Computer-assisted Decision Support Systems Research Articles

How well do decision support systems help decision makers? An examination of adopting lean manufacturing processes

Choice is crucial for industrial enterprises as their success or failure may depend on it. Consequently, emerging technologies, especially Industry 4.0 (I4.0) are making precision decisions and enabling enriching collaborations with the industry’s computer-assisted decision support systems (DSSs). However, the arrival of Industry 4.0 poses challenges to this emerging application in terms of data variations and interconnectivity. A thorough search across Scopus, Web of Science, and Emerald Science databases yielded 2023 relevant documents. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), the top 250 academic documents were meticulously filtered and analysed in this study. The analysis resulted in the classification of the four main purposes of a DSS as data evaluation, optimisation, scheduling, and selection. The research also investigated the impact of DSSs on the performance of lean manufacturing (LM). Next, this research discussed the controversies with regard to the confidence, prejudice, and discrimination of users, discipline-based DSS application bias, as well as criticisms and suggestions for the future development of DSS, especially in the manufacturing industry. It is believed that, based on its novel findings, this work will pave the way for future research in the same field.

In vitro evaluation of a computer-assisted decision support system for the primary care of polytrauma patients

IntroductionThe management of polytraumatized patients is set in a stressful environment with numerous critical decisions in a brief amount of time. Working along a standardised procedure can improve the outcome for these patients and reduce mortality. To help clinical practitioners, we developed “TraumaFlow”, a workflow management system for the primary care of polytrauma patients based on the current treatment guidelines. This study sought to validate the system and investigate its effect on user performance and perceived workload.MethodsThe computer-assisted decision support system was tested in two scenarios in a trauma room of a level 1 trauma centre by 11 final-year medical students and 3 residents. In simulated polytrauma scenarios, the participants functioned as a trauma leader. The first scenario was performed without decision support and the second with support by “TraumaFlow” via tablet. During each scenario, the performance was evaluated in a standardized assessment. After each scenario, the participants answered a questionnaire on workload [NASA Raw Task Load Index (NASA RTLX)].ResultsIn total, 14 participants (mean 28 ± 4 years, 43% female) managed 28 scenarios. During the first scenario without computer-assisted support, the participants achieved a mean of 6.6 out of 12 points (SD 1.2, range 5 to 9). With the support of TraumaFlow, the mean performance score was significantly higher with 11.6 out of 12 points (SD 0.5, range 11 to 12, p < 0.001). In the 14 scenarios performed without support, there was no run in which no errors were made. In comparison, ten of the 14 scenarios performed with TraumaFlow ran free of relevant errors. The mean improvement in the performance score was 42%. There was a significant decrease in the mean self-reported mental stress level in scenarios with support of TraumaFlow (55, SD 24) as compared to scenarios without support (72, SD 13, p = 0.041).ConclusionIn a simulated environment, computer-assisted decision-making improved the performance of the trauma leader, helped to adhere to clinical guidelines, and reduced stress in a fast-acting environment. In reality, this may improve the treatment outcome for the patient.

A Decision Support System For Detecting Stage In Hodgkin Lymphoma Patients Using Artificial Neural Network and Optimization Algorithms

Hodgkin-type lymphoma is a disease with unique histological, immunophenotypic, and clinical features. This disease occurs in nearly 30% of all lymphomas. Its treatable is high. However, the treatment plan is specified after the stage and risk status are determined. For this reason, it is an important process for doctors to decide on the stage of the disease correctly. Some of the data used for this decision are the patient's history, detailed physical examination, laboratory findings, imaging methods and bone marrow biopsy results. Hybrid FDG-PET is the other method used in the medical world. This method is used in diagnosis, evaluation of response given to treatment, staging and restaging process. However, it is radiation-based. Therefore it has the possibility of producing undesirable results in the future. In this study, an artificial intelligence-based computer-assisted decision support system is done to reduce the number of used medical methods and radiation exposure. Data were obtained from the NCBI-GEO dataset. The evaluation of these data, which contains missing values, is handled in two ways. Firstly, samples with missing values in the initial evaluation are deleted from the dataset. Then, these data are trained with “trainlm” function in artificial neural network architecture. However, reducing the error value of the estimates is important. For this, the artificial neural network architecture is retrained with the artificial bee colony algorithm, particle swarm optimization algorithm and invasive weed algorithm, respectively. Secondly, the same operations are performed again on the dataset containing missing values. As a result of the training, the maximum performance was obtained for invasive weed and particle swarm optimization algorithms with 1,45547E+14 and 1,23103E+14 average error rates, respectively.

