Information processing by community health nurses using mobile health (mHealth) tools for early identification of suicide and depression risks in Fiji Islands

Objectives

High rates of depression and suicide and a lack of trained psychiatrists have emerged as significant concerns in the low-income and middle-income countries (LMICs) such as the Pacific Island Countries (PICs). Readily available smartphones were leveraged with community health nurses (CHNs) in task-sharing for early identification of suicide and depression risks in Fiji Islands, the largest of PICs. This investigation examines how CHNs can efficiently and effectively process patient information about depression and suicide risk for making diagnostic and management decisions without compromising safety. The research is driven by the theoretical framework of text comprehension (knowledge representation and interpretation) and decision-making.

Methods

Mobile health (mHealth) Application for Suicide Risk and Depression Assessment (ASRaDA) was designed to include culturally useful clinical guidelines for these disorders. A representative sample of 48 CHNs was recruited and presented with two clinical cases (depression and suicide) in a simulated setting under three conditions: No support, paper-based and mobile-based culturally valid guideline support. Data were collected as the nurses read through the scenarios, ‘thinking aloud’, before summarising, diagnoses and follow-up recommendations. Transcribed audiotapes were analysed using formal qualitative discourse analysis methods for diagnostic accuracy, comprehension of clinical problems and reasoning patterns.

Results

Using guidelines on ASRaDA, the CHNs took less time to process patient information with more accurate diagnostic and therapeutic decisions for depression and suicide risk than with paper-based or no guideline conditions. A change in reasoning pattern for nurses’ information processing was observed with decision support.

Discussion

Although these results are shown in a mental health setting in Fiji, there are reasons to believe they are generalisable beyond mental health and other lower-to-middle income countries.

Conclusions

Culturally appropriate clinical guidelines on mHealth supports efficient information processing for quick and accurate decisions and a positive shift in reasoning behaviour by the nurses. However, translating complex qualitative patient information into quantitative scores could generate conceptual errors. These results are valid in simulated conditions.

Utility of mobile learning in Electrocardiography

Aims

Mobile learning is attributed to the acquisition of knowledge derived from accessing information on a mobile device. Although increasingly implemented in medical education, research on its utility in Electrocardiography remains sparse. In this study, we explored the effect of mobile learning on the accuracy of electrocardiogram (ECG) analysis and interpretation.

Methods and results

The study comprised 181 participants (77 fourth- and 69 sixth-year medical students, and 35 residents). Participants were randomized to analyse ECGs with a mobile learning strategy [either searching the Internet or using an ECG reference application (app)] or not. For each ECG, they provided their initial diagnosis, key supporting features, and final diagnosis consecutively. Two weeks later, they analysed the same ECGs, without access to any mobile device. ECG interpretation was more accurate when participants used the ECG app (56%), as compared to searching the Internet (50.3%) or neither (43.5%, P = 0.001). Importantly, mobile learning supported participants in revising their initial incorrect ECG diagnosis (ECG app 18.7%, Internet search 13.6%, no mobile device 8.4%, P < 0.001). However, whilst this was true for students, there was no significant difference amongst residents. Internet searches were only useful if participants identified the correct ECG features. The app was beneficial when participants searched by ECG features, but not by diagnosis. Using the ECG reference app required less time than searching the Internet (7:44 ± 4:13 vs. 9:14 ± 4:34, P < 0.001). Mobile learning gains were not sustained after 2 weeks.

Conclusion

Whilst mobile learning contributes to increased ECG diagnostic accuracy, the benefits were not sustained over time.

Mapping the territory of renal care: a formative analysis of the cognitive work involved in managing acute kidney injury

The design and evaluation of healthcare work systems requires an understanding of the cognitive work involved in healthcare tasks. Previous studies suggest that a formative approach would be particularly useful to examine healthcare activities for this purpose. In the present study, methods from cognitive work analysis and cognitive task analysis are combined in a formative examination of managing acute kidney injury, an activity that occurs across primary and secondary healthcare settings. The analyses are informed by interviews with healthcare practitioners and a review of practice guidelines. The findings highlight ways in which the task setting influenced practitioners' activity, and ways in which practitioners approached the activity (for example, how they used data to make decisions). The approach taken provided a rich understanding of the cognitive work involved, as well as generating suggestions for the design of work systems to support the clinical task. Practitioner summary: Healthcare tasks often require decision-making in complex and dynamic circumstances, potentially involving collaboration across different practitioner roles and locations. We demonstrate the use of a formative analysis to understand the cognitive work in managing a clinical syndrome across primary and secondary care settings, and consider the implications for work design.

'This is our liver patient…': use of narratives during resident and nurse handoff conversations

OBJECTIVE

Handoffs are often framed as the co-construction of a shared understanding relying on narrative storytelling. We investigated how narratives are constructed and used during resident and nurse handoff conversations.

METHOD

We audio-recorded resident (n=149) and nurse (n=126) handoffs in an inpatient medicine unit. Qualitative analysis using grounded theory was conducted to identify and characterise the structure of resident and nursing handoff narratives.

RESULTS

Handoff conversations among both residents and nurses used three types of narratives: narratives on creating clinical imagery, narratives on coordinating care continuity and narratives on integrating contextual aspects of care. Clinical imagery narratives were common during patient introductions: residents used a top-down approach relying on overarching patient clinical situations (eg, 'a liver patient'), whereas nurses used a bottom-up approach using patient-specific identifying information. Narratives on the coordination of care continuity for residents focused on managing internal and external coordination activities, whereas nurse narratives focused on internal coordination, emphasising their role as an interface between patients and their physicians. Both resident and nurse narratives on the contextual aspects of care had considerable focus on highlighting 'heads up' anticipatory information and personal patient information; such information was often not present in patient charts, but was important for ensuring effective care management.

