Systems thinking, complexity and managerial decision-making: an analytical review.

One feature that characterizes the organization and delivery of health care is its inherent complexity. All too often, with so much information and so many activities involved, it is difficult for decision-makers to determine in an objective fashion an appropriate course of action. It would appear that a holistic rather than a reductionist approach would be advantageous. The aim of this paper is to review how formal systems thinking can aid decision-making in complex situations. Consideration is given as to how the use of a number of systems modelling methodologies can help in gaining an understanding of a complex decision situation. This in turn can enhance the possibility of a decision being made in a more rational, explicit and transparent fashion. The arguments and approaches are illustrated using examples taken from the public health arena.

A proposed semantic framework for diabetes education content management, customisation and delivery within the M2DM project.

M2DM (multi access services for telematic management of diabetes mellitus, ) is an EU-funded telemedicine project that aims at increasing the quality of diabetes care by improving communication between patients and caregivers. As part of this project, we have undertaken the initial work of describing the necessary requirements (framework) of an advanced educational component for M2DM in accordance with the latest Semantic Web concepts. This paper describes our proposed semantic framework for educational content management, customisation and delivery. A big internet challenge today is to find and push situation and user-specific quality knowledge to users based on their actual individual needs, circumstances and profiles at any given time. We believe that the semantic framework presented in this paper could be a good step towards meeting this challenge. Benefits for users, both developers and end users, of adopting such framework are also discussed. The ideas discussed in this paper could be easily adapted to other similar services besides M2DM and to different health topics besides diabetes mellitus.

Anabolic role of urea in renal failure.

The amount of urea nitrogen released and the amount reincorporated into albumin has been measured in healthy and uremic individuals on both normal and low-protein diets. The albumin synthesis rate was measured simultaneously. Gut urea breakdown was only 50% higher in renal failure than in health, but the efficiency of utilization of the nitrogen thus released was increased more than 6-fold in renal failure and was higher on a low protein than on a normal protein diet. The lower the albumin synthetic rate, the greater was the efficiency of incorporation of urea nitrogen into albumin. The rate of urea nitrogen incorporation into albumin increased on average 14-fold in chronic renal failure. The absolute rate of utilization (84 mumole/hr) was, however, small and comprised on average only 2.4% of the nitrogen used in albumin synthesis. These findings suggest that although some urea derived nitrogen is incorporated into albumin, the amount is not nutritionally significant even under conditions of protein deprivation and high urea availability.

User requirements and understanding of public health networks in England.

BACKGROUND: The movement of public health professionals from health authorities to primary care trusts has increased their isolation and dependence on public health networks for communication. METHODS: A cross sectional survey of 60 public health professionals working in England was performed to determine their understanding of the term "public health network" and to explore the functions that they would like these networks to perform. It also assessed their attitudes towards a national network and towards individual, local, and national web sites to support these networks. RESULTS: The most popular functions were the support of CPD/education, the identification of expertise and maximisation of scarce resources, information sharing, and efficient information/knowledge management. The local and national networks and their web sites should provide information on current projects of the network and searches to identify people, expertise, and reports. CONCLUSION: Public health professionals have a similar but broader understanding of the term "public health network" than that of the government with greater emphasis on sharing of information. The network is more likely to be successful if its priorities are maximising scarce resources, identification of expertise, CPD/education, and knowledge management.

Diurnal variation in glucose and leucine metabolism in non-insulin-dependent diabetes.

Glucose and leucine metabolism were investigated in 5 poorly controlled non-insulin-dependent diabetics (NIDDM) following an i.v. injection of 3-[3H]glucose and 1-[14C]leucine in the morning and evening. In the morning glucose concentration (11.2 +/- 0.8 mmol/l) (mean +/- SEM) and production rate (14.2 +/- 1.3 mumol/min/kg) were significantly greater (P less than 0.001, P less than 0.05) and glucose metabolic clearance rate (MCR) (1.3 +/- 0.2 ml/min/kg) significantly lower (P less than 0.05) than in a group of control subjects. Glucose concentration was lower in the evening (P less than 0.05) as a result of a decrease in glucose production rate (P less than 0.05). Leucine concentration and production rate were not significantly different from normal but leucine oxidation rate was increased (P less than 0.05). There was no diurnal variation in leucine metabolism. Since leucine production is a measure of protein breakdown, the higher morning glucose production rate was not due to an increased supply of gluconeogenic precursors from protein catabolism.

