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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.

White blood cell count is an important indicator of each individual's health condition. An abnormal white blood cell count usually results from an infection, cancer, or other conditions that trigger systemic inflammation responses. White blood cell count also provides predictive information on the incidence of cardiovascular diseases and Type 2 diabetes. Therefore, monitoring white blood cell count on a regular basis can potentially help individuals to take preventive measures and improve healthcare outcomes. Currently, white blood cell count is primarily conducted in centralized laboratories, and it requires specialized equipment and dedicated personnel to perform the test and interpret the results. So far there has been no rapid test that allows white blood cell count in low-resource settings. In this study, we have demonstrated a vertical flow platform that quantifies white blood cells by trapping them in the paper. White blood cells were tagged with gold nanoparticles, and flowed through the paper via a small orifice. The white blood cell count was determined by measuring the colorimetric intensity of gold nanoparticles on the surface of white blood cells that were trapped in the paper mesh. Using this platform, we were able to quantify white blood cells in 15 μL of blood, and visually differentiate the abnormal count of white blood cells from the normal count. The proposed platform enabled rapid white blood cell count in low resource settings with a small sample volume requirement. Its low-cost, instrument-free operations would be attractive for point-of-care applications.

In a pilot randomized controlled trial involving overweight or obese participants with type 2 diabetes, we find that smartphone users have sharply higher adherence to self-monitoring of diet, physical activity, blood glucose, and body weight, as compared to paper diary users. By characterizing the pattern of adherence with the probability of continuation of active and inactive sequences of self-monitoring, we find that smartphone users have longer active sequences of self-monitoring of all four behaviors that were being monitored. Smartphone users are also quicker to resume self-monitoring of diet and physical activity after a lapse in self-monitoring, whereas paper diary users have shorter inactive sequences for monitoring blood glucose and body weight. The findings are informative for data collection methodology in this burgeoning area of research.

Background: A large number of nondigitized electrocardiograph (ECG) strips are routinely collected in larger cohort studies such as the ADDITION study (Anglo-Danish-Dutch Study of Intensive Treatment in People with Screen-Detected Diabetes in Primary Care). These ECG strips are routinely read manually but may contain overlooked information revealing cardiac autonomic dysfunction. The aim of this study was to investigate whether clinical information may be lost using manual R wave to R wave (RR) interval measurements in the calculation of heart rate variability (HRV) in patients with type 2 diabetes mellitus (T2DM). Method: From the Danish part of the ADDITION study, we randomly selected 120 T2DM patients at baseline of the ADDITION study. Analysis of the ECG strips was performed using two different methods: (1) by experienced technicians using rulers and (2) by experienced technicians using a high-resolution computer-assisted method. Calculation of heart rate and time domain HRV [standard deviation of normal-to-normal RR intervals (SDNN) and root mean square of successive differences (RMSSD)] were performed with the same software. Results: When comparing results from the two methods, the following values of Pearson's r are obtained: 0.98 for heart rate, 0.76 for SDNN, and 0.68 for RMSSD. These results indicate that heart rate and HRV measurements by the computer-assisted and manually based methods correlate. However, Bland-Altman plots and Pitman's test of difference in variance revealed poor agreements ( p < .01) for both HRV measurements (SDNN and RMSSD); only heart rate showed substantiated agreement ( p = .54) between the two methods. Low HRV was statistically significantly associated to high heart rate, systolic blood pressure, and diastolic blood pressure in these screen-detected T2DM patients. Conclusions: Paper ECG strips may contain overlooked clinical information on the status of autonomic function in patients with T2DM. In our study, manual measurements of RR intervals were inferior to the computer-assisted method. Based on this study, we recommend cautiousness in the clinical use and interpretation of HRV based on manual or low resolution measurements of RR intervals from ECG strips. High resolution measurements of RR intervals reveal significant associations between low HRV and high heart rate, systolic blood pressure, and diastolic blood pressure among patients with screen-detected T2DM. It is feasible to use a computer-assisted method to determine RR intervals in patients with T2DM.