The quality of data-driven hypotheses generated by inexperienced clinical researchers: A case study.

Objectives: We invited inexperienced clinical researchers to analyze coded health datasets and develop hypotheses. We recorded and analyzed their hypothesis generation process. All the hypotheses generated in the process were rated by the same group of seven experts by using the same metrics. This case study examines the higher quality (i.e., higher ratings) and lower quality of hypotheses and participants who generated them. We characterized the contextual factors associated with the quality of hypotheses. Methods: All participants (i.e., clinical researchers) completed a 2-hour study session to analyze data and generate scientific hypotheses using the think-aloud method. Participants' screen activity and audio were recorded and transcribed. These transcriptions were used to measure the time used to generate each hypothesis and to code cognitive events (i.e., cognitive activities used when generating hypotheses, for example, "Seeking for Connection" describes an attempt to draw connections between data points). The hypothesis ratings by the expert panel were used as the quality of the hypotheses during the analysis. We analyzed the factors associated with (1) the five highest and (2) five lowest rated hypotheses and (3) the participants who generated them, including the number of hypotheses per participant, the validity of those hypotheses, the number of cognitive events used for each hypothesis, as well as the participant's research experience and basic demographics. Results: Participants who generated the five highest-rated hypotheses used similar lengths of time (difference 3:03), whereas those who generated the five lowest-rated hypotheses used more varying lengths of time (difference 7:13). Participants who generated the five highest-rated hypotheses also utilized slightly fewer cognitive events on average compared to the five lowest-rated hypotheses (4 per hypothesis vs. 4.8 per hypothesis). When we examine the participants (who generated the five highest and five lowest hypotheses) and their total hypotheses generated during the 2-hour study sessions, the participants with the five highest-rated hypotheses again had a shorter range of time per hypothesis on average (0:03:34 vs. 0:07:17). They (with the five highest ratings) used fewer cognitive events per hypothesis (3.498 vs. 4.626). They (with the five highest ratings) also had a higher percentage of valid rate (75.51% vs. 63.63%) and generally had more experience with clinical research. Conclusion: The quality of the hypotheses was shown to be associated with the time taken to generate them, where too long or too short time to generate hypotheses appears to be negatively associated with the hypotheses' quality ratings. Also, having more experience seems to positively correlate with higher ratings of hypotheses and higher valid rates. Validity is a quality dimension used by the expert panel during rating. However, we acknowledge that our results are anecdotal. The effect may not be simply linear, and future research is necessary. These results underscore the multi-factor nature of hypothesis generation.

Data-Driven Hypothesis Generation in Clinical Research: What We Learned from a Human Subject Study?

Hypothesis generation is an early and critical step in any hypothesis-driven clinical research project. Because it is not yet a well-understood cognitive process, the need to improve the process goes unrecognized. Without an impactful hypothesis, the significance of any research project can be questionable, regardless of the rigor or diligence applied in other steps of the study, e.g., study design, data collection, and result analysis. In this perspective article, the authors provide a literature review on the following topics first: scientific thinking, reasoning, medical reasoning, literature-based discovery, and a field study to explore scientific thinking and discovery. Over the years, scientific thinking has shown excellent progress in cognitive science and its applied areas: education, medicine, and biomedical research. However, a review of the literature reveals the lack of original studies on hypothesis generation in clinical research. The authors then summarize their first human participant study exploring data-driven hypothesis generation by clinical researchers in a simulated setting. The results indicate that a secondary data analytical tool, VIADS-a visual interactive analytic tool for filtering, summarizing, and visualizing large health data sets coded with hierarchical terminologies, can shorten the time participants need, on average, to generate a hypothesis and also requires fewer cognitive events to generate each hypothesis. As a counterpoint, this exploration also indicates that the quality ratings of the hypotheses thus generated carry significantly lower ratings for feasibility when applying VIADS. Despite its small scale, the study confirmed the feasibility of conducting a human participant study directly to explore the hypothesis generation process in clinical research. This study provides supporting evidence to conduct a larger-scale study with a specifically designed tool to facilitate the hypothesis-generation process among inexperienced clinical researchers. A larger study could provide generalizable evidence, which in turn can potentially improve clinical research productivity and overall clinical research enterprise.

