Applications of artificial neural networks in health care organizational decision-making: A scoping review.
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ABSTRACT:
SUBMITTER: Shahid N
PROVIDER: S-EPMC6380578 | biostudies-literature | 2019
REPOSITORIES: biostudies-literature
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Project description:Neonatal sepsis remains difficult to diagnose due to its non-specific signs and symptoms. Traditional scoring systems help to discriminate between septic or not patients, but they do not consider every single patient particularity. Thus, the purpose of this study was to develop an early- and late-onset neonatal sepsis diagnosis model, based on clinical maternal and neonatal data from electronic records, at the time of clinical suspicion. A predictive model was obtained by training and validating an artificial Neural Networks (ANN) algorithm with a balanced dataset consisting of preterm and term non-septic or septic neonates (early- and late-onset), with negative and positive culture results, respectively, using 25 maternal and neonatal features. The outcome of the model was sepsis or not. The performance measures of the model, evaluated with an independent dataset, outperformed physician's diagnosis using the same features based on traditional scoring systems, with a 93.3% sensitivity, an 80.0% specificity, a 94.4% AUROC, and a regression coefficient of 0.974 between actual and simulated results. The model also performed well-relative to the state-of-the-art methods using similar maternal/neonatal variables. The top 10 factors estimating sepsis were maternal age, cervicovaginitis and neonatal: fever, apneas, platelet counts, gender, bradypnea, band cells, catheter use, and birth weight.
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Project description:In recent years, environmental awareness has increased considerably, and in order to decrease endangerments such as air and water pollution, and also global warming, green procurement should be employed. Therefore, in the assessment of suppliers, their environmental performance should be taken into consideration along with other criteria for supplier selection. Raising awareness of sustainability in production and conservation and protection of the environment is very important both for the whole environment and for the company itself by increasing its competitive advantage. And, one of the steps to achieve this is for the companies to try to select green suppliers. So, the purpose of this study is to raise awareness and tackle the need for green supplier selection and, using multiple-criteria decision-making models, to elaborate a case study regarding this. A survey was conducted in a manufacturing firm. The data were analysed, and fuzzy MCDM (multicriteria decision-making) methods and artificial neural networks were implemented. Fuzzy methods are the fuzzy analytic hierarchy process (fuzzy AHP), fuzzy TOPSIS, and fuzzy ELECTRE. ANN supports the result of fuzzy MCDM models from the profit side. ANN can make the best estimate of the current year based on historical data. Fuzzy MCDM methods will also find good solutions using the available data but will produce different solutions as there are different decision-making methods. It is aimed to produce a synergy from the solutions obtained here and to produce a better solution. Instead of a single method, it would be more accurate to produce a better solution than the solution provided by all of them. The dominant result has been obtained using the committee fuzzy MCDM and ANN to select the best green supplier.
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Project description:The decision making function is governed by the complex coupled neural circuit in the brain. The underlying energy landscape provides a global picture for the dynamics of the neural decision making system and has been described extensively in the literature, but often as illustrations. In this work, we explicitly quantified the landscape for perceptual decision making based on biophysically-realistic cortical network with spiking neurons to mimic a two-alternative visual motion discrimination task. Under certain parameter regions, the underlying landscape displays bistable or tristable attractor states, which quantify the transition dynamics between different decision states. We identified two intermediate states: the spontaneous state which increases the plasticity and robustness of changes of minds and the "double-up" state which facilitates the state transitions. The irreversibility of the bistable and tristable switches due to the probabilistic curl flux demonstrates the inherent non-equilibrium characteristics of the neural decision system. The results of global stability of decision-making quantified by barrier height inferred from landscape topography and mean first passage time are in line with experimental observations. These results advance our understanding of the stochastic and dynamical transition mechanism of decision-making function, and the landscape and kinetic path approach can be applied to other cognitive function related problems (such as working memory) in brain networks.
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Project description:ObjectiveTo illustrate the use of decision-analytic modeling to assist decision making in organizational innovations.Study setting/data sourcesRegarding an organizational innovation (shared care in hearing aid provision) available evidence from different sources was synthesized.Study designA probabilistic Markov model was constructed.Data collection/extractionWe modeled the long-term cost-effectiveness of different organizational formats of shared care as opposed to the current organization. We assessed the expected value of perfect information (EVPI) for several groups of parameters in the model.Principal findingsThe current organization had the highest probability of being cost-effective. Additional research is worthwhile, especially on access to care and safety (sensitivity to detect pathology).ConclusionsDecision-analytic modeling in an early stage of organizational innovation is a valuable tool to facilitate evidence-based decision making.
