Artificial Intelligence

The Pinnacle of Progress: AI in Healthcare

AI, often termed as the "brain" of machines, encompasses algorithms and computational power that imitate human intelligence. In healthcare, this intelligence is harnessed to analyse vast amounts of medical data, extracting meaningful insights and aiding in crucial decision-making. From diagnostics to treatment plans and administrative tasks, AI is seamlessly integrating into the healthcare ecosystem, promising remarkable improvements.

EHS Leading the AI Charge: A Commitment to Excellence

Emirates Health Service (EHS) recognizes the pivotal role of AI in advancing healthcare. Committed to delivering the highest standards of care, EHS has embraced AI technologies and integrated them into its infrastructure and services. This commitment stems from the vision to enhance patient experiences, optimize operations, and pioneer breakthroughs in healthcare delivery.

AI Projects at EHS: Transforming Care, One Innovation at a Time

1. AI-Based Cancer Diagnosis:

   Leveraging AI algorithms in mammogram devices, EHS is revolutionizing breast cancer detection. These algorithms aid in the early diagnosis of breast cancer, drastically improving the chances of successful treatment and outcomes for patients.

2. AI Heart Rate Monitor:

   EHS employs AI in heart rate monitoring devices to assess electrical heart data. The AI algorithms trigger audio warnings when a patient approaches a critical stage, enabling timely medical intervention and potentially saving lives.

3. AI Diabetes Treatment:

   EHS pioneers diabetes care by implementing AI-driven insulin pumps like the “MinimedTM 780G” system. This technology mimics the natural pancreas, maintaining a precise insulin and glucose balance. AI algorithms help prevent low blood sugar episodes, enhancing patient safety and quality of life.

4. AI Dementia Diagnosis:

   EHS employs computerized cognitive assessment technology using AI to detect early signs of cognitive impairment and dementia. Early detection enables timely interventions, improving treatment outcomes and reducing the impact of dementia on patients' lives.

5. AI Voice Recognition:

   AI-powered voice recognition is integrated into EHS systems, enabling doctors to input health data using voice commands. This innovation streamlines documentation processes, saving time and allowing doctors to focus on providing exceptional patient care.

6. AI Based Disease Prediction:

   By leveraging the power of data analysis and pattern recognition through the EHS Intelligence Platform, the AI-based disease prediction algorithms help us identify diseases at an early stage, or outbreak risks leading to timely interventions and improved patient outcomes. Detecting diseases early like Diabetes plays a crucial for successful treatment and reduced healthcare burden. Our AI disease prediction algorithms have the ability to analyse vast amounts of patient data, including medical records, lifestyle factors, and more with high accuracy of risk stratification.

7. AI Based Admission Prediction:

   EHS’ AI-powered algorithms provide admission risk prediction allowing doctors to focus on the right interventions at the right time. From Predicting the acute onset of heart failure in patients to identifying IP admission prediction scores in the emergency department, the algorithms in the EHS Intelligence platform work towards reducing the burden on both patients and the federal healthcare system. Predicting hospital admission in this patient group could enable timely intervention, with a subsequent reduction of these admissions.

8. AI for Mortality Risk Prediction:

   EHS developed a machine learning model for predicting the mortality of COVID-19 patients in critical care by using important parameters like demographics, comorbidities, medications, labs, and clinical attributes. The correlation and causation analytics along with visual insights were built to understand patterns with respect to the patient group that had fatal outcomes against those patients who survived COVID-19 in ICU / critical care settings. The AI algorithm had a very high accuracy and could easily guide ICU clinicians for timely interventions.

9. AI Based e-Visits Conversion towards Sustainability:

   Aligned with UAE 2030 strategy aiming for net zero carbon emissions, we have used the EHS intelligence platform, built with a powerful and intelligent algorithm that calculates the carbon footprint of patients. EHS used Artificial Intelligence to proactively identify potential e-visits and help convert patient visits to teleconsultation visits. This is a phenomenal and pragmatic approach that integrates with sustainable goals and results in better patient outcomes. As patients embrace sustainable innovations, we have tremendous potential and opportunity to enhance access to quality healthcare sustainably using AI.

10. AI Driven Customer Feedback Analysis:

    EHS piloted a patient feedback sentiment analysis using AI and NLP techniques to improve patient engagement and experience. The EHS Intelligence platform was used to build a novel sentiment analysis program for analysing patient feedback/comments on social media.

11. No Show Prediction Algorithm

    Using primary care historical data trends, the AI model for appointment no-show prediction was created which uses patient and appointment details from our large data repository and consumes it in various machine-learning models for meaningful outputs. It identifies the factors and indicators that create a risk of appointment no-show and stratifies every booked appointment into low to high risk of no-show prediction. Our no-show model includes 16 distinct features and has high accuracy in guiding PHC administrators to manage appointment allocations accordingly.

AI at EHS: Revolutionizing Healthcare and Enhancing Patient Services

The adoption of Artificial Intelligence (AI) technologies at the Emirates Healthcare System (EHS) is a testament to our unwavering commitment to advanced innovation and patient-centred care. As we incorporate AI across various vital domains, its impact on our services and patients has been monumental. For instance, the successful implementation of AI-driven breast cancer diagnosis in four of our hospitals led to the accurate diagnosis of 532 patients between 2019 and 2022. Notably, AI-enhanced diabetes management significantly reduced diagnosis and treatment times for diabetic patients to just two days.

Furthermore, our implementation of voice recognition systems in 82 targeted hospitals and medical centres, with training provided to 1800 physicians, resulted in 1200 active doctors, comprising 70% of our diverse medical workforce, utilizing the system. This has greatly enhanced documentation efficiency, increasing documentation input into medical records by 83%. Clinical documentation accuracy using voice recognition and AI reached 97.6% at Abdullah bin Umran Hospital and 90% at Dibba Hospital.

An impressive 88% of physicians expressed satisfaction with the new clinical documentation procedures, contributing to a 43% increase in medical documentation quality. Within a year of implementation, physician satisfaction with the documentation process reached an impressive 78%. Moreover, the voice recognition system significantly reduced patient waits times for doctor consultations, halving the time from 30 minutes to just 15 minutes.

These achievements were further acknowledged by the international "3M" award, in the category of "Optimal Physician Utilization Rate." This award is shared among all institutions using this technology and is awarded to the institution that achieves an adoption/utilization rate exceeding 50% in the first year of implementation (our institution achieved an adoption/utilization rate of 87% at the time of receiving the award).

All these remarkable advancements underscore our dedication to enhancing patient experiences and redefining healthcare standards through the integration of these cutting-edge technologies.

EHS Intelligence (PaCE)

EHS has introduced an ‘EHS Intelligence’ program that connects all data-driven & AI projects to a centralized platform that allows for timely analysis and generation of meaningful insights for clinical and administrative decision-making across the establishment. A self-analytical app is also linked to the data hubs, enabling fast access and distribution of patient statistics and operational facts. It is designed to be the catalyst for the data-driven healthcare transformation that helps end-users to use different machine learning models & insights custom-developed for solving key business challenges and providing real-time access to information & knowledge.