The novel coronavirus disease-19 (COVID-19) pandemic has altered our economy, society, and healthcare system. While this crisis has presented the U.S. healthcare delivery system with unprecedented challenges, the pandemic has catalyzed rapid adoption of telehealth, or the entire spectrum of activities used to deliver care at a distance. Using examples reported by U.S. healthcare organizations, including ours, we describe the role that telehealth has played in transforming healthcare delivery during the 3 phases of the U.S. COVID-19 pandemic: (1) stay-at-home outpatient care, (2) initial COVID-19 hospital surge, and (3) postpandemic recovery. Within each of these 3 phases, we examine how people, process, and technology work together to support a successful telehealth transformation. Whether healthcare enterprises are ready or not, the new reality is that virtual care has arrived.

Electronic Health Record (EHR) phenotyping utilizes patient data captured through normal medical practice, to identify features that may represent computational medical phenotypes. These features may be used to identify at-risk patients and improve prediction of patient morbidity and mortality. We present a novel deep multi-modality architecture for EHR analysis (applicable to joint analysis of multiple forms of EHR data), based on Poisson Factor Analysis (PFA) modules. Each modality, composed of observed counts, is represented as a Poisson distribution, parameterized in terms of hidden binary units. Information from different modalities is shared via a deep hierarchy of common hidden units. Activation of these binary units occurs with probability characterized as Bernoulli- Poisson link functions, instead of more traditional logistic link functions. In addition, we demonstrate that PFA modules can be adapted to discriminative modalities. To compute model parameters, we derive efficient Markov Chain Monte Carlo (MCMC) inference that scales efficiently, with significant computational gains when compared to related models based on logistic link functions. To explore the utility of these models, we apply them to a subset of patients from the Duke-Durham patient cohort. We identified a cohort of over 16,000 patients with Type 2 Diabetes Mellitus (T2DM) based on diagnosis codes and laboratory tests out of our patient population of over 240,000. Examining the common hidden units uniting the PFA modules, we identify patient features that represent medical concepts. Experiments indicate that our learned features are better able to predict mortality and morbidity than clinical features identified previously in a large-scale clinical trial.

OBJECTIVE: Failure to reach research subject recruitment goals is a significant impediment to the success of many clinical trials. Implementation of health-information technology has allowed retrospective analysis of data for cohort identification and recruitment, but few institutions have also leveraged real-time streams to support such activities. DESIGN: Duke Medicine has deployed a hybrid solution, The Duke Integrated Subject Cohort and Enrollment Research Network (DISCERN), that combines both retrospective warehouse data and clinical events contained in prospective Health Level 7 (HL7) messages to immediately alert study personnel of potential recruits as they become eligible. RESULTS: DISCERN analyzes more than 500000 messages daily in service of 12 projects. Users may receive results via email, text pages, or on-demand reports. Preliminary results suggest DISCERN's unique ability to reason over both retrospective and real-time data increases study enrollment rates while reducing the time required to complete recruitment-related tasks. The authors have introduced a preconfigured DISCERN function as a self-service feature for users. LIMITATIONS: The DISCERN framework is adoptable primarily by organizations using both HL7 message streams and a data warehouse. More efficient recruitment may exacerbate competition for research subjects, and investigators uncomfortable with new technology may find themselves at a competitive disadvantage in recruitment. CONCLUSION: DISCERN's hybrid framework for identifying real-time clinical events housed in HL7 messages complements the traditional approach of using retrospective warehoused data. DISCERN is helpful in instances when the required clinical data may not be loaded into the warehouse and thus must be captured contemporaneously during patient care. Use of an open-source tool supports generalizability to other institutions at minimal cost.

Healthcare is increasingly dependent upon information technology (IT), but the accumulation of data has outpaced our capacity to use it to improve operating efficiency, clinical quality, and financial effectiveness. Moreover, hospitals have lagged in adopting thoughtful analytic approaches that would allow operational leaders and providers to capitalize upon existing data stores. In this manuscript, we propose a fundamental re-evaluation of strategic IT investments in healthcare, with the goal of increasing efficiency, reducing costs, and improving outcomes through the targeted application of health analytics. We also present three case studies that illustrate the use of health analytics to leverage pre-existing data resources to support improvements in patient safety and quality of care, to increase the accuracy of billing and collection, and support emerging health issues. We believe that such active investment in health analytics will prove essential to realizing the full promise of investments in electronic clinical systems.

Health care provides many opportunities in which the sharing of data between independent sites is highly desirable. Several standards are required to produce the functional and semantic interoperability necessary to support the exchange of such data: a common reference information model, a common set of data elements, a common terminology, common data structures, and a common transport standard. This paper addresses one component of that set of standards: the ability to create a document that supports the exchange of structured data components. Unfortunately, two different standards development organizations have produced similar standards for that purpose based on different information models: Health Level 7 (HL7)'s Clinical Document Architecture (CDA) and The American Society for Testing and Materials (ASTM International) Continuity of Care Record (CCR). The coexistence of both standards might require mapping from one standard to the other, which could be accompanied by a loss of information and functionality. This paper examines and compares the two standards, emphasizes the strengths and weaknesses of each, and proposes a strategy of harmonization to enhance future progress. While some of the authors are members of HL7 and/or ASTM International, the authors stress that the viewpoints represented in this paper are those of the authors and do not represent the official viewpoints of either HL7 or of ASTM International.