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Project description:Neural networks are a machine learning method that excel in solving classification and forecasting problems. They have also been shown to be a useful tool for working with big data oriented environments such as law enforcement. This article reviews and examines existing research on the utilization of neural networks for forecasting crime and other police decision making problem solving. Neural network models to predict specific types of crime using location and time information and to predict a crime's location when given the crime and time of day are developed to demonstrate the application of neural networks to police decision making. The neural network crime prediction models utilize geo-spatiality to provide immediate information on crimes to enhance law enforcement decision making. The neural network models are able to predict the type of crime being committed 16.4% of the time for 27 different types of crime or 27.1% of the time when similar crimes are grouped into seven categories of crime. The location prediction neural networks are able to predict the zip code location or adjacent location 31.2% of the time.
| S-EPMC8529125 | biostudies-literature
Project description:BACKGROUND:Collaborative writing applications (eg, wikis and Google Documents) hold the potential to improve the use of evidence in both public health and health care. The rapid rise in their use has created the need for a systematic synthesis of the evidence of their impact as knowledge translation (KT) tools in the health care sector and for an inventory of the factors that affect their use. OBJECTIVE:Through the Levac six-stage methodology, a scoping review was undertaken to explore the depth and breadth of evidence about the effective, safe, and ethical use of wikis and collaborative writing applications (CWAs) in health care. METHODS:Multiple strategies were used to locate studies. Seven scientific databases and 6 grey literature sources were queried for articles on wikis and CWAs published between 2001 and September 16, 2011. In total, 4436 citations and 1921 grey literature items were screened. Two reviewers independently reviewed citations, selected eligible studies, and extracted data using a standardized form. We included any paper presenting qualitative or quantitative empirical evidence concerning health care and CWAs. We defined a CWA as any technology that enables the joint and simultaneous editing of a webpage or an online document by many end users. We performed qualitative content analysis to identify the factors that affect the use of CWAs using the Gagnon framework and their effects on health care using the Donabedian framework. RESULTS:Of the 111 studies included, 4 were experimental, 5 quasi-experimental, 5 observational, 52 case studies, 23 surveys about wiki use, and 22 descriptive studies about the quality of information in wikis. We classified them by theme: patterns of use of CWAs (n=26), quality of information in existing CWAs (n=25), and CWAs as KT tools (n=73). A high prevalence of CWA use (ie, more than 50%) is reported in 58% (7/12) of surveys conducted with health care professionals and students. However, we found only one longitudinal study showing that CWA use is increasing in health care. Moreover, contribution rates remain low and the quality of information contained in different CWAs needs improvement. We identified 48 barriers and 91 facilitators in 4 major themes (factors related to the CWA, users' knowledge and attitude towards CWAs, human environment, and organizational environment). We also found 57 positive and 23 negative effects that we classified into processes and outcomes. CONCLUSIONS:Although we found some experimental and quasi-experimental studies of the effectiveness and safety of CWAs as educational and KT interventions, the vast majority of included studies were observational case studies about CWAs being used by health professionals and patients. More primary research is needed to find ways to address the different barriers to their use and to make these applications more useful for different stakeholders.
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Project description:ImportanceSurgeons make complex, high-stakes decisions under time constraints and uncertainty, with significant effect on patient outcomes. This review describes the weaknesses of traditional clinical decision-support systems and proposes that artificial intelligence should be used to augment surgical decision-making.ObservationsSurgical decision-making is dominated by hypothetical-deductive reasoning, individual judgment, and heuristics. These factors can lead to bias, error, and preventable harm. Traditional predictive analytics and clinical decision-support systems are intended to augment surgical decision-making, but their clinical utility is compromised by time-consuming manual data management and suboptimal accuracy. These challenges can be overcome by automated artificial intelligence models fed by livestreaming electronic health record data with mobile device outputs. This approach would require data standardization, advances in model interpretability, careful implementation and monitoring, attention to ethical challenges involving algorithm bias and accountability for errors, and preservation of bedside assessment and human intuition in the decision-making process.Conclusions and relevanceIntegration of artificial intelligence with surgical decision-making has the potential to transform care by augmenting the decision to operate, informed consent process, identification and mitigation of modifiable risk factors, decisions regarding postoperative management, and shared decisions regarding resource use.