A Computer Assisted Decision Support System for Education Planning

The advances in technology are eliminating the demand for certain occupations and creating new opportunities. Thus, the universities, teachers, and students have to collaboratively work together to restructure their departments, course offerings, and course contents. Failure to realize the aforementioned initiatives may lead to a loss of quality and competitiveness. This study proposes a decision support system capable of maintaining the quality and competitiveness of the departments and the course offerings. The proposed system consists of three stages. The first stage is the data collection stage. At this stage, data are collected from the internet using web scraping methods. In the second stage, the collected data are turned into meaningful and processable information by natural language processing methods. In the third stage, the alternatives are ranked using multi-criteria decision-making methods. The proposed decision support system provides useful information to several educational stakeholders. First, universities are informed on which departments to create or close as well as the relevant course offerings. Second, information are provided to the teachers to create new courses or shape the course contents. Finally, students are better informed on how to go about choosing the universities, departments, courses, or career paths to pursue. The applicability and reliability of the proposed decision support system were experimentally proven through the use of computer engineering-related job postings and course contents of the universities in Turkey.

Artificial intelligence in echocardiography diagnostics – detection of takotsubo syndrome

Abstract Background Machine learning allows classifying diseases based only on raw echocardiographic imaging data and is therefore a landmark in the development of computer-assisted decision support systems in echocardiography. Purpose The present study sought to determine the value of deep (machine) learning systems for automatic discrimination of takotsubo syndrome and acute myocardial infarction. Methods Apical 2- and 4-chamber echocardiographic views of 110 patients with takotsubo syndrome and 110 patients with acute myocardial infarction were used in the development, training and validation of a deep learning approach, i.e. a convolutional autoencoder (CAE) for feature extraction followed by classical machine learning models for classification of the diseases. Results The deep learning model achieved an area under the receiver operating curve (AUC) of 0.801 with an overall accuracy of 74.5% for 5-fold cross validation evaluated on a clinically relevant dataset. In comparison, experienced cardiologists achieved AUCs in the range 0.678–0.740 and an average accuracy of 64.5% on the same dataset. Conclusions A real-time system for fully automated interpretation of echocardiographic videos was established and trained to differentiate takotsubo syndrome from acute myocardial infarction. The framework provides insight into the algorithms' decision process for physicians and yields new and valuable information on the manifestation of disease patterns in echocardiographic data. While our system was superior to cardiologists in echocardiography-based disease classification, further studies should be conducted in a larger patient population to prove its clinical application. Funding Acknowledgement Type of funding source: None

Designing a Structured System for Mammography Reporting

Background: Breast cancer is among the top causes of cancer-related mortality among females in both developed and developing countries. Screening for breast cancer plays a crucial role in the prevention of disease burden. Among various imaging modalities, mammography is currently considered the first step for the detection of breast lesions. The mammography data of breast cancer patients encompass a wide array of texts related to specific visual findings. Accordingly, unorganized mammography documents usually impede physicians’ efforts to fully review the previous information in follow-up visits; hence, this not only affects clinical judgment and treatment planning adversely, but also adds to the financial burdens and workload imposed on the healthcare system. This further highlights the great potential of structured mammography reporting in the management of breast cancer patients. Methods: In the first step, an expert panel consisting of two attending radiologists and a health informatician (all affiliated to the Shiraz University of Medical Sciences, Shiraz, Iran) reviewed related guidelines to determine the appropriate items, ontologies, and standard formats for data entry. These included radiology textbooks and the systematized nomenclature of medicine (SNOMED). In addition, 100 mammography reports were completely examined for data extraction and the creation of a template report. In the next step, the structure of the data registry was discussed among a group of radiologists and breast cancer surgeons during several sessions using Delphi technique. Results: Overall, 119 fields were selected for data entry. Our survey showed that all of the contributing physicians believed that a structured reporting system for mammography can help standardize and reduce reporting time and errors. Following the completion of the template, a user interface was developed by the expert panel for integration in the cloud software workflow, which will be deployed and assessed in the next phase of the project. Conclusion: Structured mammography reporting helps radiologists and surgeons to efficiently and confidently track the management course of their patients. In addition, with the advancement of artificial intelligence, especially deep learning for image classification, clean and labeled image databases can be used for designing computer-assisted decision support systems without significant data preprocessing.

