Identifying patients with type 2 diabetes - a chronic, progressive condition that affects more than 29 million Americans and 415 million people globally - and those at risk for diabetes, is a first step in closing care gaps and improving the health of a population.
Do you have a 360-degree view of patients available at your fingertips, to help you meet quality metrics and improve care? You might think that’s information you’d find in your electronic health record (EHR) system. But we know there is more information about patients than just the data that gets stored in the EHR when they get sick or have an annual visit. These are called Social Determinants of Health (SDoH) and they include risk factors that might never be captured by clinicians – things such as credit score, access to quality food, and where you live or work in relation to your closest clinic or hospital.
More than 75% of US providers are making diabetes a top priority in their health management programs. That's because:
40% of people with diabetes are unaware they have the disease
90% of people with prediabetes are undiagnosed
Patients with diabetes spend roughly twice the average on medical care
Clinicians typically wait weeks for reports and then aren't certain which risk criteria were used. Why not deliver the data in a consumable and exploratory format, like an application, where the clinicians themselves can search through their panel of patients for opportunities and understand quickly what goes in the often “black-box” analytical models for risk? The machine should aid their discovery – not make it for them without their input.
For example, every dot here represents a patient on Dr. McKenzie’s patient panel that has diabetes (blue) or doesn’t (grey). She can clearly tell that there are a number of patients at risk for diabetes with elevated HbA1c readings and high triglycerides:
Another major risk factor is obesity. While not all people with diabetes are obese, and not all who are obese develop diabetes, about 54% of middle-aged Americans who are obese will develop diabetes in their lifetime. This sets the stage for an alarming rise in prediabetes and diabetes, ever increasing the urgent response necessary for hospitals and health systems. There are number of comorbidities that are related to diabetes. Your analytics platform should allow you see these side by side and not require you to dig through multiple reports or dashboards:
Be critical too of communities with low rates of diabetes – this could signal that the population is under-diagnosed due to access issues such as not having a PCP (primary care physician), lack of health insurance, or limited interactions with their doctors. Population engagement requires more than just the hospitals and health systems to get involved for us to make meaningful progress in combating this disease. At the moment, the current trends of these conditions will have an even more significant impact on future healthcare costs, outcomes, and the wellbeing of individuals and populations.
View our three-minute Diabetes Discovery app video to see how easy it is to get the insights you need. You’ll understand how Qlik makes data easy to explore, visual, and most importantly actionable.
Qlik enables clinicians within their EHR (such as Epic or Cerner) to quickly and easily:
View key metrics about patient panels
Drill-down to find patients who may be undiagnosed
Explore what's behind risk models and set your own risk criteria
Reveal the social determinants of health
Start improving the health of your population and your organization today – your patients and the future of our healthcare system depend on it. See for yourself how the Qlik Sense Diabetes application makes it easy to uncover the whole story that lives within your data: https://demos.qlik.com/qliksense/DiabetesDiscovery
Start improving the health of your population and your organization today – your patients and the future of our healthcare system depend on it.
Read how #Qlik is revolutionizing diagnostics for diabetes: