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.

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.

Electronic Diabetes Management System Replaces Paper Insulin Chart: Improved Quality in Diabetes Inpatient Care Processes Due to Digitalization.

BACKGROUND: GlucoTab, an electronic diabetes management system (eDMS), supports healthcare professionals (HCPs) in inpatient blood glucose (BG) management at point-of-care and was implemented for the first time under routine conditions in a regional hospital to replace the paper insulin chart. METHOD: To investigate quality of the eDMS for inpatients with type 2 diabetes mellitus a monocentric retrospective before-after evaluation was conducted. We compared documentation possibilities by assessing a blank paper chart vs the eDMS user interface. Further quality aspects were compared by assessing filled-in paper charts (n = 106) vs filled-in eDMS documentation (n = 241). HCPs (n = 59) were interviewed regarding eDMS satisfaction. RESULTS: The eDMS represented an improvement of documentation possibilities by offering a more structured and comprehensive user interface compared to the blank paper chart. The number of good diabetes days averaged to a median value of four days in both groups (paper chart: 4.38 [0-7] vs eDMS: 4.38 [0-7] days). Median daily BG was 170 (117-297) mg/dL vs 168 (86-286) mg/dL and median fasting BG was 152 (95-285) mg/dL vs 145 (69-333) mg/dL, and 0.1% vs 0.4% BG values <54 mg/dL were documented. Diabetes documentation quality improved when using eDMS, for example, documentation of ordered BG measurement frequency (1% vs 100%) and ordered BG targets (0% vs 100%). HCPs stated that by using eDMS errors could be prevented (74%), and digital support of work processes was completed (77%). Time saving was noted by 8 out of 11 HCPs and estimated at 10-15 minutes per patient day by two HCPs. CONCLUSIONS: The eDMS completely replaced the paper chart, showed comparable glycemic control, was positively accepted by HCPs, and is suitable for inpatient diabetes management.

Diabetes Management According to Health Status in Older Adults with Type 2 Diabetes Staying in Geriatric Care Facilities.

BACKGROUND: About 25% of adults >70 years suffer from type 2 diabetes. Due to the heterogeneity of the geriatric population, guidelines emphasize the need to individualize glycemic goals and simplify treatment strategies with the main focus of avoiding hypoglycemia. The aim of this study was to assess glycemic control in patients with type 2 diabetes in geriatric care facilities based on their individual health status. METHODS: 170 medical records of older adults with type 2 diabetes in geriatric care facilities were retrospectively assessed (64.7% female, age 80 ± 9 years; glycated hemoglobin 6.8% ± 3.6% [51 ± 16 mmol/mol]; body mass index 27.9 ± 5.8 kg/m2). Based on the individual health status, patients were allocated to three groups (healthy n = 27, complex n = 86, and poor n = 57). RESULTS: The overall blood glucose (BG) value was highest in the poor health group with 188 ± 47 mg/dL (poor) vs 167 ± 42 mg/dL (complex) vs 150 ± 34 mg/dL (healthy). BG values of 1.6% (poor) vs 2.8% (complex) vs 1.5% (healthy) of patients were below 90 mg/dL. 36.8% (poor) vs 23.4% (complex) vs 18.5% (healthy) of patients received insulin as the main diabetes therapy, but of these only 14.3% (poor) vs 20% (complex) vs 40% (healthy) were treated with basal insulin. CONCLUSIONS: Overall, BG values were higher in the poor and complex health group. There were a few low BG values in all groups. Although recommended by international guidelines, basal insulin therapy with its low complexity and low hypoglycemic risk is still underused, especially in the poor health group. Therefore, simplification of diabetes therapy should be considered further.

GlucoTab-guided insulin therapy using insulin glargine U300 enables glycaemic control with low risk of hypoglycaemia in hospitalized patients with type 2 diabetes.

AIMS: To investigate efficacy, safety and usability of the GlucoTab system for glycaemic management using insulin glargine U300 in non-critically ill hospitalized patients with type 2 diabetes (T2D). MATERIALS AND METHODS: In this open, non-controlled single-arm pilot study, glycaemic control at the general ward of a tertiary care hospital was guided by a mobile decision support system (GlucoTab) for basal-bolus insulin dosing using the novel basal insulin analogue insulin glargine U300 for the first time. Glycaemic control was surveilled with capillary glucose measurements and continuous glucose monitoring (CGM). The primary endpoint was efficacy of glycaemic management, defined as the percentage of blood glucose measurements within the target range of 3.9 to 7.8 mmol/L. RESULTS: A total of 30 patients with T2D (12 female; age, 67 ± 11 years; HbA1c, 70 ± 26 mmol/mol; BMI, 31.8 ± 5.6 kg/m2 ; length of study, 8.5 ± 4.5 days) were included. In total, 894 capillary glucose values and 49 846 data points of CGM were available, of which 56.1% of all measured capillary glucose values and 54.3% of CGM values were within the target area (3.9-7.8 mmol/L). Overall capillary mean glucose was 8.5 ± 1.2 and 8.4 ± 1.2 mmol/L assessed by CGM. Time within glucose target improved continuously during the course of treatment, while time within hypoglycaemia (<3.9 mmol/L) decreased substantially. The GlucoTab-suggested total daily dose was accepted by staff in 97.3% of situations. CONCLUSIONS: Treatment with GlucoTab using insulin glargine U300 in hospitalized patients with T2D is effective and safe.

