Computerized Decision Support Tools Research Articles

Assessment of Drug-Induced QTc Prolongation in Mental Health Practice: Validation of an Evidence-Based Algorithm.

Drug-induced QTc interval prolongation (QTcIP) can lead to serious consequences and is often a concern for mental health practitioners, as access to experts such as cardiologists, for consultation is time-limiting and can delay treatment decisions. This research aimed at validating the content of an algorithm for the assessment, management and monitoring of drug-induced QTcIP in mental health practice. Following an initial face validity by content experts, a cross-sectional survey of mental health care practitioners with a 4-point Likert-type scale was used to assess the validity of the decision steps on the QTcIP algorithm (QTcIPA) by estimating the content validity index (CVI) and the modified kappa statistic (κ*). Participants' open-ended comments were also thematically analyzed. Mental health practitioners found the QTcIPA to be appropriate, safe, and evidence-based, as indicated by the high individual item CVI scores ranging from 0.89 to 1 for all of the steps/decision statements in the three domains assessed: appropriateness, safety and reliability of the references used. Five themes emerged from the qualitative analysis of the open-ended comments, of which three were identified as strengths, including practical usability, reliable references and beneficial for pharmacists. Two themes were recognized as limitations, namely, the need for additional clinical content and application barriers. These results suggest that the QTcIPA may be a useful tool for mental health clinicians at the time of prescribing medications with potential risk of QTcIP. Future research will explore the implementation of the QTcIPA into clinical practice using computerized decision support tools through web-based and mobile applications.

Nurses' sources of information to inform clinical practice: An integrative review to guide evidence-based practice.

Evidence-based practice in nursing is challenging and relies on the sources of information used by nurses to inform clinical practice. An integrative review from 2008 revealed that nurses more frequently relied on information from colleagues than information from high-level sources such as systematic reviews and evidence-based clinical practice guidelines. To describe the information sources used by registered nurses to inform their clinical practice. An integrative review was conducted according to the PRISMA guidelines, based on empirical research studies published from January 2007 until June 2021. The included studies were appraised, following which the identified sources of information from quantitative studies were compiled and ranked. Finally, the qualitative text data were summarized into categories. Fifty-two studies from various countries were included. The majority of studies employed a quantitative design and used original instruments. Peers were ranked as the number one source of information to inform nurses' clinical practice. However, computers and reference materials are now ranked among the top four most used information sources. Improvement in computer and information searching skills, as well as the availability of computerized decision support tools, may contribute to nurses' frequent use of digital sources and reference material to inform clinical practice. This review shows that nurses' most frequently reported peer nurses as their source of information in clinical practice. Information sources such as computers and reference materials were ranked higher, and information from patients was ranked lower than in the 2008 review. Developing and standardizing instruments and ensuring high-quality study design is critical for further research on nurses' sources of information for clinical practice.

SACI Protocol: Preliminary Guidelines for the Development of Digital Health Technologies for Clinical Trials in Brazil

In the last decade, evidence-based medical practice has been supported on a large scale by computerized decision support tools, aiming to reduce diagnostic and therapeutic uncertainty, complementing the actions of the health professional. With technological developments, it is now possible to consider these systems as part of clinical intervention, both for the diagnosis and treatment of diseases. The literature has described the implementation of e-health tools, that is, technological innovations in the health area such as software, applications, serious games, among others, as a strategy to improve the process and adherence to treatment. However, there is still no standardized instrument in Brazil that can be used to guide the development, from the research phase, and the implementation of these tools as a health intervention, also impacting patient outcomes. With the objective of investigating a new therapeutic and preventive form, based on intervention with a computerized system, this work proposes the creation of guidelines for the registration and implementation of e-health tools as a clinical intervention. The proposal aims to be able to assist in the reporting standardization from the development stage to the application of the e-health tool helping in the treatment of diseases, registering all the experience lived in the research and applying it in different contexts of health.

