Length of Hospital Stay Prediction at the Admission Stage for Cardiology Patients Using Artificial Neural Network.

For hospitals' admission management, the ability to predict length of stay (LOS) as early as in the preadmission stage might be helpful to monitor the quality of inpatient care. This study is to develop artificial neural network (ANN) models to predict LOS for inpatients with one of the three primary diagnoses: coronary atherosclerosis (CAS), heart failure (HF), and acute myocardial infarction (AMI) in a cardiovascular unit in a Christian hospital in Taipei, Taiwan. A total of 2,377 cardiology patients discharged between October 1, 2010, and December 31, 2011, were analyzed. Using ANN or linear regression model was able to predict correctly for 88.07% to 89.95% CAS patients at the predischarge stage and for 88.31% to 91.53% at the preadmission stage. For AMI or HF patients, the accuracy ranged from 64.12% to 66.78% at the predischarge stage and 63.69% to 67.47% at the preadmission stage when a tolerance of 2 days was allowed.

An application of multi-attribute value theory to patient-bed assignment in hospital admission management: an empirical study.

Hospital beds are considered economically scarce and hospitalists strive to balance between utilizing beds more efficiently and complying with preference of physicians and patients when pairing patients to beds. This research is to develop preference-based decision rules for patient-bed assignment in a dynamic environment. A multi-attribute value theory (MAVT) model with additive value function is proposed to quantitatively deploy hospital policies in bed management. To elicit scaling factors and value functions for attributes, a linear programming model is constructed for all preference conditions. An empirical study was conducted with real data collected from two branches of a medical center. The simulated results using value function showed greater benefits when the patient-bed ratio was high and more flexible ward assignment was allowed. Further, a detailed analysis showed that this MAVT model was better in preference matching for both physicians/nurses and patients. At least 79 percent of patients were given beds in designated wards in accordance with their attending physicians' subspecialty, and more than 48 percent of patients' room preferences were matched in the simulated assignment for one branch.

Using discrete-event simulation in strategic capacity planning for an outpatient physical therapy service.

This study uses a simulation model as a tool for strategic capacity planning for an outpatient physical therapy clinic in Taipei, Taiwan. The clinic provides a wide range of physical treatments, with 6 full-time therapists in each session. We constructed a discrete-event simulation model to study the dynamics of patient mixes with realistic treatment plans, and to estimate the practical capacity of the physical therapy room. The changes in time-related and space-related performance measurements were used to evaluate the impact of various strategies on the capacity of the clinic. The simulation results confirmed that the clinic is extremely patient-oriented, with a bottleneck occurring at the traction units for Intermittent Pelvic Traction (IPT), with usage at 58.9 %. Sensitivity analysis showed that attending to more patients would significantly increase the number of patients staying for overtime sessions. We found that pooling the therapists produced beneficial results. The average waiting time per patient could be reduced by 45 % when we pooled 2 therapists. We found that treating up to 12 new patients per session had no significantly negative impact on returning patients. Moreover, we found that the average waiting time for new patients decreased if they were given priority over returning patients when called by the therapists.