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PubMed1994

A decision support system for AIDS intervention and prevention.

In recent years, the importance of information systems has been identified as a vital issue to continuing success in AIDS intervention and prevention (AIP). The advances in information technology have resulted in integrative information systems including decision support systems (DSS). The concept of DSS for AIP was created at the intersection of two trends. The first trend was a growing belief that AIP information systems are successful in automating operations in AIP programs. The second was a continuing improvement in modeling and software development in the AIP area. This paper presents an integrated DSS for AIP. The system is integrated with a database and achieves its efficiency by incorporating various algorithms and models to support AIP decision processes. The application examples include screening AIDS-risky behaviors, evaluating educational interventions, and scheduling AIP sessions. The implementation results present evidence of the usefulness of the system in AIP.

AIDS Serodiagnosis↗

PubMed1997

Implementation problems of decision support system for nosocomial infection.

Decision support system for nosocomial infection therapy Ptah can reduce antibiotic misuse with data about bacteria resistance and antibiotic ineffectiveness. Resistance vectors in time series show epidemiological problems with resistant bacterias, named house-bacteria. Most important implementation factors are integrated hospital information system and doctors, nurses and managers interested in problems of nosocomial infection.

Anti-Bacterial Agents↗

PubMed1996

MDX--a medical diagnostic decision support system.

The MDX diagnostic decision support system contains a multitude of clinical facts as reported in a variety of medical textbooks and national medical journals. The database was custom designed for the medical diagnostic process and offers users the widest range of diagnostic knowledge available from a single source in the industry. The database consists of diseases, conditions, chemicals, drugs, and toxins that are known to cause medical illness. Each causal element has an associated file for signs, symptoms and findings. This fully referenced source of encoded clinical knowledge used in MDX comes from the world's experts as they transmit their experience in the written word of books and journals.

Databases, Factual↗

PubMed2002

Psychological cue use and implications for a clinical decision support system.

Effective clinical decision making depends upon identifying possible outcomes for a patient, selecting relevant cues, and processing the cues to arrive at accurate judgements of each outcome's probability of occurrence. These activities can be considered as classification tasks. This paper describes a new model of psychological classification that explains how people use cues to determine class or outcome likelihoods. It proposes that clinicians respond to conditional probabilities of outcomes given cues and that these probabilities compete with each other for influence on classification. The model explains why people appear to respond to base rates inappropriately, thereby overestimating the occurrence of rare categories, and a clinical example is provided for predicting suicide risk. The model makes an effective representation for expert clinical judgements and its psychological validity enables it to generate explanations in a form that is comprehensible to clinicians. It is a strong candidate for incorporation within a decision support system for mental-health risk assessment, where it can link with statistical and pattern recognition tools applied to a database of patients. The symbiotic combination of empirical evidence and clinical expertise can provide an important web-based resource for risk assessment, including multi-disciplinary education and training.

Bias↗

PubMed1994

Decision support systems: fact and fiction.

Computers perform tasks daily that aid people in the completion of their duties; computers help drive cars, cook food, entertain, and support our decision making processes. Often a silent partner, the computer is involved in tasks that a health professional provides. Modern ECG machines, infusion pumps, blood analyzers, ventilators, and imaging machines contain computers that aid in the use of these devices. Though not traditionally considered decision support systems, these diagnostic systems support the decision making process in health care. Shortliffe EH et al define the kinds of decision support systems by the roles they play in the decision making process. The authors state that any computer system that deals with clinical data or medical knowledge is intended to provide decision support. This article will discuss fact and fiction regarding decision support systems beginning with information systems that support the decision making process to more sophisticated knowledge based systems that provide interpretation and analysis of data.

Decision Support Systems, Management↗

PubMed1997

Decision support systems in health care.

This paper delineates the application of decision support systems in the healthcare arena. An overview of decision support systems in the healthcare environment in the last three to five years, mostly in the USA and the UK is provided. Some reference is made to work in South Africa. Although this report endeavours to emphasise the decision support system side, some operations research issues are also addressed. This paper is divided into several sections: decision support systems definition, techniques used, the use of operations research in specific areas of healthcare, information systems, assessment of decision support systems, in-house development, decision support in South Africa and future of decision support systems.

Decision Support Systems, Management↗

PubMed2001

Developing decision support systems for integrated coastal management in the tropics: is the ICM decision-making environment too complex for the development of a useable and useful DSS?

