-0.99], 0.98 [0.97-1.00], and 0.98 [0.97-0.99], respectively). In comparison to the targeted conditions, there was a relatively small, but significant, decrease in readmission rates for non-target conditions across all age-insurance groups. CONCLUSION/CONCLUSIONS:There was a significant decline in readmission rates across patient age-insurance groups for the 3 target conditions under the HRRP, as well as a decline in readmission rates for non-target conditions. There appears to be a systematic improvement in readmission rates for patient groups beyond the population of fee-for-service, older, Medicare beneficiaries included in the HRRP.

BACKGROUND:Hospital readmission rates are publicly reported by the Centers for Medicare & Medicaid Services (CMS); however, the implications of emergency department (ED) visits following hospital discharge on readmissions are uncertain. We describe the frequency, diagnoses, and hospital-level variation in ED visitation following hospital discharge, including the relationship between risk-standardized ED visitation and readmission rates. METHODS:This is a cross-sectional analysis of Medicare beneficiaries hospitalized for acute myocardial infarction (AMI), heart failure, and pneumonia between July 2011 and June 2012. We used Medicare Standard Analytic Files to identify admissions, readmissions, and ED visits consistent with CMS measures. Postdischarge ED visits were defined as treat-and-discharge ED services within 30 days of hospitalization without readmission. We utilized hierarchical generalized linear models to calculate hospital risk-standardized postdischarge ED visit rates and readmission rates. RESULTS:We included 157,035 patients hospitalized at 1656 hospitals for AMI, 391,209 at 3044 hospitals for heart failure, and 342,376 at 3484 hospitals for pneumonia. After hospitalization for AMI, heart failure, and pneumonia, there were 14,714 (9%), 31,621 (8%), and 26,681 (8%) ED visits, respectively. Hospital-level variation in postdischarge ED visit rates was substantial: AMI (median: 8.3%; 5th and 95th percentile: 2.8%-14.3%), heart failure (median: 7.3%; 5th and 95th percentile: 3.0%-13.3%), and pneumonia (median: 7.1%; 5th and 95th percentile: 2.4%-13.2%). There was statistically significant inverse correlation between postdischarge ED visit rates and readmission rates: AMI (-0.23), heart failure (-0.29), and pneumonia (-0.18). CONCLUSIONS:Following hospital discharge, ED treatand- discharge visits are half as common as readmissions for Medicare beneficiaries. There is wide hospital-level variation in postdischarge ED visitation, and hospitals with higher ED visitation rates demonstrated lower readmission rates.

PURPOSE: To better understand the decision making behind the ordering of CT pulmonary angiography (CTPA) for the diagnosis of pulmonary embolism (PE) in the emergency department. METHODS: We conducted semistructured interviews with our institution's emergency medicine (EM) providers and radiologists who read CTPAs performed in the emergency department. We employed the Theoretical Domains Framework-a formal, structured approach used to better understand the motivations and beliefs of physicians surrounding a complex medical decision making-to categorize the themes that arose from our interviews. RESULTS: EM providers were identified as the main drivers of CTPA ordering. Both EM and radiologist groups perceived the radiologist's role as more limited. Experience- and gestalt-based heuristics were the most important factors driving this decision and more important, in many cases, than established algorithms for CTPA ordering. There were contrasting views on the value of d-dimer in the suspected PE workup, with EM providers finding this test less useful than radiologists. EM provider and radiologist suggestions for improving the appropriateness of CTPA ordering consisted of making this process more arduous and incorporating d-dimer tests and prediction rules into a decision support tool. CONCLUSION: EM providers were the main drivers of CTPA ordering, and there was a marginalized role for the radiologist. Experience- and gestalt-based heuristics were the main influencers of CTPA ordering. Our findings suggest that a more nuanced intervention than simply including a d-dimer and a prediction score in each preimaging workup may be necessary to curb overordering of CTPA in patients suspected of PE.

