Applied Clinical Informatics最新文献

Background:  Health informatics education is pivotal in integrating diversity, equity, inclusion, and accessibility (DEIA) principles into curricula and leveraging data with equity considerations. Integrating clinically driven data with other datasets is crucial to comprehensive understanding of patient care demographics, experiences, and outcomes to create equity-minded data storytelling. Publicly available Healthy People 2030 (HP2030) resources complement academic electronic health records, supporting tailored learning activities in informatics education to enhance educational utility through a DEIA lens.

Objectives:  This case report describes the expansion of an existing diversity, equity, and inclusion (DEI) checklist to an updated DEIA checklist for preparing future informaticians to collect and critically evaluate DEIA features using this checklist in creating equity-minded data storytelling.

Methods:  The DEI-Oriented Data Storytelling Model and the HP2030 framework were utilized to develop the DEIA checklist. We employed an informal cognitive walkthrough to expand the DEIA checklist and evaluate the DEIA measures or characteristics within datasets from the HP2030 social determinants of health (SDOH) five topics using this checklist.

Results:  We reviewed 76 available SDOH-related datasets and added six measures to "demographics" and seven to "skills, abilities, and accessibility" of the DEIA checklist. Our evaluation of the DEIA checklist verified HP2030's inclusion of all measures, except "religions/beliefs." All DEIA measures were linked to equity and accessibility, one in inclusion, and the inclusion of three characteristics comprising the category "language" and six characteristics comprising the category "images."

Conclusion:  Results highlighted the accessibility and comprehensiveness of HP2030 demographic data resources, considering SDOH factors and promoting inclusive data representation to address health disparities. The DEIA checklist provides a structured tool in facilitating unbiased data collection and visualization of SDOH-related data through an equity-informed lens. Integrating an equity-minded data storytelling with frameworks like HP2030 enriches health informatics education, broadens students' understanding of health disparities, and supports evidence-based interventions for improved health outcomes.

Objectives:  This study explores the results of a rapidly implemented no-cost gratitude intervention designed to address student distress during the coronavirus disease 2019 (COVID-19) pandemic. This intervention focused on shared gratitude journaling with a postimplementation survey of well-being using elements of Seligman's PERMA (Positive emotion, Engagement, Relationships, Meaning, and Accomplishment) model of well-being.

Methods:  Journaling took place from November 2020 to April 2021 using a convenience sample (N = 57) from the Master of Science in Health Informatics program at the University of Alabama at Birmingham. An online postimplementation survey was conducted to evaluate students' perceptions of how the intervention influenced their well-being. Quantitative analysis was conducted to understand student well-being after two semesters of using an online shared gratitude board. Qualitative analysis was conducted to identify themes in the content of the student posts.

Results:  Relative to the PERMA elements, the majority of students agreed or strongly agreed that posting to the gratitude board led to improvements in Positive Emotion (85.72%), Engagement (77.2%), Relationships (67.7%), Meaning (77.2%), and Accomplishment (60%). Students who would recommend the board outweighed the number of students who would not by over 25%.

Discussion:  The gratitude board represented an opportunity to rapidly implement a no-cost opportunity based on the science of gratitude and well-being to support students' mental health and wellness. Meta-inferences gleaned from the quantitative and qualitative findings suggest that students found gratitude in different areas, that having things to do was helpful, that being able to connect with people was important, that students derived purpose from effort, and that they felt a sense of accomplishment by completing objectives.

Conclusion:  Our findings suggest that adopting an attitude of gratitude helps stimulate positive emotion to facilitate growth and learning. While this study was conducted with students in a graduate Health Informatics program, it has widespread generalizability to other programs and in other environments, especially at times when there is emotional distress.

Background:  Clinical data sharing is common and necessary for patient care, research, public health, and innovation. However, the term "data sharing" is often ambiguous in its many facets and complexities-each of which involves ethical, legal, and social issues. To our knowledge, there is no extant hierarchy of data sharing that assesses these issues.

Objective:  This study aimed to develop a hierarchy explicating the risks and ethical complexities of data sharing with a particular focus on patient data privacy.

