Over the past few decades, the burden of chronic diseases has significantly increased all over the world, contributing a substantial cost to the healthcare systems. These diseases are responsible for a major proportion of the top ten causes of death globally, especially in the middle-to-high income countries.
Chronic disease management requires a very comprehensive approach with sustained efforts to cover all segments across the spectrum of these complex diseases. While efforts are required to manage these chronic conditions, many resources are also required to prevent disease complications. To create relevant and effective disease management strategies, the role of data cannot be denied. Data is increasingly being utilized to curate healthcare programs and it continues to be a source of program evaluation and enhancements in the healthcare industry. Of the many ways employed to utilize data for insights creation, visualizations have proven to be a very effective tool for creating system insights for leaders and decision makers.
Diabetes is one of the leading chronic diseases in today’s world. With an expectation of the global burden of disease to increase manifold in the years to come, healthcare leaders are looking into insightful, evidence-based approaches to address this issue.
With a mission of providing innovative and high-quality care to their patients, Emirates Health Services (EHS) have a patient-centric focus. There is high emphasis on improved healthcare quality that is aligned with the international sustainable development goals. Realizing the increasing burden of chronic diseases and categorical increase in diabetes cases in the past few decades, EHS leadership required insights into their population statistics for the condition. These insights would be utilized as the evidence base for planning and management of diabetes care programs for our population.
To meet these expectations, we designed a data-driven solution utilizing Cerner EHR (called Wareed) data. This is a dashboard that gives EHS leaders a view of the distribution and wellbeing of their diabetic population across their entire network.
The dashboard consists of a series of visualizations that are based on several customized reports catering to different requirements of diabetes care and management. Our data spans from patient demographic details to clinical outcomes. Our team worked on creating multiple reports that could accommodate massive amounts of data over a span of six years, representing the diabetic cohort enrolled with EHS.
The major areas touched on by this dashboard include disease distribution, segregated by age, gender, location and nationality. There are insights into the service utilization frequencies and trends through visits analysis, which gives leaders an understanding of their resource consumption and requirements. Besides showcasing trends of service utilization, we used advanced analytics techniques to predict future visits that would support leaders in foreseeing the expected service utilization in future. This can serve as an effective tool for future program planning and management by program leaders.
There are visualizations that project burden of disease and trends over a period of time. These trends can be mapped against geographical location, age category and gender of patients. Moreover, the dashboard includes clinically rich analytics displayed over layers that cover patient comorbidity conditions and their risk factors for disease complications. The program leaders can utilize this dashboard to view continuity of care for their cohort, which also reflects upon compliance to recommended care practices by EHS clinicians.
Our platform includes a unique patient traceability feature that can be utilized by care coordinators to identify their vulnerable population. This function is supported by the analytics feasibility to identify coexistence of different clinical conditions at a patient level. This information can be tied back to patient management process and used to reach out to high-risk patients through information displayed.
Clinically, it is significant for the care team to identify their patients who require a tailored approach for disease control. This rich data set can be utilized to create advanced analytics models utilizing artificial intelligence. These models can help identify high risk characteristics from the EHS population that predispose them to a risk of poor disease control. To further elaborate on the significance of such capabilities, it can also be utilized to identify the financial impact of such preventive interventions by avoiding high resource consumption visits and events.
Overall, this project is aligned with EHS vision of leadership in healthcare delivery. By meaningful use of machine learning and advanced analytics techniques, this dashboard provides insights that can be utilized to plan resources, identify clinical practice compliance, find out the burden and distribution of diabetes, highlight the high-risk patients and provides the facilitation to reach out to such patients for prompt action and improved patient management. It displays a wealth of information that would support decision makers in identifying high achievements as well as areas of refined focus for future action by using epidemiological as well clinical evidence extracted from the Cerner EHR.
Dr Maryam presented this topic at the Advanced Data Analytics Special Interest Group (SIG) meeting hosted for Cerner clients by invitation only. If you are a Cerner client and would like to participate as an attendee or a speaker at a future SIG, then please reach out to firstname.lastname@example.org
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