Henry J. Michtalik

Acute Changes in N-Terminal Pro-B-Type Natriuretic Peptide During Hospitalization and Risk of Readmission and Mortality in Patients With Heart Failure

The level of N-terminal pro-B-type natriuretic peptide (NT-proBNP) is a predictor of adverse events in patients with heart failure. We examined the relation between acute changes in NT-proBNP during a single hospitalization and subsequent mortality and readmission. The data from a cohort of 241 consecutive patients aged ≥ 25 years who had been admitted to an urban tertiary care hospital with a primary diagnosis of heart failure were analyzed. Creatinine and NT-proBNP were measured at admission and at discharge of the first admission. The patient demographics, co-morbidities, and length of stay were collected. The patients were prospectively grouped into 2 categories according to the acute changes in NT-proBNP: a decrease of ≥ 50% or <50% from admission to discharge. The primary composite outcome was readmission or death within 1 year of the first hospital admission. The unadjusted hazard ratio of readmission/death was 1.40 (95% confidence interval 0.97 to 2.01; p = 0.07) for those with a < 50% decrease in NT-proBNP compared to their counterparts with a ≥ 50% decrease. After adjustment for age, gender, race, and admission creatinine and NT-proBNP, the risk of readmission/death was 57% greater for those with a < 50% decrease (hazard ratio 1.57, 95% confidence interval 1.08 to 2.28; p = 0.02). An adjustment for co-morbidity, length of stay, and left ventricular ejection fraction did not significantly change this relation. Reductions in NT-proBNP of < 50% during an acute hospitalization for heart failure might be associated with an increased hazard of readmission/death, independent of age, gender, race, creatinine, admission NT-proBNP, co-morbidities, left ventricular ejection fraction, and length of stay. In conclusion, patients with a < 50% reduction in NT-proBNP might benefit from more intensive medical treatment, monitoring, and follow-up.

Developing a Model for Attending Physician Workload and Outcomes

Medicalis. Funding/Support: This study was funded in part by grant 1UC4EB012952-01 from the National Institute of Biomedical Imaging and Bioengineering. Additional Contributions: Shelley Hurwitz, PhD, and the Harvard Catalyst Center provided statistical support. Dana Marchello, RT, and Richard Hanson, BA, provided database assistance, Cameron Farkas, BA, participated in algorithm verification, and Louise Scheider, MD, provided editorial support.

Identifying potential predictors of a safe attending physician workload: A survey of hospitalists

Attending physician workload may be compromising patient safety and quality of care. Recent studies show hospitalists, intensivists, and surgeons report that excessive attending physician workload has a negative impact on patient care.1–3 Because physician teams and hospitals differ in composition, function, and setting, it is difficult to directly compare one service to another within or between institutions. Identifying physician, team, and hospital characteristics associated with clinicians’ impressions of unsafe workload provides physician leaders, hospital administrators, and policymakers with potential “risk factors” and specific targets for interventions.4 In this study, we use a national survey of hospitalists to identify the physician, team, and hospital factors associated with physician report of an “unsafe” workload.

A Method for Attributing Patient-Level Metrics to Rotating Providers in an Inpatient Setting

BACKGROUND Individual provider performance drives group metrics, and increasingly, individual providers are held accountable for these metrics. However, appropriate attribution can be challenging, particularly when multiple providers care for a single patient. OBJECTIVE We sought to develop and operationalize individual provider scorecards that fairly attribute patient-level metrics, such as length of stay and patient satisfaction, to individual hospitalists involved in each patient’s care. DESIGN Using patients cared for by hospitalists from July 2010 through June 2014, we linked billing data across each hospitalization to assign “ownership” of patient care based on the type, timing, and number of charges associated with each hospitalization (referred to as “provider day weighted”). These metrics were presented to providers via a dashboard that was updated quarterly with their performance (relative to their peers). For the purposes of this article, we compared the method we used to the traditional method of attribution, in which an entire hospitalization is attributed to 1 provider, based on the attending of record as labeled in the administrative data. RESULTS Provider performance in the 2 methods was concordant 56% to 75% of the time for top half versus bottom half performance (which would be expected to occur by chance 50% of the time). While provider percentile differences between the 2 methods were modest for most providers, there were some providers for whom the methods yielded dramatically different results for 1 or more metrics. CONCLUSION We found potentially meaningful discrepancies in how well providers scored (relative to their peers) based on the method used for attribution. We demonstrate that it is possible to generate meaningful provider-level metrics from administrative data by using billing data even when multiple providers care for 1 patient over the course of a hospitalization.