Leora I. Horwitz

Association Between Hospital Penalty Status Under the Hospital Readmission Reduction Program and Readmission Rates for Target and Nontarget Conditions

Importance Readmission rates declined after announcement of the Hospital Readmission Reduction Program (HRRP), which penalizes hospitals for excess readmissions for acute myocardial infarction (AMI), heart failure (HF), and pneumonia. Objective To compare trends in readmission rates for target and nontarget conditions, stratified by hospital penalty status. Design, Setting, and Participants Retrospective cohort study of Medicare fee-for-service beneficiaries older than 64 years discharged between January 1, 2008, and June 30, 2015, from 2214 penalty hospitals and 1283 nonpenalty hospitals. Difference-interrupted time-series models were used to compare trends in readmission rates by condition and penalty status. Exposure Hospital penalty status or target condition under the HRRP. Main Outcomes and Measures Thirty-day risk adjusted, all-cause unplanned readmission rates for target and nontarget conditions. Results The study included 48 137 102 hospitalizations of 20 351 161 Medicare beneficiaries. In January 2008, the mean readmission rates for AMI, HF, pneumonia, and nontarget conditions were 21.9%, 27.5%, 20.1%, and 18.4%, respectively, at hospitals later subject to financial penalties and 18.7%, 24.2%, 17.4%, and 15.7% at hospitals not subject to penalties. Between January 2008 and March 2010, prior to HRRP announcement, readmission rates were stable across hospitals (except AMI at nonpenalty hospitals). Following announcement of HRRP (March 2010), readmission rates for both target and nontarget conditions declined significantly faster for patients at hospitals later subject to financial penalties compared with those at nonpenalized hospitals (for AMI, additional decrease of -1.24 [95% CI, -1.84 to -0.65] percentage points per year relative to nonpenalty discharges; for HF, -1.25 [95% CI, -1.64 to -0.86]; for pneumonia, -1.37 [95% CI, -1.80 to -0.95]; and for nontarget conditions, -0.27 [95% CI, -0.38 to -0.17]; P < .001 for all). For penalty hospitals, readmission rates for target conditions declined significantly faster compared with nontarget conditions (for AMI, additional decline of -0.49 [95% CI, -0.81 to -0.16] percentage points per year relative to nontarget conditions [P = .004]; for HF, -0.90 [95% CI, -1.18 to -0.62; P < .001]; and for pneumonia, -0.57 [95% CI, -0.92 to -0.23; P < .001]). In contrast, among nonpenalty hospitals, readmissions for target conditions declined similarly or more slowly compared with nontarget conditions (for AMI, additional increase of 0.48 [95% CI, 0.01-0.95] percentage points per year [P = .05]; for HF, 0.08 [95% CI, -0.30 to 0.46; P = .67]; for pneumonia, 0.53 [95% CI, 0.13-0.93; P = .01]). After HRRP implementation in October 2012, the rate of change for readmission rates plateaued (P < .05 for all except pneumonia at nonpenalty hospitals), with the greatest relative change observed among hospitals subject to financial penalty. Conclusions and Relevance Medicare fee-for-service patients at hospitals subject to penalties under the HRRP had greater reductions in readmission rates compared with those at nonpenalized hospitals. Changes were greater for target vs nontarget conditions for patients at the penalized hospitals but not at the other hospitals.

Explanation and elaboration of the SQUIRE (Standards for Quality Improvement Reporting Excellence) Guidelines, V.2.0: examples of SQUIRE elements in the healthcare improvement literature

Since its publication in 2008, SQUIRE (Standards for Quality Improvement Reporting Excellence) has contributed to the completeness and transparency of reporting of quality improvement work, providing guidance to authors and reviewers of reports on healthcare improvement work. In the interim, enormous growth has occurred in understanding factors that influence the success, and failure, of healthcare improvement efforts. Progress has been particularly strong in three areas: the understanding of the theoretical basis for improvement work; the impact of contextual factors on outcomes; and the development of methodologies for studying improvement work. Consequently, there is now a need to revise the original publication guidelines. To reflect the breadth of knowledge and experience in the field, we solicited input from a wide variety of authors, editors and improvement professionals during the guideline revision process. This Explanation and Elaboration document (E&E) is a companion to the revised SQUIRE guidelines, SQUIRE 2.0. The product of collaboration by an international and interprofessional group of authors, this document provides examples from the published literature, and an explanation of how each reflects the intent of a specific item in SQUIRE. The purpose of the guidelines is to assist authors in writing clearly, precisely and completely about systematic efforts to improve the quality, safety and value of healthcare services. Authors can explore the SQUIRE statement, this E&E and related documents in detail at http://www.squire-statement.org.

Association of Changing Hospital Readmission Rates With Mortality Rates After Hospital Discharge

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, 2 962 554 hospitalizations for HF, 1 229 939 for AMI, and 2 544 530 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.

