Person:

Murphy, Shawn

Background: The Health Quality Measures Format (HQMF) is a Health Level 7 (HL7) standard for expressing computable Clinical Quality Measures (CQMs). Creating tools to process HQMF queries in clinical databases will become increasingly important as the United States moves forward with its Health Information Technology Strategic Plan to Stages 2 and 3 of the Meaningful Use incentive program (MU2 and MU3). Informatics for Integrating Biology and the Bedside (i2b2) is one of the analytical databases used as part of the Office of the National Coordinator (ONC)’s Query Health platform to move toward this goal. Objective: Our goal is to integrate i2b2 with the Query Health HQMF architecture, to prepare for other HQMF use-cases (such as MU2 and MU3), and to articulate the functional overlap between i2b2 and HQMF. Therefore, we analyze the structure of HQMF, and then we apply this understanding to HQMF computation on the i2b2 clinical analytical database platform. Specifically, we develop a translator between two query languages, HQMF and i2b2, so that the i2b2 platform can compute HQMF queries. Methods: We use the HQMF structure of queries for aggregate reporting, which define clinical data elements and the temporal and logical relationships between them. We use the i2b2 XML format, which allows flexible querying of a complex clinical data repository in an easy-to-understand domain-specific language. Results: The translator can represent nearly any i2b2-XML query as HQMF and execute in i2b2 nearly any HQMF query expressible in i2b2-XML. This translator is part of the freely available reference implementation of the QueryHealth initiative. We analyze limitations of the conversion and find it covers many, but not all, of the complex temporal and logical operators required by quality measures. Conclusions: HQMF is an expressive language for defining quality measures, and it will be important to understand and implement for CQM computation, in both meaningful use and population health. However, its current form might allow complexity that is intractable for current database systems (both in terms of implementation and computation). Our translator, which supports the subset of HQMF currently expressible in i2b2-XML, may represent the beginnings of a practical compromise. It is being pilot-tested in two Query Health demonstration projects, and it can be further expanded to balance computational tractability with the advanced features needed by measure developers.

Results of medical research studies are often contradictory or cannot be reproduced. One reason is that there may not be enough patient subjects available for observation for a long enough time period. Another reason is that patient populations may vary considerably with respect to geographic and demographic boundaries thus limiting how broadly the results apply. Even when similar patient populations are pooled together from multiple locations, differences in medical treatment and record systems can limit which outcome measures can be commonly analyzed. In total, these differences in medical research settings can lead to differing conclusions or can even prevent some studies from starting. We thus sought to create a patient research system that could aggregate as many patient observations as possible from a large number of hospitals in a uniform way. We call this system the ‘Shared Health Research Information Network’, with the following properties: (1) reuse electronic health data from everyday clinical care for research purposes, (2) respect patient privacy and hospital autonomy, (3) aggregate patient populations across many hospitals to achieve statistically significant sample sizes that can be validated independently of a single research setting, (4) harmonize the observation facts recorded at each institution such that queries can be made across many hospitals in parallel, (5) scale to regional and national collaborations. The purpose of this report is to provide open source software for multi-site clinical studies and to report on early uses of this application. At this time SHRINE implementations have been used for multi-site studies of autism co-morbidity, juvenile idiopathic arthritis, peripartum cardiomyopathy, colorectal cancer, diabetes, and others. The wide range of study objectives and growing adoption suggest that SHRINE may be applicable beyond the research uses and participating hospitals named in this report.

Background: Network Tools and Applications in Biology (NETTAB) Workshops are a series of meetings focused on the most promising and innovative ICT tools and to their usefulness in Bioinformatics. The NETTAB 2011 workshop, held in Pavia, Italy, in October 2011 was aimed at presenting some of the most relevant methods, tools and infrastructures that are nowadays available for Clinical Bioinformatics (CBI), the research field that deals with clinical applications of bioinformatics. Methods In this editorial, the viewpoints and opinions of three world CBI leaders, who have been invited to participate in a panel discussion of the NETTAB workshop on the next challenges and future opportunities of this field, are reported. These include the development of data warehouses and ICT infrastructures for data sharing, the definition of standards for sharing phenotypic data and the implementation of novel tools to implement efficient search computing solutions. Results: Some of the most important design features of a CBI-ICT infrastructure are presented, including data warehousing, modularity and flexibility, open-source development, semantic interoperability, integrated search and retrieval of -omics information. Conclusions: Clinical Bioinformatics goals are ambitious. Many factors, including the availability of high-throughput "-omics" technologies and equipment, the widespread availability of clinical data warehouses and the noteworthy increase in data storage and computational power of the most recent ICT systems, justify research and efforts in this domain, which promises to be a crucial leveraging factor for biomedical research.

