Human Factors Experts Research Articles

The role of human factors engineering in patient safety.

To explore the collaboration between human factors (HFs) experts and clinicians in order to improve perioperative patient safety. Recent recommendations to integrate human factors into anesthesia in the United Kingdom emphasizes the value of applying disciplines outside of medicine to optimize the patient experience. Human factors engineering is underutilized worldwide. Patient safety would benefit from collaboration with HF experts to design resilient systems. Healthcare organizations must consider HF to develop and implement user-centered solutions to improve safety for patients and professionals.

Feelings and Physics: Emotional, Psychological, and Other Soft Human Requirements, by Model‐Based Systems Engineering

ABSTRACTTraditionally, engineering encourages requirements statements that are objective, testable, quantitative, atomic descriptions of system technical behavior. But what about “soft” requirements? When products deliver psychologically or emotionally based human experiences, subjective descriptions may frustrate engineers. This challenge is important for products appealing to senses of style, enjoyment, fulfillment, stimulation, power, safety, awareness, comfort, or similar emotional or psychological factors. Automobiles, buildings, consumer products, packaging, graphic user interfaces, airline passenger compartments and flight decks, and hospital equipment provide typical examples. This paper shows how model‐based systems engineering helps solve three related problems: (1) integrating models of “soft” human experience with hard technical product requirements, (2) describing how to score traditional “hard” technology products in terms of “fuzzier” business and competitive marketplace issues, and (3) coordinating marketing communication and promotion with the design process. The resulting framework integrates the diverse perspectives of engineers, stylists, industrial designers, human factors experts, and marketing professionals.

Classroom Interior Design: Wooden Furniture Prototype with Feedback from Students and Teachers

Studies indicate that natural wooden materials positively affect students’ well-being in classrooms. In addition, students spend a considerable amount of their time in classrooms predominantly seated, making school interiors a suitable place to reduce sedentary behaviors of children. A mixed team of experts in human factors, architecture, design and engineering designed a prototype wooden standing desk for indoor use and formed focus groups with students and teachers to gather feedback on the development process and to evaluate the suitability of the prototype. The prototype desk was well received by the primary school teachers and students. The students appreciated plywood as the main material for the construction of the desk; however, they criticized that the wooden tabletop should be more resistant. The height adjustability of the desk and the tiltable tabletop were the most appreciated features of the prototype. Further studies should be conducted to investigate the optimal material, shape and color of the school desk, especially the tabletop, and additional efforts should be made to design furniture that promotes a less sedentary classroom and improves students’ well-being at school.

Designing resilient health services supported by digital technologies: A study of the blood transfusion process

Although resilient performance (RP) in health services can be both enhanced and hindered by the use of digital technologies (DTs), little is known on how this influence occurs through design. This study introduces a framework for designing resilient health services supported by DTs, involving four steps: (i) define the motivation for the framework application, select the target system, and form a project team; (ii) modeling of the target system; (iii) identify problems and countermeasures, emphasizing the role of DTs supportive of RP; and (iv) implement countermeasures. The framework was tested in the blood transfusion process of a large tertiary hospital. Data collection for this test included participant and non-participant observations, interviews, and documentary analysis. Results shed light on the framework's utility and ease of use, also giving rise to propositions that guide the framework application. These propositions are related to using business process management notation to bridge the perspectives of DTs designers and human factors experts; supporting dynamic prioritization of orders; standardizing interactions between management information systems; using DTs to amplify rather than replace human skills; using DTs to buy time for deploying responses to variabilities; accounting for the perspectives of diverse stakeholders; and learning from applying the framework. Requirements of DTs supportive of RP were also derived from the propositions, offering guidance to designers.