Impact of a computer-assisted decision support system (CDSS) on nutrition management in critically ill hematology patients: the NUTCHOCO study (nutritional care in hematology oncologic patients and critical outcome)

BackgroundMortality of critically ill hematology (HM) patients has improved over time. Thus, those patients require an extensive diagnostic workup and the optimal use of available treatments. There are no data regarding nutrition strategy for critically ill HM patients, while nutritional support is crucial for both HM and critically ill patients. We hypothesized that the implementation of a computer-assisted decision support system (CDSS), designed to supervise a nutritional intervention by a multidisciplinary team, would be able to increase guidelines adherence and outcomes.ResultsIn this before/after study, 275 critically ill hematology patients admitted to the ICU over 5-year period were included. Energy and protein intakes were delivered using standard protocol in the 147 patients (53%) of the ‘before group’ and using a CDSS in order to reach every day predefined caloric and protein targets accordingly to the catabolic or anabolic status in the 128 patients (47%) of the ‘after group.’ Using a Poisson regression, we showed that the use of CDSS allowed to reach a relative increase in the rate of days in compliance with caloric (1.57; 95% confidence interval (CI), [1.17–2.10], p = 0.0025) and protein targets (3.86 [2.21–6.73], p < 0.0001) in the ‘after group’ by more than 50% as compared with the ‘before group.’ Interestingly, compliance rates were low and only reached 30% after intervention. Hospital mortality, ICU-acquired infection, and hospital, and ICU length of stay were similar in the two groups of patients. Importantly, exploratory analysis showed that hospital mortality was lower in the ‘after group’ for neutropenic and severely ill patients.ConclusionFor critically ill hematology patients, the use of a nutritional CDSS allowed to increase the days in compliance with caloric and protein targets as compared with no CDSS use. In this context, overall hospital mortality was not affected.

User Evaluation of a Smartphone Application for Anticoagulation Therapy.

Anticoagulation therapy with Warfarin is used for specific cardiovascular diseases to control the ability of blood clotting. Traditional ways of self-management therapy are based on paper forms and procedures. This paper presents an evaluation of the smartphone application Warfarin Guide, a computer-assisted decision-support system used to help patients in their management of anticoagulation therapy related to International Normalized Ratio (INR) values. The evaluation consisted of a usability test with 4 participants and a field test with 14 participants who used the application at home during four months. A mixed methods research approach included quantitative and qualitative analysis of the test results. The results showed that participants evaluated the Warfarin Guide as 'useful' for self-management of anticoagulation therapy, reporting key issues for further improvement.

AgroDSS: A decision support system for agriculture and farming

Decision support systems, data analysis and data mining have become significant tools for improving business in professional world. The emerging technologies are making the precision agriculture omnipresent and allow potential for enriching it with computer-assisted decision support systems for farm management. In this paper we describe a novel system AgroDSS that bridges the gap between agricultural systems and state-of-the-art decision support methodology. The described system is intended for integration into the existing farm management information systems and provides a cloud-based decision support toolbox, allowing farmers to upload their own data, utilize several data analysis methods and retrieve their outputs. The implemented tools include predictive modeling with explanation, accuracy evaluation, time series clustering and decomposition, and structural change detection. They can help users make predictions for simulated scenarios and better understand the dependencies (interactions) within their domain. We apply the AgroDSS system on a case study of pest population dynamics, illustrating the potential for its use.

