Computer-based Decision Support Tools Research Articles

Design and initial user experience of a computer-based decision-support tool to improve safety of chemotherapy delivery

Design and initial user experience of a computer-based decision-support tool to improve safety of chemotherapy delivery

Assessing the impact of COVID-19 measures on COPD management and patients: a simulation-based decision support tool for COPD services in the UK

ObjectivesTo develop a computer-based decision support tool (DST) for key decision makers to safely explore the impact on chronic obstructive pulmonary disease (COPD) care of service changes driven by restrictions...

Reliability and Validity of a New Computer-Based Triage Decision Support Tool: ANKUTRIAGE.

Triage is a tool used to determine patients' severity of illness or injury within minutes of arrival. This study aims to assess the reliability and validity of a new computer-based triage decision support tool, ANKUTRIAGE, prospectively. ANKUTRIAGE, a 5-level triage tool was established considering 2 major factors, patient's vital signs and characteristics of the admission complaint. Adult patients admitted to the ED between July and October, 2019 were consecutively and independently double triaged by 2 assessors using ANKUTRIAGE system. To measure inter-rater reliability, quadratic-weighted kappa coefficients (Kw) were calculated. For the validity, associations among urgency levels, resource use, and clinical outcomes were evaluated. The inter-rater reliability between users of ANKUTRIAGE was excellent with an agreement coefficient (Kw) greater than 0.8 in all compared groups. In the validity phase, hospitalization rate, intensive care unit admission and mortality rate decreased from level 1 to 5. Likewise, according to the urgency levels, resource use decreased significantly as the triage level decreased (P < 0.05). ANKUTRIAGE proved to be a valid and reliable tool in the emergency department. The results showed that displaying the key discriminator for each complaint to assist decision leads to a high inter-rater agreement with good correlation between urgency levels and clinical outcomes, as well as between urgency levels and resource consumptions.

Survey of barriers to adherence to international inflammatory bowel disease guidelines: does gastroenterologists' confidence translate to high adherence?

Despite the availability of evidence-based inflammatory bowel disease (IBD) guidelines, suboptimal care persists. There is little published research assessing barriers to IBD guideline adherence. To identify barriers to IBD guideline adherence including gastroenterologists' knowledge and attitudes towards guidelines. An online cross-sectional survey of 824 Australian gastroenterologists was conducted from April to August 2018, with 198 (24%) responses. A novel survey was developed that was informed by the theoretical domain's framework. Confidence in guideline recommendations was high; however, referral to them was low. The European Crohn's and Colitis Organisation guidelines were referred to most commonly (43.6%). In multivariate analysis, significant predictors of frequent versus infrequent guideline referral were: high confidence in the guideline (odds ratio (OR) 7.70; 95% confidence interval (CI): 2.43-24.39; P = 0.001), and low (≤10 years) clinical experience (OR 3.62; 95% CI: 1.11-11.79; P = 0.03). The most common barriers to guideline adherence were not having time (62%), followed by guideline specifics being difficult to remember (61%). Low confidence was reported in managing pregnancy and IBD (34%) and loss of response to therapy (29%). High confidence was reported in managing immunomodulators; however, only 43% answered the associated knowledge question correctly. Although gastroenterologists have high confidence in guidelines, they use them infrequently, primarily due to specifics being difficult to remember and lack of time. Self-reported confidence in an area of IBD management does not always reflect knowledge. An intervention targeting these barriers, for example, computer-based clinical decision support tools, might improve adherence and standardise care.

Optimization of Integrated Solid Waste Management Improvement Scenarios for Salt City in Jordan Using Computer-Based Decision Support Tool

Optimization of Integrated Solid Waste Management Improvement Scenarios for Salt City in Jordan Using Computer-Based Decision Support Tool

Review of Medical Decision Support and Machine-Learning Methods.

Machine-learning methods can assist with the medical decision-making processes at the both the clinical and diagnostic levels. In this article, we first review historical milestones and specific applications of computer-based medical decision support tools in both veterinary and human medicine. Next, we take a mechanistic look at 3 archetypal learning algorithms-naive Bayes, decision trees, and neural network-commonly used to power these medical decision support tools. Last, we focus our discussion on the data sets used to train these algorithms and examine methods for validation, data representation, transformation, and feature selection. From this review, the reader should gain some appreciation for how these decision support tools have and can be used in medicine along with insight on their inner workings.