Computerized decision support and machine learning applications for the prevention and treatment of childhood obesity: A systematic review of the literature.

Digital health interventions based on tools for Computerized Decision Support (CDS) and Machine Learning (ML), which take advantage of new information, sensing and communication technologies, can play a key role in childhood obesity prevention and treatment. We present a systematic literature review of CDS and ML applications for the prevention and treatment of childhood obesity. The main characteristics and outcomes of studies using CDS and ML are demonstrated, to advance our understanding towards the development of smart and effective interventions for childhood obesity care. A search in the bibliographic databases of PubMed and Scopus was performed to identify childhood obesity studies incorporating either CDS interventions, or advanced data analytics through ML algorithms. Ongoing, case, and qualitative studies, along with those not providing specific quantitative outcomes were excluded. The studies incorporating CDS were synthesized according to the intervention's main technology (e.g., mobile app), design type (e.g., randomized controlled trial), number of enrolled participants, target age of children, participants' follow-up duration, primary outcome (e.g., Body Mass Index (BMI)), and main CDS feature(s) and their outcomes (e.g., alerts for caregivers when BMI is high). The studies incorporating ML were synthesized according to the number of subjects included and their age, the ML algorithm(s) used (e.g., logistic regression), as well as their main outcome (e.g., prediction of obesity). The literature search identified 8 studies incorporating CDS interventions and 9 studies utilizing ML algorithms, which met our eligibility criteria. All studies reported statistically significant interventional or ML model outcomes (e.g., in terms of accuracy). More than half of the interventional studies (n = 5, 63 %) were designed as randomized controlled trials. Half of the interventional studies (n = 4, 50 %) utilized Electronic Health Records (EHRs) and alerts for BMI as means of CDS. From the 9 studies using ML, the highest percentage targeted at the prognosis of obesity (n = 4, 44 %). In the studies incorporating more than one ML algorithms and reporting accuracy, it was shown that decision trees and artificial neural networks can accurately predict childhood obesity. This review has found that CDS tools can be useful for the self-management or remote medical management of childhood obesity, whereas ML algorithms such as decision trees and artificial neural networks can be helpful for prediction purposes. Further rigorous studies in the area of CDS and ML for childhood obesity care are needed, considering the low number of studies identified in this review, their methodological limitations, and the scarcity of interventional studies incorporating ML algorithms in CDS tools.

Reducing Potentially Inappropriate Prescriptions for Older Patients Using Computerized Decision Support Tools: Systematic Review.

BackgroundOlder adults are more vulnerable to polypharmacy and prescriptions of potentially inappropriate medications. There are several ways to address polypharmacy to prevent its occurrence. We focused on computerized decision support tools.ObjectiveThe available literature was reviewed to understand whether computerized decision support tools reduce potentially inappropriate prescriptions or potentially inappropriate medications in older adult patients and affect health outcomes.MethodsOur systematic review was conducted by searching the literature in the MEDLINE, CENTRAL, EMBASE, and Web of Science databases for interventional studies published through February 2018 to assess the impact of computerized decision support tools on potentially inappropriate medications and potentially inappropriate prescriptions in people aged 65 years and older.ResultsA total of 3756 articles were identified, and 16 were included. More than half (n=10) of the studies were randomized controlled trials, one was a crossover study, and five were pre-post intervention studies. A total of 266,562 participants were included; of those, 233,144 participants were included and assessed in randomized controlled trials. Intervention designs had several different features. Computerized decision support tools consistently reduced the number of potentially inappropriate prescriptions started and mean number of potentially inappropriate prescriptions per patient. Computerized decision support tools also increased potentially inappropriate prescriptions discontinuation and drug appropriateness. However, in several studies, statistical significance was not achieved. A meta-analysis was not possible due to the significant heterogeneity among the systems used and the definitions of outcomes.ConclusionsComputerized decision support tools may reduce potentially inappropriate prescriptions and potentially inappropriate medications. More randomized controlled trials assessing the impact of computerized decision support tools that could be used both in primary and secondary health care are needed to evaluate the use of medication targets defined by the Beers or STOPP (Screening Tool of Older People’s Prescriptions) criteria, adverse drug reactions, quality of life measurements, patient satisfaction, and professional satisfaction with a reasonable follow-up, which could clarify the clinical usefulness of these tools.Trial RegistrationInternational Prospective Register of Systematic Reviews (PROSPERO) CRD42017067021; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42017067021

Implementation of the Recommendations for RBC Transfusions for Critically Ill Children From the Pediatric Critical Care Transfusion and Anemia Expertise Initiative.