A Mobile Computerized Decision Support System to Prevent Hypoglycemia in Hospitalized Patients With Type 2 Diabetes Mellitus.

BACKGROUND: Diabetes management requires complex and interdisciplinary cooperation of health care professionals (HCPs). To support this complex process, IT-support is recommended by clinical guidelines. The aim of this article is to report on results from a clinical feasibility study testing the prototype of a mobile, tablet-based client-server system for computerized decision and workflow support (GlucoTab®) and to discuss its impact on hypoglycemia prevention. METHODS: The system was tested in a monocentric, open, noncontrolled intervention study in 30 patients with type 2 diabetes mellitus (T2DM). The system supports HCPs in performing a basal-bolus insulin therapy. Diabetes therapy, adverse events, software errors and user feedback were documented. Safety, efficacy and user acceptance of the system were investigated. RESULTS: Only 1.3% of blood glucose (BG) measurements were <70 mg/dl and only 2.6% were >300 mg/dl. The availability of the system (97.3%) and the rate of treatment activities documented with the system (>93.5%) were high. Only few suggestions from the system were overruled by the users (>95.7% adherence). Evaluation of the 3 anonymous questionnaires showed that confidence in the system increased over time. The majority of users believed that treatment errors could be prevented by using this system. CONCLUSIONS: Data from our feasibility study show a significant reduction of hypoglycemia by implementing a computerized system for workflow and decision support for diabetes management, compared to a paper-based process. The system was well accepted by HCPs, which is shown in the user acceptance analysis and that users adhered to the insulin dose suggestions made by the system.

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.

OBJECTIVE: Most preventable adverse drug events and medication errors occur during medication ordering. Medication order entry and clinical decision support are available on paper or as computerized systems. In this post-hoc analysis we investigated frequency and clinical impact of blood glucose (BG) documentation- and user-related calculation errors as well as workflow deviations in diabetes management. We aimed to compare a paper-based protocol to a computerized medication management system combined with clinical workflow and decision support. METHODS: Seventy-nine hospitalized patients with type 2 diabetes mellitus were treated with an algorithm driven basal-bolus insulin regimen. BG measurements, which were the basis for insulin dose calculations, were manually entered either into the paper-based workflow protocol (PaperG: 37 patients) or into GlucoTab(®)-a mobile tablet PC based system (CompG: 42 patients). We used BG values from the laboratory information system as a reference. A workflow simulator was used to determine user calculation errors as well as workflow deviations and to estimate the effect of errors on insulin doses. The clinical impact of insulin dosing errors and workflow deviations on hypo- and hyperglycemia was investigated. RESULTS: The BG documentation error rate was similar for PaperG (4.9%) and CompG group (4.0%). In PaperG group, 11.1% of manual insulin dose calculations were erroneous and the odds ratio (OR) of a hypoglycemic event following an insulin dosing error was 3.1 (95% CI: 1.4-6.8). The number of BG values influenced by insulin dosing errors was eightfold higher than in the CompG group. In the CompG group, workflow deviations occurred in 5.0% of the tasks which led to an increased likelihood of hyperglycemia, OR 2.2 (95% CI: 1.1-4.6). DISCUSSION: Manual insulin dose calculations were the major source of error and had a particularly strong influence on hypoglycemia. By using GlucoTab(®), user calculation errors were entirely excluded. The immediate availability and automated handling of BG values from medical devices directly at the point of care has a high potential to reduce errors. Computerized systems facilitate the safe use of more complex insulin dosing algorithms without compromising usability. In CompG group, missed or delayed tasks had a significant effect on hyperglycemia, while in PaperG group insufficient precision of documentation times limited analysis. The use of old BG measurements was clinically less relevant. CONCLUSION: Insulin dosing errors and workflow deviations led to measurable changes in clinical outcome. Diabetes management systems including decision support should address nurses as well as physicians in a computerized way. Our analysis shows that such systems reduce the frequency of errors and therefore decrease the probability of hypo- and hyperglycemia.

Standardized Glycemic Management with a Computerized Workflow and Decision Support System for Hospitalized Patients with Type 2 Diabetes on Different Wards.

