Implementation of Hospital Examination Reservation System Using Data Mining Technique.
- Author:
Hyo Soung CHA
1
;
Tae Sik YOON
;
Ki Chung RYU
;
Il Won SHIN
;
Yang Hyo CHOE
;
Kyoung Yong LEE
;
Jae Dong LEE
;
Keun Ho RYU
;
Seung Hyun CHUNG
Author Information
- Publication Type:Original Article
- Keywords: Hospital Information System; Electronic Medical Records; Data Mining; Examinations and Diagnoses; Systems Integration
- MeSH: Appointments and Schedules; Data Mining*; Diagnosis; Electronic Health Records; Hospital Information Systems; Internet; Korea; Mining; Patient Satisfaction; Prescriptions; Systems Integration
- From:Healthcare Informatics Research 2015;21(2):95-101
- CountryRepublic of Korea
- Language:English
- Abstract: OBJECTIVES: New methods for obtaining appropriate information for users have been attempted with the development of information technology and the Internet. Among such methods, the demand for systems and services that can improve patient satisfaction has increased in hospital care environments. METHODS: In this paper, we proposed the Hospital Exam Reservation System (HERS), which uses the data mining method. First, we focused on carrying clinical exam data and finding the optimal schedule for generating rules using the multi-examination pattern-mining algorithm. Then, HERS was applied by a rule master and recommending system with an exam log. Finally, HERS was designed as a user-friendly interface. RESULTS: HERS has been applied at the National Cancer Center in Korea since June 2014. As the number of scheduled exams increased, the time required to schedule more than a single condition decreased (from 398.67% to 168.67% and from 448.49% to 188.49%; p < 0.0001). As the number of tests increased, the difference between HERS and non-HERS increased (from 0.18 days to 0.81 days). CONCLUSIONS: It was possible to expand the efficiency of HERS studies using mining technology in not only exam reservations, but also the medical environment. The proposed system based on doctor prescription removes exams that were not executed in order to improve recommendation accuracy. In addition, we expect HERS to become an effective system in various medical environments.