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Project description:ObjectivesCognitive and other biases can influence the quality of healthcare decision making. While substantial research has explored how biases can lead to diagnostic or other errors in medicine, fewer studies have examined how they impact the decision making of other healthcare professionals. This scoping review aimed to identify and synthesise a broad range of research investigating whether decisions made by allied health professionals are influenced by cognitive, affective or other biases.Materials and methodsA systematic literature search was conducted in five electronic databases. Title, abstract and full text screening was undertaken in duplicate, using prespecified eligibility criteria designed to identify studies attempting to demonstrate the presence of bias when allied healthcare professionals make decisions. A narrative synthesis was undertaken, focussing on the type of allied health profession, type of decision, and type of bias reported within the included studies.ResultsThe search strategy identified 149 studies. Of these, 119 studies came from the field of psychology, with substantially fewer from social work, physical and occupational therapy, speech pathology, audiology and genetic counselling. Diagnostic and assessment decisions were the most common decision types, with fewer studies assessing treatment, prognostic or other clinical decisions. Studies investigated the presence of over 30 cognitive, affective and other decision making biases, including stereotyping biases, anchoring, and confirmation bias. Overall, 77% of the studies reported at least one outcome that represented the presence of a bias.ConclusionThis scoping review provides an overview of studies investigating whether decisions made by allied health professionals are influenced by cognitive, affective or other biases. Biases have the potential to seriously impact the quality, consistency and accuracy of decision making in allied health practice. The findings highlight a need for further research particularly in professional disciplines outside of psychology, using methods that reflect real life healthcare decision making.
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Project description:BackgroundOrganizational health literacy (OHL) aims to respond to the health literacy needs of patients by improving health information and services and making them easier to understand, access, and apply. This scoping review primarily maps criteria characterizing health literate health care organizations. Secondary outcomes are the concepts and terminologies underlying these criteria as well as instruments to measure them.MethodsThe review was carried out following the JBI Manual on scoping reviews. The databases CINAHL, Cochrane Library, JSTOR, PsycINFO, PubMed, Web of Science Core Collection, and Wiley Online Library were searched in July 2020. Three researchers screened the records and extracted the data. The results were synthesized systematically and descriptively.ResultsThe literature search resulted in 639 records. After removing duplicates, screening by title and abstract, and assessing full-texts for eligibility, the scoping review included 60 publications. Criteria for OHL were extracted and assigned to six main categories (with 25 subcategories). The most prevalent topic of organizational health literacy refers to communication with service users. Exemplary criteria regarding this main category are the education and information of service users, work on easy-to-understand written materials as well as oral exchange, and verifying understanding. The six main categories were defined as 1) communication with service users; 2) easy access & navigation; 3) integration & prioritization of OHL; 4) assessments & organizational development; 5) engagement & support of service users, and 6) information & qualification of staff. The criteria were based on various concepts and terminologies. Terminologies were categorized into four conceptual clusters: 1) health literacy in various social contexts; 2) health literate health care organization; 3) organizational behavior, and 4) communication in health care. 17 different assessment tools and instruments were identified. Only some of the toolkits and instruments were validated or tested in feasibility studies.ConclusionsOrganizational health literacy includes a significant number of distinct organizational criteria. The terminologies used in the OHL literature are heterogeneous based on a variety of concepts. A comprehensive, consensus-based conceptual framework on OHL is missing.
| S-EPMC8259028 | biostudies-literature
Project description:Core competencies for public health in Canada require proficiency in evidence informed decision making (EIDM). However, decision makers often lack access to information, many workers lack knowledge and skills to conduct systematic literature reviews, and public health settings typically lack infrastructure to support EIDM activities. This research was conducted to explore and describe critical factors and dynamics in the early implementation of one public health unit's strategic initiative to develop capacity to make EIDM standard practice.This qualitative case study was conducted in one public health unit in Ontario, Canada between 2008 and 2010. In-depth information was gathered from two sets of semi-structured interviews and focus groups (n = 27) with 70 members of the health unit, and through a review of 137 documents. Thematic analysis was used to code the key informant and document data.The critical factors and dynamics for building EIDM capacity at an organizational level included: clear vision and strong leadership, workforce and skills development, ability to access research (library services), fiscal investments, acquisition and development of technological resources, a knowledge management strategy, effective communication, a receptive organizational culture, and a focus on change management.With leadership, planning, commitment and substantial investments, a public health department has made significant progress, within the first two years of a 10-year initiative, towards achieving its goal of becoming an evidence informed decision making organization.
| S-EPMC3305606 | biostudies-literature