Methods:  We surveyed the available peer-reviewed and gray literature and with our combined extensive experience in bioethics and medical informatics, created this hierarchy.

Results:  We present six ways on how data are shared and provide a tiered Data Sharing Hierarchy (DaSH) of risks, showing increasing threats to patients' privacy, clinicians, and organizations as one progresses up the hierarchy from data sharing for direct patient care, public health and safety, scientific research, commercial purposes, complex combinations of the preceding efforts, and among networked third parties. We offer recommendations to enhance the benefits of data sharing while mitigating risks and protecting patients' interests by improving consenting; developing better policies and procedures; clarifying, simplifying, and updating regulations to include all health-related data regardless of source; expanding the scope of bioethics for information technology; and increasing ongoing monitoring and research.

Conclusion:  Data sharing, while essential for patient care, is increasingly complex, opaque, and perhaps perilous for patients, clinicians, and health care institutions. Risks increase with advances in technology and with more encompassing patient data from wearables and artificial intelligence database mining. Data sharing places responsibilities on all parties: patients, clinicians, researchers, educators, risk managers, attorneys, informaticists, bioethicists, institutions, and policymakers.

Objectives:  Efforts to reduce documentation burden (DocBurden) for all health professionals (HP) are aligned with national initiatives to improve clinician wellness and patient safety. Yet DocBurden has not been precisely defined, limiting national conversations and rigorous, reproducible, and meaningful measures. Increasing attention to DocBurden motivated this work to establish a standard definition of DocBurden, with the emergence of excessive DocBurden as a term.

Methods:  We conducted a scoping review of DocBurden definitions and descriptions, searching six databases for scholarly, peer-reviewed, and gray literature sources, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extensions for Scoping Review guidance. For the concept clarification phase of work, we used the American Nursing Informatics Association's Six Domains of Burden Framework.

Results:  A total of 153 articles were included based on a priori criteria. Most articles described a focus on DocBurden, but only 18% (n = 28) provided a definition. We define excessive DocBurden as the stress and unnecessarily heavy work an HP or health care team experiences when usability of documentation systems and documentation activities (i.e., generation, review, analysis, and synthesis of patient data) are not aligned in support of care delivery. A negative connotation was attached to burden without a neutral state in included sources, which does not align with dictionary definitions of burden.

Conclusion:  Existing literature does not distinguish between a baseline or required task load to conduct patient care resulting from usability issues (DocBurden), and the unnecessarily heavy tasks and requirements that contribute to excessive DocBurden. Our definition of excessive DocBurden explicitly acknowledges this distinction, to support development of meaningful measures for understanding and intervening on excessive DocBurden locally, nationally, and internationally.

Background:  Social media networks have been found to provide emotional, instrumental, and social support, which may contribute to improved adherence to postbariatric surgery care recommendations.

Objectives:  This study aimed to evaluate the impact of an online social media-based, health care professional-led, educational and support program on patients' long-term engagement with and adherence to follow-up guidelines, self-care recommendations, and weight management after bariatric surgery.

Methods:  An observational cohort study, employing mixed methods, accompanied a 12-week interactive, structured, social media psychoeducational intervention program delivered on Facebook. Program participants, who had undergone one bariatric surgery within the past 1 to 7 years and were at least 18 years old at the time of surgery, were invited to join the program via posts online. Interested individuals were provided information about the program and the accompanying evaluation study, and those who met requirements completed study questionnaires before and after the program. Questionnaires included demographic and anthropometric information; postoperative recommendations received and their clarity and implementation; attitudes toward recommendation adherence; and well-being. Daily system data on program engagement were collected from the Facebook website.

Results:  Of the 214 participants enrolled in the program, 101 (80.2% female, mean age 43.8 ± 9.1 years and mean body mass index 30.2 ± 6.8 kg/m², 1-7 years after bariatric surgery) completed both baseline and end-of-program questionnaires and were included in the analysis. Following the program, improvements were observed in most aspects of participants' adherence to postoperative recommendations and well-being. Close to half of the participants (44.6%) reported reaching their postoperative target weight at the end of the program or maintaining it throughout the program. Video posts drew higher participant engagement than other media, and content about proteins received the highest number of reactions. However, participants' active engagement gradually declined over time.

