Journal of the American Medical Informatics Association最新文献

Objective: This study addresses the significant challenges posed by emerging SARS-CoV-2 variants, particularly in developing diagnostics and therapeutics. Drug repurposing is investigated by identifying critical regulatory proteins impacted by the virus, providing rapid and effective therapeutic solutions for better disease management.

Materials and methods: We employed a comprehensive approach combining mathematical modeling and efficient parameter estimation to study the transient responses of regulatory proteins in both normal and virus-infected cells. Proportional-integral-derivative (PID) controllers were used to pinpoint specific protein targets for therapeutic intervention. Additionally, advanced deep learning models and molecular docking techniques were applied to analyze drug-target and drug-drug interactions, ensuring both efficacy and safety of the proposed treatments. This approach was applied to a case study focused on the cytokine storm in COVID-19, centering on Angiotensin-converting enzyme 2 (ACE2), which plays a key role in SARS-CoV-2 infection.

Results: Our findings suggest that activating ACE2 presents a promising therapeutic strategy, whereas inhibiting AT1R seems less effective. Deep learning models, combined with molecular docking, identified Lomefloxacin and Fostamatinib as stable drugs with no significant thermodynamic interactions, suggesting their safe concurrent use in managing COVID-19-induced cytokine storms.

Discussion: The results highlight the potential of ACE2 activation in mitigating lung injury and severe inflammation caused by SARS-CoV-2. This integrated approach accelerates the identification of safe and effective treatment options for emerging viral variants.

Conclusion: This framework provides an efficient method for identifying critical regulatory proteins and advancing drug repurposing, contributing to the rapid development of therapeutic strategies for COVID-19 and future global pandemics.

Objective: Developing equitable, sustainable informatics solutions is key to scalability and long-term success for projects in the global health informatics (GHI) domain. This paper presents key strategies for incorporating principles of health equity in the GHI project lifecycle.

Materials and methods: The American Medical Informatics Association (AMIA) GHI Working Group organized a collaborative workshop at the 2023 AMIA Annual Symposium that included the presentation of five case studies of how principles of health equity have been incorporated into projects situated in low-and-middle-income countries and with Indigenous communities in the U.S. and best practices for operationalizing these principles into other informatics projects.

Results: We present five principles: (1) Inclusion and Participation in Ethical, Sustainable Collaborations; (2) Engaging Community-Based Participatory Research Approaches; (3) Stakeholder Engagement; (4) Scalability and Sustainability; (5) Representation in Knowledge Creation, along with strategies that informatics researchers may use to incorporate these principles into their work.

Discussion: Presented case studies and subsequent focus groups yielded key concepts and strategies to promote health equity that may be operationalized across GHI projects.

Conclusion: Equitable, sustainable, and scalable GHI projects require intentional integration of community and stakeholder perspectives in project development, implementation, and knowledge creation processes.

Objective: Modernizing and strengthening the US public health data and information infrastructure requires a strong public health informatics (PHI) workforce. The study objectives were to characterize existing PHI specialists and assess informatics-related training needs.

Materials and methods: To examine the PHI workforce, the 2021 Public Health Workforce Interests and Needs Survey (PH WINS), a nationally representative survey with 44 732 governmental public health (PH) respondents was analyzed. The survey included data from 47 state health agencies-central office, 29 large local health departments (Big Cities Health Coalition members), and 259 other local/regional health departments. Analysis focused on "public health informatics specialist" (PHI), "information system manager/information technology specialist" (IT/IS), "public health science" (PHS), and "clinical and laboratory" (CL) roles.

Results: PHI specialists account for less than 2% of the governmental PH workforce. A majority were female (68%), White (55%), and close to half in 31-50 age category (49%). Most (74%) were in non-supervisory roles and <1% in managerial/executive roles, with less than one-third (29%) earning >$75 000 salary. Skill gaps on informatics-related tasks included: identify appropriate data/information sources; collect valid data for decision making; participate in quality improvement processes; identify evidence-based approaches. The PHI specialists reported lower skill gaps in data/informatics areas when compared to other public health roles (PHS and CL), and this was consistent across state/local settings.

Discussion: Given the scale of work needed for modernization of information systems, PH agencies need more individuals in informatics roles. To attract PHI specialists, better salaries, clear PHI job classifications and permanent PHI workers are needed, which requires sustained investments from federal and state governments.

Conclusion: Efforts to train PHI specialists, recruit and retain them in the governmental public health workforce, and address hiring issues in public health agencies are essential next steps to transform the US public health enterprise.

Objectives: Digital health equity, the opportunity for all to engage with digital health tools to support good health outcomes, is an emerging priority across the world. The field of digital health equity would benefit from a comprehensive and systematic understanding of digital health, digital equity, and health equity, with a focus on real-world applications. We conducted a scoping review to identify and describe published frameworks and concepts relevant to digital health equity interventions.

Materials and methods: We conducted a scoping review of published peer-reviewed literature guided by the PRISMA Extension for Scoping Reviews. We searched 5 databases for frameworks related to or applied to digital health or equity interventions. Using deductive and inductive approaches, we analyzed frameworks and concepts based on the socio-ecological model.

Results: Of the 910 publications initially identified, we included 44 (4.8%) publications in our review that described 42 frameworks that sought to explain the ecosystem of digital and/or health equity, but none were comprehensive. From the frameworks we identified 243 concepts grouped into 43 categories including characteristics of individuals, communities, and organizations; societal context; perceived value of the intervention by and impacts on individuals, community members, and the organization; partnerships; and access to digital health services, in-person services, digital services, and data and information, among others.