562 AI Translation Advisory Board: Mastering team science to facilitate implementation of AI into clinical practice

OBJECTIVES/GOALS: Healthcare sectors are rushing to develop AI models. Yet, a dearth of coordinated practices leaves many teams struggling to implement models into practice. The Enterprise AI Translation Advisory Board uses across-disciplinary team to facilitate AI translation. METHODS/STUDY POPULATION: The Mayo Clinic Enterprise AI Translation Advisory Board was established to assess AI solutions lever aging cross-disciplinary team science to accelerate AI innovation and translation. The 23-member board reflects expertise in data science, qualitative research, user experience, IT, human factors, informatics, regulatory compliance,ethics, and clinical care, with members spanning thought leadership, decision-making, and clinical practice. Taking an approach of respectful communication, transparency, scientific debate, and open discussion, the Board has consulted onover two dozen projects at various stages of the AI life cycle. RESULTS/ANTICIPATED RESULTS: Common issues identified for projects earlier in the AI life cycle, sometimes fatal but often address able once identified, include a lack of buy-in from potential product users, a lack of planningabout integration into clinical workflow, inadequately labeled data, and attempting to use machine learning when what is desired is really a causal model for intervening. Recommendations for projects later in the AI life cycle include details of a testing plan (silent evaluation, pragmatic clinical trials), advice about clinical integration, both post-hoc and on going auditing for performance disparities, and planning for regulatory clearance. DISCUSSION/SIGNIFICANCE: Advising is more valuable for projects at the ideation phase, when multi disciplinary interrogation can identify weaknesses. But at all phases, projects have gaps related to a lack of specific disciplinary expertise. A multi disciplinary cluster like the AI Translation Advisory Board seeks to address these gaps.

ASSESSING USABILITY OF A MEDICATION ADHERENCE SYSTEM FOR PERSONS WITH MILD COGNITIVE IMPAIRMENT

Abstract Persons with mild cognitive impairment (PwMCI) are at risk of medication nonadherence due to prospective memory deficits. Over time, they are likely to experience accelerated deterioration in cognitive functions and may develop dementia when comorbid treatable health conditions, such as hypertension, are not well managed. A digital therapeutic system called Medication Education, Decision Support, Reminding, and Monitoring (MEDSReM©) has been designed for cognitively normal older adults to support hypertension medication adherence and is currently being tested in a randomized controlled trial. MEDSReM has the potential to support PwMCI with hypertension medication adherence, but it must be optimized specifically for this population. We conducted a cognitive walkthrough to identify usability issues and inform the redesign and optimization of the system for PwMCI. This usability technique is used to evaluate a system from a user’s perspective and is typically conducted by subject matter experts. We had 12 individuals with interdisciplinary expertise in cognitive aging, cognitive impairment, hypertension management, nursing, human factors, and health technology participate in the cognitive walkthrough. Usability issues related to figure-ground contrast, text complexity, and flexibility related to decision making were identified. Overall, recommendations included (1) simplifying the user interface (e.g., color contrast/textual cues), (2) eliminating options related to medication taking actions (e.g., snooze, take later) to mitigate memory-related errors, and (3) providing more direct, prescriptive instructions to minimize decision-making demands. Based on these recommendations, MEDSReM is currently being redesigned for testing with PwMCI. Our approach can inform the design of other mobile health tools.

A human factors perspective on how to keep SAE Level 3 conditional automated driving safe

The Society of Automotive Engineers (SAE) defines Conditional Automated Driving (CAD) or SAE level 3 as the next step in the transition to higher automated driving. A human factors challenge at this level is to keep the fallback-ready user aware of the driving situation. The problem with the widely accepted terminology of SAE is that it does not consider the human factors of achieving this safety-critical transition and lacks in defining a required or appropriate state of fallback readiness and how a human reaches this state.This paper aims to understand how research and industry currently perceive the problem and how they counteract it. We conducted exploratory expert interviews with N = 15 subject matter experts from universities, research centres and vehicle manufacturers.Based on the findings of the expert interviews, we discuss how fallback-ready users might be kept in the loop. The results show that the user should remain physically and sensory in the state of regular drivers to allow a safe transition, which narrows down the problem to maintaining an appropriate cognitive state, level of arousal, and motivational conditions. Lastly, the experts identify that the NDRA should be (a) limited to the onboard-entertainment system and (b) context-aware and (temporarily) restricted or interrupted. We provide concrete design recommendations grounded in (or drawing upon) theoretical frameworks and current human factors expert views and highlight specific challenges that require more research.