DISCUSSION

The presence of narrative structures highlights the need for new perspectives for the design of handoff tools that allow for both informational and cognitive support and shared awareness among conversational partners during handoff conversations. We discuss the implications of the use of narratives for patient safety and describe specific design considerations for supporting narrative interactions during handoffs.

Advancing Critical Thinking Through Learning Issues in Problem-Based Learning

Health professions educators are increasingly urged to use learning designs that promote critical thinking and the development of interpersonal competencies. Problem-based learning (PBL) has a long, albeit contested, history as a collaborative and deep think-aloud process that participants use to reach conclusions about medical cases. In order to make progress, participants must assess what they do not know and what they must learn in order to continue. Answering these learning issues (LI) requires self-direction and cognitive presence. This study analyzes the discussions that participants used in the reporting phase of the LI process in an 8-week PBL module on cardiac-renal systems. Data were drawn from 10 class sessions and analyzed for critical thinking using a model based on Garrison and Newman et al. Participants at first presented LI reports didactically but over time initiated active learning strategies. The findings indicate large increases in the numbers of LI reports in which participants engaged in collaborative thinking. There were also large increases in the amount of time devoted to critical thinking as participants aligned the LI process more closely with the intent of PBL. Participants' identity development as experts also underwent changes and the fluidity of the expert roles increased. Thoughtful design of the LI process can help learners develop the habitus of self-direction and collaborative critical thinking that they need in order to develop clinical reasoning.

Requirements and validation of a prototype learning health system for clinical diagnosis

INTRODUCTION

Diagnostic error is a major threat to patient safety in the context of family practice. The patient safety implications are severe for both patient and clinician. Traditional approaches to diagnostic decision support have lacked broad acceptance for a number of well-documented reasons: poor integration with electronic health records and clinician workflow, static evidence that lacks transparency and trust, and use of proprietary technical standards hindering wider interoperability. The learning health system (LHS) provides a suitable infrastructure for development of a new breed of learning decision support tools. These tools exploit the potential for appropriate use of the growing volumes of aggregated sources of electronic health records.

METHODS

We describe the experiences of the TRANSFoRm project developing a diagnostic decision support infrastructure consistent with the wider goals of the LHS. We describe an architecture that is model driven, service oriented, constructed using open standards, and supports evidence derived from electronic sources of patient data. We describe the architecture and implementation of 2 critical aspects for a successful LHS: the model representation and translation of clinical evidence into effective practice and the generation of curated clinical evidence that can be used to populate those models, thus closing the LHS loop.

RESULTS/CONCLUSIONS

Six core design requirements for implementing a diagnostic LHS are identified and successfully implemented as part of this research work. A number of significant technical and policy challenges are identified for the LHS community to consider, and these are discussed in the context of evaluating this work: medico-legal responsibility for generated diagnostic evidence, developing trust in the LHS (particularly important from the perspective of decision support), and constraints imposed by clinical terminologies on evidence generation.

Templates as a method for implementing data provenance in decision support systems

Decision support systems are used as a method of promoting consistent guideline-based diagnosis supporting clinical reasoning at point of care. However, despite the availability of numerous commercial products, the wider acceptance of these systems has been hampered by concerns about diagnostic performance and a perceived lack of transparency in the process of generating clinical recommendations. This resonates with the Learning Health System paradigm that promotes data-driven medicine relying on routine data capture and transformation, which also stresses the need for trust in an evidence-based system. Data provenance is a way of automatically capturing the trace of a research task and its resulting data, thereby facilitating trust and the principles of reproducible research. While computational domains have started to embrace this technology through provenance-enabled execution middlewares, traditionally non-computational disciplines, such as medical research, that do not rely on a single software platform, are still struggling with its adoption. In order to address these issues, we introduce provenance templates - abstract provenance fragments representing meaningful domain actions. Templates can be used to generate a model-driven service interface for domain software tools to routinely capture the provenance of their data and tasks. This paper specifies the requirements for a Decision Support tool based on the Learning Health System, introduces the theoretical model for provenance templates and demonstrates the resulting architecture. Our methods were tested and validated on the provenance infrastructure for a Diagnostic Decision Support System that was developed as part of the EU FP7 TRANSFoRm project.

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.

Heuristics in Managing Complex Clinical Decision Tasks in Experts' Decision Making

BACKGROUND

Clinical decision support is a tool to help experts make optimal and efficient decisions. However, little is known about the high level of abstractions in the thinking process for the experts.

OBJECTIVE

The objective of the study is to understand how clinicians manage complexity while dealing with complex clinical decision tasks.

METHOD

After approval from the Institutional Review Board (IRB), three clinical experts were interviewed the transcripts from these interviews were analyzed.

RESULTS

We found five broad categories of strategies by experts for managing complex clinical decision tasks: decision conflict, mental projection, decision trade-offs, managing uncertainty and generating rule of thumb.

CONCLUSION

Complexity is created by decision conflicts, mental projection, limited options and treatment uncertainty. Experts cope with complexity in a variety of ways, including using efficient and fast decision strategies to simplify complex decision tasks, mentally simulating outcomes and focusing on only the most relevant information.

APPLICATION

Understanding complex decision making processes can help design allocation based on the complexity of task for clinical decision support design.