BACKGROUND: This study investigated the efficacy, safety, and usability of standardized glycemic management by a computerized decision support system for non-critically ill hospitalized patients with type 2 diabetes on four different wards. MATERIALS AND METHODS: In this open, noncontrolled intervention study, glycemic management of 99 patients with type 2 diabetes (62% acute admissions; 41 females; age, 67±11 years; hemoglobin A1c, 65±21 mmol/mol; body mass index, 30.4±6.5 kg/m(2)) on clinical wards (Cardiology, Endocrinology, Nephrology, Plastic Surgery) of a tertiary-care hospital was guided by GlucoTab(®) (Joanneum Research GmbH [Graz, Austria] and Medical University of Graz [Graz, Austria]), a mobile decision support system providing automated workflow support and suggestions for insulin dosing to nurses and physicians. RESULTS: Adherence to insulin dosing suggestions was high (96.5% bolus, 96.7% basal). The primary outcome measure, percentage of blood glucose (BG) measurements in the range of 70-140 mg/dL, occurred in 50.2±22.2% of all measurements. The overall mean BG level was 154±35 mg/dL. BG measurements in the ranges of 60-70 mg/dL, 40-60 mg/dL, and <40 mg/dL occurred in 1.4%, 0.5%, and 0.0% of all measurements, respectively. A regression analysis showed that acute admission to the Cardiology Ward (+30 mg/dL) and preexisting home insulin therapy (+26 mg/dL) had the strongest impact on mean BG. Acute admission to other wards had minor effects (+4 mg/dL). Ninety-one percent of the healthcare professionals felt confident with GlucoTab, and 89% believed in its practicality and 80% in its ability to prevent medication errors. CONCLUSIONS: An efficacious, safe, and user-accepted implementation of GlucoTab was demonstrated. However, for optimized personalized patient care, further algorithm modifications are required.

Taking a Closer Look--Continuous Glucose Monitoring in Non-Critically Ill Hospitalized Patients with Type 2 Diabetes Mellitus Under Basal-Bolus Insulin Therapy.

BACKGROUND: Inpatient glucose management is based on four daily capillary blood glucose (BG) measurements. The aim was to test the capability of continuous glucose monitoring (CGM) for assessing the clinical impact and safety of basal-bolus insulin therapy in non-critically ill hospitalized patients with type 2 diabetes mellitus (T2DM). MATERIALS AND METHODS: Eighty-four patients with T2DM (age, 68±10 years; glycosylated hemoglobin, 72±28 mmol/mol; body mass index, 31±7 kg/m(2)) were treated with basal-bolus insulin. CGM was performed with the iPro(®)2 system (Medtronic MiniMed, Northridge, CA) and calibrated retrospectively. RESULTS: A remarkable consistency between CGM and BG measurements and therapy improvement was shown over the study period of 501 patient-days. The number of CGM and BG measurements (CGM/BG) in the range from 3.9-10 mmol/L increased from 67.7%/67.2% (on Day 1) to 77.5%/78.6% (on the last day) (P<0.04). The number of low glycemic episodes (3.3 to <3.9 mmol/L) during nighttime detected by CGM was 15-fold higher, and the number of episodes >13.9 mmol/L detected by CGM during night was 12.5-fold higher than the values from the BG measurements. Ninety-nine percent of data points were in the clinically accurate or acceptable Clarke Error Grid Zones A+B, and the relative numbers of correctly identified episodes of <3.9 and >13.9 mmol/L detected by CGM (sensitivity) were 47.3% and 81.5%, respectively. CONCLUSIONS: Our data exhibit a good agreement between overall CGM and BG measurements, but there were a high number of missed hypo- and hyperglycemic episodes with BG measurements, particularly during nighttime. Overall assessment of glycemic control using CGM is feasible, whereas the use of CGM for individualized therapy decisions needs further improvement.

Automatic system testing of a decision support system for insulin dosing using Google Android.

Hyperglycaemia in hospitalized patients is a common and costly health care problem. The GlucoTab system is a mobile workflow and decision support system, aiming to facilitate efficient and safe glycemic control of non-critically ill patients. Being a medical device, the GlucoTab requires extensive and reproducible testing. A framework for high-volume, reproducible and automated system testing of the GlucoTab system was set up applying several Open Source tools for test automation and system time handling. The REACTION insulin titration protocol was investigated in a paper-based clinical trial (PBCT). In order to validate the GlucoTab system, data from this trial was used for simulation and system tests. In total, 1190 decision support action points were identified and simulated. Four data points (0.3%) resulted in a GlucoTab system error caused by a defective implementation. In 144 data points (12.1%), calculation errors of physicians and nurses in the PBCT were detected. The test framework was able to verify manual calculation of insulin doses and detect relatively many user errors and workflow anomalies in the PBCT data. This shows the high potential of the electronic decision support application to improve safety of implementation of an insulin titration protocol and workflow management system in clinical wards.

Design of a mobile, safety-critical in-patient glucose management system.

Diabetes mellitus is one of the most widespread diseases in the world. People with diabetes usually have long stays in hospitals and need specific treatment. In order to support in-patient care, we designed a prototypical mobile in-patient glucose management system with decision support for insulin dosing. In this paper we discuss the engineering process and the lessons learned from the iterative design and development phases of the prototype. We followed a user-centered development process, including real-life usability testing from the outset. Paper mock-ups in particular proved to be very valuable in gaining insight into the workflows and processes, with the result that user interfaces could be designed exactly to the specific needs of the hospital personnel in their daily routine.