A50The SEIPS game: An interprofessional teaching aid to promote understanding of human factors in healthcare

As a human-factors focused simulation centre, we begin all our simulation courses with a human factors workshop introducing participants to the SEIPS model of human factors [1]. This enables them to explore systems-based impacts on clinical practice during post-scenario debriefs. However, we have noticed that some participants struggle to identify and discuss human factor themes which impact on them in their workplace. We aimed to develop an innovative teaching aid which would promote participant understanding and engagement. Previous experience has provided evidence that participants enjoy simulation games. Therefore, we chose to develop a table-top game to play with participants based on the SEIPS work system. We worked with interprofessional colleagues to identify factors that help and hinder processes in the work system and categorized them under SEIPS headings. We made a series of cards based on these factors which participants collect. The winner was the person who collected a helpful card for each SEIPS heading first. We have piloted our SEIPS game with interprofessional faculty, including those with specialist expertise in human factors in healthcare. We surveyed participants to obtain feedback. Survey results so far include data contained in ‘Play’ is a kinaesthetic way of learning and helps embed ideas and thinking. It also can create opportunities for discussion on different headings for human factors and systems thinking. The examples are fun but are also realistic so helps you see how HF is relevant. With the examples of human factors in the game it could be useful for staff with little clinical experience. Liked the competitive element and the examples helped expand on what SEIPS was and how it could be relevant to lots of areas. SEIPS game participant survey results We have developed a SEIPS game to facilitate discussion of human factors in healthcare. This novel approach has received positive initial feedback following our pilot. We are confident we can now move forward to integrate our SEIPS game into our Foundation Doctor’s simulation programme from August 2023. Following this, we intend to continue the process of data collection and analysis, with the intention of incorporating our SEIPS game more widely across simulation courses within various clinical specialties in future. Authors confirm that all relevant ethical standards for research conduct and dissemination have been met. The submitting author confirms that relevant ethical approval was granted, if applicable.

Salient Measures of Hospitalist Workload

The ideal hospitalist workload and optimal way to measure it are not well understood. To obtain expert consensus on the salient measures of hospitalist workload. This qualitative study used a 3-round Delphi technique between April 5 and July 13, 2022, involving national experts within and external to the field. Experts included hospitalist clinicians, leaders, and administrators, as well as researchers with expertise in human factors engineering and cognitive load theory. Three rounds of surveys were conducted, during which participants provided input on the salient measures of hospitalist workload across various domains. In the first round, free-text data collected from the surveys were analyzed using a directed qualitative content approach. In the second and third rounds, participants rated each measure's relevance on a Likert scale, and consensus was evaluated using the IQR. Percentage agreement was also calculated. Seventeen individuals from 14 organizations, encompassing clinicians, leaders, administrators, and researchers, participated in 3 rounds of surveys. In round 1, participants provided 135 unique qualitative comments across 10 domains, with 192 unique measures identified. Of the 192 measures presented in the second round, 6 (3%) were considered highly relevant, and 25 (13%) were considered moderately relevant. In round 3, 161 measures not meeting consensus were evaluated, with 25 (16%) considered highly relevant and 95 (59%) considered moderately relevant. Examples of measures considered highly relevant included a patient complexity score and outcome measures such as savings from hospital days avoided and clinician turnover. In this qualitative study measuring hospitalist workload, multiple measures, including those quantifying work demands and the association of those demands with outcomes, were considered relevant for measuring and understanding workloads. The findings suggest that relying on traditional measures, such as productivity-related measures and financial measures, may offer an incomplete understanding of workloads and their association with key outcomes. By embracing a broader range of measures, organizations may be able to better capture the complexity and nuances of hospitalist work demands and their outcomes on clinicians, patients, and organizations.

Hazard Assessment and Remediation Tool for Simulation-Based Healthcare Facility Design Testing.

To develop an objective, structured observational tool to enable identification and measurement of hazards in the built environment when applied to audiovisual recordings of simulations by trained raters. Simulation-based facility design testing is increasingly used to optimize safety of healthcare environments, often relying on participant debriefing or direct observation by human factors experts. Hazard categories were defined through participant debriefing and detailed review of pediatric intensive care unit in situ simulation videos. Categories were refined and operational definitions developed through iterative coding and review. Hazard detection was optimized through the use of structured coding protocols and optimized camera angles. Six hazard categories were defined: (1) slip/trip/fall/injury risk, impaired access to (2) patient or (3) equipment, (4) obstructed path, (5) poor visibility, and (6) infection risk. Analysis of paired and individual coding demonstrated strong overall reliability (0.89 and 0.85, Gwet's AC1). Reliability coefficients for each hazard category were >0.8 for all except obstructed path (0.76) for paired raters. Among individual raters, reliability coefficients were >0.8, except for slip/trip/fall/injury risk (0.68) and impaired access to equipment (0.77). Hazard Assessment and Remediation Tool (HART) provides a framework to identify and quantify hazards in the built environment. The tool is highly reliable when applied to direct video review of simulations by either paired raters or trained single clinical raters. Subsequent work will (1) assess the tool's ability to discriminate between rooms with different physical attributes, (2) develop strategies to apply HART to improve facility design, and (3) assess transferability to non-ICU acute care environments.

