pSCANNER: Patient-centered scalable national network for effectiveness research

pre-printThis article describes the patient-centered Scalable National Network for Effectiveness Research (pSCANNER), which is part of the recently formed PCORnet, a national network composed of learning healthcare systems and patient-powered research networks funded by the Patient Centered Outcomes Research Institute (PCORI). It is designed to be a stakeholder-governed federated network that uses a distributed architecture to integrate data from three existing networks covering over 21 million patients in all 50 states: (1) VA Informatics and Computing Infrastructure (VINCI), with data from Veteran Health Administration's 151 inpatient and 909 ambulatory care and community-based outpatient clinics; (2) the University of California Research exchange (UC-ReX) network, with data from UC Davis, Irvine, Los Angeles, San Francisco, and San Diego; and (3) SCANNER, a consortium of UCSD, Tennessee VA, and three federally qualified health systems in the Los Angeles area supplemented with claims and health information exchange data, led by the University of Southern California. Initial use cases will focus on three conditions: (1) congestive heart failure; (2) Kawasaki disease; (3) obesity. Stakeholders, such as patients, clinicians, and health service researchers, will be engaged to prioritize research questions to be answered through the network. We will use a privacy-preserving distributed computation model with synchronous and asynchronous modes. The distributed system will be based on a common data model that allows the construction and evaluation of distributed multivariate models for a variety of statistical analyses

Impact of Terminology Mapping on Population Health Cohorts IMPaCt

Background and Objectives: The population health care delivery model uses phenotype algorithms in the electronic health record (EHR) system to identify patient cohorts targeted for clinical interventions such as laboratory tests, and procedures. The standard terminology used to identify disease cohorts may contribute to significant variation in error rates for patient inclusion or exclusion. The United States requires EHR systems to support two diagnosis terminologies, the International Classification of Disease (ICD) and the Systematized Nomenclature of Medicine (SNOMED). Terminology mapping enables the retrieval of diagnosis data using either terminology. There are no standards of practice by which to evaluate and report the operational characteristics of ICD and SNOMED value sets used to select patient groups for population health interventions. Establishing a best practice for terminology selection is a step forward in ensuring that the right patients receive the right intervention at the right time. The research question is, “How does the diagnosis retrieval terminology (ICD vs SNOMED) and terminology map maintenance impact population health cohorts?” Aim 1 and 2 explore this question, and Aim 3 informs practice and policy for population health programs. Methods Aim 1: Quantify impact of terminology choice (ICD vs SNOMED) ICD and SNOMED phenotype algorithms for diabetes, chronic kidney disease (CKD), and heart failure were developed using matched sets of codes from the Value Set Authority Center. The performance of the diagnosis-only phenotypes was compared to published reference standard that included diagnosis codes, laboratory results, procedures, and medications. Aim 2: Measure terminology maintenance impact on SNOMED cohorts For each disease state, the performance of a single SNOMED algorithm before and after terminology updates was evaluated in comparison to a reference standard to identify and quantify cohort changes introduced by terminology maintenance. Aim 3: Recommend methods for improving population health interventions The socio-technical model for studying health information technology was used to inform best practice for the use of population health interventions. Results Aim 1: ICD-10 value sets had better sensitivity than SNOMED for diabetes (.829, .662) and CKD (.242, .225) (N=201,713, p Aim 2: Following terminology maintenance the SNOMED algorithm for diabetes increased in sensitivity from (.662 to .683 (p Aim 3: Based on observed social and technical challenges to population health programs, including and in addition to the development and measurement of phenotypes, a practical method was proposed for population health intervention development and reporting

Representing and Retrieving Patients\u27 Falls Risk Factors and Risk for Falls Among Adults in Acute Care Through the Electronic Health Record