EICU STUDY

The authors' objective is to study the effect of eICU (electronic ICU) as a supplementary remote intensive care program on improvement in clinical outcomes. The experiment is designed to determine the clinical usefulness before and after evaluation of the effects of eICU program. The setting is two adult ICU of a large 375 bed teaching community hospital in US. The study was undertaken by group of resident physicians after eICU was started to study the impact on few selected parameters in a teaching hospital. A total of 2537Patients admitted to ICU between June 2006 to June 2008 (n= 1310 before and n= 1227 after implementation of the eICU) The eICU Program used intensivists and other healthcare providers to give 24x7 supplemental monitoring and management from a remote location. Supporting software and computer based decision support tool were available. The outcome is to study and compare rate of falls, mortalities, incidence of code blues and length of stay between the two periods before and after the implementation of eICU. In the results no statistical difference was observed in the studied parameters thus showing contrary results to other previous studies. The incidence of code blue (39 vs. 54 with P value of 0.36), length of stay (3.0 vs. 3.1 P value 0.36), mortality rates (77 vs. 90 P value 0.28) and incidence of fall (0 vs. 1 P value 0.28) all show no improve outcomes before and after the implementation of eICU.

The usefulness of Decision Support Systems in participatory forest planning: a comparison between Finland and Italy

Aim of study: Participation of stakeholders is considered an essential element in producing, at different spatial and temporal scales, forest plans accepted by local community and fulfilling the requirements of Sustainable Forest Management. Increasingly, computer-based decision support systems (DSS) and tools are being introduced to assist stakeholders and decision-makers in coping with the complexities inherent in participatory forest planning. The study aimed to investigate how useful the users and researchers see DSS tools and which opportunities they perceive DSS might carry for enhancing participatory forest planning in their field of activity.Area of study: 15 Italian and Finnish researchers and practitioners were interviewed.Material and Methods: Face-to-face structured interviews were used to collect opinions and experiences. Quantitative and qualitative information were analyzed to investigate differences between Italian and Finnish respondents as well as between researchers and practitionersMain results: Results showed that in Italy there has been more focus on forest-level and medium-term problems and the intelligence phase, while in Finland there has been more attention to region-level and long-term problems and equally intelligence, design, and choice phases of decision-making. Deviations probably reflect different planning contexts and culture, variability in experiences and expertise in DSS and in availability of suitable DSS.Research highlights: The study suggests to pay attention to evaluating the success criteria of participatory planning when preparing for the use of DSS and related tools in practical forest planning processes. Experience sharing is a key to reaching more successful use of DSS.Keywords: computer-based decision support; participatory processes; spatial scale; success criteria; temporal scale; users' perception.

Expert system for predicting unstable angina based on Bayesian networks

The use of computer-based clinical decision support (CDS) tools is growing significantly in recent years. These tools help reduce waiting lists, minimise patient risks and, at the same time, optimise the cost health resources. In this paper, we present a CDS application that predicts the probability of having unstable angina based on clinical data. Due to the characteristics of the variables (mostly binary) a Bayesian network model was chosen to support the system. Bayesian-network model was constructed using a population of 1164 patients, and subsequently was validated with a population of 103 patients. The validation results, with a negative predictive value (NPV) of 91%, demonstrate its applicability to help clinicians. The final model was implemented as a web application that is currently been validated by clinician specialists.

Development of a Computer-Based Clinical Decision Support Tool for Selecting Appropriate Rehabilitation Interventions for Injured Workers

To develop a classification algorithm and accompanying computer-based clinical decision support tool to help categorize injured workers toward optimal rehabilitation interventions based on unique worker characteristics. Population-based historical cohort design. Data were extracted from a Canadian provincial workers' compensation database on all claimants undergoing work assessment between December 2009 and January 2011. Data were available on: (1) numerous personal, clinical, occupational, and social variables; (2) type of rehabilitation undertaken; and (3) outcomes following rehabilitation (receiving time loss benefits or undergoing repeat programs). Machine learning, concerned with the design of algorithms to discriminate between classes based on empirical data, was the foundation of our approach to build a classification system with multiple independent and dependent variables. The population included 8,611 unique claimants. Subjects were predominantly employed (85 %) males (64 %) with diagnoses of sprain/strain (44 %). Baseline clinician classification accuracy was high (ROC = 0.86) for selecting programs that lead to successful return-to-work. Classification performance for machine learning techniques outperformed the clinician baseline classification (ROC = 0.94). The final classifiers were multifactorial and included the variables: injury duration, occupation, job attachment status, work status, modified work availability, pain intensity rating, self-rated occupational disability, and 9 items from the SF-36 Health Survey. The use of machine learning classification techniques appears to have resulted in classification performance better than clinician decision-making. The final algorithm has been integrated into a computer-based clinical decision support tool that requires additional validation in a clinical sample.

