RT Journal Article
SR Electronic
T1 Limited waiting areas in outpatient clinics: an intervention to incorporate the effect of bridging times in blueprint schedules
JF BMJ Open Quality
JO BMJ Open Qual
FD British Medical Journal Publishing Group
SP e001703
DO 10.1136/bmjoq-2021-001703
VO 11
IS 2
A1 Sander Dijkstra
A1 Maarten Otten
A1 Gréanne Leeftink
A1 Bas Kamphorst
A1 Angelique Olde Meierink
A1 Anouk Heinen
A1 Rhodé Bijlsma
A1 Richard J Boucherie
YR 2022
UL http://bmjopenquality.bmj.com/content/11/2/e001703.abstract
AB Background Distancing measures enforced by the COVID-19 pandemic impose a restriction on the number of patients simultaneously present in hospital waiting areas.Objective Evaluate waiting area occupancy of an intervention that designs clinic blueprint schedules, in which all appointments of the pre-COVID-19 case mix are scheduled either digitally or in person under COVID-19 distancing measures, whereby the number of in-person appointments is maximised.Methods Preintervention analysis and prospective assessment of intervention outcomes were used to evaluate the outcomes on waiting area occupancy and number of in-person consultations (postintervention only) using descriptive statistics, for two settings in the Rheumatology Clinic of Sint Maartenskliniek (SMK) and Medical Oncology & Haematology Outpatient Clinic of University Medical Center Utrecht (UMCU). Retrospective data from October 2019 to February 2020 were used to evaluate the pre-COVID-19 blueprint schedules. An iterative optimisation and simulation approach was followed, based on integer linear programming and Monte Carlo simulation, which iteratively optimised and evaluated blueprint schedules until the 95% CI of the number of patients in the waiting area did not exceed available capacity.Results Under pre-COVID-19 blueprint schedules, waiting areas would be overcrowded by up to 22 (SMK) and 11 (UMCU) patients, given the COVID-19 distancing measures. The postintervention blueprint scheduled all appointments without overcrowding the waiting areas, of which 88% and 87% were in person and 12% and 13% were digitally (SMK and UMCU, respectively).Conclusions The intervention was effective in two case studies with different waiting area characteristics and a varying number of interdependent patient trajectory stages. The intervention is generically applicable to a wide range of healthcare services that schedule a (series of) appointment(s) for their patients. Care providers can use the intervention to evaluate overcrowding of waiting area(s) and design optimal blueprint schedules to continue a maximum number of in-person appointments under pandemic distancing measures.Data are available upon reasonable request.