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Concept: Hospital

327

Surgical training has always been hard on residents. During my own residency more than 20 years ago, 100-hour workweeks and in-house call every other night were routine. A resident’s life outside the hospital was simply not a priority. Residency may be even harder on patients. A large body of research has linked sleep deprivation in resident physicians to poor performance in neurobehavioral testing and, more alarmingly, to higher rates of attention failure in patient care.(1),(2) Reacting to concerns about both resident well-being and patient safety, the Accreditation Council for Graduate Medical Education (ACGME) implemented duty-hour reforms in 2003 that . . .

Concepts: Medicine, Patient, Hospital, Sleep deprivation, Physician, Medical school, Hospital accreditation, Medical education

322

Do patients' reports of their health care experiences reflect the quality of care? Despite the increasing role of such measures in research and policy, there’s no consensus regarding their legitimacy in quality assessment. Indeed, as physician and hospital compensation becomes increasingly tied to patient feedback, health care providers and academics are raising strong objections to the use of patient-experience surveys. These views are fueled by studies indicating that patient-experience measures at best have no relation to the quality of delivered care and at worst are associated with poorer patient outcomes. Conversely, other studies have found that better patient experiences - . . .

Concepts: Health care, Health care provider, Medicine, Patient, Hospital, Evaluation, Physician, Illness

316

To examine patient consultation preferences for seeing or speaking to a general practitioner (GP) or nurse; to estimate associations between patient-reported experiences and the type of consultation patients actually received (phone or face-to-face, GP or nurse).

Concepts: Patient, Hospital, Physician, Doctor-patient relationship, Philosophy of science, Knowledge, General practitioner, Medical school

310

Background The prevalence of pulmonary embolism among patients hospitalized for syncope is not well documented, and current guidelines pay little attention to a diagnostic workup for pulmonary embolism in these patients. Methods We performed a systematic workup for pulmonary embolism in patients admitted to 11 hospitals in Italy for a first episode of syncope, regardless of whether there were alternative explanations for the syncope. The diagnosis of pulmonary embolism was ruled out in patients who had a low pretest clinical probability, which was defined according to the Wells score, in combination with a negative d-dimer assay. In all other patients, computed tomographic pulmonary angiography or ventilation-perfusion lung scanning was performed. Results A total of 560 patients (mean age, 76 years) were included in the study. A diagnosis of pulmonary embolism was ruled out in 330 of the 560 patients (58.9%) on the basis of the combination of a low pretest clinical probability of pulmonary embolism and negative d-dimer assay. Among the remaining 230 patients, pulmonary embolism was identified in 97 (42.2%). In the entire cohort, the prevalence of pulmonary embolism was 17.3% (95% confidence interval, 14.2 to 20.5). Evidence of an embolus in a main pulmonary or lobar artery or evidence of perfusion defects larger than 25% of the total area of both lungs was found in 61 patients. Pulmonary embolism was identified in 45 of the 355 patients (12.7%) who had an alternative explanation for syncope and in 52 of the 205 patients (25.4%) who did not. Conclusions Pulmonary embolism was identified in nearly one of every six patients hospitalized for a first episode of syncope. (Funded by the University of Padua; PESIT ClinicalTrials.gov number, NCT01797289 .).

Concepts: Clinical trial, Lung, Hospital, Heart, Pulmonary embolism, Pulmonary artery, Human lung, Embolism

296

A 33-year-old man was admitted to the emergency department after a motorcycle accident. Examination of the intubated patient showed a hard, swollen, bluish scrotum and an externally rotated and slightly shortened left leg.