Informing Visual Display Design of Electronic Health Records: A Human Factors Cross-Industry Perspective

Background Despite their prevalence, poorly designed electronic health records (EHRs) are common, and research shows poor design consequences include clinician burnout, diagnostic error, and even patient harm. One of the major difficulties of EHR design is the visual display of information, which aims to present information in an easily digestible form for the user. High-risk industries like aviation, automotive, and nuclear have guidelines for visual displays based on human factors principles for optimized design. Purpose In this study, we reviewed the visual display guidelines from three high-risk industries—automotive, aviation, nuclear—for their applicability to EHR design and safety. Methods Human factors experts extracted guidelines related to visual displays from automotive, aviation, and nuclear human factors guideline documents. Human factors experts and a clinical expert excluded guidelines irrelevant to EHR. Human factors experts used a modified reflexive thematic analysis to group guidelines into meaningful topics. Disagreements were discussed until a consensus was reached. Results A total of 449 guidelines were extracted from the industry documents, and 283 (63.0%) were deemed relevant to EHRs. By industry, 12 of 44 (27.3%) automotive industry guidelines were relevant, 43 of 115 (37.4%) aviation industry guidelines were relevant, and 228 of 290 (78.6%) nuclear industry guidelines were relevant. Guidelines were grouped into six categories: alphanumeric; color, brightness, contrast, and luminance; comprehension; design characteristics; symbols, pictograms, and icons; and tables, figures, charts, and lists. Conclusion Our analysis identified visual display guidelines organized around six topics from the automotive, aviation, and nuclear industries to inform EHR design. Multiple stakeholders, including EHR vendors, healthcare facilities, and policymakers, can apply these guidelines to design new EHRs and optimize EHRs already in use.

Opportunities for human factors in machine learning.

The field of machine learning and its subfield of deep learning have grown rapidly in recent years. With the speed of advancement, it is nearly impossible for data scientists to maintain expert knowledge of cutting-edge techniques. This study applies human factors methods to the field of machine learning to address these difficulties. Using semi-structured interviews with data scientists at a National Laboratory, we sought to understand the process used when working with machine learning models, the challenges encountered, and the ways that human factors might contribute to addressing those challenges. Results of the interviews were analyzed to create a generalization of the process of working with machine learning models. Issues encountered during each process step are described. Recommendations and areas for collaboration between data scientists and human factors experts are provided, with the goal of creating better tools, knowledge, and guidance for machine learning scientists.

Applying Failure Mode Effects Analysis (FMEA) to Improve Choking Risk Prevention in a Mental Health Setting: Analysis Outcomes and Lessons Learned on Human Factors Collaboration

This paper describes the collaborative work performed as part of a patient safety and quality improvement choking risk prevention initiative in a specialty mental health hospital in Ontario, Canada. In 2021, Ontario Shores Centre for Mental Health Sciences (Ontario Shores), in collaboration with the Healthcare Insurance Reciprocal of Canada (HIROC), conducted a Failure Modes and Effects Analysis (FMEA) to identify potential failure modes for their choking risk prevention process. “Failure modes” refer to states in a process that have the potential for unintended consequences. The interdisciplinary project team developed and validated a current-state process map, through which identified all opportunities for process improvement. A thematic analysis of the barriers revealed 14 distinct failure modes, each of which were rated along three scales (Severity, Occurrence, and Detectability) to form a ranked list based on Risk Priority Number. As part of a prospective analysis, several system-based and people-based mitigations were generated for each of the failure modes. As a result of the FMEA, Ontario Shores developed, and is in the process of, implementing a choking risk prevention and risk mitigation strategies action plan. In addition, the authors offer some reflections on the collaborative work between the two organizations, in recognition of the opportunity for healthcare organizations to benefit from human factors expertise and principles of applied safety science, usability engineering, and user-centered design.

Implementing human factors in anaesthesia: guidance for clinicians, departments and hospitals: Guidelines from the Difficult Airway Society and the Association of Anaesthetists: Guidelines from the Difficult Airway Society and the Association of Anaesthetists.

