Identidade profissional do psicólogo judiciário: um estudo sobre configurações identitárias / Forensic psychologist´s professional identity: a study about identity configurations / Identidad profesional del psicólogo forense: un estudio sobre configuraciones de identidad

Este estudo objetivou descrever a identidade profissional de psicólogos judiciários, partindo do cenário contemporâneo da Psicologia Jurídica brasileira, contexto que envolve crises e conflitos sobre a forma de responder a atribuições e demandas do campo legal. Pela perspectiva da sociologia das identidades profissionais de Claude Dubar, sustenta-se a hipótese de que a identidade profissional do psicólogo judiciário depende de estratégias de compatibilização entre o pertencimento à categoria e as atribuições legais e institucionais. Participaram 95 psicólogos do quadro ativo do Tribunal de Justiça de São Paulo, que responderam a um formulário online sobre a percepção de si e do campo de atuação. Os dados foram submetidos à análise de conteúdo. Os resultados indicam a saliência da avaliação psicológica e da interdisciplinaridade na identidade profissional, e as rupturas identitárias diante de práticas verificatórias. Tais achados apontam a necessidade de participação da categoria na construção de suas atribuições; e dificuldades para o exercício das funções por limitações à autonomia profissional.(AU)

This study aimed to describe the professional identity of forensic psychologists, considering Brazil's Legal Psychology contemporary scenario which relates to a critical issues on how practitioners respond the demands of the legal system. Based on Claude Dubar's sociology of professional identities, we support the hypothesis that forensic psychologists' professional identity depends on strategies of compatibilization between belonging their reference group and the institutional attributions. There were 95 participants, all from the current staff of the Court of Justice of the state of São Paulo, who answered an online form. The data were subjected to content analysis. The results indicate a professional identity with noted salience on psychological assessment and interdisciplinarity, and the identity crises regarding verification practices. Such findings highlight the importance of practitioners taking part on the construction of their own tasks.(AU)

Este estudio tuvo como objetivo describir la identidad profesional de los psicólogos forenses, considerando el escenario de la Psicología Jurídica brasileña, que se relaciona con una crisis sobre si estos profesionales responden a las demandas del sistema legal. Teniendo en cuenta la sociología de las identidades profesionales de Claude Dubar, sostenemos la hipótesis de que la identidad profesional de los psicólogos forenses depende de estrategias de compatibilización entre la pertenencia a su grupo profesional y a instituciones. Participaron 95 psicólogos, quienes actuaban en el Tribunal de Justicia del Estado de São Paulo, a los cuales se aplicó un formulario en línea. Los datos se sometieron a análisis de contenido. Los resultados indican una identidad profesional saliente en cuanto a la evaluación psicológica y la interdisciplinariedad, pero también crisis de identidad en relación con las prácticas de verificación. Tales resultados señalan la importancia de que la categoría participe en la construcción de sus propias atribuciones.(AU)

Explainability does not improve biochemistry staff trust in artificial intelligence-based decision support.

BACKGROUND: Explainability, the aspect of artificial intelligence-based decision support (ADS) systems that allows users to understand why predictions are made, offers many potential benefits. One common claim is that explainability increases user trust, yet this has not been established in healthcare contexts. For advanced algorithms such as artificial neural networks, the generation of explanations is not trivial, but requires the use of a second algorithm. The assumption of improved user trust should therefore be investigated to determine if it justifies the additional complexity. METHODS: Biochemistry staff completed a wrong blood in tube (WBIT) error identification task with the help of an ADS system. One-half of the volunteers were provided with both ADS predictions and explanations for those predictions, while the other half received predictions alone. The two groups were compared in terms of their rate of agreement with ADS predictions, as an index of user trust, and WBIT error detection performance. Since the AI model used to generate predictions was known to out-perform laboratory staff, increased trust was expected to improve user performance. RESULTS: Volunteers reviewed 1590 sets of results. The volunteers provided with explanations demonstrated no difference in their rate of agreement with the ADS system compared to volunteers receiving predictions alone (83.3% versus 81.8%, p = 0.46). The two volunteer groups were also equivalent in accuracy, sensitivity and specificity for WBIT error identification (p-values >0.78). CONCLUSIONS: For a WBIT error identification task, there was no evidence to justify the additional complexity of explainability on the grounds of increased user trust.

