An exploratory assessment of the impact of a novel risk assessment test on breast cancer clinic waiting times and workflow: a discrete event simulation model

Background Breast cancer clinics across the UK have long been struggling to cope with high demand. Novel risk prediction tools – such as the PinPoint test – could help to reduce unnecessary clinic referrals. Using early data on the expected accuracy of the test, we explore the potential impact of Pi...

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Vydané v:BMC health services research Ročník 22; číslo 1; s. 1 - 9
Hlavní autori: Smith, Alison F., Frempong, Samuel N., Sharma, Nisha, Neal, Richard D., Hick, Louise, Shinkins, Bethany
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London BioMed Central 29.10.2022
BioMed Central Ltd
Springer Nature B.V
BMC
Predmet:
Biopsy
Breast cancer
Breast neoplasms
Clinics
Computer Simulation
Datasets
Health Administration
Health Informatics
Health services
Mammography
Medical screening
Medicine
Medicine & Public Health
Nursing Research
Patients
Public Health
Risk assessment
Secondary Care Centres
Simulation
Teaching hospitals
Ultrasonic imaging
United Kingdom
United Kingdom > UK
England
Secondary Care Centres
Computer Simulation
Breast neoplasms
ISSN:1472-6963, 1472-6963
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Shrnutí:Background Breast cancer clinics across the UK have long been struggling to cope with high demand. Novel risk prediction tools – such as the PinPoint test – could help to reduce unnecessary clinic referrals. Using early data on the expected accuracy of the test, we explore the potential impact of PinPoint on: (a) the percentage of patients meeting the two-week referral target, and (b) the number of clinic ‘overspill’ appointments generated (i.e. patients having to return to the clinic to complete their required investigations). Methods A simulation model was built to reflect the annual flow of patients through a single UK clinic. Due to current uncertainty around the exact impact of PinPoint testing on standard care, two primary scenarios were assessed. Scenario 1 assumed complete GP adherence to testing, with only non-referred cancerous cases returning for delayed referral. Scenario 2 assumed GPs would overrule 20% of low-risk results, and that 10% of non-referred non-cancerous cases would also return for delayed referral. A range of sensitivity analyses were conducted to explore the impact of key uncertainties on the model results. Service reconfiguration scenarios, removing individual weekly clinics from the clinic schedule, were also explored. Results Under standard care, 66.3% (95% CI: 66.0 to 66.5) of patients met the referral target, with 1,685 (1,648 to 1,722) overspill appointments. Under both PinPoint scenarios, > 98% of patients met the referral target, with overspill appointments reduced to between 727 (707 to 746) [Scenario 1] and 886 (861 to 911) [Scenario 2]. The reduced clinic demand was sufficient to allow removal of one weekly low-capacity clinic [N = 10], and the results were robust to sensitivity analyses. Conclusion The findings from this early analysis indicate that risk prediction tools could have the potential to alleviate pressure on cancer clinics, and are expected to have increased utility in the wake of heightened pressures resulting from the COVID-19 pandemic. Further research is required to validate these findings with real world evidence; evaluate the broader clinical and economic impact of the test; and to determine outcomes and risks for patients deemed to be low-risk on the PinPoint test and therefore not initially referred.
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ISSN:1472-6963
1472-6963
DOI:10.1186/s12913-022-08665-0