Computer-based Decision Support System Research Articles

Comparison of Computer-based Clinical Decision Support Systems and Content for Diabetes Mellitus

Computer-based clinical decision support (CDS) systems have been shown to improve quality of care and workflow efficiency, and health care reform legislation relies on electronic health records and CDS systems to improve the cost and quality of health care in the United States; however, the heterogeneity of CDS content and infrastructure of CDS systems across sites is not well known. We aimed to determine the scope of CDS content in diabetes care at six sites, assess the capabilities of CDS in use at these sites, characterize the scope of CDS infrastructure at these sites, and determine how the sites use CDS beyond individual patient care in order to identify characteristics of CDS systems and content that have been successfully implemented in diabetes care. We compared CDS systems in six collaborating sites of the Clinical Decision Support Consortium. We gathered CDS content on care for patients with diabetes mellitus and surveyed institutions on characteristics of their site, the infrastructure of CDS at these sites, and the capabilities of CDS at these sites. The approach to CDS and the characteristics of CDS content varied among sites. Some commonalities included providing customizability by role or user, applying sophisticated exclusion criteria, and using CDS automatically at the time of decision-making. Many messages were actionable recommendations. Most sites had monitoring rules (e.g. assessing hemoglobin A1c), but few had rules to diagnose diabetes or suggest specific treatments. All sites had numerous prevention rules including reminders for providing eye examinations, influenza vaccines, lipid screenings, nephropathy screenings, and pneumococcal vaccines. Computer-based CDS systems vary widely across sites in content and scope, but both institution-created and purchased systems had many similar features and functionality, such as integration of alerts and reminders into the decision-making workflow of the provider and providing messages that are actionable recommendations.

The introduction of a diagnostic decision support system (DXplain™) into the workflow of a teaching hospital service can decrease the cost of service for diagnostically challenging Diagnostic Related Groups (DRGs)

Background In an era of short inpatient stays, residents may overlook relevant elements of the differential diagnosis as they try to evaluate and treat patients. However, if a resident's first principal diagnosis is wrong, the patient's appropriate evaluation and treatment may take longer, cost more, and lead to worse outcomes. A diagnostic decision support system may lead to the generation of a broader differential diagnosis that more often includes the correct diagnosis, permitting a shorter, more effective, and less costly hospital stay. Methods We provided residents on General Medicine services access to DXplain, an established computer-based diagnostic decision support system, for 6 months. We compared charges and cost of service for diagnostically challenging cases seen during the fourth through sixth month of access to DXplain (intervention period) to control cases seen in the 6 months before the system was made available. Results 564 cases were identified as diagnostically challenging by our criteria during the intervention period along with 1173 cases during the control period. Total charges were $1281 lower ( p = .006), Medicare Part A charges $1032 lower ( p = 0.006) and cost of service $990 lower ( p = 0.001) per admission in the intervention cases than in control cases. Conclusions Using DXplain on all diagnostically challenging cases might save our medical center over $2,000,000 a year on the General Medicine Services alone. Using clinical diagnostic decision support systems may improve quality and decrease cost substantially at teaching hospitals.

Medical Decision Support Systems in Africa

To present an overview of the current state of computer-based medical decision support systems in Africa in the areas of public health, patient care, and consumer support. Scientific and gray literature reviews complemented by expert interviews. Various domains of decision support are developed and deployed in Sub-Saharan Africa: public health information systems, clinical decision-support systems, and patient-centred decision-support systems. Until recently, most of these systems have been deployed by international organizations without a real ownership policy entrusted to the African stakeholders. Many of these endeavours have remained or ceased at the experimentation stage. The multiplicity of organizations has led to the deployment of fragmented systems causing serious interoperability problems. In addition to basic infrastructures, these studies also highlight the importance of good organization, training and support, as key to the success and sustainability of these decision support systems.