Data-driven hypothesis generation among inexperienced clinical researchers: A comparison of secondary data analyses with visualization (VIADS) and other tools.

Objectives: To compare how clinical researchers generate data-driven hypotheses with a visual interactive analytic tool (VIADS, a visual interactive analysis tool for filtering and summarizing large datasets coded with hierarchical terminologies) or other tools. Methods: We recruited clinical researchers and separated them into "experienced" and "inexperienced" groups. Participants were randomly assigned to a VIADS or control group within the groups. Each participant conducted a remote 2-hour study session for hypothesis generation with the same study facilitator on the same datasets by following a think-aloud protocol. Screen activities and audio were recorded, transcribed, coded, and analyzed. Hypotheses were evaluated by seven experts on their validity, significance, and feasibility. We conducted multilevel random effect modeling for statistical tests. Results: Eighteen participants generated 227 hypotheses, of which 147 (65%) were valid. The VIADS and control groups generated a similar number of hypotheses. The VIADS group took a significantly shorter time to generate one hypothesis (e.g., among inexperienced clinical researchers, 258 s versus 379 s, p = 0.046, power = 0.437, ICC = 0.15). The VIADS group received significantly lower ratings than the control group on feasibility and the combination rating of validity, significance, and feasibility. Conclusion: The role of VIADS in hypothesis generation seems inconclusive. The VIADS group took a significantly shorter time to generate each hypothesis. However, the combined validity, significance, and feasibility ratings of their hypotheses were significantly lower. Further characterization of hypotheses, including specifics on how they might be improved, could guide future tool development.

Evaluation of a Patient Decision Aid for Refractive Eye Surgery.

BACKGROUND: We developed a prototype patient decision aid, EyeChoose, to assist college-aged students in selecting a refractive surgery. EyeChoose can educate patients on refractive errors and surgeries, generate evidence-based recommendations based on a user's medical history and personal preferences, and refer patients to local refractive surgeons. OBJECTIVES: We conducted an evaluative study on EyeChoose to assess the alignment of surgical modality recommendations with a user's medical history and personal preferences, and to examine the tool's usefulness and usability. METHODS: We designed a mixed methods study on EyeChoose through simulations of test cases to provide a quantitative measure of the customized recommendations, an online survey to evaluate the usefulness and usability, and a focus group interview to obtain an in-depth understanding of user experience and feedback. RESULTS: We used stratified random sampling to generate 245 test cases. Simulated execution indicated EyeChoose's recommendations aligned with the reference standard in 243 (99%). A survey of 55 participants with 16 questions on usefulness, usability, and general impression showed that 14 questions recorded more than 80% positive responses. A follow-up focus group with 10 participants confirmed EyeChoose's useful features of patient education, decision assistance, surgeon referral, as well as good usability with multimedia resources, visual comparison among the surgical modalities, and the overall aesthetically pleasing design. Potential areas for improvement included offering nuances in soliciting user preferences, providing additional details on pricing, effectiveness, and reversibility of surgeries, expanding the function of surgeon referral, and fixing specific usability issues. CONCLUSION: The initial evaluation of EyeChoose suggests that it could provide effective patient education, generate appropriate recommendations, connect to local refractive surgeons, and demonstrate good system usability in a test environment. Future research is required to enhance the system functions, fully implement and evaluate the tool in naturalistic settings, and examine the findings' generalizability to other populations.

How do clinical researchers generate data-driven scientific hypotheses? Cognitive events using think-aloud protocol.