Computer-Assisted Decision Support System in Pulmonary Cancer detection and stage classification on CT images

Pulmonary cancer is considered as one of the major causes of death worldwide. For the detection of lung cancer, computer-assisted diagnosis (CADx) systems have been designed. Internet-of-Things (IoT) has enabled ubiquitous internet access to biomedical datasets and techniques; in result, the progress in CADx is significant. Unlike the conventional CADx, deep learning techniques have the basic advantage of an automatic exploitation feature as they have the ability to learn mid and high level image representations. We proposed a Computer-Assisted Decision Support System in Pulmonary Cancer by using the novel deep learning based model and metastasis information obtained from MBAN (Medical Body Area Network). The proposed model, DFCNet, is based on the deep fully convolutional neural network (FCNN) which is used for classification of each detected pulmonary nodule into four lung cancer stages. The performance of proposed work is evaluated on different datasets with varying scan conditions. Comparison of proposed classifier is done with the existing CNN techniques. Overall accuracy of CNN and DFCNet was 77.6% and 84.58%, respectively. Experimental results illustrate the effectiveness of proposed method for the detection and classification of lung cancer nodules. These results demonstrate the potential for the proposed technique in helping the radiologists in improving nodule detection accuracy with efficiency.

Co-Design of a Computer-Assisted Medical Decision Support System to Manage Antibiotic Prescription in an ICU Ward.

About 37 thousand people die per year in Europe due to infections by resistant bacteria. Fighting antimicrobial resistances (AR) is a top priority to save lives and reduce costs. AR is triggered mostly by uncritical antibiotic prescription. This paper presents HAITool, a decision-making information system to support antibiotic prescription. The system was co-developed together with health professionals using Design Science Research Methodology, empowered with innovative data visualization techniques to improve AR management. HAITool includes integrated visualizations of patient, microbiology, and pharmacy data, facilitating clinical decision support, antibiotic prescriptions quality and antibiotic-resistant bacteria monitoring. It also includes an alert module that monitors conformance of antibiotic prescriptions with norms and guidelines. HAITool is evaluated using both the Österle principles and interviews with physicians and infection control team from three participant hospitals.

Long-term effect of computer-assisted decision support for antibiotic treatment in critically ill patients: a prospective ‘before/after’ cohort study

ObjectivesAntibiotic resistance has risen dramatically over the past years. For individual patients, adequate initial antibiotic therapy is essential for clinical outcome. Computer-assisted decision support systems (CDSSs) are advocated to support...

Non-negative matrix factorisation methods for the spectral decomposition of MRS data from human brain tumours

BackgroundIn-vivo single voxel proton magnetic resonance spectroscopy (SV 1H-MRS), coupled with supervised pattern recognition (PR) methods, has been widely used in clinical studies of discrimination of brain tumour types and follow-up of patients bearing abnormal brain masses. SV 1H-MRS provides useful biochemical information about the metabolic state of tumours and can be performed at short (< 45 ms) or long (> 45 ms) echo time (TE), each with particular advantages. Short-TE spectra are more adequate for detecting lipids, while the long-TE provides a much flatter signal baseline in between peaks but also negative signals for metabolites such as lactate. Both, lipids and lactate, are respectively indicative of specific metabolic processes taking place. Ideally, the information provided by both TE should be of use for clinical purposes. In this study, we characterise the performance of a range of Non-negative Matrix Factorisation (NMF) methods in two respects: first, to derive sources correlated with the mean spectra of known tissue types (tumours and normal tissue); second, taking the best performing NMF method for source separation, we compare its accuracy for class assignment when using the mixing matrix directly as a basis for classification, as against using the method for dimensionality reduction (DR). For this, we used SV 1H-MRS data with positive and negative peaks, from a widely tested SV 1H-MRS human brain tumour database.ResultsThe results reported in this paper reveal the advantage of using a recently described variant of NMF, namely Convex-NMF, as an unsupervised method of source extraction from SV1H-MRS. Most of the sources extracted in our experiments closely correspond to the mean spectra of some of the analysed tumour types. This similarity allows accurate diagnostic predictions to be made both in fully unsupervised mode and using Convex-NMF as a DR step previous to standard supervised classification. The obtained results are comparable to, or more accurate than those obtained with supervised techniques.ConclusionsThe unsupervised properties of Convex-NMF place this approach one step ahead of classical label-requiring supervised methods for the discrimination of brain tumour types, as it accounts for their increasingly recognised molecular subtype heterogeneity. The application of Convex-NMF in computer assisted decision support systems is expected to facilitate further improvements in the uptake of MRS-derived information by clinicians.

