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Objective: To evaluate the real-world performance of the SMART/HL7 Bulk Fast Health Interoperability Resources (FHIR) Access Application Programming Interface (API), developed to enable push button access to electronic health record data on large populations, and required under the 21st Century Cures Act Rule., Materials and Methods: We used an open-source Bulk FHIR Testing Suite at 5 healthcare sites from April to September 2023, including 4 hospitals using electronic health records (EHRs) certified for interoperability, and 1 Health Information Exchange (HIE) using a custom, standards-compliant API build. We measured export speeds, data sizes, and completeness across 6 types of FHIR., Results: Among the certified platforms, Oracle Cerner led in speed, managing 5-16 million resources at over 8000 resources/min. Three Epic sites exported a FHIR data subset, achieving 1-12 million resources at 1555-2500 resources/min. Notably, the HIE's custom API outperformed, generating over 141 million resources at 12 000 resources/min., Discussion: The HIE's custom API showcased superior performance, endorsing the effectiveness of SMART/HL7 Bulk FHIR in enabling large-scale data exchange while underlining the need for optimization in existing EHR platforms. Agility and scalability are essential for diverse health, research, and public health use cases., Conclusion: To fully realize the interoperability goals of the 21st Century Cures Act, addressing the performance limitations of Bulk FHIR API is critical. It would be beneficial to include performance metrics in both certification and reporting processes., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Environmental health (EH) services in the United States lag behind other areas of public health and health care with respect to information system interoperability and data sharing. This is partly due to an absence of well-defined use cases, the lack of direct economic drivers and resources to improve, the multiple jurisdictional elements that govern EH services across the United States, and no central organization to drive modernization of EH data. We summarize the status of EH information systems; argue for greater interoperability, including use cases for a messaging standard for environmental inspections; and present recommendations to better align EH services and data modernization efforts currently underway in other areas of public health., (Published by Oxford University Press on behalf of the American Medical Informatics Association 2024.)

Objective: The increased availability of public data and accessible visualization technologies enhanced the popularity of public health data dashboards and broadened their audience from professionals to the general public. However, many dashboards have not achieved their full potential due to design complexities that are not optimized to users' needs., Material and Methods: We used a 4-step human-centered design approach to develop a data dashboard of sexually transmitted infections for the New York State Department of Health: (1) stakeholder requirements gathering, (2) an expert review of existing data dashboards, (3) a user evaluation of existing data dashboards, and (4) an usability evaluation of the prototype dashboard with an embedded experiment about visualizing missing race and ethnicity data., Results: Step 1 uncovered data limitations and software requirements that informed the platform choice and measures included. Step 2 yielded a checklist of general principles for dashboard design. Step 3 revealed user preferences that influenced the chart types and interactive features. Step 4 uncovered usability problems resulting in features such as prompts, data notes, and displaying imputed values for missing race and ethnicity data., Discussion: Our final design was accepted by program stakeholders. Our modifications to traditional human-centered design methodologies to minimize stakeholders' time burden and collect data virtually enabled project success despite barriers to meeting participants in-person and limited public health agency staff capacity during the COVID-19 pandemic., Conclusion: Our human-centered design approach and the final data dashboard architecture could serve as a template for designing public health data dashboards elsewhere., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Objective: The annual American College of Medical Informatics (ACMI) symposium focused discussion on the national public health information systems (PHIS) infrastructure to support public health goals. The objective of this article is to present the strengths, weaknesses, threats, and opportunities (SWOT) identified by public health and informatics leaders in attendance., Materials and Methods: The Symposium provided a venue for experts in biomedical informatics and public health to brainstorm, identify, and discuss top PHIS challenges. Two conceptual frameworks, SWOT and the Informatics Stack, guided discussion and were used to organize factors and themes identified through a qualitative approach., Results: A total of 57 unique factors related to the current PHIS were identified, including 9 strengths, 22 weaknesses, 14 opportunities, and 14 threats, which were consolidated into 22 themes according to the Stack. Most themes (68%) clustered at the top of the Stack. Three overarching opportunities were