Findings from the Yearbook 2010 Section on Decision Support Systems

To summarize current excellent research in the field of computer-based decision support systems in health and healthcare. We provide a synopsis of the articles selected for the IMIA Yearbook 2010, from which we attempt to derive a synthetic overview of the activity and new trends in the field. while the state of the research in the field of medical decision support systems is illustrated by a set of fairly heterogeneous studies, it is possible to identify trends. Thus, clearly, the importance of studies related to computerized prescription order entry (CPOE) systems and guidelines management systems for both medical decision making and care providers, occupies a central role in the field, with application affecting also EHR vendors. In parallel, we observe translational interests for developing bridges with results generated by molecular biology, where the mass of data generated by high/throughput experiments and large-scale genome analysis projects, raises specific processing challenges. The best paper selection of articles on decision support shows examples of excellent research on methods concerning original development as well as quality assurance of previously reported studies. This selected set of scientific investigations demonstrates the needs for computerized applications to transform the biomedical data overflow into more operational clinical knowledge. Altogether these papers support the idea that more elaborated computer tools, likely to combine heterogeneous contextual contents, are needed.

Knowledge discovery and computer-based decision support in biomedicine

No abstract. Keywords: Knowledge discovery; Computer-based decision support systems; Data mining; Biomedical image processing; Biomedical image classification; Biomedical signal processing.

A Computerized Handheld Decision-Support System to Improve Pulmonary Embolism Diagnosis: A Randomized Trial

This cluster randomization trial conducted in 20 French emergency departments (EDs) enrolled patients with suspected pulmonary embolus (PE) to measure the change in appropriateness of diagnostic evaluation when using a computer-based decision support system. Diagnostic decision data from 1103 patients with clinically suspected PE seen during a 6-month pre-intervention period were collected for comparison with data from 1768 interventional period patients. EDs were randomized to use handheld computer-based decision support or paper guidelines of the revised Geneva score.

English

Prioritising emergency bridgeworks assessment has been a key to winning battles in combat circumstances because of soldier safety, attack or defence tactics, and logistic supply ability. However, an imprecise or vague satisfaction level of importance of criteria may also affect the prioritising evaluation of bridgeworks under military consideration. In this paper, the fuzzy set theory is employed to treat this aspect. With linguistic variables, fuzzy numbers and an enhanced fuzzy weighted average approach will be used. The proposed approach is used to investigate an example to illustrate its applications in emergency bridgeworks assessment. The approach is shown to be useful and effective. In order to make computing and ranking results easier and to increase recruiting productivity, a computer-based decision support system has been developed, which may help the commander make decisions more efficiently. Defence Science Journal, 2010, 60(4), pp.451-461 , DOI:http://dx.doi.org/10.14429/dsj.60.485

Does sex influence the allocation of life support level by dispatchers in acute chest pain?

Aim The aim of this study was to evaluate ( a) the differences between men and women in symptom profile, allocated life support level (LSL), and presence of acute myocardial infarction (AMI), life-threatening condition (LTC), or death and ( b) whether a computer-based decision support system could improve the allocation of LSL. Patients All patients in Göteborg, Sweden, who called the dispatch center because of chest pain during 3 months (n = 503) were included in this study. Methods Age, sex, and symptom profile were background variables. Based on these, we studied allocation of LSL by the dispatchers and its relationship to AMI, LTC, and death. All evaluations were made from a sex perspective. Finally, we studied the potential benefit of using a statistical model for allocating LSL. Results The advanced life support level (ALSL) was used equally frequently for men and women. There was no difference in age or symptom profile between men and women in relation to allocation. However, the allocation of ALSL was predictive of AMI and LTC only in men. The sensitivity was far lower for women than for men. When a statistical model was used for allocation, the ALSL was predictive for both men and women. Using a separate model for men and women respectively, sensitivity increased, especially for women, and specificity was kept at the same level. Conclusion This exploratory study indicates that women would benefit most from the allocation of LSL using a statistical model and computer-based decision support among patients who call for an ambulance because of acute chest pain. This needs further evaluation.