Overcoming Major Barriers to Build Efficient Decision Support Systems in Pharmacovigilance.

Many decision support methods and systems in pharmacovigilance are built without explicitly addressing specific challenges that jeopardize their eventual success. We describe two sets of challenges and appropriate strategies to address them. The first are data-related challenges, which include using extensive multi-source data of poor quality, incomplete information integration, and inefficient data visualization. The second are user-related challenges, which encompass users' overall expectations and their engagement in developing automated solutions. Pharmacovigilance decision support systems will need to rely on advanced methods, such as natural language processing and validated mathematical models, to resolve data-related issues and provide properly contextualized data. However, sophisticated approaches will not provide a complete solution if end-users do not actively participate in their development, which will ensure tools that efficiently complement existing processes without creating unnecessary resistance. Our group has already tackled these issues and applied the proposed strategies in multiple projects.

Activating the Hospital Incident Command System Response in a Community Specialty Practice: The Mayo Clinic Experience.

INTRODUCTION: The COVID-19 pandemic presented health care organizations with a unique challenge in determining effective management of a large-scale incident across an extended time period. CASE PRESENTATION: This report describes the response of a multisite integrated system to the COVID-19 pandemic through activation of the Hospital Incident Command System. DISCUSSION: A robust emergency response plan with multidisciplinary involvement can help to ensure clear lines of accountability and expedite decision-making. Consistent physician input across affected specialties allows for a robust understanding of impacted areas, peer-to-peer communication, and a sense of ownership across the medical staff. The necessity of effective communication with staff and patients during times of crisis cannot be understated. The potential for information overload in a pandemic is significant but can be overcome through consistent and transparent communication from leadership. CONCLUSION: Health systems should have a well-organized emergency response system prepared to launch in small-scale or large-scale situations. The threshold to implement the response system and accountability to make that decision must be a clearly defined organizational policy.

A decision support framework for prediction of avian influenza.

For years, avian influenza has influenced economies and human health around the world. The emergence and spread of avian influenza virus have been uncertain and sudden. The virus is likely to spread through several pathways such as poultry transportation and wild bird migration. The complicated and global spread of avian influenza calls for surveillance tools for timely and reliable prediction of disease events. These tools can increase situational awareness and lead to faster reaction to events. Here, we aimed to design and evaluate a decision support framework that aids decision makers by answering their questions regarding the future risk of events at various geographical scales. Risk patterns were driven from pre-built components and combined in a knowledge base. Subsequently, questions were answered by direct queries on the knowledge base or through a built-in algorithm. The evaluation of the system in detecting events resulted in average sensitivity and specificity of 69.70% and 85.50%, respectively. The presented framework here can support health care authorities by providing them with an opportunity for early control of emergency situations.

EXpectation Propagation LOgistic REgRession on permissioned blockCHAIN (ExplorerChain): decentralized online healthcare/genomics predictive model learning.

OBJECTIVE: Predicting patient outcomes using healthcare/genomics data is an increasingly popular/important area. However, some diseases are rare and require data from multiple institutions to construct generalizable models. To address institutional data protection policies, many distributed methods keep the data locally but rely on a central server for coordination, which introduces risks such as a single point of failure. We focus on providing an alternative based on a decentralized approach. We introduce the idea using blockchain technology for this purpose, with a brief description of its own potential advantages/disadvantages. MATERIALS AND METHODS: We explain how our proposed EXpectation Propagation LOgistic REgRession on Permissioned blockCHAIN (ExplorerChain) can achieve the same results when compared to a distributed model that uses a central server on 3 healthcare/genomic datasets, and what trade-offs need to be considered when using centralized/decentralized methods. We explain how the use of blockchain technology can help decrease some of the problems encountered in decentralized methods. RESULTS: We showed that the discrimination power of ExplorerChain can be statistically similar to its counterpart central server-based algorithm. While ExplorerChain inherited some benefits of blockchain, it had a small increased running time. DISCUSSION: ExplorerChain has the same prerequisites as a distributed model with a centralized server for coordination. In a manner similar to secure multi-party computation strategies, it assumes that participating institutions are honest, but "curious." CONCLUSION: When evaluated on relatively small datasets, results suggest that ExplorerChain, which combines artificial intelligence and blockchain technologies, performs as well as a central server-based method, and may avoid some risks at the cost of efficiency.

