Objective:This perspective summarizes key themes that arose from stakeholder discussions at the inaugural Clinical Decision Support Innovation Collaborative (CDSiC) 2023 Annual Meeting. The CDSiC is an Agency for Healthcare Research and Quality (AHRQ)-funded innovation hub for patient-centered clinical decision support (PC CDS). Materials and Methods:The meeting took place on May 16-17, 2023, and engaged 73 participants that represented a range of stakeholder groups including researchers, informaticians, federal representatives, clinicians, patients, and electronic health record developers. Each meeting session was recorded and had 2 notetakers. CDSiC leadership analyzed the compiled meeting notes to synthesize key themes. Results:Participants discussed 7 key opportunities to advance PC CDS: (1) establish feedback loops between patients and clinicians; (2) develop new workflows; (3) expand the evidence base; (4) adapt the CDS Five Rights for the patient perspective; (5) advance health equity; (6) explore perceptions on the use of artificial intelligence; and (7) encourage widespread use and scalability of PC CDS. Discussion and Conclusion:Innovative approaches are needed to ensure patients' and caregivers' voices are meaningfully included to advance PC CDS.

Objective To use workflow execution models to highlight new considerations for patient-centered clinical decision support policies (PC CDS), processes, procedures, technology, and expertise required to support new workflows.Methods To generate and refine models, we used (1) targeted literature reviews; (2) key informant interviews with 6 external PC CDS experts; (3) model refinement based on authors' experience; and (4) validation of the models by a 26-member steering committee.Results and Discussion We identified 7 major issues that provide significant challenges and opportunities for healthcare systems, researchers, administrators, and health IT and app developers. Overcoming these challenges presents opportunities for new or modified policies, processes, procedures, technology, and expertise to: (1) Ensure patient-generated health data (PGHD), including patient-reported outcomes (PROs), are documented, reviewed, and managed by appropriately trained clinicians, between visits and after regular working hours. (2) Educate patients to use connected medical devices and handle technical issues. (3) Facilitate collection and incorporation of PGHD, PROs, patient preferences, and social determinants of health into existing electronic health records. (4) Troubleshoot erroneous data received from devices. (5) Develop dashboards to display longitudinal patient-reported data. (6) Provide reimbursement to support new models of care. (7) Support patient engagement with remote devices.Conclusion Several new policies, processes, technologies, and expertise are required to ensure safe and effective implementation and use of PC CDS. As we gain more experience implementing and working with PC CDS, we should be able to begin realizing the long-term positive impact on patient health that the patient-centered movement in healthcare promises.

OBJECTIVES:To reflect on the achievements of the Agency for Healthcare Research and Quality's (AHRQ) Digital Healthcare Research Program over the past 20 years, evaluate its impact on US healthcare quality and safety, and outline current and future priorities for digital healthcare research and innovation. PROCESS:The article reviews key milestones in AHRQ's digital healthcare initiatives, including its founding and its advances in telehealthcare and clinical decision support. It highlights AHRQ's contributions to advancing technology integration in healthcare, promoting patient safety, and addressing equity gaps. The article also examines the evolving role of artificial intelligence (AI) in healthcare delivery. CONCLUSIONS:AHRQ's Digital Healthcare Research Program has significantly contributed to improving healthcare quality. As digital technologies evolve, particularly with AI, the program remains focused on enhancing safety, equity, and efficiency in healthcare. Continued research and investment will be essential to maintaining progress and addressing new challenges.

