TY - JOUR
T1 - Limited waiting areas in outpatient clinics
T2 - an intervention to incorporate the effect of bridging times in blueprint schedules
AU - Dijkstra, Sander
AU - Otten, Maarten
AU - Leeftink, Gréanne
AU - Kamphorst, Bas
AU - Olde Meierink, Angelique
AU - Heinen, Anouk
AU - Bijlsma, Rhodé
AU - Boucherie, Richard J
N1 - Funding Information:
This project was partly financed by the Netherlands Organisation for Health Research and Development (ZonMw) under grant number 10430042010015.
Publisher Copyright:
© 2022 BMJ Publishing Group. All rights reserved.
PY - 2022/6
Y1 - 2022/6
N2 - 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.
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.
KW - Ambulatory Care Facilities
KW - COVID-19/prevention & control
KW - Humans
KW - Pandemics/prevention & control
KW - Prospective Studies
KW - Retrospective Studies
KW - COVID-19
KW - Outpatients
KW - Simulation
KW - Decision support, computerised
KW - Efficiency, Organizational
UR - http://www.scopus.com/inward/record.url?scp=85132310746&partnerID=8YFLogxK
U2 - 10.1136/bmjoq-2021-001703
DO - 10.1136/bmjoq-2021-001703
M3 - Article
C2 - 35728864
VL - 11
JO - BMJ Open Quality
JF - BMJ Open Quality
IS - 2
M1 - e001703
ER -