We have built a fit-for-purpose, scalable and sustainable solution, leveraging internal knowledge and resources, built in-house for a low cost. By incorporating multiple technologies, we have a fully automated a solution that identify patient at risk of sepsis without human interference.
Replacing traditional vital signs devices with smart ones allowed us to benefit from the Internet of Thing (IOT) and integrate the data from the devices with the electronic medical record system directly instead of depending on the nurses to enter the data manually.
In addition, depending on a fully electronic based decision making tool to predict the patient at high risk of having sepsis before having the septic shock and notifying the clinical care team using a mobile application designed to send the notifications and register the time of acknowledgement by the clinical care team result into a better healthcare service.

After successfully implementing the e-alert tool in the hospital EMR system, we have reached a point where we have a sufficient accurate responses to the alerts from the primary physicians that will allow us to move on to the next level.

1. Improve the prediction model to accurately predict the development of severe sepsis or septic shock following the points below.
• Stratify risk (low, moderate, high)
• More improvement by attempting to add more features.
• Increase the early detection of sepsis and reduce false positive
2. Assess the effect of the system on clinical outcomes are per the protocol.
3. Implement the system in the emergency departments
4. Sustain the implementation in the wards and measure the effect over the next 12 months, with the hypothesis that more effect will be observed.
5. Enhancing the mobile devices and create an app on personal mobile phones.
6. Enabling BestBoard for real-time alerting.

MNGHA has collaborated with several national entities to reach a digital maturity level that facilitates the implementation and utilization of smart digital solutions. National Data Management Office (NDMO) is one example of the active collaborations that allowed MNGHA to adopt the guidelines and standards of data governances leading towards 2030 vision.

Digital innovation project was complex, require a high level of expertise, and involve various stakeholders, including:
• ICU and Inpatient department.
• Medical service.
• Data Governance office.
• Information Technology team
• Nursing
• Communication office.

The nurses’ acknowledgment to the alerts has improved from a median time of 34 minutes to a median time of 6 minutes, with several wards achieving times of less than 4 minutes; where the physicians’ confirmation has improved from a median of 6 hours to 2 hours, with several wards achieving times of less than 30 minutes. Early detection and medical intervention reduced sepsis mortality by 30%, hospital length of stay and ICU admissions. This not only improves clinical patient outcomes and experience, but also significantly reduces the cost of care. Moreover, the analysis of the data from the solution has shown that approximately 33% of those alerts were primarily in patients with an admitting diagnosis of pneumonia.

The challenges encountered with our electronic alert development and implementation include resource allocation, changing and un-agreed-upon sepsis screening tools (qSOFA and SIRS), charting behaviors, alert fatigue (false positive), inappropriate response (false negative) and differences in health care delivery models. The screening for sepsis project provides an opportunity for a novel trial design and analysis of routinely collected and entered data to evaluate the effectiveness of an intervention (sepsis alert) for a common medical problem (sepsis in ward patients). Its results may open the door for other trials on other interventions in other conditions.

Providing high quality of health care that is readily accessible, cost effective and meets the needs of the communities we serve. Provide our patients, their families and our community with extraordinary healthcare service; to ensure high quality, compassionate treatment; and to deliver care beyond their expectations. For this we developed a clear understanding of emerging technologies' potential, identifying the right use cases, building the necessary infrastructure and talent, and ensuring ethical and responsible use of AI.

The implementation of the solution has started in 2019 at King Fahad Hospital – Riyadh with a plan to expand to new locations every quarter. By the end of 2021, the solution was applied at King Abdullah Specialized Children Hospital – Riyadh, King Abdulaziz Medical City – Jeddah, King, King Abdulaziz Hospital - Al Ahsa, Imam Abdulrahman Bin Faisal Hospital – Dammam, and Prince Mohammed Bin Abdulaziz Hospital – Al Madinah. In 2023, the solution applied within King Abdullah Specialized Women’s Hospital – Riyadh. In addition, planned to expand into four new hospitals in Riyadh, Jeddah, Taif and Qassim within the upcoming two years.

The project demonstrates the power of having a built-in alert system with dashboards that removes many aspects of human intervention and ensures sustainability. The system allowed a large-scale implementation. The closed loop communication is an intervention. The implementation within a research project facilitates a robust methodology and proper assessment of the effect on outcomes.