Real-Time Analytics and AI for Managing No-Show Appointments in Primary Health Care in the United Arab Emirates: Before-and-After Study
Abstract BackgroundPrimary health care (PHC) services face operational challenges due to high patient volumes, leading to complex management needs. Patients access services through booked appointments and walk-in visits, with walk-in visits often facing longer waiting times. N...
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JMIR Publications
2025-01-01
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| Series: | JMIR Formative Research |
| Online Access: | https://formative.jmir.org/2025/1/e64936 |
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| author | Yousif Mohamed AlSerkal Naseem Mohamed Ibrahim Aisha Suhail Alsereidi Mubaraka Ibrahim Sudheer Kurakula Sadaf Ahsan Naqvi Yasir Khan Neema Preman Oottumadathil |
| author_facet | Yousif Mohamed AlSerkal Naseem Mohamed Ibrahim Aisha Suhail Alsereidi Mubaraka Ibrahim Sudheer Kurakula Sadaf Ahsan Naqvi Yasir Khan Neema Preman Oottumadathil |
| author_sort | Yousif Mohamed AlSerkal |
| collection | DOAJ |
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Abstract
BackgroundPrimary health care (PHC) services face operational challenges due to high patient volumes, leading to complex management needs. Patients access services through booked appointments and walk-in visits, with walk-in visits often facing longer waiting times. No-show appointments are significant contributors to inefficiency in PHC operations, which can lead to an estimated 3%-14% revenue loss, disrupt resource allocation, and negatively impact health care quality. Emirates Health Services (EHS) PHC centers handle over 140,000 visits monthly. Baseline data indicate a 21% no-show rate and an average patient wait time exceeding 16 minutes, necessitating an advanced scheduling and resource management system to enhance patient experiences and operational efficiency.
ObjectiveThe objective of this study was to evaluate the impact of an artificial intelligence (AI)-driven solution that was integrated with an interactive real-time data dashboard on reducing no-show appointments and improving patient waiting times at the EHS PHCs.
MethodsThis study introduced an innovative AI-based data application to enhance PHC efficiency. Leveraging our electronic health record system, we deployed an AI model with an 86% accuracy rate to predict no-shows by analyzing historical data and categorizing appointments based on no-show risk. The model was integrated with a real-time dashboard to monitor patient journeys and wait times. Clinic coordinators used the dashboard to proactively manage high-risk appointments and optimize resource allocation. The intervention was assessed through a before-and-after comparison of PHC appointment dynamics and wait times, analyzing data from 135,393 appointments (67,429 before implementation and 67,964 after implementation).
ResultsImplementation of the AI-powered no-show prediction model resulted in a significant 50.7% reduction in no-show rates (PPP
ConclusionsThis project demonstrates that integrating AI with a data analytics platform and an electronic health record systems can significantly improve operational efficiency and patient satisfaction in PHC settings. The AI model enabled daily assessments of wait times and allowed for real-time adjustments, such as reallocating patients to different clinicians, thus reducing wait times and optimizing resource use. These findings illustrate the transformative potential of AI and real-time data analytics in health care delivery. |
| format | Article |
| id | doaj-art-fdb094e167a44b9d937c3befb9e8624f |
| institution | Kabale University |
| issn | 2561-326X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Formative Research |
| spelling | doaj-art-fdb094e167a44b9d937c3befb9e8624f2025-01-13T21:00:55ZengJMIR PublicationsJMIR Formative Research2561-326X2025-01-019e64936e6493610.2196/64936Real-Time Analytics and AI for Managing No-Show Appointments in Primary Health Care in the United Arab Emirates: Before-and-After StudyYousif Mohamed AlSerkalhttp://orcid.org/0009-0000-8869-6986Naseem Mohamed Ibrahimhttp://orcid.org/0009-0007-8713-5037Aisha Suhail Alsereidihttp://orcid.org/0009-0009-8427-0799Mubaraka Ibrahimhttp://orcid.org/0009-0000-3973-726XSudheer Kurakulahttp://orcid.org/0009-0007-7061-3901Sadaf Ahsan Naqvihttp://orcid.org/0000-0002-7105-5266Yasir Khanhttp://orcid.org/0000-0003-2134-0963Neema Preman Oottumadathilhttp://orcid.org/0009-0004-3932-726X Abstract BackgroundPrimary health care (PHC) services face operational challenges due to high patient volumes, leading to complex management needs. Patients access services through booked appointments and walk-in visits, with walk-in visits often facing longer waiting times. No-show appointments are significant contributors to inefficiency in PHC operations, which can lead to an estimated 3%-14% revenue loss, disrupt resource allocation, and negatively impact health care quality. Emirates Health Services (EHS) PHC centers handle over 140,000 visits monthly. Baseline data indicate a 21% no-show rate and an average patient wait time exceeding 16 minutes, necessitating an advanced scheduling and resource management system to enhance patient experiences and operational efficiency. ObjectiveThe objective of this study was to evaluate the impact of an artificial intelligence (AI)-driven solution that was integrated with an interactive real-time data dashboard on reducing no-show appointments and improving patient waiting times at the EHS PHCs. MethodsThis study introduced an innovative AI-based data application to enhance PHC efficiency. Leveraging our electronic health record system, we deployed an AI model with an 86% accuracy rate to predict no-shows by analyzing historical data and categorizing appointments based on no-show risk. The model was integrated with a real-time dashboard to monitor patient journeys and wait times. Clinic coordinators used the dashboard to proactively manage high-risk appointments and optimize resource allocation. The intervention was assessed through a before-and-after comparison of PHC appointment dynamics and wait times, analyzing data from 135,393 appointments (67,429 before implementation and 67,964 after implementation). ResultsImplementation of the AI-powered no-show prediction model resulted in a significant 50.7% reduction in no-show rates (PPP ConclusionsThis project demonstrates that integrating AI with a data analytics platform and an electronic health record systems can significantly improve operational efficiency and patient satisfaction in PHC settings. The AI model enabled daily assessments of wait times and allowed for real-time adjustments, such as reallocating patients to different clinicians, thus reducing wait times and optimizing resource use. These findings illustrate the transformative potential of AI and real-time data analytics in health care delivery.https://formative.jmir.org/2025/1/e64936 |
| spellingShingle | Yousif Mohamed AlSerkal Naseem Mohamed Ibrahim Aisha Suhail Alsereidi Mubaraka Ibrahim Sudheer Kurakula Sadaf Ahsan Naqvi Yasir Khan Neema Preman Oottumadathil Real-Time Analytics and AI for Managing No-Show Appointments in Primary Health Care in the United Arab Emirates: Before-and-After Study JMIR Formative Research |
| title | Real-Time Analytics and AI for Managing No-Show Appointments in Primary Health Care in the United Arab Emirates: Before-and-After Study |
| title_full | Real-Time Analytics and AI for Managing No-Show Appointments in Primary Health Care in the United Arab Emirates: Before-and-After Study |
| title_fullStr | Real-Time Analytics and AI for Managing No-Show Appointments in Primary Health Care in the United Arab Emirates: Before-and-After Study |
| title_full_unstemmed | Real-Time Analytics and AI for Managing No-Show Appointments in Primary Health Care in the United Arab Emirates: Before-and-After Study |
| title_short | Real-Time Analytics and AI for Managing No-Show Appointments in Primary Health Care in the United Arab Emirates: Before-and-After Study |
| title_sort | real time analytics and ai for managing no show appointments in primary health care in the united arab emirates before and after study |
| url | https://formative.jmir.org/2025/1/e64936 |
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