Connecting Data to Value: An Operating Model for Healthcare Advanced Analytics.

Artificial intelligence offers the promise to revolutionize the way healthcare is delivered in the future. To capitalize on the value of advanced analytics and artificial intelligence, organizations must focus on building organizational capabilities. This report shares an operating model for insight and change in healthcare comprising six key components: analytics technology and operations, data governance, change and automation, advanced analytics and insights, analytics literacy and strategy and relationship management. The adoption of the proposed model will build core capabilities that will enable organizations to connect data to decision making and realize value from its investment in advanced analytics.

Improving Health Care Management in Hospitals Through a Productivity Dashboard.

Health information systems have been developed to help hospital managers steer daily operations, including key performance indicators (KPIs) for monitoring on a time-aggregated basis. Yet, current literature lacks in proposals of productivity dashboards to assist hospitals stakeholders. This research focuses on two related problems: (1) hospital organizations need access to productivity information to improve access to services; and (2) managers need productivity information to optimize resource allocation. This research consists in the development of dashboards to monitor information obtained from a hospital organization to support decision makers. To develop and evaluate the productivity dashboard, the Design Science Research (DSR) methodology was adopted. The dashboard was evaluated by stakeholders of a large Portuguese hospital who contributed to iteratively improving its design toward a useful decision support tool. Additionally, it was ascertained that monitoring productivity needs more study and that the dashboards on these themes are valuable assets at a monitoring level and subsequent decision-making process.

Simulation-based optimization to improve hospital patient assignment to physicians and clinical units.

A fundamental activity in hospital operations is patient assignment, which we define as the process of assigning hospital patients to specific physician services and clinical units based on their diagnosis. When the preferred assignment is not possible, typically due to capacity limits, hospitals often allow for overflow, which is the assignment of patients to other services and/or units. Overflow accelerates assignment, but can also reduce care quality and increase length of stay. This paper develops a discrete-event simulation model to evaluate different assignment strategies. Using a simulation-based optimization approach, we evaluate and heuristically optimize these strategies accounting for expected hospital and physician profit, care quality and patient waiting time. We apply the model using data from the University of Chicago Medical Center. We find that the strategies that use heuristically optimized designation of overflow services and units increase expected profit relative to the capacity-based strategy in which overflow patients are assigned to a service and unit with the most available capacity. We also find further improvement in the strategy that uses heuristically optimized overflow services and units as well as a holding unit that holds patients until a bed in their primary or secondary unit becomes available. Additionally, we demonstrate the effects of these strategies on other performance measures such as patient concentration, waiting time, and outcomes.

Quantitative thresholds based decision support approach for the home health care scheduling and routing problem.

