RESUMEN

Unlicensed patient care assistants (PCAs) are often tasked with providing constant observation (CO) of patients with complex health behaviors. The unique demands of CO are poorly understood, making it challenging to design effective training. Four-component instructional design theory suggests the tasks associated with CO feature non-recurrent challenges, such that effective, simulation-based deliberate practice must reflect real-life variability. This study aimed to elucidate the variety of challenges PCAs encounter during CO of patients with complex health behaviors. We used a qualitative descriptive design and developed a preliminary framework to code interviews. The data collection framework was designed to support creating numerous realistic scenarios to support generalized and transferable learning. From our interviews with 16 participants, we identified 1,066 statements associated with variations in CO tasks. We grouped our findings by two domains, "patient factors" and "environmental contexts." Our results revealed many recurring and non-recurring challenges inherent in CO, requiring a range of skills to maintain patient and staff safety. Instructional design elements may include scenarios incorporating environment and resource assessment, cognitive feedback for non-recurrent tasks, novel methods of self-harm initiated by the patient, and incorporating interdisciplinary staff in which the learner must navigate a complex conversation. This study clarifies the task demands of CO and is useful as a task analysis to guide scenario development for simulation-based experiences. A less systematic approach would risk underrepresenting the difficulties inherent in the task, reinforcing a limited set of behaviors that may not generalize well to the non-recurrent challenges in CO.

RESUMEN

The increasing cognitive load on infantry squad leaders is a common challenge in modern military operations. As this can increase health and safety risks, there is a need to study the factors responsible for the increase in cognitive load. Ecological situations inherently lack strong experimental controls; therefore, microworlds that simulate real tasks are the usual alternative to field studies. However, to the best of our knowledge, there are currently no microworlds that reproduce the main tasks of the squad leader during operations. This article adresses this gap by describing the design and validation of a new microworld: the Simulated Multitasking Environment for the Squad leader (SMES). Qualitative research was firstly conducted to highlight several squad leader's generic tasks (i.e., common to many situations in the field) that guided the design of the SMES. Psychometric validation of the SMES was then based on two experiments: (i) the first evaluated the microworld's psychometric qualities when tasks were performed individually; and (ii) the second explored concurrent tasks, reflecting real-world complexity. The results showed that the parameters manipulated for each task were relevant for inducing cognitive load, measured using a secondary detection response task and the NASA-TLX questionnaire. The SMES demonstrated satisfactory convergent and content validity in multitasking but not in single-task conditions. Performance in multitasking situations therefore does not seem to depend on task-specific skills, suggesting the existence of an independent factor-multitasking ability. Theoretical and practical implications of the SMES validation are discussed.

RESUMEN

Little research has examined specific instructional variables that influence the development and effectiveness of task-analysis instruction. We conducted two experiments using text-based task analyses to teach college students to create single-subject reversal design graphs. In Experiment 1, we tested the effects of presenting antecedent and outcome stimuli on graphing performance (accuracy, yield, time to completion). Different groups of participants experienced graphing tutorials with descriptions and pictures of (a) responses; (b) antecedent stimuli and responses; (c) responses and outcomes of correct responses; and (d) antecedent stimuli, responses, and outcomes. In Experiment 2, we compared tutorials with and without pictures. Collectively, the results suggest that graphing accuracy was positively affected by task analyses that included pictures and descriptions of antecedent stimuli and that adding outcome stimuli further benefited graphing accuracy. These results suggest critical instructional elements that should be included in future task analyses of graphing or other complex behavior chains.

