Objective: The aim of this study is to identify scheduling inefficiencies and to develop a personalized schedule based on diagnosis, service time (face-to-face time between the patient and the provider), and patient wait time using a Gantt diagram in a chronic pain clinic. Design: This is an observational prospective cohort quality improvement (QI) study. Setting: This study was carried out at a single outpatient multidisciplinary pain management clinic in a university teaching hospital. Subjects: New and established chronic pain patients at the University of Pittsburgh Medical Center (UPMC) Montefiore Chronic Pain Clinic were recruited for this study. Methods: Time tracking data for each phase of clinic visit and pain-related diagnoses were collected from 81 patients on 5 clinic days in March 2016 for patient flow analysis. Results: A Gantt diagram was created using Microsoft Excel® software. Areas of overbooking and underbooking were identified. Median service times (minutes) differed dramatically based on the diagnosis and were highest for facial pain (23 [IQR, 15–31]) and chronic abdominal and/ or pelvic pain (21.5 [IQR, 16–27]) and lowest for myalgia. Abdominal and/or pelvic pain and facial pain median service times consistently exceeded the 15-minute allocation for return visits. Conclusion: Schedule efficiency analysis using the Gantt diagram identified trends of overbooking and underbooking and inefficiencies in examination room utilization. A 15-minute appointment for all return patients is unrealistic due to variation of service times for some diagnoses. Scheduling appointments based on the diagnosis is an innovative approach that may reduce scheduling inefficiencies and improve patient satisfaction and the overall quality of care. To the best of our knowledge, this type of scheduling diagram has not been used in a chronic pain clinic.
CITATION STYLE
Hundley, H. E., Hudson, M. E., Wasan, A. D., & Emerick, T. D. (2019). Chronic pain clinic efficiency analysis: Optimization through use of the gantt diagram and visit diagnoses. Journal of Pain Research, 12, 1–8. https://doi.org/10.2147/JPR.S173345
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