Human factors is an evidence-based scientific discipline used in safety critical industries to improve safety and worker well-being. The implementation of human factors strategies in anaesthesia has the potential to reduce the reliance on exceptional personal and team performance to provide safe and high-quality patient care. To encourage the adoption of human factors science in anaesthesia, the Difficult Airway Society and the Association of Anaesthetists established a Working Party, including anaesthetists and operating theatre team members with human factors expertise and/or interest, plus a human factors scientist, an industrial psychologist and an experimental psychologist/implementation scientist. A three-stage Delphi process was used to formulate a set of 12 recommendations: these are described using a 'hierarchy of controls' model and classified into design, barriers, mitigations and education and training strategies. Although most anaesthetic knowledge of human factors concerns non-technical skills, such as teamwork and communication, human factors is a broad-based scientific discipline with many other additional aspects that are just as important. Indeed, the human factors strategies most likely to have the greatest impact are those related to the design of safe working environments, equipment and systems. While our recommendations are primarily provided for anaesthetists and the teams they work with, there are likely to be lessons for others working in healthcare beyond the speciality of anaesthesia.

A TRANSDISCIPLINARY TEAM APPROACH TO TECHNOLOGY DEVELOPMENT TO SUPPORT HEALTH AND WELL-BEING

Abstract Diverse disciplinary and experiential knowledge is required to understand the challenges and develop workable solutions to support the health and wellbeing of a rapidly aging population. We are developing a theory-based, digital therapeutic for self-management of hypertension medication adherence for older adults; namely, the Medication Education, Decision Support, Reminding, and Monitoring (MEDSReM) system. This development process has necessitated a transdisciplinary approach with members co-learning and working collaboratively to advance the MEDSReM system. Our group includes complementary expertise in pharmacy, nursing, human factors, cognitive aging, gerontology, and technology development. We describe the challenges of transdisciplinary teamwork, our collaborative processes, and strategies that led to successful integration of expertise in advancing MEDSReM. Lastly, we discuss useful approaches to technology conceptualization, and digital therapeutic development and implementation, as well as lessons learned in effective communication and coordination among the diverse team members to ensure that the MEDSReM goals are achieved.

Assessing Human Use of Partially-Automated Motor Vehicles: A Subjective Method for Use by Human Factors Experts

This paper describes a pilot test conducted of a quick assessment method in which experts evaluated the safety and usability of partially autonomous vehicles. Two PhD human factors experts drove 3 2018-model-year, partially-automated vehicles (Tesla Model X, Subaru Legacy, and Mazda CX5) and identified safety and usability problems related to the driver interface. From multiple drives of each vehicle over a well-defined course, comments were obtained, and they were classified into 5 safety/usability categories (e.g., not sure what the vehicle did, not sure why it did what it did) with 5 severity levels (cosmetic to catastrophic) in each category. The method was feasible. Planning the route and instrumenting the vehicles proceeded without difficulty, as did the coding of the data. Improvements such as using text-to-speech software (instead of time-consuming manual transcription) to support transcript generation, alternative problem categories, and revised problem severity scoring are described.

A content analysis of contributory factors reported in serious incident investigation reports in hospital care

Serious incident (SI) investigations aim to identify factors that caused or could have caused serious patient harm. This study aimed to use the Human Factors Analysis and Classification System (HFACS) to characterise the contributory factors identified in SI investigation reports. We performed a content analysis of 126 investigation reports from a multi-site NHS trust. We used a HFACS-based framework that was modified through inductive analysis of the data. Using the modified HFACS framework, 'unsafe actions' were the most commonly identified hierarchical level of contributory factors in investigation reports, which were identified 282 times across 99 (79%) incidents. 'Preconditions to unsafe acts' (identified 223 times in 91 (72%) incidents) included miscommunication and environmental factors. Supervisory factors were identified 73 times across 40 (31%) incidents, and organisational factors 115 times across 59 (47%) incidents. We identified 'extra-organisational factors' as a new HFACS level, though it was infrequently described. Analysis of SI investigation reports using a modified HFACS framework allows important insights into what investigators view as contributory factors. We found an emphasis on human error but little engagement with why it occurs. Better investigations will require independence and professionalisation of investigators, human factors expertise, and a systems approach.

