Professional Behavior and Value Erosion: A Qualitative Study of Physicians and the Electronic Health Record.

Goal: Occurrences of physician burnout have reached epidemic numbers, and the electronic health record (EHR) is a commonly cited cause of the distress. To enhance current understanding of the relationship between burnout and the EHR, we explored the connections between physicians' distress and the EHR.

Methods: In this qualitative study, physicians and graduate medical trainees from two healthcare organizations in California were interviewed about EHR-related distressing events and the impact on their emotions and actions.

Atrial fibrillation detection from raw photoplethysmography waveforms: A deep learning application.

Background: Atrial fibrillation (AF), a common cause of stroke, often is asymptomatic. Smartphones and smartwatches can detect AF using heart rate patterns inferred using photoplethysmography (PPG); however, enhanced accuracy is required to reduce false positives in screening populations.

Objective: The purpose of this study was to test the hypothesis that a deep learning algorithm given raw, smartwatch-derived PPG waveforms would discriminate AF from normal sinus rhythm better than algorithms using heart rate alone.

Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU.

Objectives: We validate a machine learning-based sepsis-prediction algorithm () for the detection and prediction of three sepsis-related gold standards, using only six vital signs. We evaluate robustness to missing data, customisation to site-specific data using transfer learning and generalisability to new settings.

Design: A machine-learning algorithm with gradient tree boosting.

Using Transfer Learning for Improved Mortality Prediction in a Data-Scarce Hospital Setting.

Algorithm-based clinical decision support (CDS) systems associate patient-derived health data with outcomes of interest, such as in-hospital mortality. However, the quality of such associations often depends on the availability of site-specific training data. Without sufficient quantities of data, the underlying statistical apparatus cannot differentiate useful patterns from noise and, as a result, may underperform.

Cost and mortality impact of an algorithm-driven sepsis prediction system.

Aims: To compute the financial and mortality impact of InSight, an algorithm-driven biomarker, which forecasts the onset of sepsis with minimal use of electronic health record data.

Methods: This study compares InSight with existing sepsis screening tools and computes the differential life and cost savings associated with its use in the inpatient setting. To do so, mortality reduction is obtained from an increase in the number of sepsis cases correctly identified by InSight.

Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach.

Background: Sepsis is one of the leading causes of mortality in hospitalized patients. Despite this fact, a reliable means of predicting sepsis onset remains elusive. Early and accurate sepsis onset predictions could allow more aggressive and targeted therapy while maintaining antimicrobial stewardship.

The role of prehospital electrocardiograms in the recognition of ST-segment elevation myocardial infarctions and reperfusion times.

Background: Clinical outcomes in ST-segment elevation myocardial infarction (STEMI) are related to reperfusion times. Given the benefit of early recognition of STEMI and resulting ability to decrease reperfusion times and improve mortality, current prehospital recommendations are to obtain electrocardiograms (ECGs) in patients with concern for acute coronary syndrome.

Objectives: We sought to determine the effect of wireless transmission of prehospital ECGs on STEMI recognition and reperfusion times.

Emergency Department Crowding is Associated with Reduced Satisfaction Scores in Patients Discharged from the Emergency Department.

Introduction: Emergency department (ED) crowding has been shown to negatively impact patient outcomes. Few studies have addressed the effect of ED crowding on patient satisfaction. Our objective was to evaluate the impact of ED crowding on patient satisfaction in patients discharged from the ED.

Evaluation of the revised trauma and injury severity scores in elderly trauma patients.

Background: Severity-of-illness scoring systems have primarily been developed for, and validated in, younger trauma patients.

Aims: We sought to determine the accuracy of the injury severity score (ISS) and the revised trauma score (RTS) in predicting mortality and hospital length of stay (LOS) in trauma patients over the age of 65 treated in our emergency department (ED).

Materials And Methods: Using the Illinois Trauma Registry, we identified all patients 65 years and older treated in our level I trauma facility from January 2004 to November 2007.

Surgical resection for multifocal (T4) non-small cell lung cancer: is the T4 designation valid?

Background: The current international staging system for lung cancer designates intralobar satellites as T4 disease. In this study, we sought to determine the impact of multifocal, intralobar non-small cell lung cancer (NSCLC) on patient survival and its potential relevance to stage designation.

Methods: We conducted a retrospective review of our thoracic surgical cancer registry from 1990 to 2005.

Long-term survival and recurrence in patients with resected non-small cell lung cancer 1 cm or less in size.

Objective: With the widespread use of computed tomography and the emergence of screening programs, non-small cell lung cancer is increasingly detected in sizes 1 cm or less. We sought to examine the long-term survival and recurrence patterns after resection of these tumors.

Methods: We conducted a retrospective review over a 15-year period to identify patients with surgically resected non-small cell lung cancer measuring 1 cm or less.

Downstaging of T or N predicts long-term survival after preoperative chemotherapy and radical resection for esophageal carcinoma.

Background: The purposes of this study were to determine the frequency of downstaging of T or N after neoadjuvant chemotherapy and radical resection in patients with carcinoma of the esophagus, and to evaluate the effect of tumor downstaging on survival.

Methods: A cohort of patients who underwent neoadjuvant chemotherapy followed by radical surgical resection for carcinoma of the esophagus was identified from a large, prospectively maintained, single-institution database of esophageal cancer patients. Patients were included if they had an accurate pretreatment clinical stage determined by the authors.