Implementation of a Computer-Based Decision Support System for Outcomes Prediction and Clinical Triage: Initial Results of Two Pilot Studies

Objective: To develop a custom-built, computer-based clinical decision-support tool to help determine the underlying cause of the patient’s clinical presentation, its severity level and predicted outcome. Materials and Methods: A novel, customized, computer-based decision-support tool, rooted in the Bayesian Network technology was constructed. Based on documented data from patients’ medical records, the probabilistic predictive model was utilized to validate patients’ triage in two different clinical scenarios: 1. Prediction of neurologic deficits in patients resuscitated from cardiac arrest following an Acute Coronary Syndrome/ST-Elevation Myocardial Infarction (ACS/STEMI), and thus likelihood of benefit from (or futility of) emergent coronary revascularization. Study group consisted of 99 patients resuscitated from NSTEMI. For this particular study, the endpoints were in-hospital death, neurologic recovery and the accurate prediction of outcomes by the program. 2. Prediction of the likelihood of presence of and readmission for Acute Decompensated Heart Failure syndrome. Study groups consisted of 20 randomly generated patient clinical profiles (Phase I) and 100 cases from our emergency room electronic medical records: 55 cases of acute heart failure, 14 cases of pneumonia, 7 cases of COPD exacerbation and 11 cases of “other” conditions. End points were the accurate prediction of the underlying diagnosis and the correct disposition. In both studies, observed data from our institutional electronic medical records registry was utilized to populate the computer program and to validate its initial results. Subsequently, multiple, random clinical case scenarios were used to validate the predictive properties of the computer tool (Phase I) Finally (Phase II), the program was tested using real-world data from our institutional emergency room electronic medical records. Results: First study: Overall, 64 (65%) patients survived, while 35 (35%) died. On initial examination, 25% of patients were alert, 14% were minimally responsive and 60% were unresponsive. First Group (5 Hypothetical Case scenarios): Diagnostic accuracy rate 4/5 (80%) Study Cohort (99 cases) 95/99% (95%); Overall Accuracy: 87% Second study: Phase I: Diagnostic accuracy rate of 85%; Disposition accuracy rate of 80%; Overall Accuracy rate 82% Phase II: Diagnostic accuracy rate of 98%; Disposition accuracy rate of 91%; Overall accuracy rate of 94% Conclusions: A custom-built, computer based predictive model, utilizing a probability engine software and based on population-wide real-life clinical data, can be a useful adjunct in clinical decision making and medical triage. This takes special significance in such high-risk, fast-paced environments such as the emergency department and the intensive care unit. Keywords: Predictive modeling, Bioinformatics, Clinical Risk Assessment, Clinical Decision-making. Read more →

Planning Tool to Support Planning the Future of Coastal Louisiana

Groves, D.G. and Sharon, C., 2013. Planning tool to support planning the future of coastal Louisiana.Coastal Louisiana's built and natural environment faces risks from catastrophic tropical storms. Concurrently, the region is experiencing a dramatic conversion of coastal land and associated habitats to open water and a loss of important services provided by such ecosystems. Louisiana's Coastal Protection and Restoration Authority (CPRA) engaged in a detailed modeling, simulation, and analysis exercise, the results of which informed Louisiana's 2012 Comprehensive Master Plan for a Sustainable Coast. The Master Plan defines a set of coastal risk-reduction and restoration projects to be implemented in the coming decades to reduce hurricane flood risk to coastal communities and restore the Louisiana coast. Risk-reduction and restoration projects were selected to provide the greatest level of risk-reduction and land-building benefits under a given budget constraint while being consistent with other objectives and principles of the Master Plan. A RAND project team, with the guidance of CPRA and other members of the Master Plan Delivery Team, developed a computer-based decision-support tool, called the CPRA Planning Tool. The Planning Tool provided technical analysis that supported the development of the Master Plan through CPRA and community-based deliberations. This article provides a summary of the Planning Tool and its application in supporting the development of Louisiana's Master Plan.