This commentary is in many ways a follow-on to, and elaboration of, the commentary published in the July issue of this journal.1 The previous commentary introduced three characteristics that contribute to the uniqueness of learning health systems (LHSs) as an approach to health improvement. The three characteristics introduced there were: "(1) a multi-stakeholder learning community that is focused on the (targeted) problem and collaboratively executes the entire cycle; (2) embracing, at the outset, the uncertainty of how to improve against the problem by undertaking a rigorous discovery process before any implementation takes place; and (3) supporting multiple co-occurring cycles with a socio-technical infrastructure to create a learning system." This commentary focuses on the very important third characteristic, infrastructure. It examines the role of infrastructure in the overall architecture of an LHS and describes LHS infrastructure in terms of 10 interconnected socio-technical services accompanied by a brief description of each. Like the previous commentary, this one seeks to bring an increased level of focus to discussions of LHSs and move an emerging field, what is coming to be called "Learning Health System Science",2 toward a sharper conception of its core principles. Critically, LHS infrastructure must extend beyond digital technology in order to support improvement of individual and population health. The infrastructure must be socio-technical in the sense that it incorporates the roles that a wide range of people must play at different levels of social organization: as individuals, as teams, as members of organizations, and as citizens of civil society.5 Technology, alone, only establishes a potential for health improvement through an LHS. Viewing its infrastructure in terms of socio-technical services could be beneficial in several ways beyond working toward a consensus view of LHS structure and function. Most notably, such a modular approach could lead to sharing of interoperable infrastructure components and the possibility that sharing of such components might promote the more rapid adoption of LHS methods. Moreover, compatibility of LHS architectures could enable smaller scale LHSs to compose into a single system that functions at larger scale. Logical next steps to mature LHS infrastructure would include building consensus around the constituent services and developing specifications for each one. The authors wish to thank the many members of the group developing an organizational maturity model for Learning Health Systems, a joint project of AcademyHealth and the Learning Health Community, for their insightful suggestions that helped to shape the ideas presented in this manuscript. They also wish to thank the staff of the Agency for Healthcare Research and Quality for their reviews and most helpful comments. The authors have no conflicts of interest to declare.

Patient-centered clinical decision support (PC CDS) exists on a continuum that reflects the degree to which its knowledge base, data, delivery, and use focus on patient needs and experiences. A new focus on value-based, whole-person care has resulted in broader development of PC CDS technologies, yet there is a limited information on how to measure their performance and effectiveness. This paper provides a new framework for measuring the performance of PC CDS technology and describes how the framework can be applied to illustrative use cases. This paper presents a new framework that incorporates patient-centered principles into traditional health information technology and clinical decision support (CDS) evaluation frameworks to create a unified guide to PC CDS performance measurement. We reviewed existing literature on health information technology, CDS, and PC CDS measurement and evaluation to develop the framework. We validated and refined the measurement framework through key informant interviews and input from an expert panel. The PC CDS Performance Measurement Framework includes six domains: safe, timely, effective, efficient, equitable, and patient-centered. Each domain contains subdomains with example measures and approaches to patient-centeredness. The framework also describes different levels at which effects can be measured, for example, at the patient, clinician, organization, and population level. This framework can be used by researchers, health system leaders, informaticians, and patients to understand the full breadth of performance and impact of PC CDS technology. As the field of PC CDS matures, researchers and evaluators can build upon the framework to assess which components of PC CDS technologies work, whether PC CDS technologies are being used as anticipated, and whether the intended outcomes of delivering evidence-based, patient-centered care are being achieved.

The design, development, implementation, use, and evaluation of high-quality, patient-centered clinical decision support (PC CDS) is necessary if we are to achieve the quintuple aim in healthcare. We developed a PC CDS lifecycle framework to promote a common understanding and language for communication among researchers, patients, clinicians, and policymakers. The framework puts the patient, and/or their caregiver at the center and illustrates how they are involved in all the following stages: Computable Clinical Knowledge, Patient-specific Inference, Information Delivery, Clinical Decision, Patient Behaviors, Health Outcomes, Aggregate Data, and patient-centered outcomes research (PCOR) Evidence. Using this idealized framework reminds key stakeholders that developing, deploying, and evaluating PC-CDS is a complex, sociotechnical challenge that requires consideration of all 8 stages. In addition, we need to ensure that patients, their caregivers, and the clinicians caring for them are explicitly involved at each stage to help us achieve the quintuple aim.