Objectives: This study aims to identify the cognitive events related to information use (e.g., "Analyze data", "Seek connection") during hypothesis generation among clinical researchers. Specifically, we describe hypothesis generation using cognitive event counts and compare them between groups. Methods: The participants used the same datasets, followed the same scripts, used VIADS (a visual interactive analysis tool for filtering and summarizing large data sets coded with hierarchical terminologies) or other analytical tools (as control) to analyze the datasets, and came up with hypotheses while following the think-aloud protocol. Their screen activities and audio were recorded and then transcribed and coded for cognitive events. Results: The VIADS group exhibited the lowest mean number of cognitive events per hypothesis and the smallest standard deviation. The experienced clinical researchers had approximately 10% more valid hypotheses than the inexperienced group. The VIADS users among the inexperienced clinical researchers exhibit a similar trend as the experienced clinical researchers in terms of the number of cognitive events and their respective percentages out of all the cognitive events. The highest percentages of cognitive events in hypothesis generation were "Using analysis results" (30%) and "Seeking connections" (23%). Conclusion: VIADS helped inexperienced clinical researchers use fewer cognitive events to generate hypotheses than the control group. This suggests that VIADS may guide participants to be more structured during hypothesis generation compared with the control group. The results provide evidence to explain the shorter average time needed by the VIADS group in generating each hypothesis.

How team clinical reasoning occurs on ward rounds: Implications for learning.

BACKGROUND: Ward rounds offer a rich environment for learning about team clinical reasoning. We aimed to assess how team clinical reasoning occurs on ward rounds to inform efforts to enhance the teaching of clinical reasoning. METHODS: We performed focused ethnography of ward rounds over a 6-week period, during which we observed five different teams. Each day team comprised one senior physician, one senior resident, one junior resident, two interns and one medical student. Twelve 'night-float' residents who discussed new patients with the day team were also included. Field notes were analysed using content analysis. FINDINGS: We analysed 41 new patient presentations and discussions on 23 different ward rounds. The median duration of case presentations and discussions was 13.0 minutes (IQR, 10.0-18.0 minutes). More time was devoted to information sharing (median 5.5 minutes; IQR, 4.0-7.0 minutes) than any other activity, followed by discussion of management plans (median 4.0 minutes; IQR, 3.0-7.8 minutes). Nineteen (46%) cases did not include discussion of a differential diagnosis for the chief concern. We identified two themes relevant to learning: (1) linear versus iterative approaches to team-based diagnosis and (2) the influence of hierarchy on participation in clinical reasoning discussions. CONCLUSION: The ward teams we observed spent far less time discussing differential diagnoses compared with information sharing. Junior learners such as medical students and interns contributed less frequently to team clinical reasoning discussions. In order to maximise student learning, strategies to engage junior learners in team clinical reasoning discussions on ward rounds may be needed.

Data-driven hypothesis generation among inexperienced clinical researchers: A comparison of secondary data analyses with visualization (VIADS) and other tools.

Objectives: To compare how clinical researchers generate data-driven hypotheses with a visual interactive analytic tool (VIADS, a visual interactive analysis tool for filtering and summarizing large data sets coded with hierarchical terminologies) or other tools. Methods: We recruited clinical researchers and separated them into "experienced" and "inexperienced" groups. Participants were randomly assigned to a VIADS or control group within the groups. Each participant conducted a remote 2-hour study session for hypothesis generation with the same study facilitator on the same datasets by following a think-aloud protocol. Screen activities and audio were recorded, transcribed, coded, and analyzed. Hypotheses were evaluated by seven experts on their validity, significance, and feasibility. We conducted multilevel random effect modeling for statistical tests. Results: Eighteen participants generated 227 hypotheses, of which 147 (65%) were valid. The VIADS and control groups generated a similar number of hypotheses. The VIADS group took a significantly shorter time to generate one hypothesis (e.g., among inexperienced clinical researchers, 258 seconds versus 379 seconds, p = 0.046, power = 0.437, ICC = 0.15). The VIADS group received significantly lower ratings than the control group on feasibility and the combination rating of validity, significance, and feasibility. Conclusion: The role of VIADS in hypothesis generation seems inconclusive. The VIADS group took a significantly shorter time to generate each hypothesis. However, the combined validity, significance, and feasibility ratings of their hypotheses were significantly lower. Further characterization of hypotheses, including specifics on how they might be improved, could guide future tool development.

