John E. Mattison

The HL7 Clinical Document Architecture

Many people know of Health Level 7 (HL7) as an organization that creates health care messaging standards. Health Level 7 is also developing standards for the representation of clinical documents (such as discharge summaries and progress notes). These document standards make up the HL7 Clinical Document Architecture (CDA). The HL7 CDA Framework, release 1.0, became an ANSI-approved HL7 standard in November 2000. This article presents the approach and objectives of the CDA, along with a technical overview of the standard. The CDA is a document markup standard that specifies the structure and semantics of clinical documents. A CDA document is a defined and complete information object that can include text, images, sounds, and other multimedia content. The document can be sent inside an HL7 message and can exist independently, outside a transferring message. The first release of the standard has attempted to fill an important gap by addressing common and largely narrative clinical notes. It deliberately leaves out certain advanced and complex semantics, both to foster broad implementation and to give time for these complex semantics to be fleshed out within HL7. Being a part of the emerging HL7 version 3 family of standards, the CDA derives its semantic content from the shared HL7 Reference Information Model and is implemented in Extensible Markup Language. The HL7 mission is to develop standards that enable semantic interoperability across all platforms. The HL7 version 3 family of standards, including the CDA, are moving us closer to the realization of this vision.

Report of the AMIA EHR 2020 task force on the status and future direction of EHRs

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 systems 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 …

An informatics research agenda to support precision medicine: seven key areas

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.

Human-computer interaction in health care: what works? what doesn't?

This panel will identify various ways Human-Computer Interaction (HCI) methods have been applied to health care. It will discuss successful and unsuccessful applications of HCI Techniques and discuss how HCI methods were modified to address the health care domain. PANEL DESCRlPTlON The goal of this panel is to determine the most successful approaches for building useful and usable health care

Person-centric clinical trials: Ethical challenges in recruitment and data transparency for improved outcomes

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.

“I don’t bother with the phone!”: Feeling Closer to Physician using Secure Messaging

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 frontand 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.

Emerging Roles in Health and Healthcare

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.

Awareness and handoffs in home care: coordination among informal caregivers

ABSTRACT 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.

Lessons learned in improving the adoption of a real-time NLP decision support system

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.