RESUMEN

BACKGROUND: Military physical fitness tests and standards often lack a scientific basis. Contrary to this traditional-historical precedent, US Air Force researchers use a "Two-Tier" health (Tier 1) and performance (Tier 2) approach to develop evidence-based criterion physical fitness tests, standards, and training. This study specifically and only addresses a physical demands analysis, the first step in a six-step process to develop Tier 2 occupationally specific, operationally relevant physical fitness tests and standards, and training guidance. OBJECTIVE: This study conducted a physical demands analysis to identify and delineate occupationally specific, operationally relevant critical physical tasks for eight physically demanding US Air Force occupational specialties. METHODS: A comprehensive five part physical demands analysis reviewed duty tasks, conducted focus groups, assessed incumbents, observed operational tasks, and interviewed senior leaders to detail critical physical tasks. RESULTS: The physical demands analysis delineated duty tasks to critical physical tasks for Special Tactics (556 to 40), Pararescue (981 to 50), Tactical Air Control (415 to 44), Special Operations Weather (586 to 50), Explosive Ordnance Disposal (1107 to 39), Security Forces (1286 to 25), Fire and Emergency Services (802 to 39), and Survival, Evasion, Resistance, and Escape (1099 to 25). CONCLUSIONS: The study approach proved efficacious for conducting a thorough physical demands analysis to identify and delineate occupationally specific, operationally relevant critical physical tasks for eight US Air Force physically demanding occupations. Critical physical tasks provide basis for the next overall research process step, developing physical task simulations to link to predictive physical fitness tests and training guidance.

RESUMEN

Advancements in information technology have facilitated the emergence of mHealth apps as crucial tools for health management and chronic disease prevention. This research work focuses on mHealth apps for the management of diabetes by patients on their own. Given that China has the highest number of diabetes patients in the world, with 141 million people and a prevalence rate of 12.8% (mentioned in the Global Overview of Diabetes), the development of a usability research methodology to assess and validate the user-friendliness of apps is necessary. This study describes a usability evaluation model that combines task analysis methods and eye movement data. A blood glucose recording application was designed to be evaluated. The evaluation was designed based on the model, and the feasibility of the model was demonstrated by comparing the usability of the blood glucose logging application before and after a prototype modification based on the improvement suggestions derived from the evaluation. Tests showed that an improvement plan based on error logs and post-task questionnaires for task analysis improves interaction usability by about 24%, in addition to an improvement plan based on eye movement data analysis for hotspot movement acceleration that improves information access usability by about 15%. The results demonstrate that this study presents a usability evaluation model for mHealth apps that enables the effective evaluation of the usability of mHealth apps.

RESUMEN

Background: Total knee arthroplasty (TKA) is a commonly performed procedure that has traditionally utilized reproducible steps using a set of mechanical instruments. The number of TKAs performed using robotic assistance is increasing, and augmented reality (AR) navigation systems are being developed. Hierarchical task analysis (HTA) aims to describe the steps of a specific task in detail to reduce errors and ensure reproducibility. The objective of this study was to develop and validate HTAs for conventional, robotic-assisted, and AR-navigated TKA. Methods: The development of HTAs for conventional TKA involved an iterative review process that incorporated the input of 4 experienced arthroplasty surgeons. The HTAs were then adapted for robotic-assisted and AR-navigated TKA by incorporating specific steps associated with the use of these systems. The accuracy and completeness of the HTAs were validated by observing 10 conventional and 10 robotic-assisted TKA procedures. Results: HTAs for conventional, robotic-assisted, and AR-navigated TKA were developed and validated. The resulting HTAs provide a comprehensive and standardized plan for each procedure and can aid in the identification of potential areas of inefficiency and risk. Robotic-assisted and AR-navigated approaches require additional steps, and there are an increased number of instances where complications may occur. Conclusions: The HTAs developed in this study can provide valuable insights into the potential pitfalls of robotic-assisted and AR-navigated TKA procedures. As AR-navigation systems are developed, they should be optimized by critical analysis using the developed HTAs to ensure maximum efficiency, reliability, accessibility, reduction of human error, and costs.

RESUMEN

Machine learning-based controllers of prostheses using electromyographic signals have become very popular in the last decade. The regression approach allows a simultaneous and proportional control of the intended movement in a more natural way than the classification approach, where the number of movements is discrete by definition. However, it is not common to find regression-based controllers working for more than two degrees of freedom at the same time. In this paper, we present the application of the adaptive linear regressor in a relatively low-dimensional feature space with only eight sensors to the problem of a simultaneous and proportional control of three degrees of freedom (left-right, up-down and open-close hand movements). We show that a key element usually overlooked in the learning process of the regressor is the training paradigm. We propose a closed-loop procedure, where the human learns how to improve the quality of the generated EMG signals, helping also to obtain a better controller. We apply it to 10 healthy and 3 limb-deficient subjects. Results show that the combination of the multidimensional targets and the open-loop training protocol significantly improve the performance, increasing the average completion rate from 53% to 65% for the most complicated case of simultaneously controlling the three degrees of freedom.

