How to scale telehealth programs by leveraging diabetes data to identify “at-risk” patients

The expansion in reimbursement coverage for both Telehealth and Remote Patient Monitoring (RPM) services offers unique opportunities for delivering enhanced care for patients with diabetes, and many healthcare organizations are seeking to take advantage.

For those organizations that are either starting or scaling virtual patient care programs, a common and recurring step in the workflow is identifying which patients are “at-risk” and could benefit from more care. By doing so, health systems are able to deliver targeted and proactive care to those patients, which can result in improved long term patient outcomes and cost-savings. Prior to the expansions in reimbursement coverage, delivering proactive, virtual care was a service few health systems could afford because of the strict constraints on what kinds of virtual appointments were reimbursable.

Three common challenges in identifying at-risk patients

The process of “identifying at-risk” patients must be implemented through a collaboration between an organization’s clinical & IT teams, that together define what it means for a patient to be “at-risk” using data stored in the EHR. While this workflow is understood in theory, three common challenges associated with operationalizing this workflow are equally understood:

  1. 1) the EHR data used for risk-triaging is often inconsistent or incomplete (Botsis et al., 2010)
  2. 2) defining what it means to be “at-risk” varies by demographic and there can be many “risk cohorts”
  3. 3) there is often insufficient IT/analyst capacity to address the data-intensive work posed by challenges 1 & 2.

These opportunities and challenges are amplified within diabetes care. “Beyond A1c” glucose & insulin metrics derived from diabetes self-management devices can be used to risk stratify diabetes populations with accuracy and latency not achievable when leveraging standard diabetes-related EHR data (e.g. A1c values, singular fasting glucose values). But because “diabetes device” data interoperability has only recently become portable, most healthcare organizations are not yet equipped to store diabetes device data with high granularity within their EHR and thus currently can’t use these data.

Glooko’s solution

To assist our users, Glooko has developed a comprehensive digital health platform for diabetes that includes the “at-risk” feature which allows healthcare organizations to risk-triage their diabetes population.

Using Risk-Triaging & Cohorting to Support RPM & Telehealth

  • 1. Prevent Emergency Room visits by identifying patients experiencing hypoglycemia

Hypoglycemia can be dangerous for people with diabetes and hypoglycemia-related ER hospitalizations are expensive for healthcare organizations (Bronstone & Graham, 2016). Hypoglycemia is especially prevalent within Type 1 diabetes adolescent populations, for whom the combination of rapid physical development and hectic schedules can result in highly variable glucose levels (Borus & Laffell, 2010; Datye et al., 2015).

With the recent changes to telehealth reimbursement policy, health systems can use Glooko’s solution to identify patients at risk for severe hypoglycemia at the cadence that they choose (e.g. every 7 days. 30 days, etc.), and subsequently schedule a newly reimbursable virtual visit, resulting in timely patient care while mitigating the patient’s (and payer’s) risk of incurring a costly hypoglycemia-related ER visit.

To easily identify this patient cohort, you can use Glooko’s Age, Time CGM Active and Hypoglycemia filters.

  • 2. Find patient candidates eligible for Remote Patient Monitoring

To run a viable RPM program, health systems must rigorously evaluate which patients are “at risk” and likely to benefit from additional care and which patients generate an adequate amount of qualified data (a minimum of 16 days worth of remotely transmitted data every 30 days is required for certain RPM reimbursement codes).

Using the Cohort feature, you can easily identify patients that meet both criteria. For example, an ideal RPM cohort might be patients that have an Average BG over 250 mg/dL over the last 30 days (cohort may benefit from RPM care) that also check their blood sugar more than 1 time per day (cohort meets the data threshold for reimbursement).

  • 3. Manage hyperglycemia for patients enrolled in the RPM program

Once patients are enrolled in an RPM program, the Cohort feature allows for easy identification of patients experiencing conditions such as hyperglycemia among patients 65 or older with Type 2 diabetes. Upon identification, clinicians can intervene to manage hyperglycemia through a combination of lifestyle coaching (diet & exercise) and/or medication adjustments.

Once a cohort has been found, Glooko offers various methods for identifying each patient that is included in that cohort and taking the appropriate actions for those patients. Stay tuned for Glooko’s next blog to learn more!

References

Borus JS, Laffel L. Adherence challenges in the management of type 1 diabetes in adolescents: prevention and intervention. Curr Opin Pediatr. 2010;22(4):405-411. doi:10.1097/MOP.0b013e32833a46a7

Botsis T, Hartvigsen G, Chen F, Weng C. Secondary Use of EHR: Data Quality Issues and Informatics Opportunities. Summit Transl Bioinform. 2010;2010:1-5. Published 2010 Mar 1.

Bronstone A, Graham C. The Potential Cost Implications of Averting Severe Hypoglycemic Events Requiring Hospitalization in High-Risk Adults With Type 1 Diabetes Using Real-Time Continuous Glucose Monitoring. J Diabetes Sci Technol. 2016;10(4):905-913. Published 2016 Jun 28. doi:10.1177/1932296816633233

Datye KA, Moore DJ, Russell WE, Jaser SS. A review of adolescent adherence in type 1 diabetes and the untapped potential of diabetes providers to improve outcomes. Curr Diab Rep. 2015;15(8):51. doi:10.1007/s11892-015-0621-6

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Vikram Singh

Vikram is on Glooko's product management team. He's passionate about building digital health solutions that are intelligent, scalable and accessible to all.