Combining rule-based reasoning and mathematical modelling in diabetes care.

A prototype computer system utilising a model of carbohydrate metabolism linked to an expert system is described. The prototype which integrates quantitative and qualitative computational methodologies can be used to predict blood glucose profiles and adjust insulin doses in insulin-dependent (type I) diabetic subjects. A feedback loop insulin-dosage optimisation procedure which allows quantitative advice to be generated is also described. Possible clinical applications for the system, which is intended for educational use and clinically as a research tool to try and attain normoglycaemia, are discussed.

Methodological issues in validating decision-support systems for insulin dosage adjustment.

Safety and reliability of advice from new computer systems should be confirmed before embarking on prospective hospital trials. This process of preliminary testing is termed 'validation'. Though it forms a fundamental stage in system development, few standards exist for choosing and implementing tests. In the present paper, a validation methodology is developed in the domain of diabetes and intended for general use in chronic health management. It is based on a peer review protocol and incorporates empirical measures indicating: applicability of results to the real environment; variation among doctors; comparisons between doctors' and computer advice; and relative merits of different computer algorithms.

A knowledge-based system coupled to a mathematical model for interpretation of laboratory data.

A knowledge-based system to interpret data regarding a patient's fluid-electrolyte balance is discussed. The prototype system meshes techniques drawn from expert systems with those of dynamic mathematical modeling. The utilization of an off-the-shelf model (MACPEE) to justify treatment suggestion provided by the prototype is described. Detailed information on the prototype system design is provided, and its evaluation is discussed.

Clinical decision support, systems methodology, and telemedicine: their role in the management of chronic disease.

In this paper, the design and evaluation of decision support systems, including those incorporating a telematic component, are considered. It is argued that effective design and evaluation are dependent upon the adoption of appropriate methodology set firmly within a systemic framework. Systems modeling is proposed as an approach to system design, with evaluation adopting an approach incorporating evaluability analysis and formative and summative evaluation, including the use of stakeholder matrix analysis. The relevance of such systemic methodology is demonstrated in the context of diabetes and end-stage renal disease as examples of the generic clinical problem of the management of chronic disease.

Decision support systems in diabetes: a systems perspective.

This paper examines, from a systems perspective, some of the major issues associated with the provision of computer-based decision support in the management of the diabetic patient. The importance of understanding the underlying dynamics is emphasised, as is the value of a systems approach to the specification, design and evaluation of decision support systems if they are to find clinical acceptance.

AIDA: an interactive diabetes advisor.

AIDA is a prototype computer system that incorporates a model of glucose-insulin interaction in type I diabetes mellitus alongside a knowledge-based system to make glycaemic predictions and to generate insulin dosage adjustment advice. The model attempts to reflect the underlying (patho)physiology of insulin action and carbohydrate absorption in quantitative terms. The prototype is intended to be used as a decision support system by clinical personnel in the context of day-to-day management of insulin-dependent diabetic patients. It is designed for use during consultations, as a simulator of patient response following changed insulin and dietary regimen and as a system for providing education on planning insulin therapy. Joe Daniels is a 41-year-old, 70-kg, male insulin-dependent diabetic patient who was diagnosed as being diabetic in 1972, at the age of 22. Joe recently found that he was having hypoglycaemic symptoms. Using self-monitoring blood glucose equipment, glycaemic levels below 3.0 mmol/l were recorded at least once a week, while hyperglycaemic readings (> 16 mmol/l) were observed two to three times per week. Joe came into hospital to have his glycaemic control improved, as doctors were concerned about the risks of him suffering a serious hypoglycaemic attack. Using some of the data collected by Joe while in hospital, we will demonstrate how AIDA might be applied either in a clinical setting to provide therapeutic advice or in an educational setting to interactively teach diabetic patients about their diabetes and educate them to adjust their own insulin injections and diet.

Information technology and computer-based decision support in diabetic management.