RESUMEN

This article discusses the importance of identifying and preventing human error in industrial environments, specifically in the sugar production process. The article emphasizes the importance of choosing the right technique for risk assessment studies resulting from human errors. A cross-sectional study was conducted using a multi-stage approach - Hierarchical Task Analysis (HTA), Human Error Calculator (HEC), and Predictive Human Error Analysis (PHEA) - to identify potential human errors in the sugar production process. The HTA, HEC, and PHEA techniques were employed to evaluate each stage of the process for potential human errors. The results of the HTA technique identified 35 tasks and 83 sub-tasks in 14 units of the sugar production process. According to HEC technique 4 tasks with 80 % probability of human error and 2 tasks with 50 % probability of human error had the highest calculated error probabilities. The factors of individual skill, task repetition and importance were the most important factors of human error in the present study. The analysis of PHEA worksheets showed that the number of human errors identified in the tasks with highest probability were 8 errors, of which 50 % were action errors, 25 % checking errors, 13 % selection errors, and 12 % retrieval errors. To mitigate the consequences of human error, it was recommended training courses, raising operator awareness of error consequences, and installing instructions in the sugar production process. Based on the findings, the article concludes that the HEC and PHEA techniques are applicable and effective in identifying and analyzing human errors in process and food industries.

RESUMEN

AIM: The aim of this study was to: (1) use cognitive task analysis to describe final year nursing students situation awareness in recognising, responding and escalating care of deteriorating patients in ward settings; and (2) make recommendations for training and practice. DESIGN: A mixed methods cognitive task analysis with a convergent triangulation design. METHOD: Data collection involved observations of 33 final year nursing students in simulated deteriorating patient scenarios and retrospective cognitive interviews. A process tracing technique was applied to identify the cues to deterioration participants perceived; how cue perception altered as situational demands increased; the extent that participants made connections between perceived cues and reached a situational understanding; and the factors that influenced and constrained participants situation awareness. Qualitative and quantitative findings are woven together and presented using descriptive statistics, illustrative quotations and timeline extractions. RESULTS: The median cue perception was 65.4% and 57.6% in the medical and surgical scenarios, respectively. Perception was negatively influenced by incomplete vital sign monitoring as situations escalated; limited physical assessments; passive scanning behaviours; poor task automaticity; and excessive cognitive demands. Incomplete perception, poor cue integration and underdeveloped mental models influenced situational understanding. Escalation calls did not always accurately reflect situations and a reporting mindset was evident. Clinical exposure to deteriorating patients was described as variable and opportunistic. REPORTING METHOD: The study is reported in accordance with the Good Reporting of a Mixed Methods Study (GRAMMS) checklist. PATIENT OR PUBLIC CONTRIBUTION: Patients and public were not involved in this research.

Asunto(s)

RESUMEN

Weaning patients from ventilation in intensive care units (ICU) is a complex task. There is a growing desire to build decision-support tools to help clinicians during this process, especially those employing Artificial Intelligence (AI). However, tools built for this purpose should fit within and ideally improve the current work environment, to ensure they can successfully integrate into clinical practice. To do so, it is important to identify areas where decision-support tools may aid clinicians, and associated design requirements for such tools. This study analysed the work context surrounding the weaning process from mechanical ventilation in ICU environments, via cognitive task and work domain analyses. In doing so, both what cognitive processes clinicians perform during weaning, and the constraints and affordances of the work environment itself, were described. This study found a number of weaning process tasks where decision-support tools may prove beneficial, and from these a set of contextual design requirements were created. This work benefits researchers interested in creating human-centred decision-support tools for mechanical ventilation that are sensitive to the wider work system.