Determinants of renal potassium excretion in critically ill patients

Insulin administration lowers plasma potassium concentration by augmenting intracellular uptake of potassium. The effect of insulin administration on renal potassium excretion is unclear. Some studies suggest that insulin has an antikaliuretic effect although plasma potassium levels were poorly controlled. Since the introduction of glycemic control in the intensive care unit, insulin use has increased. We examined the relation between administered insulin and renal potassium excretion in critically ill patients under computer-assisted glucose and potassium regulation. Prospective observational study. Twelve-bed surgical intensive care unit of a university teaching hospital. Consecutive intensive care unit patients. Potassium and glucose levels were regulated by a computer-assisted decision support system. Both potassium and insulin were continuously administered by syringe pump. Renal potassium excretion was measured daily in the 24-hr urine collections. The 24-hr urinary samples of patients with kidney failure or on renal replacement therapy were excluded. Multivariate analysis with potassium excretion as the dependent variable was performed. In 178 consecutive patients, 1,456 24-hr urinary samples, were analyzed. Mean ± SD plasma potassium was 4.2 ± 0.3 mmol/L, with 79 ± 46 mmol/d of potassium administered and a mean insulin dose of 53 ± 38 U/day. Renal potassium excretion was 126 ± 51 mmol/day. After multivariate analysis correcting for relevant variables (including diuretics, pH, potassium levels and renal sodium excretion), insulin administration was independently and positively associated with renal potassium excretion. Other significant variables were potassium levels, potassium administration, renal sodium and chloride excretion, creatinine clearance, diuretic therapy, pH, known diabetes and intensive care unit admission day (R = .52; p <. 001). Insulin administration is associated with an increase in the renal potassium excretion in critically ill patients.

Abstract P263: Computer-Assisted Decision Support in the Treatment of Acute Coronary Syndromes and Impact on Cardiovascular Outcomes

Background: Evidence from clinical trials and consensus guidelines suggest that in-hospital initiation of key therapeutics can reduce mortality and morbidity in patients admitted with acute coronary syndrome (ACS). As a result, the AHA and ACC have co-developed guideline-based “performance measures” for ACS patients, such that when every measure has been performed, the patient is considered to have achieved optimal or “perfect” care (PC). Computer-assisted decision support (CADS) is a tool that can improve quality of care and is well suited for complex algorithms governing treatment decisions. We sought to determine if CADS tailored to ACS would enhance the likelihood of achieving PC, and whether achievement of PC would translate into reduced mortality. Methods: 452 consecutive patients (mean age 68±13 years) admitted with ACS in 2009 were evaluated (unstable angina 29%, NSTEMI 61%, STEMI 10%). Physicians had the option of using either pre-printed ACS orders (standard orders) versus CADS generated orders. The CADS system utilized patient clinical data including risk scoring, to suggest specific therapeutics and drug dosing based on consensus guidelines. Endpoints were attainment of PC and 30-day mortality. Results: The 77 patients admitted using CADS generated orders were statistically similar (age, gender, ACS diagnosis, TIMI risk) to the 375 patients admitted with the standard order set. Attainment of PC was almost twice as likely when using CADS versus standard orders (84% vs. 44%, p&lt;0.05). PC patients trended towards higher TIMI risk scores (3.2 ±1.7 vs 2.9 ±1.6, p = 0.09) but had half the 30-day mortality (2% vs 4%, p=0.05) compared to patients not achieving PC. Conclusions: Use of CADS in the setting of ACS is feasible and doubles the likelihood of attaining PC. Although patients achieving PC had higher baseline risk, their mortality was reduced by 50% compared to those not achieving PC. These data support the use of CADS in the setting of ACS to improve quality of care and subsequent outcomes.