especially prominent: (1) addressing the needs for sustainable funding, (2) leveraging existing infrastructure and processes for information exchange and system development that meets public health goals, and (3) preparing the public health workforce to benefit from available resources., Discussion: The PHIS is unarguably overdue for a strategically designed, technology-enabled, information infrastructure for delivering day-to-day essential public health services and to respond effectively to public health emergencies., Conclusion: Most of the themes identified concerned context, people, and processes rather than technical elements. We recommend that public health leadership consider the possible actions and leverage informatics expertise as we collectively prepare for the future., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

The COVID-19 pandemic exposed multiple weaknesses in the nation's public health system. Therefore, the American College of Medical Informatics selected "Rebuilding the Nation's Public Health Informatics Infrastructure" as the theme for its annual symposium. Experts in biomedical informatics and public health discussed strategies to strengthen the US public health information infrastructure through policy, education, research, and development. This article summarizes policy recommendations for the biomedical informatics community postpandemic. First, the nation must perceive the health data infrastructure to be a matter of national security. The nation must further invest significantly more in its health data infrastructure. Investments should include the education and training of the public health workforce as informaticians in this domain are currently limited. Finally, investments should strengthen and expand health data utilities that increasingly play a critical role in exchanging information across public health and healthcare organizations., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Objective: Digital exposure notifications (DEN) systems were an emergency response to the coronavirus disease 2019 (COVID-19) pandemic, harnessing smartphone-based technology to enhance conventional pandemic response strategies such as contact tracing. We identify and describe performance measurement constructs relevant to the implementation of DEN tools: (1) reach (number of users enrolled in the intervention); (2) engagement (utilization of the intervention); and (3) effectiveness in preventing transmissions of COVID-19 (impact of the intervention). We also describe WA State's experience utilizing these constructs to design data-driven evaluation approaches., Methods: We conducted an environmental scan of DEN documentation and relevant publications. Participation in multidisciplinary collaborative environments facilitated shared learning. Compilation of available data sources and their relevance to implementation and operation workflows were synthesized to develop implementation evaluation constructs., Results: We identified 8 useful performance indicators within reach, engagement, and effectiveness constructs., Discussion: We use implementation science to frame the evaluation of DEN tools by linking the theoretical constructs with the metrics available in the underlying disparate, deidentified, and aggregate data infrastructure. Our challenges in developing meaningful metrics include limited data science competencies in public health, validation of analytic methodologies in the complex and evolving pandemic environment, and the lack of integration with the public health infrastructure., Conclusion: Continued collaboration and multidisciplinary consensus activities can improve the utility of DEN tools for future public health emergencies., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Objective: To develop a usability checklist for public health dashboards., Materials and Methods: This study systematically evaluated all publicly available dashboards for sexually transmitted infections on state health department websites in the United States (N = 13). A set of 11 principles derived from the information visualization literature were used to identify usability problems that violate critical usability principles: spatial organization, information coding, consistency, removal of extraneous ink, recognition rather than recall, minimal action, dataset reduction, flexibility to user experience, understandability of contents, scientific integrity, and readability. Three user groups were considered for public health dashboards: public health practitioners, academic researchers, and the general public. Six reviewers with usability knowledge and diverse domain expertise examined the dashboards using a rubric based on the 11 principles. Data analysis included quantitative analysis of experts' usability scores and qualitative synthesis of their textual comments., Results: The dashboards had varying levels of complexity, and the usability scores were dependent on the dashboards' complexity. Overall, understandability of contents, flexibility, and scientific integrity were the areas with the most major usability problems. The usability problems informed a checklist to improve performance in the 11 areas., Discussion: The varying complexity of the dashboards suggests a diversity of target audiences. However, the identified usability problems suggest that dashboards' effectiveness for different groups of users was limited., Conclusions: The usability of public health data dashboards can be improved to accommodate different user groups. This checklist can guide the development of future public health dashboards to engage diverse audiences., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Electronic case reporting (eCR) is the automated generation and transmission of case reports from electronic health records to public health for review and action. These reports (electronic initial case reports: eICRs) adhere to recommended exchange and terminology standards. eCR is a partnership of the Centers for Disease Control and Prevention (CDC), Association of Public Health Laboratories (APHL) and Council of State and Territorial Epidemiologists (CSTE). The Minnesota Department of Health (MDH) received eICRs for COVID-19 from April 2020 (3 sites, manual process), automated eCR implementation in August 2020 (7 sites), and on-boarded ∼1780 clinical units in 460 sites across 6 integrated healthcare systems (through March 2022). Approximately 20 000 eICRs/month were reported to MDH during high-volume timeframes. With increasing provider/health system implementation, the proportion of COVID-19 cases with an eICR increased to 30% (March 2022). Evaluation of data quality for select demographic variables (gender, race, ethnicity, email, phone, language) across the 6 reporting health systems revealed a high proportion of completeness (>80%) for half of variables and less complete data for rest (ethnicity, email, language) along with low ethnicity data (<50%) for one health system. Presently eCR implementation at MDH includes only one EHR vendor. Next steps will focus on onboarding other EHRs, additional eICR data extraction/utilization, detailed analysis, outreach to address data quality issues, and expanding to other reportable conditions., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Objective: There is a need for a systematic method to implement the World Health Organization's Clinical Progression Scale (WHO-CPS), an ordinal clinical severity score for coronavirus disease 2019 patients, to electronic health record (EHR) data. We discuss our process of developing guiding principles mapping EHR data to WHO-CPS scores across multiple institutions., Materials and Methods: Using WHO-CPS as a guideline, we developed the technical blueprint to map EHR data to ordinal clinical severity scores. We applied our approach to data from 2 medical centers., Results: Our method was able to classify clinical severity for 100% of patient days for 2756 patient encounters across 2 institutions., Discussion: Implementing new clinical scales can be challenging; strong understanding of health system data architecture was integral to meet the clinical intentions of the WHO-CPS., Conclusion: We describe a detailed blueprint for how to apply the WHO-CPS scale to patient data from the EHR., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

In response to the coronavirus disease-19 (COVID-19) pandemic, numerous institutions published COVID-19 dashboards for reporting epidemiological statistics at the county, state, or national level. However, statistics for smaller cities were often not reported, requiring these areas to develop their own data processing pipelines. For under-resourced departments of health, the development of these pipelines was challenging, leading them to rely on nonspecific and often delayed infection statistics during the pandemic. To avoid this issue, the Stamford, Connecticut Department of Health (SDH) contracted with the Columbia Mailman School of Public Health to develop an online dashboard that displays real-time case, death, test, vaccination, hospitalization, and forecast data for their city, allowing SDH to monitor trends for specific demographic and geographic groups. Insights from the dashboard allowed SDH to initiate timely and targeted testing/vaccination campaigns. The dashboard is widely used and highlights the benefit of public-academic partnerships in public health, especially during the COVID-19 pandemic., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Making EHR Data More Available for Research and Public Health (MedMorph) is a Centers for Disease Control and Prevention-led initiative developing and demonstrating a reference architecture (RA) and implementation, including Health Level Seven International Fast Healthcare Interoperability Resources (HL7 FHIR) implementation guides (IGs), describing how to leverage FHIR for aligned research and public health access to clinical data for automated data exchange. MedMorph engaged a technical expert panel of more than 100 members to model representative use cases, develop IGs (architectural and content), align with existing efforts in the FHIR community, and demonstrate the RA in research and public health uses. The RA IG documents common workflows needed to automatically send research data to Research Patient Data Repositories for multiple use cases. Sharing a common RA and canonical data model will improve data sharing for research and public health needs and generate evidence. MedMorph delivers a robust, reusable method to utilize data from electronic health records addressing multiple research and public health needs., (Published by Oxford University Press on behalf of the American Medical Informatics Association 2021. This work is written by a US Government employee and is in the public domain in the US.)