Impact of a Computer-Based Diagnostic Decision Support Tool on the Differential Diagnoses of Medicine Residents

Computer-based medical diagnostic decision support systems have been used for decades, initially as stand-alone applications. More recent versions have been tested for their effectiveness in enhancing the diagnostic ability of clinicians. To determine if viewing a rank-ordered list of diagnostic possibilities from a medical diagnostic decision support system improves residents' differential diagnoses or management plans. Twenty first-year internal medicine residents at Massachusetts General Hospital viewed 3 deidentified case descriptions of real patients. All residents completed a web-based questionnaire, entering the differential diagnosis and management plan before and after seeing the diagnostic decision support system's suggested list of diseases. In all 3 exercises, the actual case diagnosis was first on the system's list. Each resident served as his or her own control (pretest/posttest). For all 3 cases, a substantial percentage of residents changed their primary considered diagnosis after reviewing the system's suggested diagnoses, and a number of residents who had not initially listed a "further action" (laboratory test, imaging study, or referral) added or changed their management options after using the system. Many residents (20% to 65% depending on the case) improved their differential diagnosis from before to after viewing the system's suggestions. The average time to complete all 3 cases was 15.4minutes. Most residents thought that viewing the medical diagnostic decision support system's list of suggestions was helpful. Viewing a rank-ordered list of diagnostic possibilities from a diagnostic decision support tool had a significant beneficial effect on the quality of first-year medicine residents' differential diagnoses and management plans.

Straight vegetable oil from Jatropha curcas L. for rural electrification in Mali – A techno-economic assessment

JEALE (Jatropha Economic Assessment for Local Electrification) is a computer-based decision support tool designed to estimate the economic viability of Jatropha oil production and use for rural electrification. In many developing countries, SVO (Straight Vegetable Oil) from Jatropha curcas L. may be a serious local alternative to diesel-fuelled electricity generation, offering environmental benefits and acting as a potential driver for economic development. JEALE provides a general framework for techno-economic analysis of the Jatropha local value chain. Such an undertaking is challenging due to the high number of parameters, from seed propagation to grid-supplied electrical generation, which must be factored into such an assessment, and the very different forms and scales that SVO production can take in rural areas. The evaluation of a Malian case study shows that electrification projects are balanced on the threshold of economic competitiveness, regardless of the demand scenario, and have a high degree of sensitivity to parameters such as seed yields, petroleum prices, characteristics of geographic locations and labour costs. The highest financial risk is carried by the farming sector. Many of these parameters lie outside the control of investors; however, in the current context of the rarefaction of fossil resources combined with a strong global focus on sustainable energy solutions, the results reveal the usability of a cold-pressed and refined Jatropha SVO in adapted diesel engines.

Pilot testing of a decision support tool for patients with abdominal aortic aneurysms

Patients with abdominal aortic aneurysms (AAAs) who are surgical candidates have as many as three options: open surgery, endovascular surgery, or no surgery. As with all treatment decisions, informed patient preferences are critical. Decision support tools have the potential to better inform patients about the risks and benefits associated with each treatment option and to empower patients to participate meaningfully in the decision-making process. The objective of this study was to develop and pilot test a decision support tool for patients with AAAs. We developed a personalized, interactive, computer-based decision support tool reflecting the most current outcomes data and input from surgeons and patients. We piloted the tool with AAA repair candidates who used the tool prior to meeting with their surgeon. Patients were recruited from a university-based vascular surgery clinic and affiliated VA hospital clinic. To determine feasibility and acceptability, the following outcomes were measured: (1) percent of patients who agreed to participate, (2) length of time required to use the tool, (3) the amount of assistance required to use the tool, and (4) patients' opinions on the acceptability of the tool. To assess effectiveness of the tool, we measured change in knowledge and decisional conflict pre- and post-tool using the paired t-test. One hundred percent of patients who were approached (n = 12) agreed to participate in the study. The tool was administered in a median time of 35 minutes (range, 25-45 minutes), and all patients were able to navigate the program with minor technical assistance. Mean knowledge scores increased from 56% to 90% (P = .005), and decisional conflict scores decreased from 29% to 8% (P = .04). Overall, patients reported that the program content was balanced across treatment options, presented information clearly and concisely, helped them to organize their thoughts about the decision, and prepared them to talk to their surgeon about what mattered most to them. Preliminary evidence suggests that use of an evidence-based AAA decision support tool is feasible and acceptable to patients, increases knowledge, and decreases decisional conflict. Widespread use of such a tool might improve the content and quality of informed consent for this difficult treatment decision.

