Outpatient service demands have increased exponentially over time. There has been dramatic progress in the way outpatient transactions are managed today. For an impeccable patient experience, there’s ample opportunity to optimize resource utilization by aligning it with patient visit patterns in outpatient settings.
Emirates Health Services (EHS) is focused on providing world-class, high-quality care to its patients. Being the gateway to patient entry into the healthcare system, primary healthcare centers (PHC) experience heavy traffic of patients daily—the PHCs at EHS cater for approximately 140,000+ visits every month, which are managed by a streamlined resourcing approach at both organization and facility levels.
While patient appointments are organized, our facilities have a significant proportion of walk-in visits. Due to high turnover, patients must sometimes wait longer than expected until their consultation, which may impact the overall patient experience at our centers. On the contrary, some slots can go underutilized due to patient no-shows. These can be disruptive and costly for any health organization due to wasted capacity and resource underutilization.
With the evolution of artificial intelligence (AI) models, sophisticated cases have been taken up and managed using trends and insights from historical data that suggest interventions for the present and future.
Looking at ways to enhance our operational workflows, there was an opportunity for EHS to support primary healthcare appointments through insights generated by the organization’s electronic health record (EHR). The Oracle Health EHR—known as Wareed—prompted improvements in patient waiting times and appointment no-shows. Banking on our rich data repository, we could employ advanced data analytics to guide our operations in outpatient appointment management through a data application that creates actionable visualizations in real time.
Following this vision, we implemented the primary healthcare Resource Optimization and Efficiency Management (ROEM) data application across all PHCs at EHS. Our project aims were to identify operational bottlenecks and streamline processes such that we could reduce patient waiting times and appointment no-shows.
Our data application tracks patient volume, waiting times, and resource distribution (clinician-to-patient ratios) in real-time. It provides accurate insights into patient waiting times at every station in the health center and allows for the comparison of health center data to overall organization data, which can be used by managers to gauge alignment with organizational operations. The data can be viewed and analyzed based on location, department, and patient-level slicers.
We have also achieved real-time patient and staff attendance data integration through a customized data mart. This cross-functional data acquisition helps in estimating real-time resource constraints with greater insights. Additionally, the application utilizes financial data to enable primary healthcare facilities to forecast the timing, quantity, and source of revenue.
Using PHC historical data trends, an AI model for appointment no-show prediction was created. This model uses patient and appointment details from our sizeable 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 (based on four demographic, three patient history, and nine appointment factors) and has an accuracy of 86%, guiding PHC administrators to manage appointment allocations accordingly.
To maintain data privacy, access has been defined on user positions that align with governance policies.
This is the first instance where EHS has achieved a data analytics solution with real-time analytics, cross-functional analytics, and an operational AI model.
The real-time, relevant information guides PHC managers in the decision-making process for their daily operations and the management of their resources, which has helped them in designing a practical appointment booking approach. The application highlights waiting times for senior citizens so they can be dealt with as a priority.
This project is in line with the vision of the Emirates Health Services’ leadership, and it provides an abundance of valuable information that can help decision-makers identify significant accomplishments.
