ABSTRACT
<p><b>BACKGROUND</b>As an important determinant of patient satisfaction, waiting time, has gained increasing attention in the field of health care services. The present study aimed to illustrate the distribution characteristics of waiting time in a community hospital and explore the impact of potential measures to reduce outpatient waiting time based on a computer simulation approach.</p><p><b>METHODS</b>During a one-month study period in 2006, a cross-sectional study was conducted in a community hospital located in Shanghai, China. Baseline data of outpatient waiting time were calculated according to the records of registration time and payment time. A simulation technique was adopted to investigate the impact of perspective reform methods on reducing waiting time.</p><p><b>RESULTS</b>Data from a total of 10,092 patients and 26,816 medical consultations were collected in the study and 19,947 medical consultations were included. The average of the total visit time for outpatients in this hospital was 43.6 minutes in the morning, 19.1 minutes in the afternoon, and 34.3 minutes for the whole day studied period. The simulation results suggested that waiting time for outpatients could be greatly reduced through the introduction of appointment system and flexible demand-orientated doctor scheduling according to the numbers of patients waiting at different time of the workday.</p><p><b>CONCLUSION</b>Adoption of an appointment system and flexible management of doctor scheduling may be effective way to achieve decreased waiting time.</p>
Subject(s)
Adult , Aged , Aged, 80 and over , Female , Humans , Male , Middle Aged , Appointments and Schedules , Computer Simulation , Cross-Sectional Studies , Hospitals, Community , Outpatients , Patient Satisfaction , Time FactorsABSTRACT
Objective To establish a discriminant model and to provide a relatively accurate scientific basis for the early diagnosis of tuberculosis (TB) and detection of the close contacts. Methods Through logistic regression analysis, key factors were selected according to Bayes theory and key factors of TB incidence of the close contacts were screened as well as a discriminant model was established. Results The non-TB incidence discriminant function of the close contacts was described as: Y1= -39.831 (constant) + 1.927 X, (sputum-frequency) + 3.528 X2 (education) + 0.309 X3 (contact time) + 5.893 X4 (evade) + 2.140 X5 (ventilation) + 8.706 X6 (cough) + 30.970 X7 (fever). The discriminant function of non-TB incidence of the close contacts was as: Y2 =-57.875 (constant) + 2.343 X1 (sputum-frequency) + 3.965 X2 (education) + 0.361 X3 (contact time) + 6.296 X4 (evade) + 1.348 X5 (ventilation) + 12.984 X6 (cough) + 36.555 X7 (fever). Conduslon The diseriminant model night be used to contribute to the early diagnosis, early intervention and timely treatment on those close contacts of tuberculosis cases.