The COVID pandemic exposed the critical role T cells play in initial immunity, the establishment and maintenance of long term protection, and of durable responsiveness against novel viral variants. A growing body of evidence indicates that adding measures of cellular immunity will fill an important knowledge gap in vaccine clinical trials, likely leading to improvements in the effectiveness of the next generation vaccines against current and emerging variants. In depth cellular immune monitoring in Phase II trials, particularly for high risk populations such as the elderly or immune compromised, should result in better understanding of the dynamics and requirements for establishing effective long term protection. Such analyses can result in cellular immunity correlates that can then be deployed in Phase III studies using appropriate, scalable technologies. Measures of cellular immunity are less established than antibodies as correlates of clinical immunity, and some misconceptions persist about cellular immune monitoring usefulness, cost, complexity, feasibility, and scalability. We outline the currently available cellular immunity assays, review their readiness for use in clinical trials, their logistical requirements, and the type of information each assay generates. The objective is to provide a reliable source of information that could be leveraged to develop a rational approach for comprehensive immune monitoring during vaccine development.

COVID-19 vaccine clinical development was conducted with unprecedented speed. Immunity measurements were concentrated on the antibody response which left significant gaps in our understanding how robust and long-lasting immune protection develops. Better understanding the cellular immune response will fill those gaps, especially in the elderly and immunocompromised populations which not only have the highest risk for severe infection, but also frequently have inadequate antibody responses. Although cellular immunity measurements are more logistically complex to conduct for clinical trials compared to antibody measurements, the feasibility and benefit of doing them in clinical trials has been demonstrated and so should be more widely adopted. Adding significant cellular response metrics will provide a deeper understanding of the overall immune response to COVID-19 vaccination, which will significantly inform vaccination strategies for the most vulnerable populations. Better monitoring of overall immunity will also substantially benefit other vaccine development efforts, and indeed any therapies that involve the immune system as part of the therapeutic strategy.

First-generation anit-SARS-CoV-2 vaccines were highly successful. They rapidly met an unforeseen emergency need, saved millions of lives, and simultaneously eased the burden on healthcare systems worldwide. The first-generation vaccines, however, focused too narrowly on antibody-based immunity as the sole marker of vaccine trial success, resulting in large knowledge gaps about waning vaccine protection, lack of vaccine robustness to viral mutation, and lack of efficacy in immunocompromised populations. Detailed reviews of first-generation vaccines, including their mode of action and geographical distribution, have been published elsewhere. Second-generation clinical trials must address these gaps by evaluating a broader range of immune markers, including those representing cell-mediated immunity, to ensure the most protective and long-lasting vaccines are brought to market.

The "Taxonomy of Artificial Intelligence for Medical Services and Procedures" became part of the Current Procedural Terminology (CPT®) code set effective January 1, 2022. It provides a framework for discrete and differentiable CPT codes which; are consistent with the features of the devices' output, characterize interaction between the device and the physician or other qualified health care professional, and foster appropriate payment. Descriptors include "Assistive", "Augmentative", and "Autonomous". As software increasingly augments the provision of medical services the taxonomy will foster consistent language in coding enabling patient, provider, and payer access to the benefits of innovation.

Session Description: Efficiency is widely recognized as one of the most important concepts in economics and business, a generic definition from Knapp (1984) coins it as 'the allocation of scarce resources that maximizes the achievement of aims'. Organizations, hospitals, and health systems across the world have been implementing telehealth for their patients and employees with efficiency in mind. A variety of benefits and motivations for telehealth exist, this session explores the efficiencies realized (financial, clinical, operational, technical). Leading experts and senior leaders from UnitedHealth Group, Kaiser Permanente, and Cloudbreak Health join this exciting panel to discuss how telehealth has become a strategic asset and made breakthrough efficiency impacts and results in their own and partner/client organizations.