A Visual Analytic Tool (VIADS) to Assist the Hypothesis Generation Process in Clinical Research: Mixed Methods Usability Study.

BACKGROUND: Visualization can be a powerful tool to comprehend data sets, especially when they can be represented via hierarchical structures. Enhanced comprehension can facilitate the development of scientific hypotheses. However, the inclusion of excessive data can make visualizations overwhelming. OBJECTIVE: We developed a visual interactive analytic tool for filtering and summarizing large health data sets coded with hierarchical terminologies (VIADS). In this study, we evaluated the usability of VIADS for visualizing data sets of patient diagnoses and procedures coded in the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM). METHODS: We used mixed methods in the study. A group of 12 clinical researchers participated in the generation of data-driven hypotheses using the same data sets and time frame (a 1-hour training session and a 2-hour study session) utilizing VIADS via the think-aloud protocol. The audio and screen activities were recorded remotely. A modified version of the System Usability Scale (SUS) survey and a brief survey with open-ended questions were administered after the study to assess the usability of VIADS and verify their intense usage experience with VIADS. RESULTS: The range of SUS scores was 37.5 to 87.5. The mean SUS score for VIADS was 71.88 (out of a possible 100, SD 14.62), and the median SUS was 75. The participants unanimously agreed that VIADS offers new perspectives on data sets (12/12, 100%), while 75% (8/12) agreed that VIADS facilitates understanding, presentation, and interpretation of underlying data sets. The comments on the utility of VIADS were positive and aligned well with the design objectives of VIADS. The answers to the open-ended questions in the modified SUS provided specific suggestions regarding potential improvements for VIADS, and the identified problems with usability were used to update the tool. CONCLUSIONS: This usability study demonstrates that VIADS is a usable tool for analyzing secondary data sets with good average usability, good SUS score, and favorable utility. Currently, VIADS accepts data sets with hierarchical codes and their corresponding frequencies. Consequently, only specific types of use cases are supported by the analytical results. Participants agreed, however, that VIADS provides new perspectives on data sets and is relatively easy to use. The VIADS functionalities most appreciated by participants were the ability to filter, summarize, compare, and visualize data. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/39414.

Development, validation, and usage of metrics to evaluate the quality of clinical research hypotheses.

Objectives: Metrics and instruments can provide guidance for clinical researchers to assess their potential research projects at an early stage before significant investment. Furthermore, metrics can also provide structured criteria for peer reviewers to assess others' clinical research manuscripts or grant proposals. This study aimed to develop, test, validate, and use evaluation metrics and instruments to accurately, consistently, and conveniently assess the quality of scientific hypotheses for clinical research projects. Materials and Methods: Metrics development went through iterative stages, including literature review, metrics and instrument development, internal and external testing and validation, and continuous revisions in each stage based on feedback. Furthermore, two experiments were conducted to determine brief and comprehensive versions of the instrument. Results: The brief version of the instrument contained three dimensions: validity, significance, and feasibility. The comprehensive version of metrics included novelty, clinical relevance, potential benefits and risks, ethicality, testability, clarity, interestingness, and the three dimensions of the brief version. Each evaluation dimension included 2 to 5 subitems to evaluate the specific aspects of each dimension. For example, validity included clinical validity and scientific validity. The brief and comprehensive versions of the instruments included 12 and 39 subitems, respectively. Each subitem used a 5-point Likert scale. Conclusion: The validated brief and comprehensive versions of metrics can provide standardized, consistent, and generic measurements for clinical research hypotheses, allow clinical researchers to prioritize their research ideas systematically, objectively, and consistently, and can be used as a tool for quality assessment during the peer review process.

The Roles of a Secondary Data Analytics Tool and Experience in Scientific Hypothesis Generation in Clinical Research: Protocol for a Mixed Methods Study.

