Retinopathy of Prematurity—Using Electronic Medical Records to Efficiently Follow Patients at Multiple Hospitals

Purpose: To report the use of a centralized electronic medical record (EMR) to provide timely retinopathy of prematurity (ROP) screening in a previously fragmented monitoring system in a standalone children’s hospital in Connecticut. Methods: A chart review of 306 visits for ROP screening in 3 neonatal intensive care units (NICUs) over a time period of 24 months. Results: All infants born at <30 weeks gestational age or birth weight <1,500g (N = 107) at these NICUs were screened for ROP according to the American Academy of Pediatrics guidelines. Data was collected before the implementation of our centralized EMR list, during a transitional period, and once the list was established. Our analysis of the data found an improvement in delay of care from 16.85% to 10.83% of visits, and a decrease in number of visits done during off hours from 20.4% to 5% of visits. Conclusion: Our tool was a free and cost-efficient centralization of a once difficult-to-manage process for ROP screenings in Connecticut. The decrease in delay of care and improvement in physician scheduling will lead to better outcomes for our patients and better sustainability of practice for our providers.

Multi-Objective Physician Scheduling using Native Binary Particle Swarm Optimization and Its Variance

Physician Scheduling is one of the crucial operational activities in hospitals. The requirements of physician schedules involve numbers of physicians of different types, constraints from the physicians themselves and multiple regulations from the government and the hospital. Those requirements contribute to the complexity of physician scheduling model which makes it interesting to experiment and to solve. There have been many solutions proposed by researchers, particularly using metaheuristic algorithms, to automate physicians scheduling. Learning from the existing research results, this research experiments two metaheuristic algorithms which are considered simple, robust and effective to solve physician scheduling problem, i.e. Native Binary Particle Swarm Optimization (Native BPSO) with Sigmoid Transformation and its variance. The variation is done by removing particles’ local best positions from the velocity equation, with the intention to allow particles to move quickly towards global best position. The experiments were started from conducting the literature review of metaheuristic algorithms, followed by collecting physician schedules data from the selected hospital, designing the mathematical model of physician scheduling, developing and modifying the algorithms, testing and evaluating the results and at last, concluding the outcomes. Outcomes of the experiments comprise of three items, i.e. the mathematical model, the recommended algorithm and the best parameters’ values to be applied for the selected algorithm. Based on the experiments, the Native BPSO variant turns out to produce better result in terms of its fitness function and the number of days assigned for each physician in the schedule. Considering that the model, the algorithm and the parameters’ values have been implemented in a web-based application, it is ready for use by the selected hospital.