Dr Tilley Pain1,2, Dr Amy Brown1,2, Dr Gail Kingston1,2, Dr Stephen Perks1,2, Mr Corey Patterson1, Ms Nerida Firth1,2, Mr Luke Sherwood1, Ms Jessica Lester1, Prof Deborah Street3
1Townsville Hospital and Health Service, Townsville, Australia, 2James Cook University, Townsville, Australia, 3University of Technology, Sydney, Australia
Abstract:
Purpose of the research:
Our regional hospital and health service seeks to reduce the long wait list for medical specialist appointments by introducing allied health substitution models of care for low-acuity patients. A best worst scaling (BWS) survey was conducted to identify the most salient attributes associated with outpatient appointments and inform healthcare redesign to reduce the long wait list for medical specialist appointments.
Nature and scope of the topic:
A scoping review identified attributes associated with medical specialist outpatient appointments and allied health substitution models. An Object (or Case 1) BWS survey incorporating the attributes was electronically completed by patients waiting at specialist outpatient clinics and analysed using multinomial logit and mixed logit models.
Issue or problem under consideration:
Twelve attributes were identified in the review and one from local context. The ranking of the most important attributes was diagnostic accuracy, symptom relief, continuity of care, satisfaction with care, health care professional, manner and communication, time on waitlist and onward referral. The least important attributes were reassurance offered, appointment wait time, cost and appointment duration.
Outcome of the conclusion:
The importance of diagnostic accuracy and symptom relief and lack of importance of appointment wait time and duration suggests that patients would be willing to attend different models of care such as allied health primary contact models if clinical outcomes were equivalent. The most important attributes identified from the BWS, reduced from 13 to eight, will form a discrete choice experiment to further elicit patient preferences for substitution models.