The 2020 Focused Updates to the Asthma Management Guidelines: A Report from the National Asthma Education and Prevention Program Coordinating Committee Expert Panel Working Group provides the first new clinical practice recommendations from the National Heart, Lung, and Blood Institute (NHLBI) since the previous 2007 asthma management guidelines. Guideline implementability was a high priority for the expert panel, and many approaches were undertaken to enhance the implementability of this clinical guideline update. Within the report, specific implementation guidance sections provide expanded summaries for each recommendation to quickly assist users. The implementation guidance incorporates findings from NHLBI-sponsored focus groups conducted with people who have asthma, caregivers, and health care providers. The findings were used to identify the types of information and tools that individuals with asthma, their caregivers, and their health care providers would find most helpful; ensure that the new asthma guidelines reflect the voices of individuals with asthma and their caregivers; and identify potential barriers to uptake by individuals with asthma and their caregivers. The expert panel used a GRADE-based approach to develop evidence-to-decision tables that provided a framework for assessing the evidence and consideration of a range of contextual factors that influenced the recommendations such as desirable and undesirable effects, certainty of evidence, values, balance of effects, acceptability, feasibility, and equity. To facilitate uptake in clinical care workflow, selected recommendations were converted into structured, computer-based clinical decision support artifacts, and the new recommendations were integrated into existing treatment tables used in the 2007 asthma management guidelines, with which many users are familiar. A comprehensive approach to improve guidelines dissemination and implementation included scientific publications, patient materials, media activities, stakeholder engagement, and professional education. We developed evidence-based clinical practice guideline updates for asthma management focused on six topic areas. The guideline development processes and implementation and dissemination activities undertaken sought to enhance implementability by focusing on intrinsic factors as described by Kastner, Gagliardi, and others to produce usable, adoptable, and adaptable guidelines. Enhanced collaboration during guideline development between authors, informaticists, and implementation scientists may facilitate the development of tools that support the application of recommendations to further improve implementability.

AbstractObjectivesIntroduce the CDS-Sandbox, a cloud-based virtual machine created to facilitate Clinical Decision Support (CDS) developers and implementers in the use of FHIR- and CQL-based open-source tools and technologies for building and testing CDS artifacts.Materials and MethodsThe CDS-Sandbox includes components that enable workflows for authoring and testing CDS artifacts. Two workshops at the 2020 and 2021 AMIA Annual Symposia were conducted to demonstrate the use of the open-source CDS tools.ResultsThe CDS-Sandbox successfully integrated the use of open-source CDS tools. Both workshops were well attended. Participants demonstrated use and understanding of the workshop materials and provided positive feedback after the workshops.DiscussionThe CDS-Sandbox and publicly available tutorial materials facilitated an understanding of the leading-edge open-source CDS infrastructure components.ConclusionThe CDS-Sandbox supports integrated use of the key CDS open-source tools that may be used to introduce CDS concepts and practice to the clinical informatics community.

BACKGROUND:Clinical decision support (CDS), which provides tools to assist clinical decision-making, can improve adherence to evidence-based practices, prevent medical errors, and support high-quality and patient-centered care delivery. Publicly available CDS that uses standards to express clinical logic (i.e., standards-based CDS) has the potential to reduce duplicative efforts of translating the same clinical evidence into CDS across multiple health care institutions. Yet development of such CDS is relatively new and its potential only partially explored.OBJECTIVES:This study aimed to describe lessons learned from a national initiative promoting publicly available, standards-based CDS resources, discuss challenges, and report suggestions for improvement.METHODS:Findings were drawn from an evaluation of the Agency for Healthcare Research and Quality Patient-Centered Outcomes Research CDS Initiative, which aimed to advance evidence into practice through standards-based and publicly available CDS. Methods included literature and program material reviews, key informant interviews, and a web-based survey about a public repository of CDS artifacts and tools for authoring standards-based CDS.RESULTS:The evaluation identified important lessons for developing and implementing standards-based CDS through publicly available repositories such as CDS Connect. Trust is a critical factor in uptake and can be bolstered through transparent information on underlying evidence, collaboration with experts, and feedback loops between users and developers to support continuous improvement. Additionally, while adoption of standards among electronic health record developers will make it easier to implement standards-based CDS, lower-resourced health systems will need extra support to ensure successful implementation and use. Finally, although we found the resources developed by the Initiative to offer valuable prototypes for the field, health systems desire more information about patient-centered, clinical, and cost-related outcomes to help them justify the investment required to implement standards-based, publicly available CDS.CONCLUSION:While the standards and technology to publicly share standards-based CDS have increased, broad dissemination and implementation remain challenging.

