Using a double blind controlled clinical trial to evaluate the function of a Diabetes Advisory System: a feasible approach?

This paper assesses the feasibility of using a double blind controlled clinical trial to evaluate the function of a decision support system by applying such a design to the evaluation of a Diabetes Advisory System (DIAS). DIAS is based on a model of the human carbohydrate metabolism and is designed an interactive clinical tool, which can be used to predict the effects of changes in insulin dose or food intake on the blood glucose concentration in patients with insulin dependent diabetes. It can also be used to identify risk periods for hypoglycaemia. and to provide advice on insulin dose. The latter feature was evaluated in the present study. We believe double blind controlled clinical trials are prerequisites for clinical application of many decision support systems, and conclude that the present double blind controlled clinical trial is a suitable evaluation method for the function of DIAS.

[1]  E Salzsieder,et al.  Computer-aided systems in the management of type I diabetes: the application of a model-based strategy. , 1990, Computer methods and programs in biomedicine.

[2]  D. D. Tsiftsis.  Objective Medical Decision-Making Systems Approach in Disease , 1986, Lecture Notes in Medical Informatics.

[3]  D. Spiegelhalter,et al.  Evaluating medical expert systems: what to test and how? , 1990, Medical informatics = Medecine et informatique.

[4]  A. Schiffrin,et al.  Computer-assisted Insulin Dosage Adjustment , 1985, Diabetes Care.

[5]  E. Carson,et al.  A probabilistic approach to glucose prediction and insulin dose adjustment: description of metabolic model and pilot evaluation study. , 1994, Computer methods and programs in biomedicine.

[6]  P L Miller,et al.  Combining statistical, rule-based, and physiologic model-based methods to assist in the management of diabetes mellitus. , 1990, Computers and biomedical research, an international journal.

[7]  S. Genuth,et al.  The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. , 1993, The New England journal of medicine.

[8]  Kristian G. Olesen,et al.  HUGIN - A Shell for Building Bayesian Belief Universes for Expert Systems , 1989, IJCAI.

[9]  H P Lundsgaarde,et al.  Evaluating medical expert systems. , 1987, Social science & medicine.

[10]  Ole K. Hejlesen,et al.  Implementation of a learning procedure for multiple observations in a diabetes advisory system based on causal probabilistic networks , 1993 .

[11]  D Rodbard,et al.  Computer Simulation of Plasma Insulin and Glucose Dynamics After Subcutaneous Insulin Injection , 1989, Diabetes Care.

[12]  S Andreassen,et al.  Analysing the hypoglycaemic counter-regulation: a clinically relevant phenomenon? , 1996, Computer methods and programs in biomedicine.

[13]  S. Andreassen,et al.  Use of the DIAS model to predict unrecognised hypoglycaemia in patients with insulin-dependent diabetes. , 1996, Computer methods and programs in biomedicine.

[14]  Ole K. Hejlesen,et al.  Optimization and Evaluation of a probabilistic Computer Model of the Glucose Metabolism , 1995 .

[15]  S Andreassen,et al.  DIAS--the diabetes advisory system: an outline of the system and the evaluation results obtained so far. , 1997, Computer methods and programs in biomedicine.

[16]  Roman Hovorka,et al.  A vision of information technology support for diabetes care , 1995 .

[17]  E R Carson,et al.  Computer-assisted diabetic management: a complex approach. , 1990, Computer methods and programs in biomedicine.

[18]  E. Carson,et al.  Diabeta — An Expert System for the Management of Diabetes , 1986 .

[19]  M A Boroujerdi,et al.  A system for compartmental modelling and simulation. , 1991, Computer methods and programs in biomedicine.

[20]  O. Rienhoff,et al.  Expert Systems and Decision Support in Medicine , 1988, Lecture Notes in Medical Informatics.

[21]  E R Carson,et al.  Preliminary experience of the DIAS computer model in providing insulin dose advice to patients with insulin dependent diabetes. , 1998, Computer methods and programs in biomedicine.

[22]  Ole K. Hejlesen,et al.  Dynamic Propagation in Causal Probabilistic Networks with Instantiated Variables , 1995, AIME.

[23]  R. Engelbrecht,et al.  DIACONS — A Consultation System to Assist in the Management of Diabetes , 1988 .