Cognitive processes as integrative component for developing expert decision-making systems: A workflow centered framework

From digital health to learning health systems: four approaches to using data for digital health design

Digital health technologies, powered by digital data, provide an opportunity to improve the efficacy and efficiency of health systems at large. However, little is known about different approaches to the use of data for digital health design, or about their possible relations to system-level dynamics. In this contribution, we identify four existing approaches to the use of data for digital health design, namely the silent, the overt, the data-enabled, and the convergent. After characterising the approaches, we provide real-life examples of each. Furthermore, we compare the approaches in terms of selected desirable characteristics of the design process, highlighting relative advantages and disadvantages. Finally, we reflect on the system-level relevance of the differentiation between the approaches and point towards future research directions. Overall, the contribution provides researchers and practitioners with a broad conceptual framework to examine data-related challenges and opportunities in digital health design.

Dissecting Cardiac Surgery: A Video-based Recall Protocol to Elucidate Team Cognitive Processes in the Operating Room

OBJECTIVE

The aim of this study was to elucidate the cognitive processes involved in surgical procedures from the perspective of different team roles (surgeon, anesthesiologist, and perfusionist) and provide a comprehensive compilation of intraoperative cognitive processes.

SUMMARY BACKGROUND DATA

Nontechnical skills play a crucial role in surgical team performance and understanding the cognitive processes underlying the intraoperative phase of surgery is essential to improve patient safety in the operating room (OR).

METHODS

A mixed-methods approach encompassing semistructured interviews with 9 subject-matter experts. A cognitive task analysis was built upon a hierarchical segmentation of coronary artery bypass grafting procedures and a cued-recall protocol using video vignettes was used.

RESULTS

A total of 137 unique surgical cognitive processes were identified, including 33 decision points, 23 critical communications, 43 pitfalls, and 38 strategies. Self-report cognitive workload varied substantially, depending on team role and surgical step. A web-based dashboard was developed, providing an integrated visualization of team cognitive processes in the OR that allows readers to intuitively interact with the study findings.

CONCLUSIONS

This study advances the current body of knowledge by making explicit relevant cognitive processes involved during the intraoperative phase of cardiac surgery from the perspective of multiple OR team members. By displaying the research findings in an interactive dashboard, we provide trainees with new knowledge in an innovative fashion that could be used to enhance learning outcomes. In addition, the approach used in the present study can be used to deeply understand the cognitive factors underlying surgical adverse events and errors in the OR.

Surgical process modelling strategies: which method to choose for determining workflow?

The vital role of surgeries in healthcare requires a constant attention to improvement. Surgical process modelling is an innovative and rather recently introduced approach for tackling the issues in today's complex surgeries. This modelling field is very challenging and still under development, therefore, it is not always clear which modelling strategy would best fit the needs in which situations. The aim of this study was to provide a guide for matching the choice of modelling strategies for determining surgical workflows. In this work, the concepts associated with surgical process modelling are described, aiming to clarify them and to promote their use in future studies. The relationship of these concepts and the possible combinations of the suitable approaches for modelling strategies are elaborated and the criteria for opting for the proper modelling strategy are discussed.

Getting nowhere fast: trade-off between speed and precision in training to execute image-guided hand-tool movements

Background

The speed and precision with which objects are moved by hand or hand-tool interaction under image guidance depend on a specific type of visual and spatial sensorimotor learning. Novices have to learn to optimally control what their hands are doing in a real-world environment while looking at an image representation of the scene on a video monitor. Previous research has shown slower task execution times and lower performance scores under image-guidance compared with situations of direct action viewing. The cognitive processes for overcoming this drawback by training are not yet understood.

Methods

We investigated the effects of training on the time and precision of direct view versus image guided object positioning on targets of a Real-world Action Field (RAF). Two men and two women had to learn to perform the task as swiftly and as precisely as possible with their dominant hand, using a tool or not and wearing a glove or not. Individuals were trained in sessions of mixed trial blocks with no feed-back.

Results

As predicted, image-guidance produced significantly slower times and lesser precision in all trainees and sessions compared with direct viewing. With training, all trainees get faster in all conditions, but only one of them gets reliably more precise in the image-guided conditions. Speed-accuracy trade-offs in the individual performance data show that the highest precision scores and steepest learning curve, for time and precision, were produced by the slowest starter. Fast starters produced consistently poorer precision scores in all sessions. The fastest starter showed no sign of stable precision learning, even after extended training.

Conclusions

Performance evolution towards optimal precision is compromised when novices start by going as fast as they can. The findings have direct implications for individual skill monitoring in training programmes for image-guided technology applications with human operators.

Advantages and Disadvantages of 1-Incision, 2-Incision, 3-Incision, and 4-Incision Laparoscopic Cholecystectomy: A Workflow Comparison Study

A comparison of 1-port, 2-port, 3-port, and 4-port laparoscopic cholecystectomy techniques from the point of view of workflow criteria was made to both identify specific workflow components that can cause surgical disturbances and indicate good and bad practices. As a case study, laparoscopic cholecystectomies, including manual tasks and interactions within teamwork members, were video-recorded and analyzed on the basis of specially encoded workflow information. The parameters for comparison were defined as follows: surgery time, tool and hand activeness, operator's passive work, collisions, and operator interventions. It was found that 1-port cholecystectomy is the worst technique because of nonergonomic body position, technical complexity, organizational anomalies, and operational dynamism. The differences between laparoscopic techniques are closely linked to the costs of the medical procedures. Hence, knowledge about the surgical workflow can be used for both planning surgical procedures and balancing the expenses associated with surgery.

