DigiDiab

Improving diabetes management with a patient-centred digital assistant

The challenge

Around 422 million people worldwide live with diabetes, the majority in low-and middle-income countries. Diabetes prevalence has been steadily increasing over the past few decades in countries of all income levels.[1] In the EU, 61 million adults (1 in 11 adults) lived with diabetes in 2021 and this number is expected to reach 67 million by 2030. 1.1 million deaths were attributed to diabetes and about €179 billion were spent on diabetes treatment in 2021 in the EU.[2]

Diabetes treatment and management is complex because each patient requires individual and regular adjustments in therapy under the care of healthcare professionals (HCPs) in hospitals, nursing homes or at domiciliary care. Insulin dosing requires appropriate reactions to measured blood glucose or skipped meals in the short term as well as appropriate adjustment of insulin dosage in the long term. Successful diabetes management requires multiple in and outpatient visits with HCPs, as well as community support and self-care.

Currently, diabetes management across these healthcare sectors leads to high costs for healthcare systems, a low quality of care for patients and an immense workload for HCPs. Major obstacles to improving diabetes management include low adherence to standards of diabetes care and guidelines, a low degree of digitalisation in care processes, fragmentation of information across healthcare services, rigid hierarchical structures, as well as complex and fragmented healthcare financing. Diabetes management is a complex endeavour for HCPs, healthcare providers, and patients alike.

Literature shows that there needs to be more innovative point-of care computerised clinical decision tools to guide HCPs in implementing evidence-based guidelines, taking into account health status, age, and comorbidities.[3] [4]Diabetes guidelines and best practice-based care are not easily implemented, due to the complex and fragmented nature of the care processes or distributed information across HCP.

The solution

With EIT Health’s support, the DigiDiab project consortium aims to develop further, implement, and evaluate their solution – a digital diabetes assistant called GlucoTab – for best-practice care delivery, with digitised patient-centred diabetes care, in domiciliary nursing care and nursing homes in different European pilot regions.

The project will evaluate GlucoTab in different settings and healthcare systems, and support market access of the patient-centred digital medical device in Europe for faster market entry and wider adoption. The digital solution has reached product readiness and is available as CE marked class IIa medical device in hospitals and the team is working towards validated solutions for nursing homes and domiciliary nursing care.

GlucoTab solves multiple unmet needs for patient-centred diabetes treatment by supporting HCPs in different settings: hospitals, nursing home, domiciliary nursing care. The solution directly impacts patients and supports HCPs. Its unique decision support empowers nurses to execute the correct insulin therapy including individual dosing and dose adjustment, driven by validated algorithms and digital processes, in matters of seconds instead of days in the current healthcare processes.

GlucoTab focusses on specialised support for the treatment of type 2 diabetes. The digital diabetes assistant will be a versatile diabetes management solution applicable at different healthcare levels and usage scenarios. It is based on the GlucoTab system which provides algorithm-based workflow and decision support in the insulin therapy of hospital inpatients with type 2 diabetes for nurses and physicians. GlucoTab automatically suggests correct insulin doses for individual administrations as well as for regular therapy adjustment and supports the interdisciplinary blood glucose management workflow with defined tasks for nurses and physicians in hospitals.

Expected impact

Clinical evaluation in the hospital setting has shown that GlucoTab improves blood glucose values without causing additional hypoglycaemic episodes. A direct benefit for patients by reducing complications can be assumed. [5] [6]

GlucoTab has demonstrated avoidance of insulin dosing errors and dramatically increased safety in diabetes treatment for HCPs and patients.[7] GlucoTab will provide insulin dosing support plus several safeguarding functions for the most common insulin therapy regimens, as well as support of the treatment workflow and documentation, to make sure that the appropriate activities are timely performed and calculation and reading errors are prevented across the entire treatment spectrum.

An important change that GlucoTab creates is a shift in user roles. Usually, therapy is ordered by physicians and executed by nurses. With GlucoTab, the role of nurses is strengthened, and they can perform individual dosing and regular dose adjustment with the support of the medical device GlucoTab. In trials so far, GlucoTab was continuously used (>95% of expected tasks in workflow process completed and documented),[5] and more than 95% of dosage recommendations were followed by both, nurses, and physicians, indicating that staff trusted the system and the nurses feel empowered.[5]

The DigiDiab project team aims to further adapt GlucoTab to specific regional requirements, create versions in local language and perform pilots, where GlucoTab is used at the hospital, nursing home, and domiciliary nursing care setting in the treatment of patients with diabetes. Clinical trials will be performed to evaluate the pilot operation. At the same time, the team will gain further insights into the regional health systems and customer organisations and other factors that may affect their business model.

The team’s goal is to verify that the updated GlucoTab version is suitable, effective, and safe and well accepted by the users in different European health systems and be prepared for market entry.

Project leaders

Medical University of Graz

Start-up

decide Clinical Software GmbH

External Partners

• LAB University of Applied Sciences
• Universitätsklinikum Frankfurt am Main

References

[1] World Health Organisation, W. (2022). Diabetes online. https://www.who.int/health-topics/diabetes#tab=tab_1

[2] International Diabetes Federation, I. (2021). IDF Diabetes Atlas. https://diabetesatlas.org/

[3] Idrees, T., Castro-Revoredo, I. A., Migdal, A. L., Moreno, E. M., & Umpierrez, G. E. (2022). Update on the management of diabetes in long-term care facilities. BMJ Open Diabetes Research and Care, 10(4). https://doi.org/10.1136/bmjdrc-2021-002705

[4] Wexler, D. J., Shrader, P., Burns, S. M., & Cagliero, E. (2010). Effectiveness of a computerized insulin order template in general medical inpatients with type 2 diabetes: a cluster randomized trial. Diabetes Care, 33(10), 2181–2183. https://doi.org/10.2337/dc10-0964

[5] Neubauer, K. M., Mader, J. K., Höll, B., Aberer, F., Donsa, K., Augustin, T., Schaupp, L., Spat, S., Beck, P., Fruhwald, F. M., Schnedl, C., Rosenkranz, A. R., Lumenta, D. B., Kamolz, L. P., Plank, J., & Pieber, T. R. (2015). Standardized Glycemic Management with a Computerized Workflow and Decision Support System for Hospitalized Patients with Type 2 Diabetes on Different Wards. Diabetes Technology and Therapeutics, 17(10), 685–692. https://doi.org/10.1089/dia.2015.0027

[6] Umpierrez, G. E., Smiley, D., Jacobs, S., Peng, L., Temponi, A., Mulligan, P., Umpierrez, D., Newton, C., Olson, D., & Rizzo, M. (2011). Randomized study of basal-bolus insulin therapy in the inpatient management of  patients with type 2 diabetes undergoing general surgery (RABBIT 2 surgery). Diabetes Care, 34(2), 256–261. https://doi.org/10.2337/dc10-1407

[7] Donsa, K., Beck, P., Höll, B., Mader, J. K., Schaupp, L., Plank, J., Neubauer, K. M., Baumgartner, C., & Pieber, T. R. (2016). Impact of errors in paper-based and computerized diabetes management with decision support for hospitalized patients with type 2 diabetes. A post-hoc analysis of a before and after study. International Journal of Medical Informatics, 90, 58–67. https://doi.org/10.1016/j.ijmedinf.2016.03.007