Conclusion:  Interactive health support on social media can positively enhance patient engagement, adherence to treatment recommendations, health outcomes, and overall well-being.

Background:  N-of-1 trials have emerged as a personalized approach to patient-centered care, where patients can compare evidence-based treatments using their own data. However, little is known about optimal methods to present individual-level data from medication-related N-of-1 trials to patients to promote decision-making.

Objectives:  We conducted qualitative interviews with patients with heart failure with preserved ejection fraction undergoing N-of-1 trials to iterate, refine, and optimize a patient-facing data visualization tool for displaying the results of N-of-1 medication trials. The goal of optimizing this tool was to promote patients' understanding of their individual health information and to ultimately facilitate shared decision-making about continuing or discontinuing their medication.

Methods:  We conducted 32 semistructured qualitative interviews with 9 participants over the course of their participation in N-of-1 trials. The N-of-1 trials were conducted to facilitate a comparison of continuing versus discontinuing a β-blocker. Interviews were conducted in person or over the phone after each treatment period to evaluate participant perspectives on a data visualization tool prototype. Data were coded using directed content analysis by two independent reviewers and included a third reviewer to reach a consensus when needed. Major themes were extracted and iteratively incorporated into the patient-facing data visualization tool.

Results:  Nine participants provided feedback on how their data were displayed in the visualization tool. After qualitative analysis, three major themes emerged that informed our final interface. Participants preferred: (1) clearly stated individual symptom scores, (2) a reference image with labels to guide their interpretation of symptom information, and (3) qualitative language over numbers alone conveying the meaning of changes in their scores (e.g., better, worse).

Conclusion:  Feedback informed the design of a patient-facing data visualization tool for medication-related N-of-1 trials. Future work should include usability and comprehension testing of this interface on a larger scale.

Background:  Generative artificial intelligence (AI) tools may soon be integrated into health care practice and research. Nurses in leadership roles, many of whom are doctorally prepared, will need to determine whether and how to integrate them in a safe and useful way.

Objective:  This study aimed to develop and evaluate a brief intervention to increase PhD nursing students' knowledge of appropriate applications for using generative AI tools in health care.

Methods:  We created didactic lectures and laboratory-based activities to introduce generative AI to students enrolled in a nursing PhD data science and visualization course. Students were provided with a subscription to Chat Generative Pretrained Transformer (ChatGPT) 4.0, a general-purpose generative AI tool, for use in and outside the class. During the didactic portion, we described generative AI and its current and potential future applications in health care, including examples of appropriate and inappropriate applications. In the laboratory sessions, students were given three tasks representing different use cases of generative AI in health care practice and research (clinical decision support, patient decision support, and scientific communication) and asked to engage with ChatGPT on each. Students (n = 10) independently wrote a brief reflection for each task evaluating safety (accuracy, hallucinations) and usability (ease of use, usefulness, and intention to use in the future). Reflections were analyzed using directed content analysis.

Results:  Students were able to identify the strengths and limitations of ChatGPT in completing all three tasks and developed opinions on whether they would feel comfortable using ChatGPT for similar tasks in the future. All of them reported increasing their self-rated competency in generative AI by one to two points on a five-point rating scale.

Conclusion:  This brief educational intervention supported doctoral nursing students in understanding the appropriate uses of ChatGPT, which may support their ability to appraise and use these tools in their future work.

Background:  The Applied Public Health Informatics Competency Model lists "data analysis, visualization, and reporting" as one of the eight competencies when teaching public health informatics. Thus, public health informatics students need to develop knowledge and skills in visualizing public health data. Unfortunately, there is limited work that discusses pedagogical principles that could guide the implementation of pedagogical activities related to data visualization in public health informatics.

Objective:  This study aimed to introduce, discuss, and reflect on pedagogical principles that were implemented for a data visualization project in an undergraduate public health informatics course.

Methods:  A reflective teaching approach was used to guide the discussion and reflection on how pedagogical principles were implemented for a data visualization project in an undergraduate public health informatics course. The generic implementation framework (i.e., preimplementation, implementation, and postimplementation) was used to organize the discussion of the course's implementation.