Integrating service user and practitioner expertise within a web-based system for collaborative mental-health risk and safety management

OBJECTIVES

To develop a decision support system (DSS), myGRaCE, that integrates service user (SU) and practitioner expertise about mental health and associated risks of suicide, self-harm, harm to others, self-neglect, and vulnerability. The intention is to help SUs assess and manage their own mental health collaboratively with practitioners.

METHODS

An iterative process involving interviews, focus groups, and agile software development with 115 SUs, to elicit and implement myGRaCE requirements.

RESULTS

Findings highlight shared understanding of mental health risk between SUs and practitioners that can be integrated within a single model. However, important differences were revealed in SUs' preferred process of assessing risks and safety, which are reflected in the distinctive interface, navigation, tool functionality and language developed for myGRaCE. A challenge was how to provide flexible access without overwhelming and confusing users.

CONCLUSION

The methods show that practitioner expertise can be reformulated in a format that simultaneously captures SU expertise, to provide a tool highly valued by SUs. A stepped process adds necessary structure to the assessment, each step with its own feedback and guidance.

PRACTICE IMPLICATIONS

The GRiST web-based DSS (www.egrist.org) links and integrates myGRaCE self-assessments with GRiST practitioner assessments for supporting collaborative and self-managed healthcare.

Cognitive informatics in biomedicine and healthcare

Cognitive Informatics (CI) is a burgeoning interdisciplinary domain comprising of the cognitive and information sciences that focuses on human information processing, mechanisms and processes within the context of computing and computer applications. Based on a review of articles published in the Journal of Biomedical Informatics (JBI) between January 2001 and March 2014, we identified 57 articles that focused on topics related to cognitive informatics. We found that while the acceptance of CI into the mainstream informatics research literature is relatively recent, its impact has been significant - from characterizing the limits of clinician problem-solving and reasoning behavior, to describing coordination and communication patterns of distributed clinical teams, to developing sustainable and cognitively-plausible interventions for supporting clinician activities. Additionally, we found that most research contributions fell under the topics of decision-making, usability and distributed team activities with a focus on studying behavioral and cognitive aspects of clinical personnel, as they performed their activities or interacted with health information systems. We summarize our findings within the context of the current areas of CI research, future research directions and current and future challenges for CI researchers.

Advancing cognitive engineering methods to support user interface design for electronic health records

BACKGROUND

Despite many decades of research on the effective development of clinical systems in medicine, the adoption of health information technology to improve patient care continues to be slow, especially in ambulatory settings. This applies to dentistry as well, a primary care discipline with approximately 137,000 practitioners in the United States. A critical reason for slow adoption is the poor usability of clinical systems, which makes it difficult for providers to navigate through the information and obtain an integrated view of patient data.

OBJECTIVE

In this study, we documented the cognitive processes and information management strategies used by dentists during a typical patient examination. The results will inform the design of a novel electronic dental record interface.

METHODS

We conducted a cognitive task analysis (CTA) study to observe ten general dentists (five general dentists and five general dental faculty members, each with more than two years of clinical experience) examining three simulated patient cases using a think-aloud protocol.

RESULTS

Dentists first reviewed the patient's demographics, chief complaint, medical history and dental history to determine the general status of the patient. Subsequently, they proceeded to examine the patient's intraoral status using radiographs, intraoral images, hard tissue and periodontal tissue information. The results also identified dentists' patterns of navigation through patient's information and additional information needs during a typical clinician-patient encounter.

CONCLUSION

This study reinforced the significance of applying cognitive engineering methods to inform the design of a clinical system. Second, applying CTA to a scenario closely simulating an actual patient encounter helped with capturing participants' knowledge states and decision-making when diagnosing and treating a patient. The resultant knowledge of dentists' patterns of information retrieval and review will significantly contribute to designing flexible and task-appropriate information presentation in electronic dental records.

Collaborative co-design of emerging multi-technologies for surgery

The EU Research Training Network on Augmented Reality in Surgery (ARIS*ER) was established with two aims: (1) to develop next-generation novel image guidance (augmented reality based on medical images) and cross-linked robotic systems (automatic control loops guided by information sensed from the patient) and (2) to educate young researchers in the user-centred, multidisciplinary design of emerging technologies for minimally invasive surgery (MIS) and intervention radiology. Collaborations between engineers, Human Factors specialists, industrial designers and medical end users were foreseen, but actual methodologies had to be developed. Three applications were used as development vehicles and as demonstrators. The resulting teamwork and process of identifying requirements, finding solutions (in technology and workflow), and shifting between these to optimize and speed development towards quality of care were studied. The ARIS*ER approach solves current problems in collaborative teams, taking a systems approach, and manages the overview of requirements and solutions, which is too complex to manage centrally.