To provide context for the implementation of the Pediatric Critical Care Transfusion and Anemia Expertise Initiative recommendations for RBC transfusions including a review of prior research related to implementation of transfusion guidelines, efforts to facilitate implementation through Transfusion and Anemia Expertise Initiative, and to provide a framework for recommendation implementation. Review of existing clinical literature and description of a comprehensive approach to implementation based on Implementation Science principles. The Transfusion and Anemia Expertise Initiative recommendations on RBC transfusions are based on clinical evidence and aim to limit unnecessary and potentially harmful transfusions. Prior efforts to use transfusion guidelines include use of provider education, local guidelines, visual aids, prospective and retrospective audit and feedback as well as computerized decision support tools; however, no single approach has been identified as optimal for implementation in pediatric critical care settings. Evidence around provider beliefs and transfusion decision-making point to the need for additional provider education, emphasizing the importance of limiting transfusions, and the development of recommendations, such as the Transfusion and Anemia Expertise Initiative guidelines, that can be applied to specific clinical conditions. The Transfusion and Anemia Expertise Initiative guidelines will be broadly disseminated; however, coordinated implementation efforts will be required to impact practice. An approach that encourages involvement of a wide range of multiprofessional stakeholders, formal agreement on the implemented guidelines, selection of strategies that are practical and feasible, and active monitoring of clinical practice and outcomes throughout implementation is recommended. A formal second stage Transfusion and Anemia Expertise Initiative - Continuous Assessment of Blood-use is proposed to enhance implementation of the recommendations, follow uptake and impact on practice and patient outcomes, and ensure integration of new clinical evidence into the existing guideline as it is developed.

A Bedside Computerized Decision-Support Tool for Intravenous Insulin Infusion Management in Critically Ill Patients

Intravenous (IV) insulin infusions using a validated protocol are the recommended method for blood glucose control in critically ill patients. Computerized decision-support tools improve quality over manual paper-based protocols. However, nonproprietary computerized tools targeting the recommended blood glucose range of 140-180 mg/dL are not readily available. A bedside computerized decision-support tool was developed at a US Department of Veterans Affairs health system to assist the nursing staff with the management of patients requiring IV insulin infusion. Initial evaluation showed that the tool was useful in the safe and effective management of an IV insulin infusion protocol for blood glucose control targeting the updated blood glucose range.

Electronic health record with computerized decision support tools for the purposes of a pediatric cardiovascular heart disease screening program in Crete

Background and ObjectiveEarly detection of cardiovascular (CV) disease or associated risk factors during childhood is of paramount importance, allowing for early treatment or lifestyle modifications, respectively.The objective of this study was to describe the development of an electronic health record (EHR), with integrated computerized decision support system (CDSS), specifically designed for supporting the needs of a pilot pediatric CV disease screening program applied on primary school students of a Mediterranean island. MethodsEvidence-based knowledge, national and international practice guidelines regarding sport preparticipation CV screening of children and young athletes has been used for the design of the designated EHR. A CDSS, capable for providing alerts for further cardiology evaluation need, has been incorporated into the EHR, based on normative anthropometric and electrocardiographic data as well as predefined positive history responses. ResultsWe developed a designated EHR with integrated CDSS supporting pediatric CV disease screening, capable for documenting CV-related personal and family history responses, physical evaluation data (weight, height, blood pressure), allowing for entering electrocardiogam (ECG) measurements and for uploading of multimedia files (including ECG images and digital phonocardiogram audio files). The EHR incorporates clinical calculators and referral alerts for the presence (and degree) of adiposity, hypertension, ECG abnormalities and positive history responses indicative of high CV disease risk. In a preliminary EHR validation, performed by entering data from 53 previously available paper-based health records, the EHR was proven to be fully functional. ConclusionsThe pediatric cardiology EHR with CDSS features which we developed might serve as a model for EHR for primary health care purposes, capable to document and early detect CV disease and associated risk factors in pediatric populations.