Asunto(s)

RESUMEN

It is uncertain how the application of artificial intelligence (AI) technology transforms industrial work. We address this question from the perspective of cognitive systems, which, in this case, includes considerations of AI and process transparency, resilience, division of labor, and worker skills. We draw from a case study on glass tempering that includes a machine-vision-based quality control system and an advanced automation process control system. Based on task analysis and background literature, we develop the concept of hybrid intelligence that implies balanced AI transparency that supports upskilling and resilience. So-called fragmented intelligence, in turn, may result from the combination of the complexity of advanced automation along with the complexity of the process physics that places critical emphasis on expert knowledge. This combination can result in the so-called "double black box effect", given that designing for understandability for the line workers might not be feasible: expert networks are needed for resilience.

Asunto(s)

RESUMEN

BACKGROUND: Militaries have historically utilised generic physical fitness tests to assess physical readiness, but there has been a recent shift to develop physical employment standards (PES) based on actual job demands. OBJECTIVE: The purpose of this investigation was to characterise the physical demands of critical tasks performed by Royal Australian Air Force (RAAF) personnel to inform PES development. METHODS: Job task analysis were performed for 27 RAAF trades. Criterion tasks were identified through a systematic approach involving workshops and field-observations. The identified tasks were assessed for dominant physical capacity and grouped into movement-based clusters. Psychophysiological measures were collected from personnel performing the tasks. RESULTS: Of 87 criterion tasks, 92% were characterised as manual handling dominant. Across these 87 tasks the principal physical capacities were: muscular strength (59%), muscular endurance (52%) and cardiorespiratory endurance (39%). The most common movement clusters were Lift to Platform (44%) and Lift and Carry (38%). Lift to Platform tasks required lifting to a median height of 1.32 m (1.20 -1.65 m) and a median mass of 25.0 kg (21.0 -28.9 kg) per person. Median carry mass was 25.0 kg (22.4 -36.1 kg) per person and distance was 26.0 m (17.5 -50.0 m). Median task mean 'Vdot;O2, HR and RPE were 1.8 L.min- 1 (1.5-2.2 L.min- 1), 137 b.min- 1 (120-144) and 13 (12-14). CONCLUSIONS: The high proportion of manual handling criterion tasks emphasises the importance of these activities and the underlying physical capacities for RAAF personnel. Current fitness assessments are unlikely to predict job task performance.

Asunto(s)

RESUMEN

Transportation tasks in nursing are common, often overlooked, and directly impact patient care time in the context of staff shortages and an aging society. Current studies lack a specific focus on transportation tasks, a gap our research aims to fill. By providing detailed data on transportation needs in nursing, our study establishes a crucial foundation for the development and integration of assistive robots in clinical settings. In July and September 2023, we conducted weekly observations of nurses to assess clinical transportation needs. We aim to understand the economic impact and the methods nurses use for transportation tasks. We conducted a participant observation using a standardized app-based form over a seven-day observation period in two rural clinics. N = 1830 transports were made by nurses and examined by descriptive analysis. Non-medical supplies account for 27.05% (n = 495) of all transports, followed by medical supplies at 17.32% (n = 317), pharmacotherapy at 14.10% (n = 258) and other other categories like meals or drinks contributing 12.68% (n = 232). Most transports had a factual transport time of under a minute, with patient transport and lab samples displaying more variability. In total, 77.15% of all transports were made by hand. Requirements to collect items or connect transports with patient care were included in 5% of all transports. Our economic evaluation highlighted meals as the most costly transport, with 9596.16 € per year in the observed clinics. Budget-friendly robots would amortize these costs over one year by transporting meals. We support understanding nurses' transportation needs via further research on assistive robots to validate our findings and determine the feasibility of transport robots.

