Total 595 words used.
Advanced analytics driven clinical program for diabetes management
The program is the first of its kind advanced analytics driven clinical program for diabetes management that is implemented in a large-scale governmental healthcare system. The program provides security-controlled insights at operational, tactical, and strategic levels to the decision makers - healthcare providers, care coordinators and leadership. The key insights in the program are –
- Overall Diabetes Disease burden and distribution across northern emirates population of UAE
- Services utilization / current resource needs for diabetes and associated comorbidities
- Disease trends across population with multiple segmentations of demographics and geographics
- Disease risk factors based on clinical guidelines and data modelling
- Compliance with the care plan/treatment protocol
- Prediction of demand for different services
- Patient traceability feature to trace and contact the patient
- Risk stratification to identify patients at higher clinical risks due to uncontrolled diabetes
The insights into the service utilization frequencies and trends through visits analysis gives leaders an understanding of their resource consumption and requirements. The platform includes clinically rich analytics used by different care providers both in the primary care setting and tertiary care setting to measure the continuity of care for their patient cohort which also reflects upon internal and international compliance with the recommended care practices by EHS clinicians.
The platform has a unique patient traceability capability, that helps care providers to identify the patient groups which need attention and thus schedule the follow-up care. The program was designed on the Data->Information->Knowledge->Wisdom (DIKW) methodology
A modular approach was developed to enhance the disease distribution insights with the financial burden, disease control, disease complexity and service utilization. Insights were developed across descriptive, predictive, and prescriptive analytics for clinical care providers, hospital managers and executive decision makers.
A unique AI model was built that utilizes historical data to identify features of patients at risk of poor disease control. The model predicts individuals who are at risk of deterioration in the future and patient traceability features help in both developing care plans and to bring these patients back into the health system.
Benefits Realized
- The platform indicated that the cost of delayed medical intervention for diabetics for the at-risk groups will increase the financial burden on the institution by 8 million dirhams in the next five years.
- A large population of 62000+ diabetic patients were studied, disease trends analyzed, and risk profiling is done through the platform
- Patient traceability helped create patient groups for focused care plans/treatment regimes
- Future service utilization prediction shows expected trends of EHS services that will be utilized by the diabetic populations in the coming years
- % Population was identified at risk of deterioration of the disease
- Improved Resource planning, patient care and management and better health outcomes
Novelties of the Program
- The practical use of artificial intelligence innovatively and proactively applied for the first time in the field of diabetes management at the level of the Arab countries and the countries of the Middle East
- Use of clinically rich prescriptive analytics displayed over layers that cover patient comorbid conditions and their risk factors for disease complications
- This solution has a unique patient traceability feature, that helps users in identifying their population of interest within the diabetic cohort and provides links for patient tracking and follow up
- AI model that predicts the deterioration of controlled diabetes patients into severely uncontrolled diabetes patients helps in the identification of high-risk patients
- For the first time an in-depth analysis of the clinical risk factors correlated with the financial burden of disease is being done and how early interventions can reduce the overall financial burden in the region