Integrated coastal management in the tropics requires the conservation of vulnerable and diverse ecosystems such as coral reefs and mangroves as well as the management of land and marine-based human activities. Decision-making for integrated coastal management involves multiple decision-makers and multiple stakeholders often with conflicting needs and interests. Decision support systems can be developed to improve our understanding of the inter-relationships between the natural and socio-economic variables and hence result in improved decision-making. The question is whether this decision making environment is actually too complex for the development of useful and useable decision support systems. This paper describes the components of the decision making environment and the components of a decision support system. It also explores the various techniques available to deal with different modelling needs, the constraints of inadequate data and the multi-objective decision making environment. In addition, different techniques of developing decision support systems can play important roles within integrated coastal management. Three coastal decision support systems are evaluated in terms of their design and role in integrated coastal management and are used to evaluate the potential to develop decision support systems for integrated coastal management.

Animals↗

PubMed2006

Automated test selection in decision-support systems: a case study in oncology.

Decision-support systems in medicine should be equipped with a facility that provides patient-tailored information about which test had best be performed in which phase of the patient's management. A decision-support system with a good test-selection facility may result in ordering fewer tests, decreasing financial costs, improving a patient's quality of life, and in an improvement of medical care in general. In close cooperation with two experts in oncology, we designed such a facility for a decision-support system for the staging of cancer of the oesophagus. The facility selects tests based upon a patient's health status and closely matches current routines. We feel that by extending our decision-support system with the facility, it provides further support for a patient's management and will be more interesting for use in daily medical practice. In this paper, we describe the test-selection facility that we designed for our decision-support system in oncology and present some initial results.

Decision Support Systems, Clinical↗

PubMed2006

The safety and quality of decision support systems.

OBJECTIVES: The use of clinical decision support systems (CDSS) can improve the overall safety and quality of health care delivery, but may also introduce machine-related errors. Recent concerns about the potential for CDSS to harm patients have generated much debate, but there is little research available to identify the nature of such errors, or quantify their frequency or clinical impact. METHODS: A review of recent literature into electronic prescribing systems, as well as related literature in decision support. RESULTS: There seems to be some evidence for variation in the outcomes of using CDSS, most likely reflecting variations in clinical setting, culture, training and organizational process, independent of technical variables. There is also preliminary evidence that poorly implemented CDSS can lead to increased mortality in some settings. Studies in the US, UK and Australia have found commercial prescribing systems often fail to uniformly detect significant drug interactions, probably because of errors in their knowledge base. Electronic medication management systems may generate new types of error because of user-interface design, but also because of events in the workplace such as distraction affecting the actions of system users. Another potential source of CDSS influenced errors are automation biases, including errors of omission where individuals miss important data because the system does not prompt them to notice them, and errors of commission where individuals do what the decision aid tells to do, even when this contradicts their training and other available data. Errors of dismissal occur when relevant alerts are ignored. On-line decision support systems may also result in errors where clinicians come to an incorrect assessment of the evidence, possibly shaped in part by cognitive decision biases. CONCLUSIONS: The effectiveness of decision support systems, like all other health IT, cannot be assessed purely by evaluating the usability and performance of the software, but is the outcome of a complex set of cognitive and socio-technical interactions. A deeper understanding of these issues can result in the design of systems which are not just intrinsically 'safe' but which also result in safe outcomes in the hands of busy or poorly resourced clinicians.

Decision Support Systems, Clinical↗

PubMed2004

Clinical decision support systems: perspectives in dentistry.

Clinical decision-support systems (CDSSs) are computer programs that are designed to provide expert support for health professionals making clinical decisions. The goal of these systems is to help health professionals analyze patient data and make decisions regarding diagnosis, prevention, and treatment of health problems. This article discusses the characteristics of such systems, addresses the challenges in developing them, identifies potential barriers for their use in clinical practice, and provides perspectives for the future.

Decision Support Systems, Clinical↗

PubMed1999

Design of a decision support system for test ordering in general practice: choices and decisions to make.

The increased availability of tests in the past years has been accompanied by an increased number of blood tests ordered by general practitioners. Dutch investigators report a lack of general practitioners' knowledge concerning the indications for blood tests leading to inappropriate and inadequate use of diagnostic tests. Taking advantage of the use of electronic patient records by Dutch general practitioners, the authors replaced the traditional paper forms for test ordering by a decision-support system. The objective of the decision-support system is to change test-ordering behavior. Designing a system to change test-ordering behavior, however, required the selection of a method to provide support. To study different methods for changing test-ordering behavior, the authors developed two versions of the decision-support system BloodLink. The first version, BloodLink-Restricted, is based on the notion of restricting the number of choices presented to the general practitioners. The second version, BloodLink-Guideline, is based on the guidelines provided by the Dutch college of general practitioners.