A multi-criteria decision-making model for classifying wood products with respect to their impact on environment

Background, aim and scope Although life cycle assessment is frequently used in scientific studies of product comparison, many practitioners are looking for improvements in the normalisation, grouping and weighting of life cycle inventory results. Local conditions, which are well known to local experts, are very important to these steps. The goal of this work was to develop a computer-based decision support system for classifying wood products according to their influence on the environment in their whole life cycle. The model specifically addresses local conditions in the Republic of Slovenia and was developed by Slovenian experts.

Use of Diagnostic Decision Support Systems in Medical Education

Diagnostic decision support systems are designed to assist physicians with making diagnoses. This article illustrates some of the issues that will be faced as diagnostic decision support systems become used in medical education. The objectives of this article are to examine 1) the skills that are needed to properly use these programs as part of the students' clinical experiences; 2) the changes that will be necessary in our curricula once these programs are more extensively utilized, including the implications of using these systems as an educational resource or simulation tool, and 3) the research issues that arise when these systems become an established part of our educational programs. This is a critical analysis of the literature on diagnostic decision support systems and medical education. To optimally use diagnostic decision support programs, students will need grounding in the basic knowledge and skills that have always been necessary to become a physician, such as the ability to accurately gather and interpret clinical information from the patient. In addition, students will need specific skills in 1) selecting appropriate system vocabulary and functions, and 2) applying the diagnostic system's suggestions to their particular patient. When computer-based decision support systems are incorporated in medical education, they will likely lead to changes in the traditional medical curriculum. Research will be needed on how use of these programs changes the students' knowledge, problem-solving and information-seeking skills.

A Framework for Data and Mined Knowledge Interoperability in Clinical Decision Support Systems

Due to reliance on human knowledge, the practice of medicine is subject to errors that endanger patients’ health and cause substantial financial loss to healthcare institutions. Computer-based decision support systems assist healthcare personnel to improve quality of clinical practice. Currently, most clinical guideline modeling languages represent decision-making knowledge in terms of basic logical expressions. In this paper, we focus on encoding, sharing, and using results of data mining analyses to influence decision making within Clinical Decision Support Systems. A knowledge management framework is proposed that addresses the issues of data and knowledge interoperability by adopting healthcare and data mining modeling standards. In a further step, data mining results are incorporated into a guideline-based decision support system. A prototype tool has been developed to provide an environment for clinical guideline authoring and execution. Also, three real world case studies have been presented, one of which is used as a running example throughout the paper.

COWEL: a decision support system to assess welfare of husbandry systems for dairy cattle

AbstractAnimals have various behavioural and physiological needs that are important for welfare. Fulfilment of these needs depends on the quality of housing, management and animal characteristics. The objective of this study was to develop a model to assign welfare scores to husbandry systems for dairy cattle, based on scientific results, and thereby supporting the design of new, welfare-friendly systems. COWEL is a computer-based decision support system that contains attributes regarding housing and management conditions. These attributes are technical specifications that contain various technical units called levels. These levels are ranked from best-to-worst regarding welfare, based on scientific information about animal-based parameters. This information, inserted in the model as statements, was weighted depending on the impact it has on welfare by using weighting categories. Thereafter, a weighting factor was calculated for each attribute which determines how important an attribute is for welfare. The COWEL model contains 2,343 statements on dairy cattle welfare from 476 sources found during a literature survey. The model was applied to four husbandry systems, namely a tie-stall, cubicle housing, a straw yard and a pasture-based system. The welfare scores, calculated by COWEL for these husbandry systems, correspond with the general opinion about these systems. A tie-stall receives a low and a pasture-based system a high welfare score: 211 and 271, respectively. A husbandry system can receive a maximum of 313 on the welfare scale of COWEL. We conclude that COWEL can be used to rank husbandry systems on a welfare scale, and may be a useful tool to develop new, sustainable and welfare-friendly systems for dairy cattle.