Exploring New Opportunities for Evidence-Based Decision Making

In this issue of the American Journal of Preventive Medicine, Yancey et al.1 describe the creation of a graphical, computer-based decision-support tool for state and local public health decision makers to evaluate policies supporting increased population rates of physical activity. Currently, decision makers have to take action with insufficient evidence to determine salient policy strategies to promote physical activity, or to reduce complementary sedentary behaviors, in their states or communities.

Balancing nutrient supply and nutrient requirements in grazing sheep

Attempting to achieve a balance between the supply of and requirements for the major nutrients implies that demands for major nutrients can be adequately quantified, that the nutritive value of the consumed diet can be described in similar terms and that the diet actually consumed by the grazing sheep can be estimated, or at least predicted, from pasture and animal conditions. Several systems exist for defining the nutrient requirements of sheep and, whilst there are real differences between them, all are sufficiently accurate to allow their use in ration formulation and in grazing animal management. Similarly, nutritive value of feed components can be defined accurately. In grazing sheep, the major difficulty in balancing the supply of consumed nutrients against nutrient requirements is the estimation of diet composition and intake. Computer-based decision support tools are a useful aid to overcoming this problem. It is suggested that since profit derives from animal production per hectare and not per animal, the balancing of nutrient supply and requirements should not be attempted on an individual-animal basis.

Intensive Care Nurses’ Interest in Clinical Personal Digital Assistants

Intensive Care Nurses’ Interest in Clinical Personal Digital Assistants

Multiobjective Groundwater Management Using Evolutionary Algorithms

Sustainable management of groundwater resources is of crucial importance for regions where freshwater supply is naturally limited. Long-term planning of groundwater usage requires computer-based decision support tools: on the one hand, they must be able to predict the complex system dynamics with sufficient accuracy, on the other, they must allow exploring management scenarios with respect to different criteria such as sustainability, cost, etc. In this paper, we present a multiobjective evolutionary algorithm for groundwater management that optimizes the placement and the operation of pumping facilities over time, while considering multiple neighboring regions which are economically independent. The algorithm helps in investigating the cost tradeoffs between the different regions by providing an approximation of the Pareto-optimal set, and its capabilities are demonstrated on a three-region problem. The application of the proposed methodology can also serve as a benchmark problem as shown in this paper. The corresponding implementation is freely available as a precompiled module at http://www.tik.ee.ethz.ch/pisa.

Multicenter Validation of a Computer-Based Clinical Decision Support Tool for Glucose Control in Adult and Pediatric Intensive Care Units

Hyperglycemia during critical illness is common, and intravenous insulin therapy (IIT) to normalize blood glucose improves outcomes in selected populations. Methods differ widely in complexity, insulin dosing approaches, efficacy, and rates of hypoglycemia. We developed a simple bedside-computerized decision support protocol (eProtocol-insulin) that yields promising results in the development center. We examined the effectiveness and safety of this tool in six adult and five pediatric intensive care units (ICUs) in other centers. We required attending physicians of eligible patients to independently intend to use intravenous insulin to normalize blood glucose. We used eProtocol-insulin for glucose control for a duration determined by the clinical caregivers. Adults had an anticipated length of stay of 3 or more days. In pediatric ICUs, we also required support or intended support with mechanical ventilation for greater than 24 hours or with a vasoactive infusion. We recorded all instances in which eProtocol-insulin instructions were not accepted and all blood glucose values. An independent data safety and monitoring board monitored study results and subject safety. Bedside nurses were selected randomly to complete a paper survey describing their perceptions of quality of care and workload related to eProtocol-insulin use. Clinicians accepted 93% of eProtocol-insulin instructions (11,773/12,645) in 100 adult and 48 pediatric subjects. Forty-eight percent of glucose values were in the target range. Both of these results met a priori-defined efficacy thresholds. Only 0.18% of glucose values were < or =40 mg/dl. This is lower than values reported in prior IIT studies. Although nurses reported eProtocol-insulin required as much work as managing a mechanical ventilator, most nurses felt eProtocol-insulin had a low impact on their ability to complete non-IIT nursing activities. A multicenter validation demonstrated that eProtocol-insulin is a valid, exportable tool that can assist clinicians in achieving control of glucose in critically ill adults and children.