Towards a standardized protocol for conducting randomized clinical trial for software

In the last decade, evidence-based medical practice has been aided on a large scale by computerized decision support tools, aimed at reducing diagnostic and therapeutic uncertainty, complementing the actions of the health professional. With the technological evolution, it is already possible to consider these systems as part of the clinical intervention, both for the diagnostic stage and for the treatment of diseases. This article proposes the creation of a specific clinical trial protocol to evaluate the effects of the software as clinical intervention in different medical settings. The proposal is unprecedented and extremely relevant in the scientific literature and able to standardize from the development stage to the application of the software aiding in the treatment of diseases.

R2-IBMOLS applied to a practical case of the multiobjective knapsack problem

The social and medico-social sector is experiencing a fast evolution due to the continuing growth of older population. Yet, social and medico-social structures suffer from a real lack of computerized decision support tools. This work deals with the key issue of elaborating efficient action plans in these structures, which aims to improve the whole quality of these structures. An efficient action plan is a set of actions chosen among many candidate actions which optimize several conflicting objectives and satisfy some imperative constraints. To assist managers to optimize their action plans, we develop a multiobjective decision support system as part of a commercial software. According to the objectives and constraints defined by the decision maker and a set of feasible actions, the software is used to select the actions that optimize the given objectives while satisfying the constraints. After providing a description and a formal model of the action plan optimization problem, we present a solution method using the iterated local search based on quality indicators (IBMOLS). We assess the proposed approach on problem instances with 2–8 objectives and up to 500 candidate actions and demonstrate its usefulness as a key component of a decision support system for social and medico-social structures.

TH-A-207B-02: QIBA Ultrasound Elasticity Imaging System Biomarker Qualification and User Testing of Systems

