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Evaluating waiting time with real-world health information in a high-volume cancer center

 Kyu-Pyo Kim  ;  Yu Rang Park  ;  Jung Bok Lee  ;  Hae Reong Kim  ;  Yongman Lyu  ;  Jeong-Eun Kim  ;  Yong Sang Hong  ;  Jae-Lyun Lee  ;  Tae Won Kim 
 MEDICINE, Vol.99(39) : e21796, 2020-09 
Journal Title
Issue Date
Aged ; Appointments and Schedules* ; Cancer Care Facilities / statistics & numerical data* ; Colorectal Neoplasms / therapy* ; Female ; Humans ; Male ; Medical Oncology / methods ; Middle Aged ; Time Factors
Wait time and scheduling for outpatient chemotherapy administration depends on various factors including infusion room hours of operation, availability of oncologists, nursing and pharmacy staffing, and physical space limitations. The aim of this study was to use the electronic event log of patients on health information system (HIS) to map and analyze patient flow in advanced metastatic colorectal patients at an academic cancer center. From January 2009 to December 2014, patients who were diagnosed with metastatic colorectal cancer and received outpatient chemotherapy confined to FOLFIRI (fluorouracil, leucovorin, and irinotecan) or FOLFOX (folinic acid, fluorouracil, and oxaliplatin) were identified. From the HIS, patient flow was mapped by collection of event records including blood collection and pretreatment laboratory test, arrival to outpatient clinics, outpatient session (interview, drug accountability and appointment scheduling), and initiation of chemotherapy. A total of 10,638 patients were analyzed for 136,281 outpatient visits. The total office stay time from outpatient registration to initiation of chemotherapy was 92.58 ± 87.96 (mean ± standard deviation) minutes. Each outpatient session lasted 23.75 ± 51.55 minutes. After completing the outpatient session, patients waited 1,657.23 ± 3,027.65 minutes before chemotherapy and 46.66 ± 75.94 minutes within infusion room. Compared to the prior first come first serve rule, the new reservation system showed an improvement in overall waiting time from 2,432.3 ± 4,822.9 to 2,386.7 ± 143.4 minutes; however, waiting time within infusion room slightly increased from 36.68 ± 49.33 to 48.13 ± 46.32 minutes. Our findings indicate that transaction data analytics from HIS can be used to evaluate patient flow within oncology outpatient practice based on real-world hospital data.
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1. College of Medicine (의과대학) > Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) > 1. Journal Papers
Yonsei Authors
Park, Yu Rang(박유랑) ORCID logo https://orcid.org/0000-0002-4210-2094
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