Association of hospital volume with readmission rates: a retrospective cross-sectional study

Objective To examine the association of hospital volume (a marker of quality of care) with hospital readmission rates. Design Retrospective cross-sectional study. Setting 4651US acute care hospitals. Study data 6 916 644 adult discharges, excluding patients receiving psychiatric or medical cancer treatment. Main outcome measures We used Medicare fee-for-service data from 1 July 2011 to 30 June 2012 to calculate observed-to-expected, unplanned, 30 day, standardized readmission rates for hospitals and for specialty cohorts medicine, surgery/gynecology, cardiorespiratory, cardiovascular, and neurology. We assessed the association of hospital volume by quintiles with 30 day, standardized readmission rates, with and without adjustment for hospital characteristics (safety net status, teaching status, geographic region, urban/rural status, nurse to bed ratio, ownership, and cardiac procedure capability. We also examined associations with the composite outcome of 30 day, standardized readmission or mortality rates. Results Mean 30 day, standardized readmission rate among the fifth of hospitals with the lowest volume was 14.7 (standard deviation 5.3) compared with 15.9 (1.7) among the fifth of hospitals with the highest volume (P<0.001). We observed the same pattern of lower readmission rates in the lowest versus highest volume hospitals in the specialty cohorts for medicine (16.6 v 17.4, P<0.001), cardiorespiratory (18.5 v 20.5, P<0.001), and neurology (13.2 v 14.0, p=0.01) cohorts; the cardiovascular cohort, however, had an inverse association (14.6 v 13.7, P<0.001). These associations remained after adjustment for hospital characteristics except in the cardiovascular cohort, which became non-significant, and the surgery/gynecology cohort, in which the lowest volume fifth of hospitals had significantly higher standardized readmission rates than the highest volume fifth (difference 0.63 percentage points (95% confidence interval 0.10 to 1.17), P=0.02). Mean 30 day, standardized mortality or readmission rate was not significantly different between highest and lowest volume fifths (20.4 v 20.2, P=0.19) and was highest in the middle fifth of hospitals (range 20.6–20.8). Conclusions Standardized readmission rates are lowest in the lowest volume hospitals—opposite from the typical association of greater hospital volume with better outcomes. This association was independent of hospital characteristics and was only partially attenuated by examining mortality and readmission together. Our findings suggest that readmissions are associated with different aspects of care than mortality or complications.

Development and Validation of an Algorithm to Identify Planned Readmissions From Claims Data.

BACKGROUND It is desirable not to include planned readmissions in readmission measures because they represent deliberate, scheduled care. OBJECTIVES To develop an algorithm to identify planned readmissions, describe its performance characteristics, and identify improvements. DESIGN Consensus-driven algorithm development and chart review validation study at 7 acute-care hospitals in 2 health systems. PATIENTS For development, all discharges qualifying for the publicly reported hospital-wide readmission measure. For validation, all qualifying same-hospital readmissions that were characterized by the algorithm as planned, and a random sampling of same-hospital readmissions that were characterized as unplanned. MEASUREMENTS We calculated weighted sensitivity and specificity, and positive and negative predictive values of the algorithm (version 2.1), compared to gold standard chart review. RESULTS In consultation with 27 experts, we developed an algorithm that characterizes 7.8% of readmissions as planned. For validation we reviewed 634 readmissions. The weighted sensitivity of the algorithm was 45.1% overall, 50.9% in large teaching centers and 40.2% in smaller community hospitals. The weighted specificity was 95.9%, positive predictive value was 51.6%, and negative predictive value was 94.7%. We identified 4 minor changes to improve algorithm performance. The revised algorithm had a weighted sensitivity 49.8% (57.1% at large hospitals), weighted specificity 96.5%, positive predictive value 58.7%, and negative predictive value 94.5%. Positive predictive value was poor for the 2 most common potentially planned procedures: diagnostic cardiac catheterization (25%) and procedures involving cardiac devices (33%). CONCLUSIONS An administrative claims-based algorithm to identify planned readmissions is feasible and can facilitate public reporting of primarily unplanned readmissions.

Comparison of Approaches for Heart Failure Case Identification From Electronic Health Record Data.

