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SHR Neuro Krebs Kardio Lipid Stoffw Microb

Lichtenegger, KM; Aberer, F; Tuca, AC; Donsa, K; Höll, B; Schaupp, L; Plank, J; Beck, P; Fruhwald, FM; Kamolz, LP; Pieber, TR; Mader, JK, ;on behalf of the Hypo-RESOLVE, Consortium;AiDAPT, AP@home04, CLOuD, DAN05, DAN06, and, KidsAP, consortia;HYPO-RESOLVE, Consortium;KidsAP, Consortium.
Safe and Sufficient Glycemic Control by Using a Digital Clinical Decision Support System for Patients With Type 2 Diabetes in a Routine Setting on General Hospital Wards.
J Diabetes Sci Technol. 2021; 15(2):231-235 Doi: 10.1177/1932296820955243 [OPEN ACCESS]
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Führende Autor*innen der Med Uni Graz
Lichtenegger Katharina
Co-Autor*innen der Med Uni Graz
Aberer Felix
Fruhwald Friedrich
Höll Bernhard
Kamolz Lars-Peter
Mader Julia
Pieber Thomas
Schaupp Lukas
Tuca Alexandru

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The aim was to investigate the applicability of a clinical decision support system in a real-world inpatient setting for patients with type 2 diabetes on general hospital wards.A total of 150 patients with type 2 diabetes requiring subcutaneous insulin therapy were treated with basal-bolus insulin therapy guided by a decision support system (GlucoTab) providing automated workflow tasks and suggestions for insulin dosing to health care professionals.By using the system, a mean daily blood glucose (BG) of 159 ± 32 mg/dL was achieved. 68.8% of measurements were in the target range (70 to <180 mg/dL). The percentage of BG values <40, <70, and ≥300 mg/dL was 0.02%, 2.2%, and 2.3%, respectively. Health care professionals' adherence to suggested insulin doses and workflow tasks was high (>93% and 91%, respectively).The decision support system facilitates safe and efficacious inpatient diabetes care by standardizing treatment workflow and providing decision support for basal-bolus insulin dosing.

Find related publications in this database (Keywords)
type 2 diabetes
insulin therapy
decision support
mobile applications
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