Public health faces unprecedented challenges in its efforts to control COVID-19 through a national vaccination campaign. Addressing these challenges will require fundamental changes to public health data systems. For example, of the core data systems for immunization campaigns is the immunization information system (IIS); however, IISs were designed for tracking the vaccinated, not finding the patients who are high risk and need to be vaccinated. Health systems have this data in their electronic health records (EHR) systems and often have a greater capacity for outreach. Clearly, a partnership is needed. However, successful collaborations will require public health to change from its historical hierarchical information supply chain model to an ecosystem model with a peer-to-peer exchange with population health providers. Examples of the types of informatics innovations necessary to support such an ecosystem include a national patient identifier, population-level data exchange for immunization data, and computable electronic quality measures. Rather than think of these components individually, a comprehensive approach to rapidly adaptable tools for collaboration is needed., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Objective: We sought to support public health surveillance and response to coronavirus disease 2019 (COVID-19) through rapid development and implementation of novel visualization applications for data amalgamated across sectors., Materials and Methods: We developed and implemented population-level dashboards that collate information on individuals tested for and infected with COVID-19, in partnership with state and local public health agencies as well as health systems. The dashboards are deployed on top of a statewide health information exchange. One dashboard enables authorized users working in public health agencies to surveil populations in detail, and a public version provides higher-level situational awareness to inform ongoing pandemic response efforts in communities., Results: Both dashboards have proved useful informatics resources. For example, the private dashboard enabled detection of a local community outbreak associated with a meat packing plant. The public dashboard provides recent trend analysis to track disease spread and community-level hospitalizations. Combined, the tools were utilized 133 637 times by 74 317 distinct users between June 21 and August 22, 2020. The tools are frequently cited by journalists and featured on social media., Discussion: Capitalizing on a statewide health information exchange, in partnership with health system and public health leaders, Regenstrief biomedical informatics experts rapidly developed and deployed informatics tools to support surveillance and response to COVID-19., Conclusions: The application of public health informatics methods and tools in Indiana holds promise for other states and nations. Yet, development of infrastructure and partnerships will require effort and investment after the current pandemic in preparation for the next public health emergency., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Objective: The study sought to describe the contributions of clinical informatics (CI) fellows to their institutions' coronavirus disease 2019 (COVID-19) response., Materials and Methods: We designed a survey to capture key domains of health informatics and perceptions regarding fellows' application of their CI skills. We also conducted detailed interviews with select fellows and described their specific projects in a brief case series., Results: Forty-one of the 99 CI fellows responded to our survey. Seventy-five percent agreed that they were "able to apply clinical informatics training and interest to the COVID-19 response." The most common project types were telemedicine (63%), reporting and analytics (49%), and electronic health record builds and governance (32%). Telehealth projects included training providers on existing telehealth tools, building entirely new virtual clinics for video triage of COVID-19 patients, and pioneering workflows and implementation of brand-new emergency department and inpatient video visit types. Analytics projects included reports and dashboards for institutional leadership, as well as developing digital contact tracing tools. For electronic health record builds, fellows directly contributed to note templates with embedded screening and testing guidance, adding COVID-19 tests to order sets, and validating clinical triage workflows., Discussion: Fellows were engaged in projects that span the breadth of the CI specialty and were able to make system-wide contributions in line with their educational milestones., Conclusions: CI fellows contributed meaningfully and rapidly to their institutions' response to the COVID-19 pandemic., (© The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Objectives: This review summarizes past and current informatics activities at the Centers for Disease Control and Prevention National Program of Cancer Registries to inform readers about efforts to improve, standardize, and automate reporting to public health cancer registries., Target Audience: The target audience includes cancer registry experts, informaticians, public health professionals, database specialists, computer scientists, programmers, and system developers who are interested in methods to improve public health surveillance through informatics approaches., Scope: This review provides background on central cancer registries and describes the efforts to standardize and automate reporting to these registries. Specific topics include standardized data exchange activities for physician and pathology reporting, software tools for cancer reporting, development of a natural language processing tool for processing unstructured clinical text, and future directions of cancer surveillance informatics., (Published by Oxford University Press on behalf of the American Medical Informatics Association 2020. This work is written by US Government employees and is in the public domain in the US.)