Blood Chemical Analysis↗

PubMed1996

Current status of mechanical ventilation decision support systems: a review.

Objectives of computerized decision support systems for mechanical ventilation are discussed. Questions considered are: Why is computerized decision support for mechanical ventilation important? What parameter(s) should be optimized? What are the differences between a single attribute and a multiattribute value function used for optimization? How is it possible to achieve optimization in clinical practice with existing ventilators? How does one solve the problem of acquiring measurement of data needed for closed loop control? The possibilities and limitations of three existing decision support systems are discussed. 1) Computerized protocols from LDS Hospital in Salt Lake City, Utah, USA. 2) Optimization Program (OPTPROG) developed jointly at the Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland and Medical Intensive Care Unit, Department of Medicine at Karolinska Institute, South Hospital, Stockholm, Department of Medical Informatics Linkoping University, Sweden. 3) Ventilator Therapy Planner (VENT-PLAN) from the Section on Medical Informatics at Stanford University, Palo Alto, California, USA. Strategies leading to an optimal computerized decision support system are proposed. These strategies include development of better measurement methods for blood gases and cardiac output, improvement of man-machine and machine-machine interaction and the selection of optimization criteria. Finally, research directed towards building quantitative, dynamic patient models based on computerized databases of mechanically ventilated patients are discussed.

Algorithms↗

PubMed2003

The development and evaluation of a computerised decision support system for primary care based upon 'patient profile decision analysis'.

OBJECTIVE: To develop and evaluate in primary care a computerised decision support system for the management of stroke patients based upon 'patient profile decision analysis'. DESIGN: The decision support system incorporated the findings of 960 Markov models examining the decision to prescribe aspirin in the secondary prevention of stroke. The models reflected each combination of nine risk factors that determined a patient's profile. The evaluation comprised a qualitative interview and a questionnaire administered before and after the general practitioners (GPs) were given access to the support system. SETTING: Primary care. PARTICIPANTS: 15 GPs from the West Midlands. MAIN OUTCOME MEASURES: Decision certainty scoring of hypothetical patient vignettes. Qualitative perceptions of the applicability and acceptability of the system for primary care. RESULTS: After using the system, GPs were more certain of their decision making and made decisions more in line with national guidelines. Quantitative results further suggested that the system made decision making easier, improved feelings of being supported, improved the quality of decision making and increased satisfaction. Qualitative themes included that GPs thought the system could clarify their own decision making and improve GP-patient dialogue. CONCLUSIONS: The feasibility of individualised decision analysis for general practice has been questioned. Patient profile decision analysis, however, may be a valuable means of harnessing some of the advantages of the methodology to produce more patient-specific guidelines for primary care.

Aspirin↗

PubMed1993

A decision support system using milk progesterone tests to improve fertility in commercial dairy herds.

MOIRA (the Management of Insemination through Routine Analysis) is a computer program that is a module of DAISY, the Dairy Information System. This decision support system uses the results of milk progesterone tests to determine when to inseminate cows. The MOIRA program plans a series of weekly tests for each cow, to check for cyclicity. Subsequently, the program lists cows for alternate day tests to identify the days when they should be served, and if necessary, a cow can be served without being seen in heat. The work which led to the development of MOIRA showed that ovulation detection rates of 98 per cent were achieved; in commercial herds the rate is around 85 per cent. As pregnancy rates were unchanged, the effect of MOIRA was to reduce calving to conception interval from 90 to 85 days and the culling rate for failure to conceive from 13 per cent to 5 per cent in one herd and from 104 to 100 days and 21 per cent to 12 per cent in another herd. The use of the program produced an estimated extra net profit for the farmers of pounds 52/cow in the first herd and pounds 54/cow in the second.

Animal Husbandry↗

PubMed1998

Design of a modular, extensible decision support system for arrhythmia therapy.

We developed a decision-support system for evaluation of treatment alternatives for supraventricular and ventricular arrhythmias. The system uses independent decision models that evaluate the costs and benefits of treatment for recurrent atrioventricular-node reentrant tachycardia (AVNRT), and of therapies to prevent sudden cardiac death (SCD) in patients at risk for life-threatening ventricular arrhythmias. Each of the decision models is accessible through a web-based interface that enables remote users to browse the model's underlying evidence and to perform analyses of effectiveness, cost effectiveness, and sensitivity to input variables. Because the web-based interface is independent of the models, we can extend the functionality of the system by adding decision models. This system illustrates that the use of a library of web-accessible decision models provides decision support economically to widely dispersed users.

