A landscape survey of planned SMART/HL7 bulk FHIR data access API implementations and tools

The Office of National Coordinator for Health Information Technology final rule implementing the interoperability and information blocking provisions of the 21st Century Cures Act requires support for two SMART (Substitutable Medical Applications, Reusable Technologies) application programming interfaces (APIs) and instantiates Health Level Seven International (HL7) Fast Healthcare Interoperability Resources (FHIR) as a lingua franca for health data. We sought to assess the current state and near-term plans for the SMART/HL7 Bulk FHIR Access API implementation across organizations including electronic health record vendors, cloud vendors, public health contractors, research institutions, payors, FHIR tooling developers, and other purveyors of health information technology platforms. We learned that many organizations not required through regulation to use standardized bulk data are rapidly implementing the API for a wide array of use cases. This may portend an unprecedented level of standardized population-level health data exchange that will support an apps and analytics ecosystem. Feedback from early adopters on the API’s limitations and unsolved problems in the space of population health are highlighted.     

Building capacity of community health centers to overcome data challenges with the development of an agile COVID-19 public health registry: a multistate quality improvement effort

ObjectiveDuring the coronavirus disease 2019 (COVID-19) pandemic, federally qualified health centers rapidly mobilized to provide SARS-CoV-2 testing, COVID-19 care, and vaccination to populations at increased risk for COVID-19 morbidity and mortality. We describe the development of a reusable public health data analytics system for reuse of clinical data to evaluate the health burden, disparities, and impact of COVID-19 on populations served by health centers.Materials and MethodsThe Multistate Data Strategy engaged project partners to assess public health readiness and COVID-19 data challenges. An infrastructure for data capture and sharing procedures between health centers and public health agencies was developed to support existing capabilities and data capacities to respond to the pandemic.ResultsBetween August 2020 and March 2021, project partners evaluated their data capture and sharing capabilities and reported challenges and preliminary data. Major interoperability challenges included poorly aligned federal, state, and local reporting requirements, lack of unique patient identifiers, lack of access to pharmacy, claims and laboratory data, missing data, and proprietary data standards and extraction methods.DiscussionEfforts to access and align project partners’ existing health systems data infrastructure in the context of the pandemic highlighted complex interoperability challenges. These challenges remain significant barriers to real-time data analytics and efforts to improve health outcomes and mitigate inequities through data-driven responses.ConclusionThe reusable public health data analytics system created in the Multistate Data Strategy can be adapted and scaled for other health center networks to facilitate data aggregation and dashboards for public health, organizational planning, and quality improvement and can inform local, state, and national COVID-19 response efforts.     

Moving toward multimedia electronic health records: how do we get there?

This report, based on a workshop jointly sponsored the National Institute of Biomedical Imaging and Biomedical Engineering and the Office of the National Coordinator for Health Information Technology, examines the role and value of images as multimedia data in electronic health records (EHRs). The workshop, attended by a wide range of stakeholders, was motivated in part by the absence of image data from discussions of meaningful use of health information technology. Collectively, the workshop presenters and participants argued that images are not ancillary data and should be central to health information systems to facilitate clinical decisions and higher quality, efficiency, and safety of care. They emphasized that the imaging community has already developed standards that form the basis of interoperability. Despite the apparent value of images, workshop participants also identified challenges and barriers to their implementation within EHRs. Weighing the opportunities and challenges, workshop participants provided their perspectives on possible paths forward toward fully multimedia EHRs.     

The emerging role of clinical informatics fellows in service learning during the COVID-19 pandemic

ObjectiveThe study sought to describe the contributions of clinical informatics (CI) fellows to their institutions’ coronavirus disease 2019 (COVID-19) response.Materials and MethodsWe 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.ResultsForty-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.DiscussionFellows 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.ConclusionsCI fellows contributed meaningfully and rapidly to their institutions’ response to the COVID-19 pandemic.     