Background Patient-centered clinical decision support (PC CDS) aims to assist with tailoring decisions to an individual patient's needs. Patient-generated health data (PGHD), including physiologic measurements captured frequently by automated devices, provide important information for PC CDS. The volume and availability of such PGHD is increasing, but how PGHD should be presented to clinicians to best aid decision-making is unclear. Objectives Identify best practices in visualizations of physiologic PGHD, for designing a software application as a PC CDS tool. Methods We performed a scoping review of studies of PGHD dashboards that involved clinician users in design or evaluations. We included only studies that used physiologic PGHD from single patients for usage in decision-making. Results We screened 468 titles and abstracts, 63 full-text papers, and identified 15 articles to include in our review. Some research primarily sought user input on PGHD presentation; other studies garnered feedback only as a side effort for other objectives (e.g., integration with electronic health records [EHRs]). Development efforts were often in the domains of chronic diseases and collected a mix of physiologic parameters (e.g., blood pressure and heart rate) and activity data. Users' preferences were for data to be presented with statistical summaries and clinical interpretations, alongside other non-PGHD data. Recurrent themes indicated that users desire longitudinal data display, aggregation of multiple data types on the same screen, actionability, and customization. Speed, simplicity, and availability of data for other purposes (e.g., documentation) were key to dashboard adoption. Evaluations were favorable for visualizations using common graphing or table formats, although best practices for implementation have not yet been established. Conclusion Although the literature identified common themes on data display, measures, and usability, more research is needed as PGHD usage grows. Ensuring that care is tailored to individual needs will be important in future development of clinical decision support.

Supporting healthcare decision-making that is patient-centered and evidence-based requires investments in the development of tools and techniques for dissemination of patient-centered outcomes research findings via methods such as clinical decision support (CDS). This article explores the technical landscape for patient-centered CDS (PC CDS) and the gaps in making PC CDS more shareable, standards-based, and publicly available, with the goal of improving patient care and clinical outcomes. This landscape assessment used: (1) a technical expert panel; (2) a literature review; and (3) interviews with 18 CDS stakeholders. We identified 7 salient technical considerations that span 5 phases of PC CDS development. While progress has been made in the technical landscape, the field must advance standards for translating clinical guidelines into PC CDS, the standardization of CDS insertion points into the clinical workflow, and processes to capture, standardize, and integrate patient-generated health data.

Background Patient use of mobile health applications is increasing. To promote patient-centered care, data from these apps must be integrated into clinician workflows within the electronic health record (EHR). Health Level 7 Fast Healthcare Interoperability Resources (FHIR) offers a standards-based application programming interface (API) that may support such integration. Objective We aimed to use interoperability standards to integrate a patient mobile application (coronavirus 2019 [COVID-19] Tracker) with an EHR. The COVID-19 Tracker engages patients by sending introductory and reminder text messages, collecting vital signs and symptom data from COVID-19 patients, and providing actionable guidance if concerning issues are identified. This case report explored the use of FHIR APIs to integrate the app into EHR-enabled clinical workflows. Methods The authors used notes from project meetings and from semistructured discussions among the application development team to track the design and implementation processes. Seven points of integration between the application and the EHR were identified, and approaches using FHIR to perform these integrations were delineated. Results Although this clinical decision support integration project benefited from its standards-based approach, many challenges were encountered. These were due to (1) partial implementation of the FHIR standard in the EHR, particularly, components needed for patient engagement applications; (2) limited experience with the adoption of FHIR standards; and (3) gaps in the current FHIR standard. Alternative approaches, often not based on interoperability standards, were developed to overcome these limitations. Conclusion Despite the challenges encountered due to the early stages of FHIR development and adoption, FHIR standards provide a promising mechanism for overcoming longstanding barriers and facilitating the integration of patient engagement apps with EHRs. To accelerate the integration of apps into clinical workflows, additional components of the FHIR standard must be implemented within the EHR and other clinical systems. Continued expansion of available FHIR resources will help with tighter workflow integration.