Background Interdisciplinary collaborations bring lots of benefits to researchers in multiple areas, including precision medicine. Objective This viewpoint aims at studying how cross-institution team science would affect the development of precision medicine. Methods Publications of organizations on the eHealth Catalogue of Activities were collected in 2015 and 2017. The significance of the correlation between coleadership and coauthorship among different organizations was calculated using the Pearson chi-square test of independence. Other nonparametric tests examined whether organizations with coleaders publish more and better papers than organizations without coleaders. Results A total of 374 publications from 69 organizations were analyzed in 2015, and 7064 papers from 87 organizations were analyzed in 2017. Organizations with coleadership published more papers (P<.001, 2015 and 2017), which received higher citations (Z=–13.547, P<.001, 2017), compared to those without coleadership. Organizations with coleaders tended to publish papers together (P<.001, 2015 and 2017). Conclusions Our findings suggest that organizations in the field of precision medicine could greatly benefit from institutional-level team science. As a result, stronger collaboration is recommended.

Drawing on a landscape analysis of existing data-sharing initiatives, in-depth interviews with expert stakeholders, and public deliberations with community advisory panels across the U.S., we describe features of the evolving medical information commons (MIC). We identify participant-centricity and trustworthiness as the most important features of an MIC and discuss the implications for those seeking to create a sustainable, useful, and widely available collection of linked resources for research and other purposes.

The transition from a system focused on the delivery of sickness and illness services to one with a heavy focus of helping people become and remain healthier requires a major shift in how we view the patient and person. The health care system attempts to magically transform us from persons to patients in a context of sickness and disease, in need of medical procedures and interventions. Those few hours we spend a year in formal medical and health care contexts do not define us in the broader life space. We contend that "person-centricity" is more reflective of the life space and as such better supports that shift than do models of consumer or patient empowerment, centeredness, engagement, or activation. "Person-centricity" represents the complexity of how individuals make decisions including health and health care decisions, within the broader context of their lives, and accurately addresses the needs and aspirations of people throughout their life journey. This is not simply a shift in semantics, but an entirely new paradigm that frees the individual from assuming and succumbing to the passive and subservient patient role and dramatically changes the way in which we view ourselves and interact with the health care system.The changes required to create a healthier America and affect costs associated with lifestyle-related diseases need to happen on a personal level, coupled with a supportive infrastructure and public policies to promote and sustain them. This shift is critical to our transition from health care to a healthier way of living and of controlling avoidable costs.

Informal caregivers, such as family members and friends, are important for maintaining the health and wellness of the elderly and the chronically ill. However, the complexity of informal caregiving has been under-studied in prior research. In this study, we attempt to answer two main questions - what makes the coordination of patient care so difficult among multiple informal caregivers, and what are the challenges that should be considered in designing technologies to facilitate informal care coordination? To answer these questions, we conducted a qualitative interview study with 15 individuals with caregiving experience. The findings of the study identified the main challenges faced by caregivers in maintaining awareness, coordinating handoffs among informal caregivers, and easing tensions among caregivers and patients. These findings revealed considerations unique for informal caregiver coordination, and helped inform the design of technologies to support the care coordination.

This study explores the use of phone and secure messaging via an online patient portal in mediating the communication between patients and their healthcare providers. In analyzing the messages handling processes, we found that although both phone and secure messages were answered in similar manners, the interplay of the front- and back-end roles in collaborative work resulted in patients' preference for secure messages in communication as they believed it offered direct and empowered communication experiences. This study offers insights on the choice of how different communication media affect patients' perception toward the quality of the communication and patient-provider relationship.

National and international public–private partnerships, consortia, and government initiatives are underway to collect and share genomic, personal, and healthcare data on a massive scale. Ideally, these efforts will contribute to the creation of a medical information commons (MIC), a comprehensive data resource that is widely available for both research and clinical uses. Stakeholder participation is essential in clarifying goals, deepening understanding of areas of complexity, and addressing long-standing policy concerns such as privacy and security and data ownership. This article describes eight core principles proposed by a diverse group of expert stakeholders to guide the formation of a successful, sustainable MIC. These principles promote formation of an ethically sound, inclusive, participant-centric MIC and provide a framework for advancing the policy response to data-sharing opportunities and challenges.