This paper describes the application of computer-based techniques within an intelligent, knowledge-based framework to the management of diabetes. The objectives are to structure data collection and storage so that the relevant patient-specific data are collected and made accessible as needed, and to provide clinical decision support on either a day-by-day or longer timescale as appropriate; these objectives relating to both hospital clinic and general practice. For longer-term management, a prototype rule set (greater than 500 rules) has been developed (coded in Sigma PROLOG), validated and tested on patient data. The data collection programs (written in SCULPTOR) to feed the ruleset have been tested in the hospital clinic and compared with the resident data collection system for usability, and impact on the running of the clinic. Links between the data collection programs and the ruleset program have been written and tested. The computer system will also incorporate a module, combining knowledge-based advisory system and glucose/insulin model as patient simulator, that can be tested as a potential decision aid for adjusting insulin dosage on a daily basis.

A consultation system for insulin therapy.

This paper describes a computer system to advice on insulin therapy for diabetic in-patients. A mathematical model was developed to describe the effect of insulin on blood glucose (BG) level. The system uses an adaptive approach to analyse the response to an applied insulin dosage. It learns the patient's individual parameters. All conventional injection and insulin pump regimens are supported. The individualised model is used to predict BG level of the proposed insulin dosage. The system uses a generate-reject strategy to output optimum insulin therapy in terms of optimum BG. The predictive capability of the system was tested and it is able to predict BG with a precision of 2.5 mmol/l after 3 days and 6 days of insulin pump treatment and conventional injection therapy, respectively.

Acid-base chemistry of the blood--a general model.

This paper describes a general model of acid-base chemistry of the blood which can be used to simulate physiological perturbation of acid-base chemistry on addition or removal of any buffer acid or base. In particular, it is shown how this model can be used to estimate the concentrations of buffer acid or base. In particular, it is shown how this model can be used to estimate the concentrations of buffer acids and bases when blood is equilibrated to a new pCO2, when hydrogen ions H+ are added to the blood, or when two pools of blood with different concentrations of buffer acids and bases are mixed. The ability of the model to represent the addition or removal of any acid or base is a significant increase in functionality above the Siggaard-Andersen nomogram which is limited to simulating the effects of equilibrating the blood to a new pCO2. When used to represent the situation where blood is equilibrated at a new pCO2 the model enables calculation of the amount CO2 removed during equilibration, a further increase in functionality above the Siggaard-Andersen nomogram. In two experimental situations, equilibrating blood to a new pCO2 and addition of H+ ions, the model predictions are shown to be consistent with existing experimental data in the form of the Siggaard-Andersen nomogram.

A probabilistic approach to glucose prediction and insulin dose adjustment: description of metabolic model and pilot evaluation study.

A model of carbohydrate metabolism has been implemented as a causal probabilistic network, allowing explicit representation of the uncertainties involved in the prediction of 24-h blood glucose profiles in insulin-dependent diabetic subjects. The parameters of the model were based on experimental data from the literature describing insulin and carbohydrate absorption, renal loss of glucose, insulin-independent glucose utilisation and insulin-dependent glucose utilisation and production. The model can be adapted to the observed glucose metabolism in the individual patient and can be used to generate predicted 24-h blood glucose profiles. A penalty is assigned to each level of blood glucose, to indicate that high and low blood glucose levels are undesirable. The system can be asked to find the insulin doses that result in the most desirable 24-h blood glucose profile. In a series of 12 patients, the system predicted blood glucose with a mean error of 3.3 mmol/l. The insulin doses suggested by the system seemed reasonable and in several cases seemed more appropriate than the doses actually administered to the patients.

Computer-assisted diabetic management: a complex approach.

This paper describes the architecture of, and the main reasoning methods involved in, a computer system developed to assist in diabetic management. The system integrates (i) a database module used for blood glucose monitoring, (ii) an interpreter module used to analyse the adequacy of diet and insulin treatment for diabetics, and (iii) an advisory module suggesting alterations in diet and/or insulin regimen in order to improve glycaemic control. The analysis of blood glucose profiles and hypoglycaemic episodes, as well as the suggestions for altered diet and insulin therapy, are based on qualitative and quantitative models of insulin effect and carbohydrate absorption using meal-time related glucose balance and distance from the preselected target (DFT) glucose values as focal concepts in the reasoning process. During the sequence of consultations with the system, a dynamic model of carbohydrate metabolism is gradually adjusted in order to constitute an appropriate simulation for the specific patient. This model is used to confirm the suggestions made by the ADVISOR program and to assist the health care professional in selecting the best control action by predicting the blood glucose profiles resulting from alternative control policies.