In the domain of Home Health Care (HHC), precise decisions regarding patient's selection, staffing level, and scheduling of health care staff have a significant impact on the efficiency and effectiveness of the HHC system. However, decentralized planning, the absence of well defined decision rules, delayed decisions and lack of interactive tools typically lead towards low satisfaction level among all the stakeholders of the HHC system. In order to address these issues, we propose an integrated three phase decision support methodology for the HHC system. More specifically, the proposed methodology exploits the structure of the HHC problem and logistic regression based approaches to identify the decision rules for patient acceptance, staff hiring, and staff utilization. In the first phase, a mathematical model is constructed for the HHC scheduling and routing problem using Mixed-Integer Linear Programming (MILP). The mathematical model is solved with the MILP solver CPLEX and a Variable Neighbourhood Search (VNS) based method is used to find the heuristic solution for the HHC problem. The model considers the planning concerns related to compatibility, time restrictions, distance, and cost. In the second phase, Bender's method and Receiver Operating Characteristic (ROC) curves are implemented to identify the thresholds based on the CPLEX and VNS solution. While the third phase creates a fresh solution for the HHC problem with a new data set and validates the thresholds predicted in the second phase. The effectiveness of these thresholds is evaluated by utilizing performance measures of the widely-used confusion matrix. The evaluation of the thresholds shows that the ROC curves based thresholds of the first two parameters achieved 67% to 71% accuracy against the two considered solution methods. While the Bender's method based thresholds for the same parameters attained more than 70% accuracy in cases where probability value is small (p ≤ 0.5). The promising results indicate that the proposed methodology is applicable to define the decision rules for the HHC problem and beneficial to all the concerned stakeholders in making relevant decisions.

Knowledge Extraction of Cohort Characteristics in Research Publications.

When healthcare providers review the results of a clinical trial study to understand its applicability to their practice, they typically analyze how well the characteristics of the study cohort correspond to those of the patients they see. We have previously created a study cohort ontology to standardize this information and make it accessible for knowledge-based decision support. The extraction of this information from research publications is challenging, however, given the wide variance in reporting cohort characteristics in a tabular representation. To address this issue, we have developed an ontology-enabled knowledge extraction pipeline for automatically constructing knowledge graphs from the cohort characteristics found in PDF-formatted research papers. We evaluated our approach using a training and test set of 41 research publications and found an overall accuracy of 83.3% in correctly assembling the knowledge graphs. Our research provides a promising approach for extracting knowledge more broadly from tabular information in research publications.

Artificial Intelligence Decision Support for Medical Triage.

Applying state-of-the-art machine learning and natural language processing on approximately one million of teleconsultation records, we developed a triage system, now certified and in use at the largest European telemedicine provider. The system evaluates care alternatives through interactions with patients via a mobile application. Reasoning on an initial set of provided symptoms, the triage application generates AI-powered, personalized questions to better characterize the problem and recommends the most appropriate point of care and time frame for a consultation. The underlying technology was developed to meet the needs for performance, transparency, user acceptance and ease of use, central aspects to the adoption of AI-based decision support systems. Providing such remote guidance at the beginning of the chain of care has significant potential for improving cost efficiency, patient experience and outcomes. Being remote, always available and highly scalable, this service is fundamental in high demand situations, such as the current COVID-19 outbreak.

Continuous Glucose Monitoring Sensors for Diabetes Management: A Review of Technologies and Applications.

By providing blood glucose (BG) concentration measurements in an almost continuous-time fashion for several consecutive days, wearable minimally-invasive continuous glucose monitoring (CGM) sensors are revolutionizing diabetes management, and are becoming an increasingly adopted technology especially for diabetic individuals requiring insulin administrations. Indeed, by providing glucose real-time insights of BG dynamics and trend, and being equipped with visual and acoustic alarms for hypo- and hyperglycemia, CGM devices have been proved to improve safety and effectiveness of diabetes therapy, reduce hypoglycemia incidence and duration, and decrease glycemic variability. Furthermore, the real-time availability of BG values has been stimulating the realization of new tools to provide patients with decision support to improve insulin dosage tuning and infusion. The aim of this paper is to offer an overview of current literature and future possible developments regarding CGM technologies and applications. In particular, first, we outline the technological evolution of CGM devices through the last 20 years. Then, we discuss about the current use of CGM sensors from patients affected by diabetes, and, we report some works proving the beneficial impact provided by the adoption of CGM. Finally, we review some recent advanced applications for diabetes treatment based on CGM sensors.

Real-time forecasting of emergency department arrivals using prehospital data.