Imaging of tissue elastic properties is a relatively new and powerful approach to one of the oldest and most important diagnostic tools. Imaging of shear wave speed with ultrasound is has been added to most high-end ultrasound systems. Understanding this exciting imaging mode aiding its most effective use in medicine can be a rewarding effort for medical physicists and other medical imaging and treatment professionals. Assuring consistent, quantitative measurements across the many ultrasound systems in a typical imaging department will constitute a major step toward realizing the great potential of this technique and other quantitative imaging. This session will target these two goals with two presentations. A. Basics and Current Implementations of Ultrasound Imaging of Shear Wave Speed and Elasticity - Shigao Chen, Ph.D. Learning objectives-To understand: 1. Introduction: • Importance of tissue elasticity measurement • Strain vs. shear wave elastography (SWE), beneficial features of SWE • The link between shear wave speed and material properties, influence of viscosity 2. Generation of shear waves • External vibration (Fibroscan) • ultrasound radiation force • Point push • Supersonic push (Aixplorer) • Comb push (GE Logiq E9) 3. Detection of shear waves • Motion detection from pulse-echo ultrasound • Importance of frame rate for shear wave imaging • Plane wave imaging detection • How to achieve high effective frame rate using line-by-line scanners 4. Shear wave speed calculation • Time to peak • Random sample consensus (RANSAC) • Cross correlation 5. Sources of bias and variation in SWE • Tissue viscosity • Transducer compression or internal pressure of organ • Reflection of shear waves at boundaries B. Elasticity Imaging System Biomarker Qualification and User Testing of Systems – Brian Garra, M.D. Learning objectives-To understand: 1. Goals • Review the need for quantitative medical imaging • Provide examples of quantitative imaging biomarkers • Acquaint the participant with the purpose of the RSNA Quantitative Imaging Biomarker Alliance and the need for such an organization • Review the QIBA process for creating a quantitative biomarker • Summarize steps needed to verify adherence of site, operators, and imaging systems to a QIBA profile 2. Underlying Premise and Assumptions • Objective, quantifiable results are needed to enhance the value of diagnostic imaging in clinical practice • Reasons for quantification i. Evidence based medicine requires objective, not subjective observer data ii. Computerized decision support tools (eg CAD) generally require quantitative input. iii. Quantitative, reproducible measures are more easily used to develop personalized molecular medical diagnostic and treatment systems 3. What is quantitative imaging? • Definition from Imaging Metrology Workshop 4. The Quantitative Imaging Biomarker Alliance • Formation 2008 • Mission • Structure • Example Imaging Biomarkers Being Explored • Biomarker Selection • Groundwork • Draft Protocol for imaging and data evaluation • QIBA Profile Drafting • Equipment and Site Validation i. Technical ii. Clinical • Site and Equipment QA and Compliance Checking 5. Ultrasound Elasticity Estimation Biomarker • US Elasticity Estimation Background • Current Status and Problems • Biomarker Selection-process and outcome 6. US SWS for Liver Fibrosis Biomarker Work • Groundwork i. Literature search and analysis results ii. Phase I phantom testing-Elastic phantoms iii. Phase II phantom testing-Viscoelastic phantoms iv. Digital Simulated Data • Protocol and Profile Drafting i Protocol: based on UPICT and existing literature and standards bodies protocols ii. Profile-Current claims, Manufacturer specific appendices 7. What comes after the profile • Profile Validation i Technical validation ii. Clinical validation • QA and Compliance i. Possible approaches 1. Site a. Operator testing b. Site protocol re-evaluation 2. Imaging system a. Manufacturer testing and attestation b. User acceptance testing and periodic QA i. Phantom Tests ii. Digital Phantom Based Testing iii. Standard QA Testing iv. Remediation Schemes 8. Profile Evolution • Towards additional applications • Towards higher accuracy and precision Supported in part by NIH contract HHSN268201300071C from NIBIB. Collaboration with GE Global Research, no personal support.; S. Chen, Some technologies described in this presentation have been licensed. Mayo Clinic and Dr. Chen have financial interests these technologies.

Making a Case for Pediatric Antimicrobial Stewardship Programs.

Although antimicrobials are commonly used in children, it is important to remember that they can have a profound impact on this unique patient population. Inadvertent consequences of antiinfective use in children include antimicrobial resistance, infection caused by Clostridium difficile, increased risk of obesity, and adverse drug events. In addition, compared with adults, children have different dosing requirements, antimicrobial formulation needs, pharmacokinetics, and antimicrobial susceptibility profiles. Therefore, pediatric-specific antimicrobial stewardship efforts are needed to promote appropriate use of antimicrobials in children. The primary purposes of this review article are to provide a rationale behind pediatric-focused antimicrobial stewardship and to describe currently available evidence regarding the initiatives of pediatric antimicrobial stewardship programs (ASPs). A literature search of the Medline database was performed (from inception through March 2015). The studies included in this review focus on antimicrobial stewardship interventions in inpatient pediatric settings. Ten inpatient studies involving pediatric-focused antimicrobial stewardship interventions were identified from the published literature. Four studies used the core strategy of prospective audit with feedback; two used prior approval. The remaining four used supplemental antimicrobial stewardship strategies (guidelines, clinical pathways, and computerized decision support tools). In general, the interventions resulted in decreased antimicrobial use, reduced antimicrobial costs, and fewer prescribing errors. Children have unique medical needs related to antimicrobials and deserve focused ASP efforts. The literature regarding pediatric antimicrobial stewardship interventions is limited, but published interventions may serve as paradigms for developing pediatric ASPs as demonstrated by the general success of these interventions.