Design of a Decision Support System to Help Clinicians Manage Glycemia in Patients with Type 2 Diabetes Mellitus

We sought to develop a computerized clinical decision support for clinicians treating patients with type 2 diabetes mellitus (T2DM). We designed, developed, and tested a computer-assisted decision support (CADS) system using statistical analyses of self-monitoring of blood glucose data, laboratory data, medical and medication history, and individualized hemoglobin A1c goals. A rule-based expert system generated recommendations for changes in therapy and accompanying explanations. A clinical decision support system (CADS) was developed that considers 9 classes of medications and 69 regimens with combinations of up to 4 therapeutic agents. The preferred sequences of regimens can be customized. The program is integrated with a "comprehensive diabetes management system," electronic medical record systems, and a method for uploading data from memory glucose meters via telephone without use of a computer or the Internet. The software provides a report to the clinician regarding the overall quality of glycemic control and identifies problems, e.g., hypoglycemia, hyperglycemia, glycemic variability, and insufficient data. The program can recommend continuation of current therapy, adjustment of dosages of current medications, or change of regimen and can provide explanations for its recommendations. If the user rejects the recommendations, the program will recommend alternative approaches. The CADS also provides access to Food and Drug Administration-approved prescribing information, guidelines from professional organizations, and selections from the general medical literature. The system has been extensively tested with real and synthetic data and is ready for evaluation in multicenter clinical trials. A clinical decision support system to assist with the management of patients with T2DM was designed, developed, tested, and found to perform well.

Systematic Review to Identify Validated Manual Warfarin Maintenance Dosing Tools.

AbstractAbstract 3800 Rationale:Vitamin K antagonists, of which warfarin is the most widely used in North America, are some of the most difficult medications to control and require careful monitoring to keep the international normalized ratio (INR) in the therapeutic range. Anticoagulation clinics and computer-assisted decision support systems have been associated with an improved time patients spend in the therapeutic INR range, but these systems can be expensive. Also, patient self-management delivers the best INR control, but is only useful for patients who are capable to perform self testing and management. Simple, inexpensive and easy to use dosing algorithms have the potential to overcome some of these logistical barriers and offer a tool that could be used without the infrastructure of a formal anticoagulation clinic. We performed a systematic review to identify validated manual warfarin maintenance dosing algorithms that do not require computer support. Methods:MEDLINE was searched, without language restriction, by two independent reviewers for observational and experimental reports of warfarin dosing algorithms, nomograms or formulas. Studies that reported efficacy of anticoagulation clinics, patient self management and computer assisted warfarin dosing were reviewed for references for an underlying dosing process. Inclusion criteria for studies for this review were: 1) the tool needed to provide advice on maintenance dose adjustment and next INR testing, 2) the manual dosing tool needed to be compared to a control group, 3) time in therapeutic range or patient outcomes were reported, 4) the effect of the dosing method could be separated from other interventional aspects of warfarin management. Inter-rater agreement for inclusion of candidate studies was measured with the kappa statistic and disagreement was resolved by consensus. Results:Twenty-five studies were identified and 23 either did not report a manually useful tool (computer-based algorithm or complex formula), did not have a control comparator, did not report the time in therapeutic range or patient outcomes, or the effect of the tool could not be separated from other interventional aspects of anticoagulation clinics, computer systems or self-management. Only 2 studies fulfilled all of the inclusion criteria and there was 100% agreement between the two independent reviewers for their selection. Both studies were single center studies and used practice performance before implementation of the dosing tool as the comparator. One study (n=72) showed an improvement in the proportion of INRs in the therapeutic range from 32% to 46% (p < 0.05). The other study (n=1961) showed an improvement in the time in therapeutic range in patients with a target INR range of 2–3 from 67% to 73% (p < 0.001) and in patients with a range of 2.5–3.5, form 50% to 64% (p < 0.001). Data of the two studies were not pooled due to differences in the dosing tool, quality level of care and calculation of the primary outcome, and the negligible effect of the smaller study. Conclusion:We identified only 2 manually useful VKA maintenance dosing tools that have been compared with a control group. Both studies showed an improvement in the quality of INR control with a simple dosing algorithm, but the studies were limited by their pre/post interventional design. Our results stress the need for a randomized trial to validate the usefulness of a manual dosing algorithm which could yield a simple and inexpensive evidence-based method for many physicians managing patients taking a vitamin K antagonist. Disclosures:No relevant conflicts of interest to declare.