Defining fall risk factors and predicting fall risk status among patients in acute care has been a topic of research for decades. With increasing pressure on hospitals to provide quality care and prevent hospital-acquired conditions, the search for effective fall prevention interventions continues. Hundreds of risk factors for falls in acute care have been described in the literature. However, due to variations in the terms utilized to represent each fall risk factor, an effort to compare findings across settings and replicate research is hampered. As the expectations for the effective use of electronic health records increase, an opportunity exists to create infrastructure within clinical information systems, constructed with evidence-based knowledge and standardized terms, that will support interoperability between systems and enable comparative research. The purpose of this study is to identify to what extent selected fall risk factors and the problem, `risk for falls\u27 are represented and retrievable, in patients\u27 electronic health record, in one acute care setting. Specifically, this study sought to answer three questions: 1) How can the selected fall risk factors and the problem, `risk for falls\u27 be represented through selected standardized terminologies? 2) How are the selected fall risk factors and problem, `risk for falls\u27 represented in a clinical information system? and 3) Which of the selected fall risk factors and problem, `risk for falls\u27 can be retrieved from the electronic health record? The study was guided by the Knowledge Based Nursing Initiative (KBNI) framework. The study was conducted at a local health system within the hospital division, utilizing electronic, patient clinical data. Five selected fall risk factors and the problem, `risk for falls,\u27 were mapped to five standardized terminologies utilizing lexical matching. The terms mapped from the five terminologies were compared to the terms, located in discrete fields within the study site\u27s clinical information system. In addition to SNOMED CT and ICD-9 CM terms, a mixture of vendor and site-specific terms that represented the problem, `risk for falls,\u27 and the five selected fall risk factors were located in the study site\u27s clinical information system. The mapped ICD-9 CM terms and fourteen of the twenty-two SNOMED CT terms were located in the `Problem List\u27 and `Medical History\u27 sections of the clinical information system, while the vendor and site-specific terms were located in `Orders,\u27 `Nursing Flow Sheet,\u27 and `Rehabilitation Flow Sheet\u27 sections. Although both the ICD-9 CM and SNOMED CT terminologies were visible to the clinicians, one of the two mapped SNOMED CT terms representing the problem, `risk for falls,\u27 and fourteen of the twenty-two mapped fall risk factors were not visible because they did not correspond to ICD-9 CM terms. Site-specific terms representing `cognitive impairment\u27 and `impaired gait\u27 were located in both the `Nursing Flow Sheet\u27 and `Rehabilitation Flow Sheet\u27 section. While the terms were lexically similar, the terms were not exact matches and the machine-readable codes differed.Data recorded in 995 episodes of care were retrieved from the electronic data warehouse for analysis. While the SNOMED CT terms were not available for retrieval from the electronic data warehouse, the ICD-9 CM, vendor, and site-specific terms were available. As there were not SNOMED CT terms available for retrieval from the electronic data warehouse, the representation of the problem, `risk for falls,\u27 was not retrievable as a standardized term; however, it was retrieved as a Morse Fall Scale score of 40 or greater among 64.7% of the sample. The percentage of the five fall risk factors represented with the ICD-9 CM terms was lower than the percentage of fall risk factors represented with vendor and site-specific terms. While it is promising that two standardized terminologies have been embedded in the study site\u27s system, limiting the SNOMED CT terms to those that have corresponding ICD-9 terms limits the representation of both the problem, `risk for falls,\u27 and the five selected fall risk factors. It is recommended that hospital administrators embed standardized terminologies in their entirety to allow for adequate representation of terms. Accepting terminologies in their entirety would allow for interoperability between health systems and enable comparative research. Additionally, if vendor and site-specific terms are embedded, clinical information analysts in partnership with clinicians should assure that terms representing the same clinical data (e.g., disorientation), match across different sections of the clinical information system or a cross-mapping of those terms exist in order to support interoperability within the system

An interoperable electronic medical record-based platform for personalized predictive analytics

Indiana University-Purdue University Indianapolis (IUPUI)Precision medicine refers to the delivering of customized treatment to patients based on their individual characteristics, and aims to reduce adverse events, improve diagnostic methods, and enhance the efficacy of therapies. Among efforts to achieve the goals of precision medicine, researchers have used observational data for developing predictive modeling to best predict health outcomes according to patients’ variables. Although numerous predictive models have been reported in the literature, not all models present high prediction power, and as the result, not all models may reach clinical settings to help healthcare professionals make clinical decisions at the point-of-care. The lack of generalizability stems from the fact that no comprehensive medical data repository exists that has the information of all patients in the target population. Even if the patients’ records were available from other sources, the datasets may need further processing prior to data analysis due to differences in the structure of databases and the coding systems used to record concepts. This project intends to fill the gap by introducing an interoperable solution that receives patient electronic health records via Health Level Seven (HL7) messaging standard from other data sources, transforms the records to observational medical outcomes partnership (OMOP) common data model (CDM) for population health research, and applies predictive models on patient data to make predictions about health outcomes. This project comprises of three studies. The first study introduces CCD-TOOMOP parser, and evaluates OMOP CDM to accommodate patient data transferred by HL7 consolidated continuity of care documents (CCDs). The second study explores how to adopt predictive model markup language (PMML) for standardizing dissemination of OMOP-based predictive models. Finally, the third study introduces Personalized Health Risk Scoring Tool (PHRST), a pilot, interoperable OMOP-based model scoring tool that processes the embedded models and generates risk scores in a real-time manner. The final product addresses objectives of precision medicine, and has the potentials to not only be employed at the point-of-care to deliver individualized treatment to patients, but also can contribute to health outcome research by easing collecting clinical outcomes across diverse medical centers independent of system specifications