The principles and prototyping of a knowledge-based diabetes management system.

This paper describes the principles and prototyping of a computer-based system being developed to assist in the management of diabetes mellitus. Unlike other approaches based upon mathematical modelling or the use of computer algorithms, this system adopts one derived from artificial intelligence, seeking to incorporate the dynamics of glucose and insulin in a manner which reflects their clinical importance. The resultant logical model (qualitative algebra) defines the relationships between changes in insulin dose and site and time of injection and glycaemic response. In this manner the computer-based system, implemented in Prolog, can be used to provide advice concerning insulin therapy by means of making qualitative predictions of patient outcome of blood glucose profile resulting from alternative insulin regimens.

Time series analysis and control of blood glucose levels in diabetic patients.

This paper describes features of a computer-based decision support system which is being developed to assist in the management of insulin-dependent diabetic patients. The clinical context is the provision of advice on the adjustment of the basic insulin regimen such as occurs at regular visits to the clinician. The integrated system combines data processing and interpretation, generation of qualitative advice and testing the implications of that advice using a glucose/insulin dynamic simulator. The two major features described in this paper are time series analysis of blood glucose data, and their interpretation in relation to the provision of advice for controlling the patient's blood glucose level. It is demonstrated that two approaches may be adopted in such time series analysis: an intuitive approach, manipulating symbolic representations of the data, and formal time series methods which decompose the series into clinically related components.

Preliminary experience of the DIAS computer model in providing insulin dose advice to patients with insulin dependent diabetes.

The Diabetes Advisory System (DIAS) is a model of human glucose metabolism which predicts hourly blood glucose concentrations and provides advice on insulin dose. Its ability to provide appropriate advice was assessed in 20 well-controlled IDDM patients (mean (SD) age 38 (11), duration 17 (9) years; HbA1 8.8 (0.9)%, reference range 5.4-7.6%). Patients recorded blood glucose measurements, insulin dose and food intake for 4 days. These data were used to generate insulin dose advice by both DIAS and a diabetes specialist nurse. Patients were then allocated to follow either DIAS or nurse advice for a further 4 days. There was no significant difference in mean recorded blood glucose values or frequency of reported hypoglycaemia between the DIAS and nurse groups either before or after insulin dose adjustment. The DIAS model, however, generated significantly lower insulin dose advice than the nurse (median (range)% change in insulin dose: DIAS group -13.3% (-25.0 to +11.6) versus nurse group 0% (-8.7 to +2.5), P < 0.05). We conclude that, in the patients studied, DIAS provided insulin dose advice which maintained good short term control of diabetes, despite significant reductions in dose in some cases.

Clinical-HINTS: integrated intelligent ICU patient monitoring and information management system.

Clinical-HINTS (Health Intelligence System) is a horizontally integrated decision support system (DSS) designed to meet the requirements for intelligent real-time clinical information management in critical care medical environments and to lay the foundation for the development of the next generation of intelligent medical instrumentation. The system presented was developed to refine and complement the information yielded by clinical laboratory investigations, thereby benefiting the management of the intensive care unit (ICU) patient. More specifically, Clinical-HINTS was developed to provide computer-based assistance with the acquisition, organisation and display, storage and retrieval, communication and generation of real-time patient-specific clinical information in an ICU. Clinical-HINTS is an object-oriented system developed in C+2 to run under Microsoft Windows as an embryo intelligent agent. Current generic reasoning skills include perception and reactive cognition of patient status but exclude therapeutic action. The system monitors the patient by communicating with the available sources of data and uses generic reasoning skills to generate intelligent alarms, or HINTS, on various levels of interpretation of an observed dysfunction, even in the presence of complex disorders. The system's communication and information management capabilities are used to acquire physiological data, and to store them along with their interpretations and any interventions for the dynamic recognition of interrelated pathophysiological states or clinical events.