Objective: To optimally leverage the scalability and unique features of the electronic health records (EHR) for research that would ultimately improve patient care, we need to accurately identify patients and extract clinically meaningful measures. Using multiple sclerosis (MS) as a proof of principle, we showcased how to leverage routinely collected EHR data to identify patients with a complex neurological disorder and derive an important surrogate measure of disease severity heretofore only available in research settings. Methods: In a cross-sectional observational study, 5,495 MS patients were identified from the EHR systems of two major referral hospitals using an algorithm that includes codified and narrative information extracted using natural language processing. In the subset of patients who receive neurological care at a MS Center where disease measures have been collected, we used routinely collected EHR data to extract two aggregate indicators of MS severity of clinical relevance multiple sclerosis severity score (MSSS) and brain parenchymal fraction (BPF, a measure of whole brain volume). Results: The EHR algorithm that identifies MS patients has an area under the curve of 0.958, 83% sensitivity, 92% positive predictive value, and 89% negative predictive value when a 95% specificity threshold is used. The correlation between EHR-derived and true MSSS has a mean R2 = 0.38±0.05, and that between EHR-derived and true BPF has a mean R2 = 0.22±0.08. To illustrate its clinical relevance, derived MSSS captures the expected difference in disease severity between relapsing-remitting and progressive MS patients after adjusting for sex, age of symptom onset and disease duration (p = 1.56×10−12). Conclusion: Incorporation of sophisticated codified and narrative EHR data accurately identifies MS patients and provides estimation of a well-accepted indicator of MS severity that is widely used in research settings but not part of the routine medical records. Similar approaches could be applied to other complex neurological disorders.

We have developed an interface to serve patient data from Informatics for Integrating Biology and the Bedside (i2b2) repositories in the Fast Healthcare Interoperability Resources (FHIR) format, referred to as a SMART-on-FHIR cell. The cell serves FHIR resources on a per-patient basis, and supports the “substitutable” modular third-party applications (SMART) OAuth2 specification for authorization of client applications. It is implemented as an i2b2 server plug-in, consisting of 6 modules: authentication, REST, i2b2-to-FHIR converter, resource enrichment, query engine, and cache. The source code is freely available as open source. We tested the cell by accessing resources from a test i2b2 installation, demonstrating that a SMART app can be launched from the cell that accesses patient data stored in i2b2. We successfully retrieved demographics, medications, labs, and diagnoses for test patients. The SMART-on-FHIR cell will enable i2b2 sites to provide simplified but secure data access in FHIR format, and will spur innovation and interoperability. Further, it transforms i2b2 into an apps platform.

Objective: Understanding population-level health trends is essential to effectively monitor and improve public health. The Office of the National Coordinator for Health Information Technology (ONC) Query Health initiative is a collaboration to develop a national architecture for distributed, population-level health queries across diverse clinical systems with disparate data models. Here we review Query Health activities, including a standards-based methodology, an open-source reference implementation, and three pilot projects. Materials and methods Query Health defined a standards-based approach for distributed population health queries, using an ontology based on the Quality Data Model and Consolidated Clinical Document Architecture, Health Quality Measures Format (HQMF) as the query language, the Query Envelope as the secure transport layer, and the Quality Reporting Document Architecture as the result language. Results: We implemented this approach using Informatics for Integrating Biology and the Bedside (i2b2) and hQuery for data analytics and PopMedNet for access control, secure query distribution, and response. We deployed the reference implementation at three pilot sites: two public health departments (New York City and Massachusetts) and one pilot designed to support Food and Drug Administration post-market safety surveillance activities. The pilots were successful, although improved cross-platform data normalization is needed. Discussions This initiative resulted in a standards-based methodology for population health queries, a reference implementation, and revision of the HQMF standard. It also informed future directions regarding interoperability and data access for ONC's Data Access Framework initiative. Conclusions: Query Health was a test of the learning health system that supplied a functional methodology and reference implementation for distributed population health queries that has been validated at three sites.