Development of a human factors-based tool for evaluating and improving infection prevention and control protocols

Background: Infection prevention and control (IPC) protocols and guidelines are important quality management tools for educating care professionals and standardizing care processes. However, most of the actual care (ie, work as done) differ from protocol recommendations (ie, work as imagined). No tool or set of criteria has been established for how to develop human-centered IPC protocols. The goal of this research was to develop a standardized human-factors analysis method to provide healthcare organizations with a tangible framework to improve protocol usefulness and usability. Methods: The proposed analysis method combines principles from human-factors engineering (ie, usability heuristics, systems ambiguity framework) and instructional design. Relevant literature was analyzed by experts in human factors and clinical experts to develop a tool with criteria such as visualization and method ambiguity. Overall, 5 IPC-related protocols from a large academic hospital were selected from an electronic database and were evaluated using the proposed criteria. Results: During application of the analysis method, 70 human-factors–related problems were identified across 5 IPC protocols (eg, heater cooler cleaning), including violation of design heuristics and the presence of ambiguity. Frequently violated human-factors design principles included appearance and/or visibility (ie, visual display of content), visualization (ie, providing illustrative examples), and method ambiguity (ie, lack of clarity on how to complete a task). Figure 1 provides a sample of the human-factors problems identified and suggested solutions. Only minor modifications (ie, clarification of criteria definitions) were needed on the final tool. Conclusions: The human-factors–based tool developed in this study can be used both to develop new IPC protocols and to evaluate and improve existing protocols.Funding: The CDC funded this work. This material is based upon work supported by the Naval Sea Systems Command under Contract No. N00024-13-D-6400, Task Order NH076. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Naval Sea Systems Command (NAVSEA) or the US CDC.Disclosures: None

The Application of Human Factors Engineering to Reduce Operating Room Turnover in Robotic Surgery.

Challenges associated with turnover time are magnified in robotic surgery. The introduction of advanced technology increases the complexity of an already intricate perioperative environment. We applied a human factors approach to develop systematic, data-driven interventions to reduce robotic surgery turnover time. Researchers observed 40 robotic surgery turnovers at a tertiary hospital [20 pre-intervention (Jan 2018 to Apr 2018), 20 post-intervention (Jan 2019 to Jun 2019)]. Components of turnover time, including cleaning, instrument and room set-up, robot preparation, flow disruptions, and major delays, were documented and analyzed. Surveys and focus groups were used to investigate staff perceptions of robotic surgery turnover time. A multidisciplinary team of human factors experts and physicians developed targeted interventions. Pre- and post-intervention turnovers were compared. Median turnover time was 67min (mean: 72, SD: 24) and 22 major delays were noted (1.1/case). The largest contributors were instrument setup (25.5min) and cleaning (25min). Interventions included an electronic dashboard for turnover time reporting, clear designation of roles and simultaneous completion of tasks, process standardization of operating room cleaning, and data transparency through monthly reporting. Post-intervention turnovers were significantly shorter (U = 57.5, p = .000) and ten major delays were noted. Human factors analysis generated interventions to improve turnover time. Significant improvements were seen post-intervention with a reduction in turnover time by a 26min and decrease in major delays by over 50%. Future opportunities to intervene and further improve turnover time include targeting pre- and post-operative care phases.

Reviewing the User-Centered Design Process for a Comprehensive Gastroesophageal Reflux Disease (GERD) App.

The objective of this study was to design a user-centered mobile health (mHealth) application for individuals with gastroesophageal reflux disease (GERD) and evaluate its design features and effectiveness for use by doctors. Prior to designing, our team undertook a discovery process that involved creating personas, conducting a competitor analysis and heuristic evaluation of existing apps, along with interviews with acid reflux patients. Then, we created a low-fidelity prototype, which was revised on the basis of several rounds of user testing. During the design phase, each round of user testing included a mix of surveys, concurrent think-alouds, and interviews to gather user feedback on the prototypes. Lastly, an evaluation phase consisting of gathering feedback on the user-centered design approach from user experience experts and medical doctors specialized in GERD was conducted. Overall, the final GERD app includes important features for tracking symptoms and triggers, analytics, data export, and community information, while promoting individualization, accessibility, and usability. The documentation of the design process of this app serves as a reference point for future medical app developers as it followed an empirically supported user-centered design strategy and resulted in an app which received positive feedback from users and human factors experts. We also intend to share some of the limitations due to the constrained resources, as well as potential ways to strengthen the design process for mHealth applications.