Results: Four pedagogical principles were implemented as part of a data visualization project in an undergraduate public health informatics course: scaffolding (i.e., outputs built on top of each other), constructivism (i.e., students apply knowledge and work in teams to create a dashboard), critical consciousness (i.e., embedding social determinants of health (SDOH) in their dashboard), and equity and inclusion (i.e., using a free data visualization software that is easy to use for beginners and is used by public health institutions). Postimplementation reflection revealed areas of improvement, such as enhancing group advising, adding more SDOH variables in the dashboard, and plans for scalability.

Conclusion:  A data visualization project in an undergraduate public health informatics course could benefit from implementing multiple pedagogical principles. Overall, creating dashboards can be a learning tool to enhance data visualization skills among undergraduate public health informatics students. Dashboards can also emphasize the impact of health disparities and inequities in public health by incorporating the principles of SDOH.

Background:  Health-related social needs (HRSNs) are the unmet social and economic needs (e.g., housing instability) that affect individuals' health and well-being. HRSNs are associated with more emergency department (ED) visits, longer stays, and worse health outcomes. More than a third of ED patients have at least one HRSN, yet patients are rarely screened for HRSNs in the ED. A clinical decision support (CDS) system with predictive modeling offers a promising approach to identifying patients systematically and efficiently with HRSNs in the ED.

Objective:  This study aimed to identify ED clinician and staff preferences for designing and implementing an HRSN-related CDS system.

Methods:  A multistep, user-centered design study involving qualitative semistructured interviews, observations of ED workflows, and a multidisciplinary design workshop.

Results:  We conducted 16 semistructured interviews with ED clinicians and staff. Following the interviews, three research team members observed ED workflows, focusing on patient entry and clinician and staff usage of the electronic health record (EHR) system. Finally, we conducted a 3-hour multidisciplinary design workshop. An HRSN-related CDS system should be visually appealing, color-coordinated, and easily accessible in the EHR. An HRSN-related CDS system should target a select group of ED patients (to be discharged from the ED) and highlight a select set of critical HRSN issues early in the workflow to adjust clinical care adequately. An HRSN-related CDS system should provide a list of actions and the ability to notify the clinical team if the patient's HRSNs were addressed.

Conclusion:  The user-centered design identified a set of specific preferences for an HRSN-related CDS system to be implemented in the ED. Future work will focus on implementing and refining the CDS system and assessing the rates of changes in clinical care (e.g., rates of referrals) to address patient HRSNs in the ED.

Objective:  Graph databases for electronic health record (EHR) data have become a useful tool for clinical research in recent years, but there is a lack of published methods to transform relational databases to a graph database schema. We developed a graph model for the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) that can be reused across research institutions.

Methods:  We created and evaluated four models, representing two different strategies, for converting the standardized clinical and vocabulary tables of OMOP into a property graph model within the Neo4j graph database. Taking the Successful Clinical Response in Pneumonia Therapy (SCRIPT) and Collaborative Resource for Intensive care Translational science, Informatics, Comprehensive Analytics, and Learning (CRITICAL) cohorts as test datasets with different sizes, we compared two of the resulting graph models with respect to database performance including database building time, query complexity, and runtime for both cohorts.

Results:  Utilizing a graph schema that was optimized for storing critical information as topology rather than attributes resulted in a significant improvement in both data creation and querying. The graph database for our larger cohort, CRITICAL, can be built within 1 hour for 134,145 patients, with a total of 749,011,396 nodes and 1,703,560,910 edges.

Discussion:  To our knowledge, this is the first generalized solution to convert the OMOP CDM to a graph-optimized schema. Despite being developed for studies at a single institution, the modeling method can be applied to other OMOP CDM v5.x databases. Our evaluation with the SCRIPT and CRITICAL cohorts and comparison between the current and previous versions show advantages in code simplicity, database building, and query speed.

Conclusion:  We developed a method for converting OMOP CDM databases into graph databases. Our experiments revealed that the final model outperformed the initial relational-to-graph transformation in both code simplicity and query efficiency, particularly for complex queries.