Population-level prevention activities are often publicly invisible and excluded in planning and policymaking. This creates an incomplete picture of prevention service-related inputs, particularly at the local level. We describe the process and lessons learned by the Public Health Activities and Services Tracking team in promoting adoption of standardized service delivery measures developed to assess public health inputs and guide system transformations. The 3 factors depicted in our Public Health Activities and Services Tracking model-data need and use, data access, and standardized measures-must be realized to promote collection of standard public health system data. Bureaucratic, resource, system, and policy challenges hampered our efforts toward adoption of the standardized measures we promoted. Substantial investments of time, resources, and coordination appear necessary for systems to adopt changes needed for collecting comparable service delivery data. Lessons from our process of promoting adoption of standardized measures provide recommendations to support future efforts to measure public health system contributions to the public's health., (© The Author(s) 2019. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: As digitalization is progressively permeating all aspects of society, how can be it fruitfully employed to sustain the public health goals of quality, accessibility, efficiency and equity in health care and prevention? In this paper, we reflect on the potential of applying digital tools to public health and discuss some key challenges., Methods: We first define 'digitalization' in its broader sense, as well as applied to public health. We then build a conceptual framework where key public health domains are associated to available digital technologies in a matrix that help to identify digital features that bolster public health action. We also provide illustrative data and evidence on the application of digital solutions on selected public health areas. In the second part, we identify the strategic pillars for a successful European strategy for public health digitalization and we outline how the approach being pursued by the European Public Health Association (EUPHA) applies to digital health., Results: From a public health perspective, digitalization is being touted as providing several potential benefits and advantages, including support for the transition from cure to prevention, helping to put people and patients at the center of care delivery, supporting patient empowerment and making healthcare system more efficient, safer and cheaper. These benefits are enabled through the following features of digital technologies: (i) Personalization and precision; (ii) Automation; (iii) Prediction; (iv) Data analytics and (v) Interaction., Conclusion: A successful European strategy for public health digitalization should integrate the following pillars: political commitment, normative frameworks, technical infrastructure, targeted economic investments, education, research, monitoring and evaluation. EUPHA acknowledges digitalization is an asset for public health and is working both to promote the culture of "public health digitalization", as well as to enable its planning, implementation and evaluation at the research, practice and policy level., (© The Author(s) 2019. Published by Oxford University Press on behalf of the European Public Health Association.)

The spread of the written word, facilitated by the introduction of the printing press, was an information revolution with profound implications for European society. Now, a second information revolution is underway, a digital transformation that is shaping the way Europeans live and interact with each other and the world around them. We are confronted with an unprecedented expansion in ways to share and access information and experiences, to express ourselves and communicate. Yet while these changes have undoubtedly provided many benefits for health, from information sharing to improved surveillance and diagnostics, they also open up many potential threats. These come in many forms. Here we review some the pressing issues of concern; discrimination; breaches of privacy; iatrogenesis; disinformation and misinformation or 'fake news' and cyber-attacks. These have the potential to impact negatively on the health and wellbeing of individuals as well as entire communities and nations. We call for a concerted European response to maximize the benefits of the digital revolution while minimizing the harms, arguably one of the greatest challenges facing the public health community today., (© The Author(s) 2019. Published by Oxford University Press on behalf of the European Public Health Association.)

Objective: Rural public health system leaders struggle to access and use data for understanding local health inequities and to effectively allocate scarce resources to populations in need. This study sought to determine these rural public health system leaders' data access, capacity, and training needs., Materials and Methods: We conducted qualitative interviews across Alaska, Idaho, Oregon, and Washington with individuals expected to use population data for analysis or decision-making in rural communities. We used content analysis to identify themes., Results: We identified 2 broad themes: (1) challenges in accessing or using data to monitor and address health disparities and (2) needs for training in data use to address health inequities. Participants faced challenges accessing or using data to address rural disparities due to (a) limited availability or access to data, (b) data quality issues, (c) limited staff with expertise and resources for analyzing data, and (d) the diversity within rural jurisdictions. Participants also expressed opportunities for filling capacity gaps through training-particularly for displaying and communicating data., Discussion: Rural public health system leaders expressed data challenges, many of which can be aided by informatics solutions. These include interoperable, accessible, and usable tools that help capture, access, analyze, and display data to support health equity efforts in rural communities., Conclusion: Informatics has the potential to address some of the daunting data-related challenges faced by rural public health system leaders working to enhance health equity. Future research should focus on developing informatics solutions to support data access and use in rural communities., (© The Author(s) 2019. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Objective: Examine whether individual, geographic, and economic phenotypes predict missing data on specific drug involvement in overdose deaths, manifesting inequities in overdose mortality data, which is a key data source used in measuring the opioid epidemic., Materials and Methods: We combined national data sources (mortality, demographic, economic, and geographic) from 2014-2016 in a multi-method analysis of missing drug classification in the overdose mortality records (as defined by the use of ICD-10 T50.9 on death certificates). We examined individual disparities in decedent-level multivariate logistic regression models, geographic disparities in spatial analysis (heat maps), and economic disparities in a combination of temporal trend analyses (descriptive statistics) and both decedent- and county-level multivariate logistic regression models., Results: Our analyses consistently found higher rates of unclassified overdoses in decedents of female gender, White race, non-Hispanic ethnicity, with college education, aged 30-59 and those from poorer counties. Despite the fact that unclassified drug overdose death rates have reduced over time, gaps persist between the richest and poorest counties. There are also striking geographic differences both across and within states., Discussion: Given the essential role of mortality data in measuring the scale of the opioid epidemic, it is important to understand the individual and community inequities underlying the missing data on specific drug involvements. Knowledge of these inequities could enhance our understanding of the opioid crisis and inform data-driven interventions and policies with more equitable resource allocations., Conclusion: Multiple individual, geographic, and economic disparities underlie unclassified overdose deaths, with important implications for public health informatics and addressing the opioid crisis., (© The Author(s) 2019. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Objective: Participants enrolled into randomized controlled trials (RCTs) often do not reflect real-world populations. Previous research in how best to transport RCT results to target populations has focused on weighting RCT data to look like the target data. Simulation work, however, has suggested that an outcome model approach may be preferable. Here, we describe such an approach using source data from the 2 × 2 factorial NAVIGATOR (Nateglinide And Valsartan in Impaired Glucose Tolerance Outcomes Research) trial, which evaluated the impact of valsartan and nateglinide on cardiovascular outcomes and new-onset diabetes in a prediabetic population., Materials and Methods: Our target data consisted of people with prediabetes serviced at the Duke University Health System. We used random survival forests to develop separate outcome models for each of the 4 treatments, estimating the 5-year risk difference for progression to diabetes, and estimated the treatment effect in our local patient populations, as well as subpopulations, and compared the results with the traditional weighting approach., Results: Our models suggested that the treatment effect for valsartan in our patient population was the same as in the trial, whereas for nateglinide treatment effect was stronger than observed in the original trial. Our effect estimates were more efficient than the weighting approach and we effectively estimated subgroup differences., Conclusions: The described method represents a straightforward approach to efficiently transporting an RCT result to any target population., (© The Author(s) 2019. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

The field of biomedical informatics experienced a productive 2015 in terms of research. In order to highlight the accomplishments of that research, elicit trends, and identify shortcomings at a macro level, a 19-person team conducted an extensive review of the literature in clinical and consumer informatics. The result of this process included a year-in-review presentation at the American Medical Informatics Association Annual Symposium and a written report (see supplemental data). Key findings are detailed in the report and summarized here. This article organizes the clinical and consumer health informatics research from 2015 under 3 themes: the electronic health record (EHR), the learning health system (LHS), and consumer engagement. Key findings include the following: (1) There are significant advances in establishing policies for EHR feature implementation, but increased interoperability is necessary for these to gain traction. (2) Decision support systems improve practice behaviors, but evidence of their impact on clinical outcomes is still lacking. (3) Progress in natural language processing (NLP) suggests that we are approaching but have not yet achieved truly interactive NLP systems. (4) Prediction models are becoming more robust but remain hampered by the lack of interoperable clinical data records. (5) Consumers can and will use mobile applications for improved engagement, yet EHR integration remains elusive., (© The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Objective: Understanding population-level health trends is essential to effectively monitor and improve public health. The Office of the National Coordinator for Health Information Technology (ONC) Query Health initiative is a collaboration to develop a national architecture for distributed, population-level health queries across diverse clinical systems with disparate data models. Here we review Query Health activities, including a standards-based methodology, an open-source reference implementation, and three pilot projects., Materials and Methods: Query Health defined a standards-based approach for distributed population health queries, using an ontology based on the Quality Data Model and Consolidated Clinical Document Architecture, Health Quality Measures Format (HQMF) as the query language, the Query Envelope as the secure transport layer, and the Quality Reporting Document Architecture as the result language., Results: We implemented this approach using Informatics for Integrating Biology and the Bedside (i2b2) and hQuery for data analytics and PopMedNet for access control, secure query distribution, and response. We deployed the reference implementation at three pilot sites: two public health departments (New York City and Massachusetts) and one pilot designed to support Food and Drug Administration post-market safety surveillance activities. The pilots were successful, although improved cross-platform data normalization is needed., Discussions: This initiative resulted in a standards-based methodology for population health queries, a reference implementation, and revision of the HQMF standard. It also informed future directions regarding interoperability and data access for ONC's Data Access Framework initiative., Conclusions: Query Health was a test of the learning health system that supplied a functional methodology and reference implementation for distributed population health queries that has been validated at three sites., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.)