Duty hour monitoring is required in accredited training programs, however trainee self-reporting is onerous and vulnerable to bias. The objectives of this study were to use an automated, validated algorithm to measure duty hour violations of pediatric trainees over a full academic year and compare to self-reported violations. Duty hour violations calculated from electronic health record (EHR) logs varied significantly by trainee role and rotation. Block-by-block differences show 36.8% (222/603) of resident-blocks with more EHR-defined violations (EDV) compared to self-reported violations (SRV), demonstrating systematic under-reporting of duty hour violations. Automated duty hour tracking could provide real-time, objective assessment of the trainee work environment, allowing program directors and accrediting organizations to design and test interventions focused on improving educational quality.
Introduction: Hospitalized children experience frequent sleep disruptions. We aimed to reduce caregiver-reported sleep disruptions of children hospitalized on the pediatric hospital medicine service by 10% over 12 months. Methods: In family surveys, caregivers cited overnight vital signs (VS) as a primary contributor to sleep disruption. We created a new VS frequency order of "every 4 hours (unless asleep between 2300 and 0500)"as well as a patient list column in the electronic health record indicating patients with this active VS order. The outcome measure was caregiver-reported sleep disruptions. The process measure was adherence to the new VS frequency. The balancing measure was rapid responses called on patients with the new VS frequency. Results: Physician teams ordered the new VS frequency for 11% (1,633/14,772) of patient nights on the pediatric hospital medicine service. Recorded VS between 2300 and 0500 was 89% (1,447/1,633) of patient nights with the new frequency ordered compared to 91% (11,895/13,139) of patient nights without the new frequency ordered (P = 0.01). By contrast, recorded blood pressure between 2300 and 0500 was only 36% (588/1,633) of patient nights with the new frequency but 87% (11,478/13,139) of patient nights without the new frequency (P < 0.001). Overall, caregivers reported sleep disruptions on 24% (99/419) of reported nights preintervention, which decreased to 8% (195/2,313) postintervention (P < 0.001). Importantly, there were no adverse safety issues related to this initiative. Conclusion: This study safely implemented a new VS frequency with reduced overnight blood pressure readings and caregiver-reported sleep disruptions.
Objective Excess physician work hours contribute to burnout and medical errors. Self-report of work hours is burdensome and often inaccurate. We aimed to validate a method that automatically determines provider shift duration based on electronic health record (EHR) timestamps across multiple inpatient settings within a single institution. Methods We developed an algorithm to calculate shift start and end times for inpatient providers based on EHR timestamps. We validated the algorithm based on overlap between calculated shifts and scheduled shifts. We then demonstrated a use case by calculating shifts for pediatric residents on inpatient rotations from July 1, 2015 through June 30, 2016, comparing hours worked and number of shifts by rotation and role. Results We collected 6.3 × 10 7 EHR timestamps for 144 residents on 771 inpatient rotations, yielding 14,678 EHR-calculated shifts. Validation on a subset of shifts demonstrated 100% shift match and 87.9 ± 0.3% overlap (mean ± standard error [SE]) with scheduled shifts. Senior residents functioning as front-line clinicians worked more hours per 4-week block (mean ± SE: 273.5 ± 1.7) than senior residents in supervisory roles (253 ± 2.3) and junior residents (241 ± 2.5). Junior residents worked more shifts per block (21 ± 0.1) than senior residents (18 ± 0.1). Conclusion Automatic calculation of inpatient provider work hours is feasible using EHR timestamps. An algorithm to assess provider work hours demonstrated criterion validity via comparison with scheduled shifts. Differences between junior and senior residents in calculated mean hours worked and number of shifts per 4-week block were also consistent with differences in scheduled shifts and duty-hour restrictions.
Introduction:
Communication between pediatric hospitalists and primary care physicians (PCPs) at discharge is an essential part of a successful transition to home. While many hospitals require communicating with PCPs for all admitted patients, it is unknown if PCPs find such communication valuable or if it improves outcomes. Our global aim was to improve discharge communication for patients that pediatric hospitalists and PCPs deemed appropriate.
Methods:
We sent surveys to 422 outpatient pediatricians in our care network to understand their communication preferences. Survey results informed local guidelines for when hospitalists should directly contact PCPs. We determined the proportion of inpatient discharges meeting those guidelines and set a target for our primary process metric: the proportion of discharges with attempted direct PCP contact. We engaged in Plan-Do-Study-Act cycles, including a discharge documentation tool in the electronic health record, education of inpatient teams, email reminders including group performance data, asynchronous Health Insurance Portability and Accountability Act-compliant messaging application, and competitions that shared blinded individual data.
Results:
We increased the percentage of documented direct communication with the PCPs from 2% to 33% and from 4% to 65% for those who met guidelines for direct communication.
Conclusions:
PCPs only want direct communication on a subset of discharges. Interventions focused on high-yield populations improved discharge communication in our institution.