RESUMEN

OBJECTIVES: The 2022 Oncology Nursing Society Oncology Navigation Standards of Professional Practice offer a framework for role delineation in oncology navigation. The goal of completing a job task analysis using the standards with four independent navigation teams was to identify a core map of work which would align skills, experience and knowledge with clinical nurse navigators, social work navigators, and patient navigators. Role delineation reduces suboptimal use of resources and inconsistent navigation services. METHODS: An independent job task analysis was conducted with each of the four oncology navigation teams. Patient navigators and clinical nurse navigators were asked to report on each requested task over a 2-week period. The team discussed and determined alignment or misalignment with the standards. This discussion included the request and skill level of each navigator. RESULTS: Sixty percent of the tasks identified in the job task analysis were in alignment with the standards for role and level of care. Thirty percent of the tasks aligned for role, but not for level of care, with nurse navigators performing a high number of non-nursing/clerical tasks. Ten percent were outside the scope of navigation. CONCLUSIONS: Four enterprise opportunities were identified: (1) formalize standards for Tumor Board management, (2) create a core model for essential metrics, (3) establish standardized process for medical record retrieval for new oncology patients, and (4) explore alternative staffing models. IMPLICATIONS FOR NURSING PRACTICE: Using a job task analysis allows time for meaningful exploration of roles and scope of work completed by the team. High work volume for navigation teams often leads to a "this is the way we've always done it" mentality. A job task analysis provides a structured approach with dedicated time and a safe space for navigators to "think critically" about their daily work, identify opportunities for change, and progress using this framework.

Asunto(s)

RESUMEN

BACKGROUND: Video-based review is paramount for operative performance assessment but can be laborious when performed manually. Hierarchical Task Analysis (HTA) is a well-known method that divides any procedure into phases, steps, and tasks. HTA requires large datasets of videos with consistent definitions at each level. Our aim was to develop an AI model for automated segmentation of phases, steps, and tasks for laparoscopic cholecystectomy videos using a standardized HTA. METHODS: A total of 160 laparoscopic cholecystectomy videos were collected from a publicly available dataset known as cholec80 and from our own institution. All videos were annotated for the beginning and ending of a predefined set of phases, steps, and tasks. Deep learning models were then separately developed and trained for the three levels using a 3D Convolutional Neural Network architecture. RESULTS: Four phases, eight steps, and nineteen tasks were defined through expert consensus. The training set for our deep learning models contained 100 videos with an additional 20 videos for hyperparameter optimization and tuning. The remaining 40 videos were used for testing the performance. The overall accuracy for phases, steps, and tasks were 0.90, 0.81, and 0.65 with the average F1 score of 0.86, 0.76 and 0.48 respectively. Control of bleeding and bile spillage tasks were most variable in definition, operative management, and clinical relevance. CONCLUSION: The use of hierarchical task analysis for surgical video analysis has numerous applications in AI-based automated systems. Our results show that our tiered method of task analysis can successfully be used to train a DL model.

Asunto(s)

RESUMEN

This study aimed to identify the most important, frequently performed, and physically demanding tasks performed by Brazilian firefighters and to identify tasks that could be used to assess physical fitness. A subjective task analysis was conducted. Five hundred twenty-four firefighters (84% male; 16% females) responded to an online survey and rated 37 tasks across three domains (most important, most frequent, and most physically demanding). A dichotomous decision analysis was used to inform the proposed physical fitness tests. Wildland firefighting tasks presented the highest overall mean rate. Traffic control was considered the most important and frequently performed task. Lifeguard rescue was considered the most physically demanding task. The dichotomous analysis identified 14 essential tasks (seven structural firefighting and seven automobile accidents). The tasks identified may be helpful in developing criterion physical fitness tests and training programs related to firefighters' demands.Practitioner summary: The unpredictability, variability, and dangerousness of firefighting make it challenging to observe the physical demands imposed on firefighters. A subjective task analysis was conducted to identify essential tasks performed by Brazilian firefighters. Wildland firefighting, lifeguard rescue, automobile accidents, and structural firefighting tasks were the most important, frequent, and physically demanding.

Asunto(s)

