Effects of two different levels of computerized decision support on blood glucose regulation in critically ill patients

INTRODUCTION Although the use of computerized decision support systems (CDSS) in glucose control in the ICU has been reported, little is known about the effect of the systems' operating modes on the quality of glucose control. The objective of this study was to evaluate the effect of providing patient-specific and patient non-specific computerized advice on timing of blood glucose level (BGL) measurements. Our hypothesis was that both levels of support would be effective for improving the quality of glucose regulation and safety, with patient specific advice being the most effective strategy. PATIENTS AND METHODS A prospective study was performed in a 30-bed mixed medical-surgical intensive care unit (ICU) of a university hospital. In phase 1 the CDSS provided non-specific advice and thereafter, in phase 2, the system provided specific advice on timing of BGL measurements. The primary outcome measure was delay in BGL measurements before and after the two levels of support. Secondary endpoints were sampling frequency, mean BGL, BGL within pre-defined targets, time to capture target, incidences of severe hypoglycemia and hyperglycemia. These indicators were analyzed over the course of time using Statistical Control Charts. The analysis was restricted to patients with at least two blood glucose measurements. RESULTS Data of 3934 patient admissions were evaluated, which corresponded to 119,116 BGL measurements. The BGL sampling interval, delays in BG sampling, and percentage of hypoglycemia all decreased after introducing either of the two levels of decision support. The effect was however larger for the patient specific CDSS. Mean BGL, time to capture target, hyperglycemia index, percentage of hyperglycemia events and "in range" measurements remained unchanged and stable after introducing both patient non-specific and patient specific decision support. CONCLUSION Adherence to protocol sampling rules increased by using decision support with a larger effect at the patient specific level. This led to a decrease in the percentage of hypoglycemia events and improved safety. The use of the CDSS at both levels, however, did not improve the quality of glucose control as measured by our indicators. More research is needed to investigate whether other socio-technical factors are in play.

[1]  Rajesh Garg,et al.  Delay in blood glucose monitoring during an insulin infusion protocol is associated with increased risk of hypoglycemia in intensive care units. , 2009, Journal of hospital medicine.

[2]  J. Benneyan,et al.  Statistical process control as a tool for research and healthcare improvement , 2003, Quality & safety in health care.

[3]  Rattan Juneja,et al.  Computerized intensive insulin dosing can mitigate hypoglycemia and achieve tight glycemic control when glucose measurement is performed frequently and on time , 2009, Critical care.

[4]  M. Berg,et al.  ICT in health care: sociotechnical approaches. , 2003, Methods of Information in Medicine.

[5]  Nicolette de Keizer,et al.  The impact of computerized physician medication order entry in hospitalized patients - A systematic review , 2008, Int. J. Medical Informatics.

[6]  Paul Taylor,et al.  Research Paper: Use of a Computerized Guideline for Glucose Regulation in the Intensive Care Unit Improved Both Guideline Adherence and Glucose Regulation , 2004, J. Am. Medical Informatics Assoc..

[7]  Ameen Abu-Hanna,et al.  Implementing glucose control in intensive care: a multicenter trial using statistical process control , 2010, Intensive Care Medicine.

[8]  O. Tanner Intensive versus Conventional Glucose Control in Critically Ill Patients , 2009 .

[9]  Raymond G. Carey,et al.  How Do You Know That Your Care Is Improving? Part I: Basic Concepts in Statistical Thinking , 2002 .

[10]  M A Mohammed,et al.  Plotting basic control charts: tutorial notes for healthcare practitioners , 2008, Quality & Safety in Health Care.

[11]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[12]  Deborah J. Cook,et al.  Intensive insulin therapy and mortality among critically ill patients: a meta-analysis including NICE-SUGAR study data , 2009, Canadian Medical Association Journal.

[13]  Anthony Delaney,et al.  Benefits and Risks of Tight Glucose Control in Critically Ill Adults: A Metaanalysis , 2009 .

[14]  Scott K Aberegg,et al.  Intensive insulin therapy in the medical ICU. , 2006, The New England journal of medicine.

[15]  M Schetz,et al.  Intensive insulin therapy in critically ill patients. , 2001, The New England journal of medicine.

[16]  Raymond G. Carey,et al.  Improving Healthcare with Control Charts: Basic and Advanced SPC Methods and Case Studies , 2002 .

[17]  Ameen Abu-Hanna,et al.  Tight glycemic control and computerized decision-support systems: a systematic review , 2009, Intensive Care Medicine.

[18]  Benjamin M. Adams,et al.  Advanced Topics in Statistical Process Control : The Power of Shewhart's Charts , 1995 .

[19]  Arie Hasman,et al.  A parallel guideline development and formalization strategy to improve the quality of clinical practice guidelines , 2009, Int. J. Medical Informatics.

[20]  Ameen Abu-Hanna,et al.  A systematic review on quality indicators for tight glycaemic control in critically ill patients: need for an unambiguous indicator reference subset , 2008, Critical care.

[21]  E. Shortliffe Computer programs to support clinical decision making. , 1990, JAMA.

[22]  Liu Xinbing,et al.  Intensive insulin therapy for the critically ill patients with stress hyperglycemia , 2008 .