KFSH&RC AI Powered No-Show Prediction System
Osama Alswailem, MD, MA, Cert.Dir
CIO, King Faisal Specialist Hospital & Research Center
According to a collaborative study published in the Annals of Saudi Medicine in 2019, the no-show rate for outpatient clinic appointments in a tertiary care center in Saudi Arabia was 11.3%. ??In general, no-show levels in healthcare tend to be a persistent challenge for healthcare providers worldwide.? Efforts to reduce no-show levels typically involve implementing strategies such as appointment reminders (phone calls, SMS messages, or emails), optimizing scheduling practices, offering flexible appointment options, improving patient communication and education, and addressing barriers to access, such as transportation or financial constraints.
The financial impact of these no-shows has a significant impact on the national healthcare system. According to the same study, the average cost of a missed appointment in a public hospital in Riyadh was SAR 200 (USD 53) and the total cost of missed appointments in a hospital was approximately SAR 1.2 million (USD 320,000) per year and it went up exponentially as you moved to more specialized care.?
By utilizing AI, the analysis identified that the key factors linked to no-shows at King Faisal Specialist Hospital and Research Center were a patient's history of prior no-shows, the location and/or time of the appointment, and the specific specialty involved.?KFSH&RC successfully managed to decrease the no-show rate in July from 11% to 10.6% and similarly in August, the rate dropped from 11.5% to 9.9%.
By adopting a comprehensive approach that combines advanced technologies, patient engagement strategies, and continuous improvement efforts, we aim to further decrease the no-show rate and enhance the overall efficiency and quality of care at KFSH&RC.?
Stay tuned as the KFSH&RC vs Patient No-Show continues …
Pharmacist, Educator & Researcher | Pushing Pharmacotherapy Boundaries, Health Tech, and AI to Revolutionize Patient Care
1 年The most interesting thing I have seen in a while, very intriguing !
Director | Confidential Government | PMP (Anticipated 2017)
1 年Intersting, I think other factors will improve the rate as :weather , social status, age, diagnoses and the stage of treatment. Moreover, i thnik it will be depend alot on patient behaveor, so the patient who missed appointment will defneitly have a high rate later.
Business Analyst at KFSHRC | MIS | Business Analysis | Data Analysis | Process Specialist | Automation & Digital Transformation
1 年KFSH is a real gold mine to apply ML
BI/Reporting Specialist
1 年Interesting
Health Data Analyst | PhD Researcher | Machine Learning | Instructor
1 年This is an interesting use of ML. I would love to see how calibrated the models are & if the results will change with a relatively new dataset. Thanks for sharing!