Studies have shown that frequent patient no-shows put significant strain on clinical operations as they could sometimes result in going to A&E for
both primary and chronic care. This drives up significant costs and is an inefficient use of limited hospital manpower and resources. With this growing
demand at outpatient clinics, it is all the more necessary for institutions to optimise staff allocation and clinic workloads.
IHiS developed a Smart Health predictive model that identifies patients based on their risk scores who are likely no-shows to better optimise hospital
resources, and provide timely appointments for other patients.
The model is being piloted at KK Women’s and Children’s Hospital (KKH) and the National Dental Centre of Singapore. Singapore General Hospital
(SGH) is in the midst of tailoring its intervention process. The model would soon be implemented at SingHealth Polyclinics (SHP) and Sengkang
General Hospital (SKGH).
How It WorksThe predictive model provides healthcare institutions with a no-show risk score for each patient’s appointment. Once the model identifies high risk patients, administrators would contact these patients to remind them about their appointments or check if they need to reschedule their appointments.
About one year’s worth of data, or about three million records, was used to develop the no-show model. Another year’s data was used to validate the model, which has an accuracy of 77 per 100 appointments predicted.
To Patients & CaregiversPatients with higher risk of not showing up for their appointments are able to receive timelier reminders from healthcare staff. Other patients may also be allocated with the cancelled appointments, meaning that patients may just get an earlier appointment.
To Healthcare StaffHealthcare professionals save clinic resources and time as the predictive model allows for better allocation and the time saved can be better spent on direct patient care.