title: Utilizing distributional analytics and electronic records to assess timeliness of inpatient blood glucose monitoring in non-critical care wards.
abstract: BACKGROUND
  Regular and timely monitoring of blood glucose (BG) levels in hospitalized patients with diabetes mellitus is crucial to optimizing inpatient glycaemic control. However, methods to quantify timeliness as a measurement of quality of care are lacking. We propose an analytical approach that utilizes BG measurements from electronic records to assess adherence to an inpatient BG monitoring protocol in hospital wards.
  
  
  METHODS
  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 applied distributional analytics to evaluate daily adherence to BG monitoring timings. A one-sample Kolmogorov-Smirnov (1S-KS) test was performed to test daily BG timings against non-adherence represented by the uniform distribution. This test was performed among wards with high power, determined through simulation. 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. We also applied mixture modelling to identify the key features in daily BG timings.
  
  
  RESULTS
  We found that 11 out of the 24 wards had high power. Among these wards, 1S-KS test with cdf plots indicated adherence to BG monitoring protocols. 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. 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. This agnostic analysis provided additional evidence that the wards were adherent to BG monitoring protocols.
  
  
  CONCLUSIONS
  We demonstrated the utility of our proposed analytical approach as a monitoring tool. It provided information to healthcare providers regarding the timeliness of daily BG measurements. From the real data application, there were empirical evidences suggesting adherence of BG timings to protocol among wards with adequate power for assessing timeliness. Our approach is extendable to other areas of healthcare where timeliness of patient care processes is important.
authors.affiliation: Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, S117549, Singapore.
authors.forename: Ying
authors.initials: Y
authors.lastname: Chen
authors.affiliation: Division of Endocrinology, Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore, S119228, Singapore.
authors.forename: Shih Ling
authors.initials: SL
authors.lastname: Kao
authors.affiliation: Division of Endocrinology, Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore, S119228, Singapore.
authors.forename: E-Shyong
authors.initials: ES
authors.lastname: Tai
authors.affiliation: Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, S117549, Singapore.|Department of Pharmacy, National University of Singapore, Singapore, S119543, Singapore.
authors.forename: Hwee Lin
authors.initials: HL
authors.lastname: Wee
authors.affiliation: Department of Pharmacy, National University of Singapore, Singapore, S119543, Singapore.
authors.forename: Eric Yin Hao
authors.initials: EY
authors.lastname: Khoo
authors.affiliation: Division of Endocrinology, Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore, S119228, Singapore.
authors.forename: Yilin
authors.initials: Y
authors.lastname: Ning
authors.affiliation: Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, S117549, Singapore.
authors.forename: Mark Kevin
authors.initials: MK
authors.lastname: Salloway
authors.affiliation: Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, S117549, Singapore.
authors.forename: Xiaodong
authors.initials: X
authors.lastname: Deng
authors.affiliation: Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, S117549, Singapore. chuen_seng_tan@nuhs.edu.sg.
authors.forename: Chuen Seng
authors.initials: CS
authors.lastname: Tan
mesh_terms: D001786:Blood Glucose / Q000032:analysis*; D003920:Diabetes Mellitus / Q000097:blood* / Q000503:physiopathology; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D005260:Female; D006757:Hospital Units; D006801:Humans; D007297:Inpatients / Q000706:statistics & numerical data*; D008297:Male; D015233:Models, Statistical; D008991:Monitoring, Physiologic / Q000379:methods; D017063:Outcome Assessment, Health Care; D012846:Singapore; D062606:Tertiary Care Centers; D013997:Time Factors
chemical_list: D001786:Blood Glucose
keywords: Diabetes mellitus; Distributional analytics; Electronic medical records; Inpatient; Quality of care; Timeliness