Lung CT Screening Reporting and Data System Speed and Accuracy Are Increased With the Use of a Semiautomated Computer Application

PurposeThe Lung CT Screening Reporting and Data System (Lung-RADS™) is an algorithm that can be used to classify lung nodules in patients with significant smoking histories. It is published in table format but can be implemented as a computer program. The aim of this study was to assess the efficiency and accuracy of the use of a computer program versus the table in categorizing lung nodules. MethodsThe Lung-RADS algorithm was implemented as a computer program. Through the use of a survey tool, respondents were asked to categorize 13 simulated lung nodules using the computer program and the Lung-RADS table as published. Data were gathered regarding time to completion, accuracy of each nodule’s categorization, users’ subjective categorization confidence, and users’ perceived efficiency using each method. ResultsThe use of a computer program to categorize lung nodules resulted in significantly increased interpretation speed (80.8 ± 37.7 vs 156 ± 105 seconds, P < .0001), lung nodule classification accuracy (99.6% vs 76.5%, P < .0001), and perceived confidence and efficiency compared with the use of the table. There were no significant differences in accuracy when comparing thoracic radiologists with the remainder of the group. ConclusionsRadiologists were both more efficient and more accurate in lung nodule categorization when using computerized decision support tools. The authors propose that other institutions use computerized implementations of Lung-RADS in the interests of both efficiency and patient outcomes through proper management. Furthermore, they suggest the ACR design future iterations of the Lung-RADS algorithm with computerized decision support in mind.

Computerized decision support in adult and pediatric critical care.

Computerized decision support (CDS) is the most advanced form of clinical decision support available and has evolved with innovative technologies to provide meaningful assistance to medical professionals. Critical care clinicians are in unique environments where vast amounts of data are collected on individual patients, and where expedient and accurate decisions are paramount to the delivery of quality healthcare. Many CDS tools are in use today among adult and pediatric intensive care units as diagnostic aides, safety alerts, computerized protocols, and automated recommendations for management. Some CDS use have significantly decreased adverse events and improved costs when carefully implemented and properly operated. CDS tools integrated into electronic health records are also valuable to researchers providing rapid identification of eligible patients, streamlining data-gathering and analysis, and providing cohorts for study of rare and chronic diseases through data-warehousing. Although the need for human judgment in the daily care of critically ill patients has limited the study and realization of meaningful improvements in overall patient outcomes, CDS tools continue to evolve and integrate into the daily workflow of clinicians, and will likely provide advancements over time. Through novel technologies, CDS tools have vast potential for progression and will significantly impact the field of critical care and clinical research in the future.

Rationale and design of the STAR randomized controlled trial to accelerate adoption of childhood obesity comparative effectiveness research

BackgroundComparative effectiveness research (CER) evidence on childhood obesity provides the basis for effective screening and management strategies in pediatric primary care. The uses of health information technology including decision support tools in the electronic health records (EHRs), as well as remote and mobile support to families, offer the potential to accelerate the adoption of childhood obesity CER evidence. Methods/designThe Study of Technology to Accelerate Research (STAR) is a three-arm, cluster-randomized controlled trial being conducted in 14 pediatric offices in Massachusetts designed to enroll 800, 6 to 12year old children with a body mass index (BMI)≥95th percentile seen in primary care at those practices. We will examine the extent to which computerized decision support tools in the EHR delivered to primary care providers at the point of care, with or without direct-to-parent support and coaching, will increase adoption of CER evidence for management of obese children. Direct-to-parent intervention components include telephone coaching and twice-weekly text messages. Point-of-care outcomes include obesity diagnosis, nutrition and physical activity counseling, and referral to weight management. One-year child-level outcomes include changes in BMI and improvements in diet, physical activity, screen time, and sleep behaviors, as well as cost and cost-effectiveness. ConclusionsSTAR will determine the extent to which decision support tools in EHRs with or without direct-to-parent support will increase adoption of evidence-based obesity management strategies in pediatric practice and improve childhood obesity-related outcomes.