BACKGROUND: Crowding in emergency departments (EDs) is a challenge globally. To counteract crowding in day-to-day operations, better tools to improve monitoring of the patient flow in the ED is needed. The objective of this study was the development of a continuously updated monitoring system to forecast emergency department (ED) arrivals on a short time-horizon incorporating data from prehospital services. METHODS: Time of notification and ED arrival was obtained for all 191,939 arrivals at the ED of a Norwegian university hospital from 2010 to 2018. An arrival notification was an automatically captured time stamp which indicated the first time the ED was notified of an arriving patient, typically by a call from an ambulance to the emergency service communication center. A Poisson time-series regression model for forecasting the number of arrivals on a 1-, 2- and 3-h horizon with continuous weekly and yearly cyclic effects was implemented. We incorporated time of arrival notification by modelling time to arrival as a time varying hazard function. We validated the model on the last full year of data. RESULTS: In our data, 20% of the arrivals had been notified more than 1 hour prior to arrival. By incorporating time of notification into the forecasting model, we saw a substantial improvement in forecasting accuracy, especially on a one-hour horizon. In terms of mean absolute prediction error, we observed around a six percentage-point decrease compared to a simplified prediction model. The increase in accuracy was particularly large for periods with large inflow. CONCLUSIONS: The proposed model shows increased predictability in ED patient inflow when incorporating data on patient notifications. This approach to forecasting arrivals can be a valuable tool for logistic, decision making and ED resource management.

Methodology and model-based DSS to managing the reallocation of inventory to orders in LHP situations. Application to the ceramics sector.

Lack of homogeneity in the product (LHP) is a problem when customers require homogeneous units of a single product. In such cases, the optimal allocation of inventory to orders becomes much more complex. Furthermore, in an MTS environment, an optimal initial allocation may become less than ideal over time, due to different circumstances. This problem occurs in the ceramics sector, where the final product varies in tone and calibre. This paper proposes a methodology for the reallocation of inventory to orders in LHP situation (MERIO-LHP) and a model-based decision-support system (DSS) to support the methodology, which enables an optimal reallocation of inventory to order lines to be carried out in real businesses environments in which LHP is inherent. The proposed methodology and model-based DSS were validated by applying it to a real case at a ceramics company. The analysis of the results indicates that considerable improvements can be obtained with regard to the quantity of orders fulfilled and sales turnover.

Ecological Interface Design: Thirty-Plus Years of Refinement, Progress, and Potential.

OBJECTIVE: The objective is to provide a review of ecological interface design (EID), to illustrate its value to human factors/ergonomics, and to identify areas for future research and development. BACKGROUND: EID uses mature interface technologies to provide decision making and problem solving support. A variety of theoretical concepts and analytical tools have been developed to meet the associated challenges. EID provides support that is simultaneously grounded in the practical realities of a work domain and tailored to human capabilities and limitations. METHOD: EID's theoretical foundation is discussed briefly. Concrete examples of ecological and traditional interfaces are provided. Different categories of work domains are described, as well as the associated implications for interface design. A targeted literature review is conducted and the experimental outcomes are summarized. A representative evaluation is discussed, and interpretations of performance are provided. RESULTS: The evidence reveals that EID has been remarkably successful in significantly improving performance for work domains with constraints that are law driven (e.g., process control). In contrast, work domains that are intent-driven (e.g., information retrieval) have, by and large, been ignored. Also, few studies have addressed nonvisual displays. CONCLUSION: EID has not yet realized its potential to improve safety and efficiency across the entire continuum of work domains. APPLICATION: EID provides a single integrated framework that is (a) sufficiently comprehensive to deal with complicated work domains and (b) capable of producing innovative support that will generalize to actual work settings.

Relative Technical Efficiency Assessment of Mental Health Services: A Systematic Review.

The current prevalence of mental disorders demands improved ways of the management and planning of mental health (MH) services. Relative technical efficiency (RTE) is an appropriate and robust indicator to support decision-making in health care, but it has not been applied significantly in MH. This article systematically reviews the empirical background of RTE in MH services following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Finally, 13 studies were included, and the findings provide new standard classifications of RTE variables, efficiency determinants and strategies to improve MH management and planning.