Nurse-led medication reviews and the quality of drug treatment of elderly hospitalized patients

To evaluate if nurses after receiving training in clinical pharmacology can improve the quality of the drug therapy in elderly hospitalized patients. Nurses were given a 1-day training in clinical pharmacology to identify drug-related problems (DRPs).All patients admitted to the ward aged 65 or more were studied. Patients at the same ward before the intervention were considered as control group. Outcome variables were re-hospitalized 3 months from discharge, drug-related readmissions, the proportion of inappropriate drug use (IDU), and DRPs found by the nurses. Of 460 patients (250 intervention group and 210 in the control group) 38 and 36%, respectively, had at least one re-admission to hospital (p=0.86) and 24% of the patients died. Eighteen and 17% (43/37), respectively, used one or more inappropriate drug (p 0.90). The nurses found 86 clinically significant DRPs not detected by the usual care. A substantial part of the DRPs detected by the nurses were revealed with assistance of Symptoms Assessment Form (SYM). There were no statistical difference in the number of drug-related re-admissions between the groups, 14/16, respectively, (p=0.40). Nurses are able to detect a high proportion of clinically relevant DRPs not detected by the usual care and thereby increase the quality of the drug treatment in elderly hospitalized patients. Our study showed no effect on re-hospitalization or IDU. By using a SYM nurses can find DRPs that computer-based decision support systems miss.

A decision support system for engineering design based on an enhanced fuzzy MCDM approach

Design concept is an important wealth-creating activity in companies and infrastructure. However, the process of designing is very complex. Besides, the information required during the conceptual stage is incomplete, imprecise, and fuzzy. Hence, fuzzy set theory should be used to handle linguistic problem at this stage. This paper presents a fuzzy integrated approach to assess the performance of design concepts. And those criteria rating, relative weights and performance levels are captured by fuzzy numbers, and the overall performance of each alternative is calculated through an enhanced fuzzy weighted average (FWA) approach. A practical numerical example is provided to demonstrate the usefulness of this study. In addition, this paper, in order to make computing and ranking results easier to increase the recruiting productivity, develops a computer-based decision support system to help make decisions more efficiently.

Evaluation of agricultural land resources by implementing a computer-based spatial decision support system for national deciders in Benin, West Africa

The marginality index for agricultural land use, which was originally defined on a global scale, was used to evaluate agricultural land resources of Benin. For the assessment of the index, several biophysical factors limiting agricultural production under low capital input are analyzed using a fuzzy logic based algorithm. For Benin, we determined the marginality index (MI) successfully in a spatial resolution of 1km x 1km using influencing factors with a higher spatial resolution and an adapted fuzzy logic based algorithm. The results of the approach proved that the chosen indicators on a global scale are also useful indicators on a national scale. The necessary modifications were slight and mostly with the aim to increase the tangibility for national decision makers. On a national scale, data derived from remote sensing like MODIS or SRTM are interesting and embolden sources to derive input data. To support national decision makers, input data and algorithms were implemented within a computer-based Spatial Decision Support System (SDSS). With the developed SDSS 'AGROLAND' the user is able to visualize and analyze agricultural land resources based on the MI. Additionally, advanced model based raster analyses as well as the possibility of user interactions during runtime are implemented.

Bidding process and integrated fuzzy model for global sourcing based on customer preferences

Due to the increasing competition of globalisation and fast technological improvements, world markets demand companies to have quality and professional supply chain partners. This can only be achieved by employing potential bidding process for global sourcing. In this paper, we proposed a generic bidding process with respect to customer preferences for global sourcing. We develop an integrated fuzzy model leveraging fuzzy analytic hierarchy process and data envelopment analysis based on the model of Parthiban et al. (2008) for evaluation and selection of supply chain partner for global sourcing. The integrated fuzzy model is applied and illustrated for a real-life case study. A practical computer-based decision support system is developed for the model to help managers make better decisions under fuzzy circumstances.