Rapid and affordable tumor molecular profiling has led to an explosion of clinical and genomic data poised to enhance the diagnosis, prognostication and treatment of cancer. A critical point has now been reached at which the analysis and storage of annotated clinical and genomic information in unconnected silos will stall the advancement of precision cancer care. Information systems must be harmonized to overcome the multiple technical and logistical barriers to data sharing. Against this backdrop, the Global Alliance for Genomic Health (GA4GH) was established in 2013 to create a common framework that enables responsible, voluntary and secure sharing of clinical and genomic data. This Perspective from the GA4GH Clinical Working Group Cancer Task Team highlights the data-aggregation challenges faced by the field, suggests potential collaborative solutions and describes how GA4GH can catalyze a harmonized data-sharing culture.

Healthcare has reached a tipping point where incremental change is not achieving the required improvements in healthcare quality, population health and affordability. The desired state of hyper-collaboration, team based, person-centered and health focused care enabled by big data and advanced analytics is described. However, gaps currently exist between the current and future states that provide opportunities for new roles both within and outside existing healthcare professions. The most significant new role will belong to informed, engaged, and activated consumers of health and healthcare pursing their desired states of health and resilience through strategies that are evidence-based, consistent with their values, goals, and preferences, and effective in their personal and social milieus. Empowered and technology savvy individuals as well as the underserved should receive the best evidence based and personalized care across the continuum of care. New team based care models require new roles and revision of existing ones to improve care and lower costs. Community Connectors, Health Coaches, Mobile Health Application Developers, Data Scientists, Informaticians and Care Experience roles are described. Virtual reality and avatars will be integrated into training and motivation of both caregivers and care receivers, and augment the health and resilience of all population segments. The roles of physicians and nurses will change in fundamental ways and become increasingly specialized and reliant on virtual care. Existing leadership roles will shift to address new values, new competencies, emerging trends and demands for consumers as co-designers of care.

The recent announcement of the Precision Medicine Initiative by President Obama has brought precision medicine (PM) to the forefront for healthcare providers, researchers, regulators, innovators, and funders alike. As technologies continue to evolve and datasets grow in magnitude, a strong computational infrastructure will be essential to realize PM's vision of improved healthcare derived from personal data. In addition, informatics research and innovation affords a tremendous opportunity to drive the science underlying PM. The informatics community must lead the development of technologies and methodologies that will increase the discovery and application of biomedical knowledge through close collaboration between researchers, clinicians, and patients. This perspective highlights seven key areas that are in need of further informatics research and innovation to support the realization of PM.

Over the last 5 years, stimulated by the changing healthcare environment and the Health Information Technology for Economic and Clinical Health (HITECH) Meaningful Use (MU) Electronic Health Record (EHR) Incentive program, EHR adoption has increased remarkably, and there is early evidence that such adoption has resulted in healthcare safety and quality benefits.1,2 However, with this broad adoption, many clinicians are voicing concerns that EHR use has had unintended clinical consequences, including reduced time for patient-clinician interaction,3 new and burdensome data entry tasks being transferred to front-line clinicians,4,5 and lengthened clinician workdays.6–8 Additionally, interoperability between different EHR systems has languished despite large efforts towards that goal.9,10 These challenges are contributing to physicians’ decreased satisfaction with their work lives.11–13 In professional journals,14 press reports,15–17 on wards, and in clinics, we have heard of the difficulties that the transition from paper records to EHRs has created.18 As a result, clinicians are seeking help to get through their work days, which often extend into evenings devoted to writing notes. Examples of comments we have received from clinicians and patients include: “Computers always make things faster and cheaper. Not this time,” and “My doctor pays more attention to the computer than to me.” Ultimately the healthcare system's goal is to create a robust, integrated, and interoperable healthcare system that includes patients, physician practices, public health, population management, and support for clinical and basic sciences research. This ecosystem has been referred to as the “learning health system.”19 EHRs are an important part of the learning health system, along with many other clinical systems, but future ways in which information is transformed into knowledge will likely require all parts of the system working together. Potentially every patient encounter could present an …