EviQ cancer treatments online: How does the web‐based protocol system fare in a comprehensive quality assessment?

There have been few evaluations of the quality of computerized decision support tools in medical oncology despite their widespread use in clinical practice. In this article, we provide an in-depth quality assessment of eviQ, an Australian web-based protocol system. We used the Appraisal of Guidelines for Research and Evaluation (AGREE-II) instrument to examine the quality of eviQ chemotherapy protocols across six domains: scope and purpose, stakeholder involvement, rigor of development, clarity of presentation, applicability, editorial independence and overall quality rating. Domain scores were categorized as high quality (scores of >70%), moderate quality (30-70%), and low quality (<30%). eviQ was rated highly with respect to articulating its scope and purpose, clarity of presentation, applicability and on the overall rating item (scores of 72-80%). Domains addressing the involvement of stakeholders in the development process and the rigor with which protocols were developed were of moderate quality (scores of 34-37%) and the management of editorial independence and conflict of interest issues were rated in the low quality range (22%). Our evaluation demonstrated the eviQ protocol system is generally of high quality. However, we identified a number of areas in which eviQ could improve either its development processes or the means by which these processes are communicated to end-users. This research represents the first comprehensive evaluation of oncology protocols and paves the way for continued quality assurance research to ensure that these tools meet their objective of facilitating evidence-based medicine, standardizing treatment and, ultimately, improving patient outcomes.

Medical Imaging Data Reconciliation, Part 1: Innovation Opportunity

The concept of data reconciliation has been well described in the industrial and pharmaceutical literature and is designed to ensure the integrity, reliability, and accuracy of sequential data in a multistep process. This concept can similarly be applied to the delivery of medical imaging services, which begins with order entry and ends with reporting and communication. The ultimate goal of medical imaging data reconciliation is to ensure continuity of care, extending across multiple service providers and imaging and information system technologies. The longitudinal analysis of standardized data across the imaging continuum presents an opportunity to improve communication between service providers, education and training, workflow, and clinical outcomes. In addition, the derived data can in turn be used to create data-driven best-practice guidelines and computerized decision support tools.

Modeling Search in Group Decision Support Systems

Groups using group decision support systems (GDSS) to address particular tasks can be viewed as performing a search. Such tasks involve arriving at a solution or decision within the context of a complex search space, warranting the use of computerized decision support tools. The type of search undertaken by the groups appears to be a form of adaptive, rather than enumerative, search. Recently, efforts have been made to incorporate this adaptation into an analytical model of GDSS usage. One possible method for incorporating adaptation into an analytical model is to use an evolutionary algorithm, such as a genetic algorithm (GA), as an analogy for the group problem-solving process. In this paper, a test is made to determine whether GDSS behaves similarly to a GA process utilizing rank selection, uniform crossover, and uniform mutation operators. A Markov model for GAs is used to make this determination. Using GDSS experimental data, the best-fit transition probabilities are estimated and various hypotheses regarding the relation of GA parameters to GDSS functionality are proposed and tested. Implications for researchers in both GAs and group decision support systems are discussed.

Evolution in Groups: A Genetic Algorithm Approach to Group Decision Support Systems

Certain tasks undertaken by groups using Group Decision Support Systems (GDSS) can be viewed as search problems. These tasks involve arriving at a solution or decision where the problem is complex enough to warrant the use of computerized decision support tools. For these types of GDSS tasks, we propose to model the information exchange and convergence toward a solution by the group as a simple genetic algorithm. The simple genetic algorithm is a generalized search technique that is based on the principles of evolution and natural selection. Simply put, the best points in the current population are more likely to be selected and combined through genetic operators to determine new points. We propose that groups using GDSS to address certain tasks behave like a simple genetic algorithm in the manner in which possible solutions are generated, enhanced and altered in attempting to reach a decision or consensus.