Medical Informatics: A Model Developed for Diabetes Education Via Telemedicine

Fast developments in information and communication technology (ICT) have made it possible to develop new services for people. One of the most interesting areas is health care. Medical informatics is the discipline concerned with the systematic processing of data, information and knowledge in medicine and health care. Information services, medical decision support systems and telemedicine are becoming important tools for medical professionals and also people who are interested in health related information. Medical decision support aims at providing health care professionals with therapy guidelines directly at the point of care. Telemedicine is the use of modern telecommunications and information technologies (IT) for the provision of clinical care to individuals at a distance and transmission of information to provide that care. In the present study, usage of IT in medicine, medical decision support systems, computerized medical measurements, patient education and network connectivity were described. A model for risk evaluation, data collection and education of undiagnosed diabetes using the world wide web (www) was presented.     

Feasibility of capturing real-world data from health information technology systems at multiple centers to assess cardiac ablation device outcomes: A fit-for-purpose informatics analysis report

ObjectiveThe study sought to conduct an informatics analysis on the National Evaluation System for Health Technology Coordinating Center test case of cardiac ablation catheters and to demonstrate the role of informatics approaches in the feasibility assessment of capturing real-world data using unique device identifiers (UDIs) that are fit for purpose for label extensions for 2 cardiac ablation catheters from the electronic health records and other health information technology systems in a multicenter evaluation.Materials and MethodsWe focused on data capture and transformation and data quality maturity model specified in the National Evaluation System for Health Technology Coordinating Center data quality framework. The informatics analysis included 4 elements: the use of UDIs for identifying device exposure data, the use of standardized codes for defining computable phenotypes, the use of natural language processing for capturing unstructured data elements from clinical data systems, and the use of common data models for standardizing data collection and analyses.ResultsWe found that, with the UDI implementation at 3 health systems, the target device exposure data could be effectively identified, particularly for brand-specific devices. Computable phenotypes for study outcomes could be defined using codes; however, ablation registries, natural language processing tools, and chart reviews were required for validating data quality of the phenotypes. The common data model implementation status varied across sites. The maturity level of the key informatics technologies was highly aligned with the data quality maturity model.ConclusionsWe demonstrated that the informatics approaches can be feasibly used to capture safety and effectiveness outcomes in real-world data for use in medical device studies supporting label extensions.     

Factors Influencing Computer Literacy of Taiwan and South Korea Nurses

Healthcare is experiencing a major transformation in its information technology base. Hospitals are adopting information technology (IT) to reduce costs and increase competitiveness. IT applications in healthcare are trending towards electronic patient records and even health records. Therefore, practices in nursing are also affected by IT. Many researchers have studied what computer literacy a nurse should possess, but have focused less on factors that actually impact computer literacy. The purposes of this study are to examine current computer literacy levels of nurses, and to indicate what variables influence their computer literacy. Taiwan and South Korea both implemented a national health insurance system, and used state-of-the art IT to provide higher volume and better quality of services. The data were collected from two case hospitals which are located in Taiwan and South Korea, respectively. By using a structured questionnaire, a total of 203 nurses responded; 104 from Taiwan and 99 from South Korea. The results revealed that personal innovativeness in IT, computer education, and age are significant factors that affected computer literacy levels. These factors serve as reference for administrators and executives in hospitals, or nursing educators seeking the data necessary to make decisions on curriculum.     

HIT Implementation in Critical Access Hospitals: Extent of Implementation and Business Strategies Supporting IT Use

Small rural hospitals face considerable financial and personnel resource shortages which hinder their efforts to implement complex health information technology (HIT) systems. A survey on the use of HIT was completed by 85% of Iowa’s 82 Critical Access Hospitals (CAH). Analyses indicate that low IT staffing in CAHs is a barrier to implementing HIT solutions. CAHs with fewer staff tend to employ alternative business strategies. There is a clear relationship between having IT staff at a CAH and the types of technologies used. Many CAHs report having difficulty expanding upon HIT functionalities due to the challenges of finding IT staff with healthcare expertise. Most CAHs are in the transition point of planning for or beginning implementation of complex clinical information systems. Strategies for addressing these challenges will need to evolve as the HIT investments by rural hospitals race to keep pace with the goals for the nation.     

