Demand, capacity, and access of the outpatient clinic: A framework for analysis and improvement

Abstract

RationaleWhile theoretical frameworks for optimization of the outpatient processes are abundant, practical step‐by‐step analyses to give leads for improvement, to forecast capacity, and to support decision making are sparse.Aims and objectivesThis article demonstrates how to evaluate and optimize the triad of demand, (future) capacity, and access time of the outpatient clinic using a structured six‐step method.MethodsAll individual logistical patient data of an orthopaedic outpatient clinic of one complete year were analysed using a 6‐step method to evaluate demand, supply, and access time. Trends in the data were retrospectively analysed and evaluated for potential improvements. A model for decision making was tested. Both the analysis of the method and actual results were considered as main outcomes.ResultsMore than 25 000 appointments were analysed. The 6‐step method showed to be sufficient to result in valuable insights and leads for improvement. While the overall match between demand and capacity was considered adequate, the variability in capacity was much higher than in demand, thereby leading to delays in access time. Holidays and subsequent weeks showed to be of great influence for demand, capacity, and access time. Using the six‐step method, several unfavourable characteristics of the outpatient clinic were revealed and a better match between demand, supply, and access time could have been reached with only minor adjustments. Last, a clinic specific prediction and decision model for demand and capacity was made using the 6‐step method.ConclusionsThe 6‐step analysis can successfully be applied to redesign and improve the outpatient health care process. The results of the analysis showed that national holidays and variability in demand and capacity have a big influence on the outpatient clinic. Using the 6‐step method, practical improvements in outpatient logistics were easily found and leads for future decision making were contrived.

An Analysis of Complaints in Two Large Tertiary University Teaching Hospital ENT Departments: A Two-Year Retrospective Review

“…Current strategies unfortunately involve further stretching available resources to fit the needs of more patients, which is not a International Journal of Otolaryngology 3 sustainable model. A new model has been proposed in which clinic appointments are released based on demand rather than predicted capacity; this, however, needs further data regarding reliability and usability of the results [10]. Patient experience has been shown to correlate with quality of care and clinical effectiveness in a recent systematic review across multiple specialties.…”

A systematized approach for reduction of medical appointment waiting list

“…In a similar approach as to this study, van Bussel et al (2018) make a great contribution on analysing real outpatient demand, supply and access time. The authors present a 6-step model to gain insights for guidance in redesigning the access to a medical appointment system and a better match between demand, supply and access time.…”

“…-Operations management approaches: queue management (Geng et al, 2017); statistical modelling (Priyan, 2017); operations research (Bowerman et al, 2007;Cipriano et al, 2008;Elwyn et al, 1996;Escobar et al, 2009;Frost, 1980;Green, 2005;Neby et al, 2015;Raymond et al, 2016;Tatham et al, 2012); demand (outpatient) management (van Bussel et al, 2018); capacity planning (Elkhuizen et al, 2007); master production scheduling (Spratt & Kozan, 2016); priority management (Bowers, 2011).…”

“…Being recognized the difficulty of managing the waiting lists in an empirical way (van Bussel et al, 2018;Elkhuizen et al, 2007), this article aims developing a systematized approach for reduction of medical appointment waiting list. This approach proposes an optimization method for determining the minimum number of hours * doctor needed in a period of time, to reduce and maintain the waiting list for first medical appointment within predefined dynamic limits.…”

Prediction model for COVID-19 patient visits in the ambulatory setting

“…Time series forecasting in ambulatory visits prior to the COVID19 pandemic have been described in a few reports. [15][16][17][18][19][20][21][22] The most used method for time series forecasting is the Box-Jenkins method otherwise known as the AutoRegressive Integrated Moving Average (ARIMA) model. 23 The ARIMA model has been used for its simplicity and exibility in capturing linear patterns in a time series.…”