BACKGROUND:Partnerships among patients, families, caregivers, and clinicians are critical to helping patients lead their best lives given their specific genetics, conditions, circumstances, and the environments in which they live, work, and play. These partnerships extend to the development of health information technology, including clinical decision support (CDS). Design of these technologies, however, often occurs without a profound understanding of the true needs, wants, and concerns of patients and family members. Patient perspective is important not only for patient-facing applications but for provider-facing applications, especially those intended to support shared decision-making. OBJECTIVES:Our objective is to describe models for effectively engaging patients and caregivers during CDS development and implementation and to inspire CDS developers to partner with patients and caregivers to improve the potential impact of CDS. METHODS:This article serves as a case study of how two patient activists successfully implemented models for engaging patients and caregivers in a federal program designed to increase the uptake of research evidence into clinical practice through CDS. Models included virtual focus groups, social media, agile software development, and attention to privacy and cybersecurity. RESULTS:Impact on the federal program has been substantial and has resulted in improved CDS training materials, new prototype CDS applications, prioritization of new functionality and features, and increased engagement of patient and caregiver communities in ongoing projects. Among these opportunities is a group of developers and patient activists dedicated and committed to exploring strategic and operational opportunities to codesign CDS applications. CONCLUSION:Codesign and implementation of CDS can occur as a partnership among developers, implementers, patients, cybersecurity and privacy activists, and caregivers. Several approaches are viable, and an iterative process is most promising. Additional work is needed to investigate scalability of the approaches explored by this case study and to identify measures of meaningful inclusion of patients/caregivers in CDS projects.

BACKGROUND:Healthcare systems devote substantial resources to the development of clinical decision support (CDS) largely independently. The process of translating evidence-based practice into useful and effective CDS may be more efficient and less duplicative if healthcare systems shared knowledge about the translation, including workflow considerations, key assumptions made during the translation process, and technical details.OBJECTIVE:Describe how a national repository of CDS can serve as a public resource for healthcare systems, academic researchers, and informaticists seeking to share and reuse CDS knowledge resources or "artifacts."METHODS:In 2016, the Agency for Healthcare Research and Quality (AHRQ) launched CDS Connect as a public, web-based platform for authoring and sharing CDS knowledge artifacts. Researchers evaluated early use and impact of the platform by collecting user experiences of AHRQ-sponsored and community-led dissemination efforts and through quantitative/qualitative analysis of site metrics. Efforts are ongoing to quantify efficiencies gained by healthcare systems that leverage shared, interoperable CDS artifacts rather than developing similar CDS de novo and in isolation.RESULTS:Federal agencies, academic institutions, and others have contributed over 50 entries to CDS Connect for sharing and dissemination. Analysis indicates shareable CDS resources reduce team sizes and the number of tasks and time required to design, develop, and deploy CDS. However, the platform needs further optimization to address sociotechnical challenges. Benefits of sharing include inspiring others to undertake similar CDS projects, identifying external collaborators, and improving CDS artifacts as a result of feedback. Organizations are adapting content available through the platform for continued research, innovation, and local implementations.CONCLUSION:CDS Connect has provided a functional platform where CDS developers are actively sharing their work. CDS sharing may lead to improved implementation efficiency through numerous pathways, and further research is ongoing to quantify efficiencies gained.