Discussion: We suggest a consolidated definition of digital health equity, highlight illustrative frameworks, and suggest concepts that may be needed to enhance digital health equity intervention development and evaluation.

Conclusion: The expanded understanding of frameworks and relevant concepts resulting from this study may inform communities and stakeholders who seek to achieve digital inclusion and digital health equity.

Objective: To assess the prevalence of recommended design elements in implemented electronic health record (EHR) interruptive alerts across pediatric care settings.

Materials and methods: We conducted a 3-phase mixed-methods cross-sectional study. Phase 1 involved developing a codebook for alert content classification. Phase 2 identified the most frequently interruptive alerts at participating sites. Phase 3 applied the codebook to classify alerts. Inter-rater reliability (IRR) for the codebook and descriptive statistics for alert design contents were reported.

Results: We classified alert content on design elements such as the rationale for the alert's appearance, the hazard of ignoring it, directive versus informational content, administrative purpose, and whether it aligned with one of the Institute of Medicine's (IOM) domains of healthcare quality. Most design elements achieved an IRR above 0.7, with the exceptions for identifying directive content outside of an alert (IRR 0.58) and whether an alert was for administrative purposes only (IRR 0.36). IRR was poor for all IOM domains except equity. Institutions varied widely in the number of unique alerts and their designs. 78% of alerts stated their purpose, over half were directive, and 13% were informational. Only 2%-20% of alerts explained the consequences of inaction.

Discussion: This study raises important questions about the optimal balance of alert functions and desirable features of alert representation.

Conclusion: Our study provides the first multi-center analysis of EHR alert design elements in pediatric care settings, revealing substantial variation in content and design. These findings underline the need for future research to experimentally explore EHR alert design best practices to improve efficiency and effectiveness.

Objectives: Digital health research involves collecting vast amounts of personal health data, making data management practices complex and challenging to convey during informed consent.

Materials and methods: We conducted eight semi-structured focus groups to explore whether dataflow diagrams (DFD) can complement informed consent and improve participants' understanding of data management and associated risks (N = 34 participants).

Results: Our analysis found that DFDs could supplement text-based information about data management and sharing practices, such as by helping raise new questions that prompt conversation between prospective participants and members of a research team. Participants in the study emphasized the need for clear, simple, and accessible diagrams that are participant centered. Third-party access to data and sharing of sensitive health data were identified as high-risk areas requiring thorough explanation. Participants generally agreed that the design process should be led by the research team, but it should incorporate many diverse perspectives to ensure the diagram was meaningful to potential participants who are likely unfamiliar with data management. Nearly all participants rejected the idea that artificial intelligence could identify risks during the design process, but most were comfortable with it being used as a tool to format and simplify the diagram. In short, DFDs may complement standard text-based informed consent documents, but they are not a replacement.

Discussion: Prospective research participants value diverse ways of learning about study risks and benefits. Our study highlights the value of incorporating information visualizations, such as DFDs, into the informed consent procedures to participate in research.

Conclusion: Future research should explore other ways of visualizing consent information in ways that help people to overcome digital and data literacy barriers to participating in research. However, creating a DFD requires significant time and effort from research teams. To alleviate these costs, research sponsors can support the creation of shared infrastructure, communities of practice, and incentivize researchers to develop better consent procedures.

Objectives: To highlight the often overlooked role of user interface (UI) design in mitigating bias in artificial intelligence (AI)-based clinical decision support (CDS).

Materials and methods: This perspective paper discusses the interdependency between AI-based algorithm development and UI design and proposes strategies for increasing the safety and efficacy of CDS.

Results: The role of design in biasing user behavior is well documented in behavioral economics and other disciplines. We offer an example of how UI designs play a role in how bias manifests in our machine learning-based CDS development.

Discussion: Much discussion on bias in AI revolves around data quality and algorithm design; less attention is given to how UI design can exacerbate or mitigate limitations of AI-based applications.

Conclusion: This work highlights important considerations including the role of UI design in reinforcing/mitigating bias, human factors methods for identifying issues before an application is released, and risk communication strategies.

Objective: Our study aimed to expedite data sharing requests of Limited Data Sets (LDS) through the development of a streamlined platform that allows distributed, immutable management of network activities, provides transparent and intuitive auditing of data access history, and systematically evaluated it on a multi-capacity network setting for meaningful efficiency metrics.

Materials and methods: We developed a blockchain-based system with six types of smart contracts to automate the LDS sharing process among major stakeholders. Our workflow included metadata initialization, access-request processing, and audit-log querying. We evaluated our system using synthetic data on three machines with varying specifications to emulate real-world scenarios. The data employed included ∼1000 researcher requests and ∼360 000 log queries.

Results: On average, it took ∼2.5 s to register and respond to a researcher access request. The average runtime for an audit-log query with non-empty output was ∼3 ms. The runtime metrics at each institution showed general trends affiliated with their computational capacity.

Discussion: Our system can reduce the LDS sharing request time from potentially hours to seconds, while enhancing data access transparency in a multi-institutional setting. There were variations in performance across sites that could be attributed to differences in hardware specifications. The performance gains became marginal beyond certain hardware thresholds, pointing to the influence of external factors such as network speeds.

Conclusion: Our blockchain-based system can potentially accelerate clinical research by strengthening the data access process, expediting access and delivery of data links, increasing transparency with clear audit trails, and reinforcing trust in medical data management. Our smart contracts are available at: https://github.com/graceyufei/LDS-Request-Management.