Concepts: Hospital, Tram accident, Ireland, Dublin

292

Background Thirty-day risk-standardized mortality rates after acute myocardial infarction are commonly used to evaluate and compare hospital performance. However, it is not known whether differences among hospitals in the early survival of patients with acute myocardial infarction are associated with differences in long-term survival. Methods We analyzed data from the Cooperative Cardiovascular Project, a study of Medicare beneficiaries who were hospitalized for acute myocardial infarction between 1994 and 1996 and who had 17 years of follow-up. We grouped hospitals into five strata that were based on case-mix severity. Within each case-mix stratum, we compared life expectancy among patients admitted to high-performing hospitals with life expectancy among patients admitted to low-performing hospitals. Hospital performance was defined by quintiles of 30-day risk-standardized mortality rates. Cox proportional-hazards models were used to calculate life expectancy. Results The study sample included 119,735 patients with acute myocardial infarction who were admitted to 1824 hospitals. Within each case-mix stratum, survival curves of the patients admitted to hospitals in each risk-standardized mortality rate quintile separated within the first 30 days and then remained parallel over 17 years of follow-up. Estimated life expectancy declined as hospital risk-standardized mortality rate quintile increased. On average, patients treated at high-performing hospitals lived between 0.74 and 1.14 years longer, depending on hospital case mix, than patients treated at low-performing hospitals. When 30-day survivors were examined separately, there was no significant difference in unadjusted or adjusted life expectancy across hospital risk-standardized mortality rate quintiles. Conclusions In this study, patients admitted to high-performing hospitals after acute myocardial infarction had longer life expectancies than patients treated in low-performing hospitals. This survival benefit occurred in the first 30 days and persisted over the long term. (Funded by the National Heart, Lung, and Blood Institute and the National Institute of General Medical Sciences Medical Scientist Training Program.).

Concepts: Mortality rate, Myocardial infarction, Hospital, Life expectancy

290

Risk sharing arrangements between hospitals and payers together with penalties imposed by the Centers for Medicare and Medicaid (CMS) are driving an interest in decreasing early readmissions. There are a number of published risk models predicting 30 day readmissions for particular patient populations, however they often exhibit poor predictive performance and would be unsuitable for use in a clinical setting. In this work we describe and compare several predictive models, some of which have never been applied to this task and which outperform the regression methods that are typically applied in the healthcare literature. In addition, we apply methods from deep learning to the five conditions CMS is using to penalize hospitals, and offer a simple framework for determining which conditions are most cost effective to target.

Concepts: Scientific method, Regression analysis, Medicare, Health insurance, Hospital, Prediction, Centers for Medicare and Medicaid Services, Medicaid

281

Background The Hospital Readmissions Reduction Program, which is included in the Affordable Care Act (ACA), applies financial penalties to hospitals that have higher-than-expected readmission rates for targeted conditions. Some policy analysts worry that reductions in readmissions are being achieved by keeping returning patients in observation units instead of formally readmitting them to the hospital. We examined the changes in readmission rates and stays in observation units over time for targeted and nontargeted conditions and assessed whether hospitals that had greater increases in observation-service use had greater reductions in readmissions. Methods We compared monthly, hospital-level rates of readmission and observation-service use within 30 days after hospital discharge among Medicare elderly beneficiaries from October 2007 through May 2015. We used an interrupted time-series model to determine when trends changed and whether changes differed between targeted and nontargeted conditions. We assessed the correlation between changes in readmission rates and use of observation services after adoption of the ACA in March 2010. Results We analyzed data from 3387 hospitals. From 2007 to 2015, readmission rates for targeted conditions declined from 21.5% to 17.8%, and rates for nontargeted conditions declined from 15.3% to 13.1%. Shortly after passage of the ACA, the readmission rate declined quickly, especially for targeted conditions, and then continued to fall at a slower rate after October 2012 for both targeted and nontargeted conditions. Stays in observation units for targeted conditions increased from 2.6% in 2007 to 4.7% in 2015, and rates for nontargeted conditions increased from 2.5% to 4.2%. Within hospitals, there was no significant association between changes in observation-unit stays and readmissions after implementation of the ACA. Conclusions Readmission trends are consistent with hospitals' responding to incentives to reduce readmissions, including the financial penalties for readmissions under the ACA. We did not find evidence that changes in observation-unit stays accounted for the decrease in readmissions.

Concepts: Patient, Hospital

276

Physicians frequently search PubMed for information to guide patient care. More recently, Google Scholar has gained popularity as another freely accessible bibliographic database.

Concepts: Health care, Medicine, Patient, Hospital, Physician, Doctor-patient relationship, Bibliographic databases, Reference management software

267

Studies finding higher mortality rates for patients admitted to hospital at weekends rely on routine administrative data to adjust for risk of death, but these data may not adequately capture severity of illness. We examined how rates of patient arrival at accident and emergency (A&E) departments by ambulance-a marker of illness severity-were associated with in-hospital mortality by day and time of attendance.

Concepts: Patient, Hospital