Biomedical subjects
Publications and source records attributed to W M Hancock.
Inpatient admissions, surgical scheduling, and outpatient scheduling are three of the most important patient-scheduling functions in the hospital. In this paper, the key elements of state-of-the-art scheduling systems are discussed, along with a rationale for their importance. Our purpose is to show how well-designed patient-scheduling systems can contribute to the improvement of hospital operations.
We present a practical approach to personnel scheduling problems arising in hospital units with demand that is of an urgent nature, cannot be backlogged, and is highly dependent on the time of day. A simple rounding heuristic is combined with a simulated annealing algorithm to obtain near-optimal solutions to large linear integer programming models of these personnel scheduling problems in a reasonable amount of time on a personal computer. The models are designed to complement the current state-of-the-art of commercially available hospital staff scheduling systems.
A computer-aided methodology for integrating the budgeting, staffing, and labor productivity systems of Ancillary Services using Respiratory Therapy as an example is presented. The data needed, staffing computations and schedules, and productivity analysis are presented and discussed. A summary of the predicted savings and other benefits for the application hospital are presented with comparisons to present productivity systems.
A methodology to determine and maintain the schedule times used in a computer-based OR Scheduling System is presented. State-of-the-art equations to be used for different scheduling situations are presented and discussed with the attendant data base reduction methodology to provide the scheduling parameters. An example is given of the data reduction methodology as well as suggestions for maintenance of the data base.
A methodology is presented with examples of the productivity, the staffing required, the resultant productivity, and costs that can be obtained for hospital units that are subject to random work demands such as laboratory, radiology, physical therapy, and nuclear medicine. The methodology assumes that the hospital has a labor productivity system that produces the RVUs or earned hours of work accomplished daily by shift. Factors considered are the distribution of the capabilities of the work force, the fatigue and delay allowances of the work standards, the quality of the work standards, the maximum amount of overtime that people will be asked to work, staffing policies such as constant or different staffing levels for each day of the week, and worker selection processes. Predicted results are compared with present practice, which indicates that substantial cost reductions can occur with the use of the methodology.
Many hospital admissions scheduling or admissions control systems reported in the literature rely on estimates of future discharges to help control the variance in daily patient census. One of the two most frequently reported methods of estimating discharges attempts to explain the variance in historical length of stay (LPS) data. This paper explores the relationship between LOS variance explanation and the ability to predict discharges and concludes that even a large improvement in the ability to explain LOS variance will only marginally reduce the errors in the associated discharge predictions. In drawing this conclusion, a general discharge prediction model is developed and a more relevant statistic than per cent variance explained is introduced.
Explore the source record for details and available documents.
Simulation is used to investigate the effects on hospital occupancy of the number of beds in the facility, the percentage of patients who are emergencies, the percentage of elective patients who are scheduled, and the average lengths of stay of emergency and elective patients. A practical method is presented for estimating the optimum size of a short-term hospital on the basis of expected demand, and use of the results in planning is discussed.
A methodology for measuring and predicting work load on nursing units utilizes perceptions of head nurses as measured by a specially designed questionnaire. The data were collected in a 300-bed shortterm general hospital over a seven week period. The results indicated that head nurses' perceptions of need on nursing units were predominantly influenced by unit census and available staff on the unit. Other patient load affecting variables, such as patient classification, number of new admissions, or postoperative patients, did not play a major role in shaping head nurses' perceptions.