BACKGROUND:The assessment of clinical guideline adherence for the evaluation of pulmonary embolism (PE) via computed tomography pulmonary angiography (CTPA) currently requires either labor-intensive, retrospective chart review or prospective collection of PE risk scores at the time of CTPA order. The recording of clinical data in a structured manner in the electronic health record (EHR) may make it possible to automate the calculation of a patient's PE risk classification and determine whether the CTPA order was guideline concordant. OBJECTIVES/OBJECTIVE:The objective of this study was to measure the performance of automated, structured-data-only versions of the Wells and revised Geneva risk scores in emergency department encounters during which a CTPA was ordered. The hypothesis was that such an automated method would classify a patient's PE risk with high accuracy compared to manual chart review. METHODS:We developed automated, structured-data-only versions of the Wells and revised Geneva risk scores to classify 212 emergency department (ED) encounters during which a CTPA was performed as "PE Likely" or "PE Unlikely." We then combined these classifications with D-dimer ordering data to assess each encounter as guideline concordant or discordant. The accuracy of these automated classifications and assessments of guideline concordance were determined by comparing them to classifications and concordance based on the complete Wells and revised Geneva scores derived via abstractor manual chart review. RESULTS:The automatically derived Wells and revised Geneva risk classifications were 91.5% and 92% accurate compared to the manually determined classifications, respectively. There was no statistically significant difference between guideline adherence calculated by the automated scores as compared to manual chart review (Wells: 70.8 vs. 75%, p = 0.33 | Revised Geneva: 65.6 vs. 66%, p = 0.92). CONCLUSION/CONCLUSIONS:The Wells and revised Geneva score risk classifications can be approximated with high accuracy using automated extraction of structured EHR data elements in patients who received a CTPA. Combining these automated scores with D-dimer ordering data allows for the automated assessment of clinical guideline adherence for CTPA ordering in the emergency department, without the burden of manual chart review.

BACKGROUND:Hospitalization and readmission rates have decreased in recent years, with the possible consequence that hospitals are increasingly filled with high-risk patients. OBJECTIVE:We studied whether readmission reduction has affected the risk profile of hospitalized patients and whether readmission reduction was similarly realized among hospitalizations with low, medium, and high risk of readmissions. DESIGN/METHODS:Retrospective study of hospitalizations between January 2009 and June 2015. PATIENTS/METHODS:Hospitalized fee-for-service Medicare beneficiaries, categorized into 1 of 5 specialty cohorts used for the publicly reported hospital-wide readmission measure. MEASUREMENTS/METHODS:Each hospitalization was assigned a predicted risk of 30-day, unplanned readmission using a risk-adjusted model similar to publicly reported measures. Trends in monthly mean predicted risk for each cohort and trends in monthly observed to expected readmission for hospitalizations in the lowest 20%, middle 60%, and highest 20% of risk of readmission were assessed using time series models. RESULTS:Of 47,288,961 hospitalizations, 16.2% (n = 7,642,161) were followed by an unplanned readmission within 30 days. We found that predicted risk of readmission increased by 0.24% (P = .03) and 0.13% (P = .004) per year for hospitalizations in the surgery/ gynecology and neurology cohorts, respectively. We found no significant increase in predicted risk for hospitalizations in the medicine (0.12%, P = .12), cardiovascular (0.32%, P = .07), or cardiorespiratory (0.03%, P = .55) cohorts. In each cohort, observed to expected readmission rates steadily declined, and at similar rates for patients at low, medium, and high risk of readmission. CONCLUSIONS:Hospitals have been effective at reducing readmissions across a range of patient risk strata and clinical conditions. The risk of readmission for hospitalized patients has increased for 2 of 5 clinical cohorts.

BACKGROUND: Interventions to reduce readmissions following acute heart failure hospitalization require early identification of patients. The purpose of this study was to develop and test accuracies of various approaches to identify patients with acute decompensated heart failure (ADHF) using data derived from the electronic health record. METHODS AND RESULTS: We included 37,229 hospitalizations of adult patients at a single hospital in 2013-2015. We developed four algorithms to identify hospitalization with a principal discharge diagnosis of ADHF: 1) presence of one of three clinical characteristics; 2) logistic regression of 31 structured data elements; 3) machine learning with unstructured data; 4) machine learning with both structured and unstructured data. In data validation, 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, while both machine learning algorithms had AUCs of 0.99. Based on a brief survey of three providers who perform chart review for ADHF, we estimated providers spent 8.6 minutes per chart review; using this this parameter, we estimated providers would spend 61.4, 57.3, 28.7, and 25.3 minutes on secondary chart review for each case of ADHF if initial screening was done with algorithms 1, 2, 3, and 4, respectively. CONCLUSION: Machine learning algorithms with unstructured notes had best performance for identification of ADHF and can improve provider efficiency for delivery of quality improvement interventions.