BACKGROUND: Scientific hypothesis generation is a critical step in scientific research that determines the direction and impact of any investigation. Despite its vital role, we have limited knowledge of the process itself, thus hindering our ability to address some critical questions. OBJECTIVE: This study aims to answer the following questions: To what extent can secondary data analytics tools facilitate the generation of scientific hypotheses during clinical research? Are the processes similar in developing clinical diagnoses during clinical practice and developing scientific hypotheses for clinical research projects? Furthermore, this study explores the process of scientific hypothesis generation in the context of clinical research. It was designed to compare the role of VIADS, a visual interactive analysis tool for filtering and summarizing large data sets coded with hierarchical terminologies, and the experience levels of study participants during the scientific hypothesis generation process. METHODS: This manuscript introduces a study design. Experienced and inexperienced clinical researchers are being recruited since July 2021 to take part in this 2×2 factorial study, in which all participants use the same data sets during scientific hypothesis-generation sessions and follow predetermined scripts. The clinical researchers are separated into experienced or inexperienced groups based on predetermined criteria and are then randomly assigned into groups that use and do not use VIADS via block randomization. The study sessions, screen activities, and audio recordings of participants are captured. Participants use the think-aloud protocol during the study sessions. After each study session, every participant is given a follow-up survey, with participants using VIADS completing an additional modified System Usability Scale survey. A panel of clinical research experts will assess the scientific hypotheses generated by participants based on predeveloped metrics. All data will be anonymized, transcribed, aggregated, and analyzed. RESULTS: Data collection for this study began in July 2021. Recruitment uses a brief online survey. The preliminary results showed that study participants can generate a few to over a dozen scientific hypotheses during a 2-hour study session, regardless of whether they used VIADS or other analytics tools. A metric to more accurately, comprehensively, and consistently assess scientific hypotheses within a clinical research context has been developed. CONCLUSIONS: The scientific hypothesis-generation process is an advanced cognitive activity and a complex process. Our results so far show that clinical researchers can quickly generate initial scientific hypotheses based on data sets and prior experience. However, refining these scientific hypotheses is a much more time-consuming activity. To uncover the fundamental mechanisms underlying the generation of scientific hypotheses, we need breakthroughs that can capture thinking processes more precisely. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/39414.

Development of a Patient Decision Aid for Refractive Eye Surgery.

To address the needs of patient decision aid for refractive eye surgery, we developed a web-based tool, EyeChoose, which provides patient education, assists in selection of a specific surgical modality, generates customized recommendations, and links patients to local surgeons, targeting specifically the population of college students. We conducted a focus group interview for needs assessment. We designed a scoring algorithm to provide customized recommendation of surgical modalities based on a patient's medical history and personal preferences. We completed a prototype implementation of the tool. Initial data from a validation study indicated that the system achieved 99.18% accuracy in its recommendation. A study to examine the usefulness and usability of EyeChoose is ongoing. Future research is required to implement the tool in naturalistic settings and to examine the generalizability of the findings to other populations.

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.DiscussionAlthough 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.

Physician Workflow in Two Distinctive Emergency Departments: An Observational Study.

OBJECTIVES: We characterize physician workflow in two distinctive emergency departments (ED). Physician practices mediated by electronic health records (EHR) are explored within the context of organizational complexity for the delivery of care. METHODS: Two urban clinical sites, including an academic teaching ED, were selected. Fourteen physicians were recruited. Overall, 62 hours of direct clinical observations were conducted characterizing clinical activities (EHR use, team communication, and patient care). Data were analyzed using qualitative open-coding techniques and descriptive statistics. Timeline belts were used to represent temporal events. RESULTS: At site 1, physicians, engaged in more team communication, followed by direct patient care. Although physicians spent 61% of their clinical time at workstations, only 25% was spent on the EHR, primarily for clinical documentation and review. Site 2 physicians engaged primarily in direct patient care spending 52% of their time at a workstation, and 31% dedicated to EHRs, focused on chart review. At site 1, physicians showed nonlinear complex workflow patterns with a greater frequency of multitasking and interruptions, resulting in workflow fragmentation. In comparison, at site 2, a less complex environment with a unique patient assignment system, resulting in a more linear workflow pattern. CONCLUSION: The nature of the clinical practice and EHR-mediated workflow reflects the ED work practices. Physicians in more complex organizations may be less efficient because of the fragmented workflow. However, these effects can be mitigated by effort distribution through team communication, which affords inherent safety checks.