Data from clinical notes: a perspective on the tension between structure and flexible documentation

Clinical documentation is central to patient care. The success of electronic health record system adoption may depend on how well such systems support clinical documentation. A major goal of integrating clinical documentation into electronic heath record systems is to generate reusable data. As a result, there has been an emphasis on deploying computer-based documentation systems that prioritize direct structured documentation. Research has demonstrated that healthcare providers value different factors when writing clinical notes, such as narrative expressivity, amenability to the existing workflow, and usability. The authors explore the tension between expressivity and structured clinical documentation, review methods for obtaining reusable data from clinical notes, and recommend that healthcare providers be able to choose how to document patient care based on workflow and note content needs. When reusable data are needed from notes, providers can use structured documentation or rely on post-hoc text processing to produce structured data, as appropriate.     

The Triangle Model for evaluating the effect of health information technology on healthcare quality and safety

With the proliferation of relatively mature health information technology (IT) systems with large numbers of users, it becomes increasingly important to evaluate the effect of these systems on the quality and safety of healthcare. Previous research on the effectiveness of health IT has had mixed results, which may be in part attributable to the evaluation frameworks used. The authors propose a model for evaluation, the Triangle Model, developed for designing studies of quality and safety outcomes of health IT. This model identifies structure-level predictors, including characteristics of: (1) the technology itself; (2) the provider using the technology; (3) the organizational setting; and (4) the patient population. In addition, the model outlines process predictors, including (1) usage of the technology, (2) organizational support for and customization of the technology, and (3) organizational policies and procedures about quality and safety. The Triangle Model specifies the variables to be measured, but is flexible enough to accommodate both qualitative and quantitative approaches to capturing them. The authors illustrate this model, which integrates perspectives from both health services research and biomedical informatics, with examples from evaluations of electronic prescribing, but it is also applicable to a variety of types of health IT systems.     

新医改形势下对公共卫生教育的思考

在新医改形势下,公共卫生教育面临着前所未有的挑战。公共卫生信息、全球健康等多学科交叉的复合型人才需求与日俱增。除专业知识的熏陶外,对于公共卫生人才尤其应注重人文素质、信息素质、三创综合素质的培养。     

市民健康信息系统网上服务平台的实现

市民健康信息系统服务平台是一种高效的网络化技术管理平台，它可以方便地存储和管理市民个人健康信息，不但为医疗机构提高服务质量提供了技术支持，还可满足疾控、医疗保险、个人保健、卫生统计、查询分析、临床科研、行政管理等各方面需求。该平台可使有限的医疗卫生资源得到最大程度的利用，有助于政府管理部门科学决策，提高服务质量和效率。     

Healthcare Process Modeling to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals): Development and evaluation of a conceptual framework

ObjectiveThere are signals of clinicians’ expert and knowledge-driven behaviors within clinical information systems (CIS) that can be exploited to support clinical prediction. Describe development of the Healthcare Process Modeling Framework to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals).Materials and MethodsWe employed an iterative framework development approach that combined data-driven modeling and simulation testing to define and refine a process for phenotyping clinician behaviors. Our framework was developed and evaluated based on the Communicating Narrative Concerns Entered by Registered Nurses (CONCERN) predictive model to detect and leverage signals of clinician expertise for prediction of patient trajectories.ResultsSeven themes—identified during development and simulation testing of the CONCERN model—informed framework development. The HPM-ExpertSignals conceptual framework includes a 3-step modeling technique: (1) identify patterns of clinical behaviors from user interaction with CIS; (2) interpret patterns as proxies of an individual’s decisions, knowledge, and expertise; and (3) use patterns in predictive models for associations with outcomes. The CONCERN model differentiated at risk patients earlier than other early warning scores, lending confidence to the HPM-ExpertSignals framework.DiscussionThe HPM-ExpertSignals framework moves beyond transactional data analytics to model clinical knowledge, decision making, and CIS interactions, which can support predictive modeling with a focus on the rapid and frequent patient surveillance cycle.ConclusionsWe propose this framework as an approach to embed clinicians’ knowledge-driven behaviors in predictions and inferences to facilitate capture of healthcare processes that are activated independently, and sometimes well before, physiological changes are apparent.     

