Building patient trust through accurate bill estimates
Care Process & Redesign
Technology
National Healthcare Innovation and Productivity Medals
Private Hospital
31 December 2024
Achieve 90% accuracy in medical bill estimations, reducing errors, improving transparency, and enhancing patient. FeeAdvisor.ai has significantly improved billing accuracy and efficiency, fostering patient trust.
Year Submitted: 2024
Published Date: 31 December 2024
Tags: Technology, Care Process ＆ Redesign, Digitalisation, Automation, Artificial Intelligence, Value Based Care, Productivity, Patient Satisfaction, Time Saving
About this Content
Aims
Achieve 90% accuracy in medical bill estimations, reducing errors, improving transparency, and enhancing patient satisfaction.
Background
Manual bill estimation processes relied on static historical data, leading to inefficiencies, errors, and patient disputes. Business Office staff spent excessive time on manual bill estimations, encountering inaccuracies.
Methods
FeeAdvisor.ai was developed through interdisciplinary collaboration, integrating machine learning and hospital workflow insights to enhance billing accuracy. A seven-step strategy was implemented for system refinement and seamless integration.
Results
Bill estimate accuracy improved from 23% to 87%, bill disputes reduced by 50%, and estimation time was cut from 40 to 25 minutes, saving 1185.4 days of man-hours annually. Error rates declined from 77% to 53% post-implementation.
Conclusion
FeeAdvisor.ai has significantly improved billing accuracy and efficiency, fostering patient trust. Future enhancements will focus on refining the model for complex cases, integrating new medical procedures, and adapting to evolving healthcare demands.
Lessons Learnt
Cases without surgery and TOSP codes may lead to bill variations. Continuous refinement of AI models and collaboration between data scientists, IT, and healthcare teams ensure ongoing improvements.
Keywords
Manual processes, Inaccuracies, Bill estimation, Accuracy, Real-world medical practice, Operational workflow
Innovators' Details
Innovators' Details
Healthcare Cluster(s) | Private Hospital |
Organization(s) Involved | IHH Healthcare |
Platform(s) | National Healthcare Innovation and Productivity Medals |
Healthcare Professional Group(s) | Allied Health, Healthcare Administration |
Applicable Specialty or Discipline | Healthcare Administrators |
Project Lead(s) | Apple Teo |
Project Member(s) | Allen Tan |
Connect with this contributor!
Apple Teo - allen.tan@ihhhealthcare.com
Project Attachment
146_IHH_NHIP_2024_Building_patient_trust_through_accurate_bill_estimates.pdf