Arrhythmias, Cardiac↗

PubMed1995

Computerized decision support systems in primary care.

Computerized decision support can be passive or active. Passive decision support occurs when a computer facilitates access to relevant patient data or clinical knowledge for interpretation by the physician. Examples include CPR systems and reference texts or literature databases on CD-ROM. Effective passive decision support may ultimately prove to have a significant impact on physician decision making, but its potential to do so has been largely unexplored. Active decision support implies some higher level of information processing, or inference, by the computer. Examples include reminder / alert systems and diagnostic decision support systems. Inference processing in active decision support systems is generally rule-based, but probabilistic inference has been successfully used as well. Reminder systems have been consistently demonstrated to improve dramatically physician guideline compliance, generally by reducing oversight or error. The same potential for large-scale, systematic impact on physician decision-making by diagnostic decision support systems probably does not exist, but these systems may prove to be extremely useful in individual cases. Current applicability of diagnostic decision support systems to primary care is limited by the incompleteness and inaccuracies of the knowledge bases of these systems with respect to primary care. The applicability of computerized decision support in general to primary care is limited by more practical considerations. Widespread computerized decision support will not occur without CPR systems coupled with appropriate data standards and nomenclatures that will permit decision support tools to be accessed effortlessly during the routine process of patient care.

Clinical Medicine↗

PubMed2006

Developing an evidence-based decision support system for rational insecticide choice in the control of African malaria vectors.

The emergence of Anopheles species resistant to insecticides widely used in vector control has the potential to impact directly on the control of malaria. This may have a particularly dramatic effect in Africa, where pyrethroids impregnated onto bed-nets are the dominant insecticides used for vector control. Because the same insecticides are used for crop pests, the extensive use and misuse of insecticides for agriculture has contributed to the resistance problem in some vectors. The potential for resistance to develop in African vectors has been apparent since the 1950s, but the scale of the problem has been poorly documented. A geographical information system-based decision support system for malaria control has recently been established in Africa and used operationally in Mozambique. The system incorporates climate data and disease transmission rates, but to date it has not incorporated spatial or temporal data on vector abundance or insecticide resistance. As a first step in incorporating this information, available published data on insecticide resistance in Africa has now been collated and incorporated into this decision support system. Data also are incorporated onto the openly available Mapping Malaria Risk in Africa (MARA) Web site (http://www.mara.org.za). New data, from a range of vector population-monitoring initiatives, can now be incorporated into this open access database to allow a spatial understanding of resistance distribution and its potential impact on disease transmission to benefit vector control programs.

Africa↗

PubMed1996

Implementation and evaluation of a decision support system for type II diabetes.

A decision support system for the management of oral hypoglycaemic therapy in type II diabetes was evaluated. The ruleset contained therein forms the basis of a prototype computer programme, but in order to assess the robustness of the individual rules, it was decided it was necessary to use a paper-based form of the ruleset. A nurse with no previous experience of managing type II diabetes was trained to use the system and then undertook the exclusive management of half of all new type II diabetics, from a district population of 300,000, over a 16-month period. General practices within this area were divided into two groups, study and control, matching for size, geographical area and standards of existing diabetes care. Patients (n = 102) from the study group practices were then assigned to her care. Those patients (n = 116) in the control group of practices were treated according to their normal procedures. The decision support system for oral hypoglycaemic therapy was based on the following criteria: the current type of treatment (six levels); current glycaemic control (HbA1 and FBS)-whether improving, steady or worsening; and weight-%IBW, whether rising, steady or falling. Each of these parameters was carefully defined on the basis of established practice and clinical experience. Patients after initial education were seen at their usual clinic by the nurse only, on a monthly basis, until satisfactory glycaemic control was established and thereafter reviewed 3 monthly. She was also responsible for ensuring the organisation of Diabetes Annual Review procedures. The medical records of the control group patients were examined at the end of the study and data on glycaemic control and Annual Reviews extracted. In the study group 98% patients achieved HbA1 levels within the normal range and all patients had full annual reviews performed. The control practices achieved much poorer degrees of metabolic control (P < 0.01) and completed fewer annual reviews. The study group did not demonstrate a significantly increased frequency of clinical hypoglycaemia consequent upon better blood sugar control. No exceptions to the ruleset, as initially defined, were detected. In conclusion, this decision support system was successful at achieving standards of diabetes control and care equal to or better than conventional structures of diabetes care. Implementation of such a system, on a simple computer platform, could greatly assist and possibly improve diabetes management in general practice.

Aged↗