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Good practice Imported

EHS Footstep Insights — AI appointment no-show predictor in UAE primary health care

United Arab Emirates · Sharjah · See the United Arab Emirates profile

Evidence: Observational / pre–post Top 32% 67/100 · Ask Evidence Copilot about this practice

The UAE's Emirates Health Services deployed AI to predict appointment no-shows across all primary health centres in October 2022. No-show rates fell 51% (21%→10%), average wait time dropped 5.7 min (54→48.5 min), saving 387,000 patient-minutes in 3 months.

21%
Baseline appointment no-show rate
>140,000
Monthly patient visits across PHCs
67,429
Pre-implementation appointments studied (Jul-Sep 2022)
67,964
Post-implementation appointments studied (Nov 2022-Jan 2023)
20.82% to 10.25% percentage points
No-show rate reduction
50.7%
Relative reduction in no-shows
OR 0.43 (95% CI 0.42-0.45, P<.001)
Odds ratio
54 to 48.5 min
Average patient wait time
387,394
Patient-minutes saved (3 months)
86%
Model accuracy
EHS Footstep Insights — AI appointment no-show predictor in UAE primary health care

Details

Maturity
Established
Promoter
Emirates Health Services (EHS)
Period
2022-present
Keywords
appointment management, no-show prediction, primary health care, random forest, real-time analytics, healthcare operations, patient access

Context

Emirates Health Services (EHS), the UAE federal authority for primary health care in the Northern Emirates, manages over 140,000 patient visits monthly across its primary health centres and faced a 21% baseline appointment no-show rate causing resource waste, longer waits, and reduced access.

Objectives

Reduce appointment no-shows and improve resource allocation through predictive risk alerts.

Activities

In October 2022, EHS deployed the 'Footstep Insights Solution', a random-forest classification model trained on 16 demographic, clinical, and appointment-specific features, categorising patients as high-risk (≥90%), medium-risk (80-89%), or low-risk (70-79%) of no-show; clinical staff receive proactive alerts to contact high-risk patients or reallocate slots via a real-time dashboard.

Results

A peer-reviewed before-after study (JMIR Formative Research, January 2025) compared 67,429 pre-implementation appointments (Jul-Sep 2022) with 67,964 post-implementation appointments (Nov 2022-Jan 2023): no-show rates fell from 20.82% to 10.25% (a statistically significant 50.7% reduction; OR 0.43, 95% CI 0.42-0.45, P<.001); average wait time fell from 54 to 48.5 minutes, saving 387,394 patient-minutes over three months; the model achieved 86% accuracy.

Conclusions

The study received Dubai Research Ethics Committee approval and EHS has published an organisational AI Policy; limitations include the mitigation workflow operating outside the EHR system and the absence of long-term follow-up, financial-impact analysis, or demographic bias assessment.

Implementation

Implementation detail (cost, timeline, staffing, conditions for success) is not yet available for this practice.

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Data sources

Where this practice's information was retrieved from, and when.

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