Abdul V. Roudsari

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.

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 patients 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.

Automation bias: a systematic review of frequency, effect mediators, and mitigators

Automation bias (AB)--the tendency to over-rely on automation--has been studied in various academic fields. Clinical decision support systems (CDSS) aim to benefit the clinical decision-making process. Although most research shows overall improved performance with use, there is often a failure to recognize the new errors that CDSS can introduce. With a focus on healthcare, a systematic review of the literature from a variety of research fields has been carried out, assessing the frequency and severity of AB, the effect mediators, and interventions potentially mitigating this effect. This is discussed alongside automation-induced complacency, or insufficient monitoring of automation output. A mix of subject specific and freetext terms around the themes of automation, human-automation interaction, and task performance and error were used to search article databases. Of 13 821 retrieved papers, 74 met the inclusion criteria. User factors such as cognitive style, decision support systems (DSS), and task specific experience mediated AB, as did attitudinal driving factors such as trust and confidence. Environmental mediators included workload, task complexity, and time constraint, which pressurized cognitive resources. Mitigators of AB included implementation factors such as training and emphasizing user accountability, and DSS design factors such as the position of advice on the screen, updated confidence levels attached to DSS output, and the provision of information versus recommendation. By uncovering the mechanisms by which AB operates, this review aims to help optimize the clinical decision-making process for CDSS developers and healthcare practitioners.

UTOPIA: A consultation system for visit-by-visit diabetes management

UTOPIA (UTilities for OPtimizing Insulin Adjustment) is a prototype computer system proposed to support home data analysis and therapy recommendations for the individual patient. The paper describes methods of analysis and their incorporation into an overall system design that matches the iterative practices at the physician-patient consultation from visit to visit. Four modules support home data display and comparison with clinical measurements; extraction of blood glucose trends and daily cycles using time series analysis, learning relationships between insulin adjustments and changes in time series patterns via a parametric, linear systems model; and advice generation by solving the linear equation for candidate insulin adjustments. Concepts and methods are placed in context, with a discussion of comparable and related research.

The applicability of a multi‐attribute classification framework in the healthcare industry

Purpose – To introduce the applicability of the Ned‐MASTA classification method for medicines within the environment of a hospital pharmacy and the virtual pharmacy inventory system that forms a virtual pharmacy inventory of hospitals within the same geographical region providing the infrastructure for the cooperation of hospital pharmacies in order to improve the efficiency of their operations.Design/methodology/approach – A survey that was conducted in Greek hospitals identified the inefficiencies of their logistics systems that are similar to inefficiencies identified through surveys in hospitals worldwide. It was considered vital and necessary to investigate the solutions that are provided in other industries facing similar problems. The case of spare parts inventory for production machines was found to present similarities with the management of medicine stock within the hospital pharmacy. The approach that was followed for the case of spare parts was modified and included in the system that forms a ...

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.

Validation of a metabolic prototype to assist in the treatment of insulin-dependent diabetes mellitus

This paper describes the principles and prototyping of a computer system to assist in the treatment of patients with insulin-dependent (type 1) diabetes mellitus. The system adopts a mixed approach involving rule-based qualitative algebra and a dynamic mathematical model to define the relationships between insulin dosage, diet and glycaemic response. The rule-based system (KBS), implemented in PROLOG, can be used to generate qualitative therapeutic advice. These suggestions are quantified and rank-ordered by the use of a mathematical model of glucose-insulin interaction in type 1 diabetes mellitus, with parameters adjusted for individual patients. In this paper an overview of the integrated prototype, linking the KBS and model, is provided and a case study used to demonstrate the principles of the system in operation. The results of verification and validation work performed on the KBS are described.

Automation bias: Empirical results assessing influencing factors

OBJECTIVE To investigate the rate of automation bias - the propensity of people to over rely on automated advice and the factors associated with it. Tested factors were attitudinal - trust and confidence, non-attitudinal - decision support experience and clinical experience, and environmental - task difficulty. The paradigm of simulated decision support advice within a prescribing context was used. DESIGN The study employed within participant before-after design, whereby 26 UK NHS General Practitioners were shown 20 hypothetical prescribing scenarios with prevalidated correct and incorrect answers - advice was incorrect in 6 scenarios. They were asked to prescribe for each case, followed by being shown simulated advice. Participants were then asked whether they wished to change their prescription, and the post-advice prescription was recorded. MEASUREMENTS Rate of overall decision switching was captured. Automation bias was measured by negative consultations - correct to incorrect prescription switching. RESULTS Participants changed prescriptions in 22.5% of scenarios. The pre-advice accuracy rate of the clinicians was 50.38%, which improved to 58.27% post-advice. The CDSS improved the decision accuracy in 13.1% of prescribing cases. The rate of automation bias, as measured by decision switches from correct pre-advice, to incorrect post-advice was 5.2% of all cases - a net improvement of 8%. More immediate factors such as trust in the specific CDSS, decision confidence, and task difficulty influenced rate of decision switching. Lower clinical experience was associated with more decision switching. Age, DSS experience and trust in CDSS generally were not significantly associated with decision switching. CONCLUSIONS This study adds to the literature surrounding automation bias in terms of its potential frequency and influencing factors.

Lexical patterns, features and knowledge resources for coreference resolution in clinical notes

Generation of entity coreference chains provides a means to extract linked narrative events from clinical notes, but despite being a well-researched topic in natural language processing, general-purpose coreference tools perform poorly on clinical texts. This paper presents a knowledge-centric and pattern-based approach to resolving coreference across a wide variety of clinical records from two corpora (Ontology Development and Information Extraction (ODIE) and i2b2/VA), and describes a method for generating coreference chains using progressively pruned linked lists that reduces the search space and facilitates evaluation by a number of metrics. Independent evaluation results give an F-measure for each corpus of 79.2% and 87.5%, respectively. A baseline of blind coreference of mentions of the same class gives F-measures of 65.3% and 51.9% respectively. For the ODIE corpus, recall is significantly improved over the baseline (p<0.05) but overall there was no statistically significant improvement in F-measure (p>0.05). For the i2b2/VA corpus, recall, precision, and F-measure are significantly improved over the baseline (p<0.05). Overall, our approach offers performance at least as good as human annotators and greatly increased performance over general-purpose tools. The system uses a number of open-source components that are available to download.

Computer-aided learning for the education of patients and family practice professionals in the personal care of diabetes.

Diabetes Mellitus is approaching pandemic proportions across the globe. It is a disproportionately expensive condition, accounting for 5-9% of annual NHS expenditure. Family practices often play a huge role in the care of diabetic patients. Many GPs elect to play a larger role in diabetes care, but the increasing burden on the multidisciplinary secondary care team means that some of the burden has to fall to family practitioners. In order to provide a high standard of care, the practitioner requires access to continuing education regarding diabetes care. The value of patient education is undisputed. In light of this situation a computer-aided learning (CAL) system is being developed for the education of both patients and practitioners concerning diabetes and its care. The proposed system takes a two pronged approach, being aimed at both patient and practitioner. This interactive system employs multimedia technology to teach practical skills and promote and consolidate theoretical understanding. It is hoped this system will improve patient self-care, and in the long-term reduce the incidence of diabetic complications and their associated costs.