search query: @keyword supervised learning / total: 4
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| Author: | Strahl, Jonathan |
| Title: | Patient appointment scheduling system: with supervised learning prediction |
| Publication type: | Master's thesis |
| Publication year: | 2015 |
| Pages: | x + 78 s. + liitt. 48 Language: eng |
| Department/School: | Perustieteiden korkeakoulu |
| Main subject: | Machine Learning and Data Mining (SCI3015) |
| Supervisor: | Kaski, Samuel |
| Instructor: | Wagner, Paul ; Dobrinkat, Marcus |
| Electronic version URL: | http://urn.fi/URN:NBN:fi:aalto-201506303595 |
| Location: | P1 Ark Aalto 2917 | Archive |
| Keywords: | appointment scheduling outpatient scheduling supervised learning local search optimisation scheduling simulation appointment rules |
| Abstract (eng): | Large waiting times at hospital outpatient clinics are a cause of dissatisfaction to patients, cause additional stress to hospital staff, increase the risk of contagion and add complications for patients with medical conditions. Reducing waiting times and surgeon idle time improves the quality of service and efficiency of a hospital: this is a recently growing focus in healthcare. Oulu hospital wants to identify and reduce large waiting times at their out-patient clinic. For the past few years the clinic has used a self-service system whereby patients register on arrival and hospital staff use a patient call-in system.The past schedules are analyzed using this data: information visualizations and performance measures are provided. The worst performing clinic sessions are the subject of the scheduling optimization prototype system. The scheduling optimization focuses on predicting the duration of an appointment and the late arrival of the surgeon. These two factors have been identified as causes of long patient waiting times. The variance of the duration is identified to be high, therefore supervised-learning regression is used for both simple inference and prediction. The features that are good predictors and the results of the prediction accuracy are reported. With the predicted appointment durations, and surgeon arrival times, a scheduling optimization approach is used to improve the existing schedule; a simple greedy hill-climbing approach is evaluated. It is found that using the historical data to simulate a real day, appointment rules and scheduling optimization the patient waiting time is reduced with this method. Showing the system to be potentially promising. |
| ED: | 2015-08-16 |
INSSI record number: 52011
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