RESUMEN

Introducción: Los intensivistas son gerentes asistenciales que toman decisiones en situaciones de estrés, por lo que su desempeño no solo depende del conocimiento técnico, sino también de las competencias gerenciales que alcancen. Objetivo: Identificar las competencias profesionales gerenciales para el desempeño de los intensivistas cubanos en la actualidad. Métodos: Se realizó una investigación de desarrollo con enfoque mixto, desde junio hasta octubre del 2021. Se emplean métodos teóricos y empíricos, entre los que se encuentran sujetos clave y un cuestionario en el que se pone a consideración de expertos, por método Delphi, las dieciséis competencias gerenciales del modelo por competencias profesionales del especialista en MIE, se utilizó Alfa de Cronbach y análisis de desligamiento multidimensional. Resultados: El Alfa de Cronbach global y por ítems del cuestionario resultó ser alto. En el análisis de desligamiento multidimensional resultó que, de las 16 competencias analizadas, diez se correlacionan entre sí y seis se disgregan del resto. Conclusión: Fueron identificadas tres competencias profesionales gerenciales con diez elementos de competencias imprescindibles para el desempeño de los especialistas en MIE y se evidencia que el desarrollo de la esfera gerencial es esencial en el desempeño de los intensivistas en la actualidad(AU)

Introduction: Intensivists are care managers who make decisions in stressful situations, so their performance not only depends on technical knowledge, but also on the managerial skills they accomplish. Objective: To identify the managerial professional proficiencies for the performance of Cuban intensivists nowadays. Methods: A development research was carried out with a mixed approach, from June to October 2021. Theoretical and empirical methods are used, among which are key individuals are considered beside a questionnaire assessed by experts, by Delphi method, sixteen managerial capabilities of the model by professional capabilities of a MIE specialist. Cronbach's Alpha and multidimensional disengagement analysis were used. Results: The global Cronbach's Alpha and by items of the questionnaire turned out to be high. In the multidimensional decoupling analysis, it turned out that, out of the 16 competencies analyzed, 10 are correlated with each other and six are disaggregated from the rest. Conclusion: Three managerial professional proficiencies were identified with 10 elements of essential proficiencies for the performance of MIE specialists and it is evident that the development of the managerial sphere is essential in the performance of intensivists these days(AU)

Asunto(s)

RESUMEN

Background: Gastroscopy to investigate dyspepsia without alarm symptoms rarely results in clinically actionable findings or sustained health-related quality-of-life improvements among patients aged 18-60 years and is, therefore, not recommended. Despite this, referrals for and performance of gastroscopy among this patient population remain high. The purpose of this study was to understand family physicians' and gastroenterologists' mental models of dyspepsia and the drivers behind referring or performing gastroscopy. Methods: Cognitive task analysis routine critical decision method interviews with family physicians (n = 8) and gastroenterologists (n = 4). Results: Family physicians and gastroenterologists hold rich mental models of dyspepsia that rely on sensemaking; however, gaps in information continuity affect their ability to plan and coordinate patient care. Drivers behind decisions to refer or perform gastroscopy were: eliminating risk for serious pathology, providing reassurance, perceived preference by patients to receive information and reassurance from gastroenterologists, maintaining relationships with patients, and saving costs to the health system. Conclusions: Family physicians refer for dyspepsia when they are seeking support from gastroenterologists, they believe that alternative factors may be impacting the patient's health or view it as a cost-saving measure. Likewise, gastroenterologists perform gastroscopy for dyspepsia when they perceive it as a cost-saving measure, they want to support their primary care colleagues and provide their colleagues and patients with reassurance. An improved degree of communication between speciality and primary care could allow for continuity in the transfer of information about patients and reduce referrals for dyspepsia.

RESUMEN

Cognitive task analysis (CTA) methods are traditionally used to conduct small-sample, in-depth studies. In this case study, CTA methods were adapted for a large multi-site study in which 102 anesthesiologists worked through four different high-fidelity simulated high-consequence incidents. Cognitive interviews were used to elicit decision processes following each simulated incident. In this paper, we highlight three practical challenges that arose: (1) standardizing the interview techniques for use across a large, distributed team of diverse backgrounds; (2) developing effective training; and (3) developing a strategy to analyze the resulting large amount of qualitative data. We reflect on how we addressed these challenges by increasing standardization, developing focused training, overcoming social norms that hindered interview effectiveness, and conducting a staged analysis. We share findings from a preliminary analysis that provides early validation of the strategy employed. Analysis of a subset of 64 interview transcripts using a decompositional analysis approach suggests that interviewers successfully elicited descriptions of decision processes that varied due to the different challenges presented by the four simulated incidents. A holistic analysis of the same 64 transcripts revealed individual differences in how anesthesiologists interpreted and managed the same case.