澳大利亚的临床服务质量指标体系

介绍了澳大利亚的临床服务质量指标体系（CI）。与国内目前使用的医疗质量指标相比较,分析了CI的关注机构服务质量和临床技术质量两个方面的质量评价丁作;注重医疗服务结果和患者利益,指标体系将对具体的临床问题起到筛查、标识和警示的作用;非常重视临床技术指标的分析、结果的沟通和在质量管理中的决策支持作用,利用数据和信息来促进医疗技术质量的改进;指标的选择标准较为严谨,强蒯指标的可比性,关注负性事件的影响等4个主要特点。     

Characterizing Mammography Reports for Health Analytics

As massive collections of digital health data are becoming available, the opportunities for large-scale automated analysis increase. In particular, the widespread collection of detailed health information is expected to help realize a vision of evidence-based public health and patient-centric health care. Within such a framework for large scale health analytics we describe the transformation of a large data set of mostly unlabeled and free-text mammography data into a searchable and accessible collection, usable for analytics. We also describe several methods to characterize and analyze the data, including their temporal aspects, using information retrieval, supervised learning, and classical statistical techniques. We present experimental results that demonstrate the validity and usefulness of the approach, since the results are consistent with the known features of the data, provide novel insights about it, and can be used in specific applications. Additionally, based on the process of going from raw data to results from analysis, we present the architecture of a generic system for health analytics from clinical notes.     

Meeting the challenge: Health information technology’s essential role in achieving precision medicine

Precision medicine can revolutionize health care by tailoring treatments to individual patient needs. Advancing precision medicine requires evidence development through research that combines needed data, including clinical data, at an unprecedented scale. Widespread adoption of health information technology (IT) has made digital clinical data broadly available. These data and information systems must evolve to support precision medicine research and delivery. Specifically, relevant health IT data, infrastructure, clinical integration, and policy needs must be addressed. This article outlines those needs and describes work the Office of the National Coordinator for Health Information Technology is leading to improve health IT through pilot projects and standards and policy development. The Office of the National Coordinator for Health Information Technology will build on these efforts and continue to coordinate with other key stakeholders to achieve the vision of precision medicine. Advancement of precision medicine will require ongoing, collaborative health IT policy and technical initiatives that advance discovery and transform healthcare delivery.     

Clinical Information System Availability and Use in Urban and Rural Hospitals

Clinical information systems (CIS) capture clinical data to support more efficient and effective decision making and clinical care delivery. Only a few estimates of CIS availability and use in hospitals are available nationally. The purpose of the current research is to examine differences in CIS availability and use between urban and rural hospitals. A survey addressing this purpose was completed by 74 (63.7%) of Iowa hospitals. Rural hospitals lag behind urban hospitals in terms of many CIS applications. More than 80% of the urban hospitals, but only between 30 and 40% of the rural hospitals, reported using computers to collect basic clinical information for potential use in an electronic medical record (EMR) and computerized provider order entry (CPOE) system. Comparison of CIS within one state’s urban and rural hospitals sheds light on variation in clinical support applications, decision support, and electronic medical record “readiness” in these settings.     

Brief communication: PEDSnet: a National Pediatric Learning Health System

A learning health system (LHS) integrates research done in routine care settings, structured data capture during every encounter, and quality improvement processes to rapidly implement advances in new knowledge, all with active and meaningful patient participation. While disease-specific pediatric LHSs have shown tremendous impact on improved clinical outcomes, a national digital architecture to rapidly implement LHSs across multiple pediatric conditions does not exist. PEDSnet is a clinical data research network that provides the infrastructure to support a national pediatric LHS. A consortium consisting of PEDSnet, which includes eight academic medical centers, two existing disease-specific pediatric networks, and two national data partners form the initial partners in the National Pediatric Learning Health System (NPLHS). PEDSnet is implementing a flexible dual data architecture that incorporates two widely used data models and national terminology standards to support multi-institutional data integration, cohort discovery, and advanced analytics that enable rapid learning.