Importance Accurate, real-time case identification is needed to target interventions to improve quality and outcomes for hospitalized patients with heart failure. Problem lists may be useful for case identification but are often inaccurate or incomplete. Machine-learning approaches may improve accuracy of identification but can be limited by complexity of implementation. Objective To develop algorithms that use readily available clinical data to identify patients with heart failure while in the hospital. Design, Setting, and Participants We performed a retrospective study of hospitalizations at an academic medical center. Hospitalizations for patients 18 years or older who were admitted after January 1, 2013, and discharged before February 28, 2015, were included. From a random 75% sample of hospitalizations, we developed 5 algorithms for heart failure identification using electronic health record data: (1) heart failure on problem list; (2) presence of at least 1 of 3 characteristics: heart failure on problem list, inpatient loop diuretic, or brain natriuretic peptide level of 500 pg/mL or higher; (3) logistic regression of 30 clinically relevant structured data elements; (4) machine-learning approach using unstructured notes; and (5) machine-learning approach using structured and unstructured data. Main Outcomes and Measures Heart failure diagnosis based on discharge diagnosis and physician review of sampled medical records. Results A total of 47 119 hospitalizations were included in this study (mean [SD] age, 60.9 [18.15] years; 23 952 female [50.8%], 5258 black/African American [11.2%], and 3667 Hispanic/Latino [7.8%] patients). Of these hospitalizations, 6549 (13.9%) had a discharge diagnosis of heart failure. Inclusion of heart failure on the problem list (algorithm 1) had a sensitivity of 0.40 and a positive predictive value (PPV) of 0.96 for heart failure identification. Algorithm 2 improved sensitivity to 0.77 at the expense of a PPV of 0.64. Algorithms 3, 4, and 5 had areas under the receiver operating characteristic curves of 0.953, 0.969, and 0.974, respectively. With a PPV of 0.9, these algorithms had associated sensitivities of 0.68, 0.77, and 0.83, respectively. Conclusions and Relevance The problem list is insufficient for real-time identification of hospitalized patients with heart failure. The high predictive accuracy of machine learning using free text demonstrates that support of such analytics in future electronic health record systems can improve cohort identification.

Association Between End-of-Rotation Resident Transition in Care and Mortality Among Hospitalized Patients

Importance Shift-to-shift transitions in care among house staff are associated with adverse events. However, the association between end-of-rotation transition (in which care of the patient is transferred) and adverse events is uncertain. Objective To examine the association of end-of-rotation house staff transitions with mortality among hospitalized patients. Design, Setting, and Participants Retrospective multicenter cohort study of patients admitted to internal medicine services (N = 230 701) at 10 university-affiliated US Veterans Health Administration hospitals (2008-2014). Exposures Transition patients (defined as those admitted prior to an end-of-rotation transition who died or were discharged within 7 days following transition) were stratified by type of transition (intern only, resident only, or intern + resident) and compared with all other discharges (control). An alternative analysis comparing admissions within 2 days before transition with admissions on the same 2 days 2 weeks later was also conducted. Main Outcomes and Measures The primary outcome was in-hospital mortality. Secondary outcomes included 30-day and 90-day mortality and readmission rates. A difference-in-difference analysis assessed whether outcomes changed after the 2011 Accreditation Council for Graduate Medical Education (ACGME) duty hour regulations. Adjustments included age, sex, race/ethnicity, month, year, length of stay, comorbidities, and hospital. Results Among 230 701 patient discharges (mean age, 65.6 years; men, 95.8%; median length of stay, 3.0 days), 25 938 intern-only, 26 456 resident-only, and 11 517 intern + resident end-of-rotation transitions occurred. Overall mortality was 2.18% in-hospital, 9.45% at 30 days, and 14.43% at 90 days. Adjusted hospital mortality was significantly greater in transition vs control patients for the intern-only group (3.5% vs 2.0%; odds ratio [OR], 1.12 [95% CI, 1.03-1.21]) and the intern + resident group (4.0% vs 2.1%; OR, 1.18 [95% CI, 1.06-1.33]), but not for the resident-only group (3.3% vs 2.0%; OR, 1.07 [95% CI, 0.99-1.16]). Adjusted 30-day and 90-day mortality rates were greater in all transition vs control comparisons (30-day mortality: intern-only group, 14.5% vs 8.8%, OR, 1.17 [95% CI, 1.13-1.22]; resident-only group, 13.8% vs 8.9%, OR, 1.11 [95% CI, 1.04-1.18]; intern + resident group, 15.5% vs 9.1%, OR, 1.21 [95% CI, 1.12-1.31]; 90-day mortality: intern-only group, 21.5% vs 13.5%, OR, 1.14 [95% CI, 1.10-1.19]; resident-only group, 20.9% vs 13.6%, OR, 1.10 [95% CI, 1.05-1.16]; intern + resident group, 22.8% vs 14.0%, OR, 1.17 [95% CI, 1.11-1.23]). Duty hour changes were associated with greater adjusted hospital mortality for transition patients in the intern-only group and intern + resident group than for controls (intern-only: OR, 1.11 [95% CI, 1.02-1.21]; intern + resident: OR, 1.17 [95% CI, 1.02-1.34]). The alternative analyses did not demonstrate any significant differences in mortality between transition and control groups. Conclusions and Relevance Among patients admitted to internal medicine services in 10 Veterans Affairs hospitals, end-of-rotation transition in care was associated with significantly higher in-hospital mortality in an unrestricted analysis that included most patients, but not in an alternative restricted analysis. The association was stronger following institution of ACGME duty hour regulations.

Early Lessons on Bundled Payment at an Academic Medical Center

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

Association of Occupation as a Physician With Likelihood of Dying in a Hospital

3,017 (95% confidence interval [CI], −

Self-care after hospital discharge: knowledge is not enough

6,066 to