Decision support system for agricultural land use and fertilisation optimisation: a case study on barley production in Estonia

The success of the decision support systems, developed within GIS with application of different models, depends on the quality of initial data and the models themselves as well as on the possibilities of their linking. The aim of the present study was to analyse the application of different agro-economic models in a computer-based decision support system, developed for optimisation of agricultural land use and fertilisation, on the example of barley production of Kullamaa rural municipality in Estonia. The algorithms used in the agronomical models were obtained from the regression analysis of numerous field experiments. The calculated new agronomical values serve as a basis for the application of economic models. GIS and modelling remain as two separate systems with the capacity for information exchange between them. Profitability of barley cultivation varied in a very broad range in the study area. The optimal fertiliser amounts established for each field allow increasing crop productivity in the region and at the same time preventing environmental pollution due to production intensification. The proposed decision support system can be further supplemented by several agro-economic models and implemented throughout Estonia.;

A fuzzy AHP approach to personnel selection problem

Due to the increasing competition of globalization and fast technological improvements, world markets demand companies to have quality and professional human resources. This can only be achieved by employing potentially adequate personnel. In this paper, we proposed a personnel selection system based on Fuzzy Analytic Hierarchy Process (FAHP). The FAHP is applied to evaluate the best adequate personnel dealing with the rating of both qualitative and quantitative criteria. The result obtained by FAHP is compared with results produced by Yager's weighted goals method. In addition to above-mentioned methods, a practical computer-based decision support system is introduced to provide more information and help manager make better decisions under fuzzy circumstances.

A decision support system improves the interpretation of myocardial perfusion imaging

The aim of this study was to investigate the influence of a computer-based decision support system (DSS) on performance and inter-observer variability of interpretations regarding ischaemia and infarction in myocardial perfusion scintigraphy (MPS). Seven physicians independently interpreted 97 MPS studies, first without and then with the advice of a DSS. Four physicians had long experience and three had limited experience in the interpretation of MPS. Each study was interpreted regarding myocardial ischaemia and infarction in five myocardial regions. The patients had undergone a gated MPS using a 2-day stress/gated rest (99m)Tc sestamibi protocol. The gold standard used was the interpretations made by one experienced nuclear medicine specialist on the basis of all available clinical and image information. The sensitivity for ischaemia of the seven readers increased from 81% without the DSS to 86% with the DSS (p = 0.01). The increase in sensitivity was higher for the three inexperienced physicians (9%) than for the four experienced physicians (2%). There was no significant change in specificity between the interpretations. The interpretations of ischaemia made with the advice of the DSS showed less inter-observer variability than those made without advice. This study shows that a DSS can improve performance and reduces the inter-observer variability of interpretations in myocardial perfusion imaging. Both experienced and, especially, inexperienced physicians can improve their interpretation with the advice from such a system.

The Impact of Computer‐assisted Auscultation on Physician Referrals of Asymptomatic Patients with Heart Murmurs

As many as 50-70% of asymptomatic children referred for specialist evaluation or echocardiography because of a murmur have no heart disease. Computer-assisted auscultation (CAA) can improve the sensitivity and specificity of referrals for evaluation of heart murmurs. Seven board-certified primary care physicians were evaluated both without and with use of a computer-based decision-support system using 100 prerecorded patient heart sounds (55 innocent murmurs, 30 pathological murmurs, 15 without murmur). The sensitivity and specificity of their murmur referral decisions relative to American College of Cardiology/American Heart Association (ACC/AHA) guidelines, and sensitivity and specificity of murmur detection and characterization (innocent versus pathological) were measured. Sensitivity for detection of murmurs significantly increased with use of CAA from 76.6 to 89.1% (p <0.001), while specificity remained unaffected (80.0 versus 81.0%). Computer-assisted auscultation improved sensitivity of correctly identifying pathological murmur cases from 82.4 to 90.0%, and specificity of correctly identifying benign cases (with innocent or no murmurs) from 74.9 to 88.8%. (p <0.001). Referral sensitivity increased from 86.7 to 92.9%, while specificity increased from 63.5 to 78.6% using CAA (p <0.001). Computer-assisted auscultation appears to be a promising new technology for informing the referral decisions of primary care physicians.