Comparison of an oncology clinical decision-support system's recommendations with actual treatment decisions.

OBJECTIVE: IBM(R) Watson for Oncology (WfO) is a clinical decision-support system (CDSS) that provides evidence-informed therapeutic options to cancer-treating clinicians. A panel of experienced oncologists compared CDSS treatment options to treatment decisions made by clinicians to characterize the quality of CDSS therapeutic options and decisions made in practice. METHODS: This study included patients treated between 1/2017 and 7/2018 for breast, colon, lung, and rectal cancers at Bumrungrad International Hospital (BIH), Thailand. Treatments selected by clinicians were paired with therapeutic options presented by the CDSS and coded to mask the origin of options presented. The panel rated the acceptability of each treatment in the pair by consensus, with acceptability defined as compliant with BIH's institutional practices. Descriptive statistics characterized the study population and treatment-decision evaluations by cancer type and stage. RESULTS: Nearly 60% (187) of 313 treatment pairs for breast, lung, colon, and rectal cancers were identical or equally acceptable, with 70% (219) of WfO therapeutic options identical to, or acceptable alternatives to, BIH therapy. In 30% of cases (94), 1 or both treatment options were rated as unacceptable. Of 32 cases where both WfO and BIH options were acceptable, WfO was preferred in 18 cases and BIH in 14 cases. Colorectal cancers exhibited the highest proportion of identical or equally acceptable treatments; stage IV cancers demonstrated the lowest. CONCLUSION: This study demonstrates that a system designed in the US to support, rather than replace, cancer-treating clinicians provides therapeutic options which are generally consistent with recommendations from oncologists outside the US.

Impact of a mobile-based (mHealth) tool to support community health nurses in early identification of depression and suicide risk in Pacific Island Countries.

OBJECTIVE: To convert screening tools for depression and suicide risk into algorithmic decision support on smartphones for use by community health nurses (CHNs), and to evaluate the efficiency, effectiveness, and usability of the mHealth tool in providing mental health (MH) care. METHOD: Two scenarios of depression and suicide risk were developed and presented to 48 nurses using paper-based and mobile-based guidelines under laboratory (nonclinical) conditions. Participants read through the case scenarios to provide summaries, diagnoses, and management recommendations. Audiotapes were transcribed and analyzed for accuracy in scoring guidelines, therapy decisions, and time for tasks completion. The validated System Usability Scale (SUS) was used to measure mobile app usability. RESULTS: Using mHealth-based guidelines, nurses took significantly less time to complete their tasks, and generated no errors of addition, as compared to paper-based guidelines. Although coding errors were noted when using the mHealth app, it did not influence treatment recommendations. The system usability scores for both guidelines were over 84%. CONCLUSIONS: Usable mHealth technology can support task-sharing for CHNs in Fiji, for the efficient and accurate screening of patients for depression and suicide risks in a nonclinical setting. Studies on clinical implementation of the mHealth tool are needed and planned.

Development and usability of a mobile tool for identification of depression and suicide risk in Fiji.

BACKGROUND: In Fiji and other South Pacific island countries, depression and suicide are of great concern. There is a pressing need to rapidly identify those at risk and provide treatment as soon as possible. OBJECTIVE: Design, develop and test a mobile health tool that enables CHNs to easily and rapidly identify individuals at risk for suicide and depression and provide guidelines for their treatment. METHODS: Using Android Studio, a native app called ASRaDA was developed that encoded two validated scales: Center for Epidemiological Studies-Depression (CES-D), and Suicide Behavior Questionnaire-Revised (SBQ-R). The usability of the app was measured using the System Usability Scale by community health nurses in Fiji. RESULTS: Out of a maximim possible of 100 on SUS, ASRaDA was scored at 86.79. CONCLUSION: Mobile tools with high usability can be designed to aid community health nurses in Fiji and Pacific island counties rapidly identify those at risk for depression and suicide.