We describe the architecture of the Patient Centered Outcomes Research Institute (PCORI) funded Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS, http://www.SCILHS.org) clinical data research network, which leverages the $48 billion dollar federal investment in health information technology (IT) to enable a queryable semantic data model across 10 health systems covering more than 8 million patients, plugging universally into the point of care, generating evidence and discovery, and thereby enabling clinician and patient participation in research during the patient encounter. Central to the success of SCILHS is development of innovative ‘apps’ to improve PCOR research methods and capacitate point of care functions such as consent, enrollment, randomization, and outreach for patient-reported outcomes. SCILHS adapts and extends an existing national research network formed on an advanced IT infrastructure built with open source, free, modular components.

Introduction: A visible example of a successfully disseminated research project in the healthcare space is Informatics for Integrating Biology and the Bedside, or i2b2. The project serves to provide the software that can allow a researcher to do direct, self-serve queries against the electronic healthcare data form a hospital. The goals of these queries are to find cohorts of patients that fit specific profiles, while providing for patient privacy and discretion. Sustaining this resource and keeping its direction has always been a challenge, but ever more so as the ten year National Centers for Biomedical Computing (NCBCs) sunset their funding. Findings: Building on the i2b2 structures has helped the dissemination plans for grants leveraging it because it is a disseminated national resource. While this has not directly increased the support of i2b2 internally, it has increased the ability of institutions to leverage the resource and generally leads to increased institutional support. Discussion: The successful development, use, and dissemination i2b2 has been significant in clinical research and informatics. Its evolution has been from a local research data infrastructure to one disseminated more broadly than any other product of the National Centers for Biomedical Computing, and an infrastructure spawning larger investments than were originally used to create it. Throughout this, there were two main lessons about the benefits of dissemination: that people have great creativity in utilizing a resource in different ways and that broader system use can make the system more robust. One option for long-term sustainability of the central authority would be to translate the function to an industry partner. Another option currently being pursued is to create a foundation that would be a central authority for the project. Conclusion: Over the past 10 years, i2b2 has risen to be an important staple in the toolkit of health care researchers. There are now over 110 hospitals that use i2b2 for research. This open-source platform has a community of developers that are continuously enhancing the analytic capacities of the platform and inventing new functionality. By understanding how i2b2 has been sustained, we hope that other research infrastructure projects may better navigate options in making those initiatives sustainable over time.

For certain research questions related to long-term outcomes or to rare disorders, designing prospective studies is impractical or prohibitively expensive. Such studies could instead utilize clinical and magnetic resonance imaging data (MRI) collected as part of routine clinical care, stored in the electronic medical record (EMR). Using major depressive disorder (MDD) as a disease model, we examined the feasibility of studying brain morphology and associations with remission using clinical and MRI data exclusively drawn from the EMR. Advanced automated tools were used to select MDD patients and controls from the EMR who had brain MRI data, but no diagnosed brain pathology. MDD patients were further assessed for remission status by review of clinical charts. Twenty MDD patients (eight full-remitters, six partial-remitters, and six nonremitters), and fifteen healthy control subjects met all study criteria for advanced morphometric analyses. Compared to controls, MDD patients had significantly smaller right rostral-anterior cingulate volume, and level of non-remission was associated with smaller left hippocampus and left rostral-middle frontal gyrus volume. The use of EMR data for psychiatric research may provide a timely and cost-effective approach with the potential to generate large study samples reflective of the real population with the illness studied.

We are fortunate to be living in an era of twin biomedical data surges: a burgeoning representation of human phenotypes in the medical records of our healthcare systems, and high-throughput sequencing making rapid technological advances. The difficulty representing genomic data and its annotations has almost by itself led to the recognition of a biomedical “Big Data” challenge, and the complexity of healthcare data only compounds the problem to the point that coherent representation of both systems on the same platform seems insuperably difficult. We investigated the capability for complex, integrative genomic and clinical queries to be supported in the Informatics for Integrating Biology and the Bedside (i2b2) translational software package. Three different data integration approaches were developed: The first is based on Sequence Ontology, the second is based on the tranSMART engine, and the third on CouchDB. These novel methods for representing and querying complex genomic and clinical data on the i2b2 platform are available today for advancing precision medicine.