![]() ![]() We demonstrated the utility of our proposed analytical approach as a monitoring tool. This agnostic analysis provided additional evidence that the wards were adherent to BG monitoring protocols. From mixture modelling among wards with high power, we consistently identified four components with high concentration of BG measurements taken before mealtimes and around bedtime. Integrating both 1S-KS and 2S-KS information within a moving window consisting of two consecutive days did not suggest frequent potential change from or towards non-adherence to protocol. Among these wards, 1S-KS test with cdf plots indicated adherence to BG monitoring protocols. We found that 11 out of the 24 wards had high power. We also applied mixture modelling to identify the key features in daily BG timings. The 1S-KS test was coupled with visualization via the cumulative distribution function (cdf) plot and a two-sample Kolmogorov-Smirnov (2S-KS) test, enabling comparison of the BG timing distributions between two consecutive days. ![]() This test was performed among wards with high power, determined through simulation. A one-sample Kolmogorov-Smirnov (1S-KS) test was performed to test daily BG timings against non-adherence represented by the uniform distribution. ![]() We applied distributional analytics to evaluate daily adherence to BG monitoring timings. We applied our proposed analytical approach to electronic records obtained from 24 non-critical care wards in November and December 2013 from a tertiary care hospital in Singapore. We propose an analytical approach that utilizes BG measurements from electronic records to assess adherence to an inpatient BG monitoring protocol in hospital wards. However, methods to quantify timeliness as a measurement of quality of care are lacking. Regular and timely monitoring of blood glucose (BG) levels in hospitalized patients with diabetes mellitus is crucial to optimizing inpatient glycaemic control. ![]()
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