November 14 is World Diabetes Day, so there’s no better time to explore the relationship between diabetes — a disease that afflicts 37.3 million, or about 1 in 10, Americans — and healthcare data analytics. Whether you’re in value-based care or using a fee-for-service model, you’ll need to treat diabetic patients and prevent the slew of comorbidities that can develop in tandem.
We talked with Nicole Geier, Arcadia’s Director of Client Business Management, to dive into the nuances of measuring, monitoring, and treating this common-but-complicated condition in a way that keeps populations and programs in good health.
Diabetes is a focal point in healthcare not just because it impacts so many lives, but because it can be a gateway to other conditions. It raises the risk for all manner of comorbidities, from hypertension to peripheral vascular disease. Of course, the ideal is to prevent it altogether, but whether it’s Type I (which isn’t preventable, and where the pancreas essentially stops producing insulin) or Type II (where the pancreas produces less insulin and cells receive it less effectively), the earlier the detection, the better.
Why? Because left untreated (or poorly treated), things go downhill fast. The worst-case scenario is death, from kidney failure or ketoacidosis, and other outcomes could be amputation, nerve damage, or blindness.
The connection between diabetes and SDoH
Like so many healthcare struggles, it has a relationship to SDoH (social determinants of health). A study published in Diabetes Care found that “those lower on the SES (socioeconomic status) ladder are more likely to develop T2DM, experience more complications, and die sooner than those higher up on the SES ladder. The higher a person’s income, the greater their educational attainment, and the higher their occupational grade, the less likely they are to develop T2DM (Type II diabetes mellitus) or to experience its complications.”
It’s not all doom and gloom, though. Timely detection and medical technology mean there are new, effective treatment options, like wearable insulin pumps and continuous glucose monitors. It’s a burden, to be sure, but it’s one patients and their providers can shoulder together, to an extent.
“It’s not a death sentence, if it’s managed very well,” Geier adds. “And if you’re doing all the appropriate steps, you don’t have to develop those complications.”
Diabetes is a major healthcare challenge across the board, but in value-based care, where healthcare systems are tasked with treatment, prevention, and overall health, it’s particularly critical.
“Payment models have diabetes as incentive measures because diabetes is so complex,” not in spite of that, says Geier. When healthcare organizations are motivated to lessen patient risk and prevent future illness — along with treating present issues — diabetes becomes a focal point.
The difficulty comes in establishing and tracking good electronic medical data. There are so many types and styles of notation that it’s easy to lose a patient or cluster in the shuffle. According to Geier, there are three key ways to approach this.
“The easiest way is claims,” Geier says. “So if you can show that your diabetics are managed well through claims, that’s probably the best way to do it.”
Here, providers and administrators can take advantage of specific CPT-II or ICD-10 codes to provide context beyond “moderate control” or “healthy.” Specifics like “A1C levels less than 8” or a diagnosis of “diabetic retinopathy” (as opposed to a more generic “bad vision” code) will show payers that you’ve got your finger on the pulse. This allows a healthcare system to know precisely where the population it serves stands, how many people suffer from specific conditions, and helps guide next steps to mediate the population’s disease burdens.
A supplemental file full of EMR data is the second approach. This entails gleaning important information from each patient’s record, assembling it into a digital dossier that gives payers a full picture. Fair warning: it requires an in-house team of electronic medical data wizards, ready to take on a large project.
“You have to have a very good analytics team that can extract data from your EMR,” Geier says. Think, “A1C scores, urine microalbumins to test for proteinuria, diabetic retinopathy exam results, medications. If you can put those into a supplemental data file and get them to the payer to prove how well you’re doing with your diabetics, then that’s excellent.”
No in-house team? Want to think beyond codes, and consider SDoH factors? Want to make sure you’re pulling in data from multiple sources, with a strict process to vet quality and deduplicate records? The third option is to work with a data lakehouse platform (like Arcadia) to cut down on administrative time and avail yourself of lots of shareable, insightful data.
If keeping people healthy is the chief mission of any healthcare organization, then a streamlined, high-functioning system comes in a close second place. You need a way to deliver that great care — process matters.
In value-based managed care or at an ACO, the diabetic patient population can be a case study in the performance of the overall organization. If a system can master this complicated reporting and care management, it’s equipped for other challenges.
“If you’re not sending correct information to the payers, like the CPT codes and the diagnosis codes, you’re not going to perform as well in your value-based care models, because you’re not going to either, one, meet the incentive measures,” says Geier, “or two, you’re going to be documenting a healthier patient with a lower risk score than what your patient population is.”
Get the full picture with detailed reporting
In short: good, succinct, interoperable data means a VBC healthcare organization knows where it needs to focus its resources. But it also means that payers can see granular detail, gaining confidence in the accuracy and specificity of the reporting they receive.
“You want to make sure that you’re documenting all of the patient’s medical conditions as they are, including their complications and comorbidities, so that your risk score is actually appropriate to your patient population,” Geier adds.
Bad data has impacts beyond missed diagnoses or late lab tests. It can also establish unrealistic benchmarks, or misrepresent the true challenge that an ACO will confront in a certain period of time. Conversely, good data puts healthcare organizations and payers on the same page, working towards the same concrete goals.
Diabetes is paradoxical — a hugely common diagnosis that requires highly tailored, individualized treatment. No two diabetic patients will need identical interventions, and that’s a major challenge for value-based managed care.
Whether it’s A1C scores or social determinants of health, learn more about how Arcadia uses healthcare data analytics to confront a formidable challenge and work towards healthier days ahead.