Practitioners participating in clinical studies are faced with a number of ethical issues related to recruitment, informed consent, handling and transparency of data. Practitioners educated in Good Clinical Practice, applying the philosophy of person-centricity within a network utilizing risk-based monitoring and remote data entry can provide the requisite infrastructure and oversight to support person-centric clinical studies. While patient-centered clinical studies allow for a broader clinical outcome perspective beyond the investigator, the person-centric approach, accounts for the comprehensiveness and complexity of how we make health and healthcare decisions. Augmenting person centricity with comparative effectiveness studies allow for the inclusion of individual data significantly contributing to the aggregation of multiple data sets about individuals and populations. This enables more powerful and personal analytics and care and everyone is afforded the opportunity and privilege to contribute to improve clinical outcomes and in controlling and containing costs. Policy and institutional investment in infrastructure are prerequisite to accommodate these opportunities, to minimize abuses, and provide pathways for analyzing alternative healthcare patterns. Data provided will be comprehensive and robust, representative of use, with safety data more easily discernible from persons with a known past medical and health history.

The Nationwide Health Information Network allow for the secure exchange of Electronic Health Records over the Internet. The Department of Veterans Affairs, Department of Defense, and Kaiser Permanente, participated in an implementation of the NwHIN specifications in San Diego, California. This paper focuses primarily on patient involvement. Specifically, it describes how the shared patients were identified, were invited to participate and to provide consent for disclosing parts of their medical record, and were matched across organizations. A total 1,144 were identified as shared patients. Invitation letters containing consent forms were mailed and resulted in 42% participation. Invalid consent forms were a significant issue (25%). Initially, the identity matching algorithms yielded low success rate (5%). However, elimination of certain traits and abbreviations and probabilistic algorithms have significantly increased matching rate. Access to information from external sources better informs providers, improves decisions and efficiency, and helps meet the meaningful use criteria.

While most research in the NLP domain focuses on information accuracy, the adoption of NLP applications in healthcare extends beyond technical innovations. This study investigates the adoption issues of an NLP application in three different field sites. Using both quantitative log analysis and qualitative user interviews, we identified four main factors that affect NLP adoption: organizational culture and support, system usability, information quality and system reliability. These factors must be considered to ensure successful adoption of NLP applications that provide real-time decision support in a clinical care setting.

Part-of-speech (POS) tagging is a fundamental step required by various NLP systems. The training of a POS tagger relies on sufficient quality annotations. However, the annotation process is both knowledge-intensive and time-consuming in the clinical domain. A promising solution appears to be for institutions to share their annotation efforts, and yet there is little research on associated issues. We performed experiments to understand how POS tagging performance would be affected by using a pre-trained tagger versus raw training data across different institutions. We manually annotated a set of clinical notes at Kaiser Permanente Southern California (KPSC) and a set from the University of Pittsburg Medical Center (UPMC), and trained/tested POS taggers with intra- and inter-institution settings. The cTAKES POS tagger was also included in the comparison to represent a tagger partially trained from the notes of a third institution, Mayo Clinic at Rochester. Intra-institution 5-fold cross-validation estimated an accuracy of 0.953 and 0.945 on the KPSC and UPMC notes respectively. Trained purely on KPSC notes, the accuracy was 0.897 when tested on UPMC notes. Trained purely on UPMC notes, the accuracy was 0.904 when tested on KPSC notes. Applying the cTAKES tagger pre-trained with Mayo Clinic's notes, the accuracy was 0.881 on KPSC notes and 0.883 on UPMC notes. After adding UPMC annotations to KPSC training data, the average accuracy on tested KPSC notes increased to 0.965. After adding KPSC annotations to UPMC training data, the average accuracy on tested UPMC notes increased to 0.953. The results indicated: first, the performance of pre-trained POS taggers dropped about 5% when applied directly across the institutions; second, mixing annotations from another institution following the same guideline increased tagging accuracy for about 1%. Our findings suggest that institutions can benefit more from sharing raw annotations but less from sharing pre-trained models for the POS tagging task. We believe the study could also provide general insights on cross-institution data sharing for other types of NLP tasks.