Computer-Assisted Decision Support for Changing Practice in Severe Sepsis and Septic Shock

Computer-assisted decision support systems (CDSS) are designed to improve infection management. The aim of this prospective, clinical pre- and post-intervention study was to investigate the influence of CDSS on infection management of severe sepsis and septic shock in intensive care units (ICUs). Data were collected for a total of 180 days during two study periods in 2006 and 2007. Of the 186 patients with severe sepsis or septic shock, 62 were stratified into a low adherence to infection management standards group (LAG) and 124 were stratified into a high adherence group (HAG). ICU mortality was significantly increased in LAG versus HAG patients (Kaplan-Meier analysis). Following CDSS implementation, adherence to standards increased significantly by 35%, paralleled with improved diagnostics, more antibiotic-free days and a shortened time until antibiotics were administered. In conclusion, adherence to infection standards is beneficial for patients with severe sepsis or septic shock and CDSS is a useful tool to aid adherence.

Development and exploitation of a clinical decision support system for the management of renal anaemia

The management of renal disease and its comorbidities lends itself to the use of computer-assisted decision support systems (CDSS); however, several issues with regard to computer-based treatment algorithms remain unresolved. This review examines the development and application of a clinical decision support system for the management of renal anaemia. Studies illustrate the dependence of outcome on prespecified haemoglobin (Hb) intervention values (thresholds) and the use of a computer program containing treatment algorithms to manage Epoetin doses and iron supplements. Early experimental studies and randomized, controlled studies are discussed that examine the use of clinical measures of haemodialysis (HD) and peritoneal dialysis (PD), including Hb, serum ferritin and red cell hypochromia or transferrin saturation. Broad flexibility of erythropoietic agent, dosing, route of administration and frequency has been built into computer programs written for clinical and experimental application. While further studies with the system are anticipated, predictability and sustainability of Hb outcomes and a capacity to manage large patient groups have been demonstrated and wider application appears promising.

Information technology, the internet, and the future of neurology.

This review addresses the impact of information technology and the Internet on the current and future practice of neurology. Information technology is influencing medical practice in ways that could be both beneficial and harmful. Scenarios are presented to depict some of the ways in which the practice of neurology is being influenced by the growth of technology. First, the advantages and disadvantages of e-mail as a means of doctor-patient communication are presented. Some of the ethical and legal issues arising in this context are discussed.Second, the Internet is changing neurologists' relationships to other professionals in the health care industry. Geographical isolation is less problematic than in the past. Telemedicine, including remote consulting via the Web, has special implications for neurologists in several areas, including stroke management, movement disorders, and epilepsy.Third, the growing availability of large databases, powerful search engines, and online full-text journals is discussed. Skill in navigating and managing these resources will become increasingly important. New computer-assisted decision support systems will continue to be implemented. Applications exist or are being developed for use by clinicians for many specific neurologic disorders.Finally, some of the problematic issues concerning medical use of the Internet are discussed, including availability, portability, security, quality, and outcomes. Medical information systems, with their attendant advantages and limitations, will become increasingly significant in the practice of neurology. Despite overall improvement in access to information, major barriers still exist to the proper implementation and utilization of truly integrated information systems.