Information Quality in Secondary Use of EHR Data : A Case Study of Quality Management in a Norwegian Hospital

The motivation for undertaking this study relates to my experiences from practice in a public hospital, where I have observed variations in reaching organizational goals of quality management informed by electronic health records (EHR) data. For example, while some departments and units have long-time traditions in meeting the quality goals that are set locally, regionally, or nationally, other departments and units struggle to meet the same quality goals. Thus, generating actionable information by reusing routinely collected EHR data does not necessary lead to action in response to the information. This process of generating information from existing EHR data, and communicating and using such information for organizational purposes, may be challenging in a highly complex environment such as health care organizations. Within this process, information quality (IQ) may influence actors’ perceptions of action possibilities the information offers, thus influencing the actual use of the information required to reach organizational goals. EHR data can be used for clinical purposes at the point-of-care (i.e., primary use) and reused for purposes that do not involve patient treatment directly (i.e., secondary use). Examples of such secondary use includes quality management, research, and policy development. Though it is widely accepted that IQ influences the use of EHR systems and the information generated by EHR systems, research on the implications of IQ on health care processes is limited: the focus of the current literature is concerned with defining and assessing IQ in primary use of EHR data, whereas the role of IQ in secondary use of EHR data remains unclear. Thus, this dissertation investigates the role of IQ in secondary use of EHR data in an organizational context. This dissertation addresses this practical and theoretical challenge by focusing on the overall research objective of understanding the role of IQ in secondary use of EHR data. To address this research objective, this dissertation explores the following research questions: RQ1. How do human actors influence in transformation of IQ while generating, communicating, and using information in secondary use of EHR data? RQ2. What are the underlying generative mechanisms through which IQ transforms in the process of secondary use of EHR data?publishedVersio

Predictive Relationships Between Electronic Health Records Attributes and Meaningful Use Objectives

The use of electronic health records (EHR) has the potential to improve relationships between physicians and patients and significantly improve care delivery. The purpose of this study was to analyze the relationships between hospital attributes and EHR implementation. The research design for this study was the cross-sectional approach. Secondary data from the Health Information and Management Systems Society (HIMSS) Analytics Database was utilized (n = 169) in a correlational crosssectional research design. Normalization Process Theory (NPT) and implementation theory were the theoretical underpinnings used in this study. Multiple linear regressions results showed statistically significant relationships between the 4 independent variables (region, ownership status, number of staffed beds [size], and organizational control) and the outcomes for the dependent variables of EHR software application attributes (Clinical Decision Support Systems (CDSS) components), EHR software application attributes (major systems), and successful implementation of Meaningful Use (MU) (p = .001). A statistically significant relationship (p = .001) was also found between the 2 independent variables (EHR software application attributes [CDSS components] and EHR software application attributes [major systems]) and the outcome of successful implementation of MU when combined. This evidence should provide policy makers and health practitioners support for their attempts to implement EHR systems to result in positive Meaningful Use which has been shown to be more cost effective and result in better quality of care for patients.The potential social change is improved medication prescribing and administration for hospitals and, lower cost and better quality of care for patients

Postmarketing Safety Study Tool: A Web Based, Dynamic, and Interoperable System for Postmarketing Drug Surveillance Studies

Postmarketing drug surveillance is a crucial aspect of the clinical research activities in pharmacovigilance and pharmacoepidemiology. Successful utilization of available Electronic Health Record (EHR) data can complement and strengthen postmarketing safety studies. In terms of the secondary use of EHRs, access and analysis of patient data across different domains are a critical factor; we address this data interoperability problem between EHR systems and clinical research systems in this paper. We demonstrate that this problem can be solved in an upper level with the use of common data elements in a standardized fashion so that clinical researchers can work with different EHR systems independently of the underlying information model. Postmarketing Safety Study Tool lets the clinical researchers extract data from different EHR systems by designing data collection set schemas through common data elements. The tool interacts with a semantic metadata registry through IHE data element exchange profile. Postmarketing Safety Study Tool and its supporting components have been implemented and deployed on the central data warehouse of the Lombardy region, Italy, which contains anonymized records of about 16 million patients with over 10-year longitudinal data on average. Clinical researchers in Roche validate the tool with real life use cases.Publisher's Versio

UK phenomics platform for developing and validating electronic health record phenotypes: CALIBER

Objective: Electronic health records (EHRs) are a rich source of information on human diseases, but the information is variably structured, fragmented, curated using different coding systems, and collected for purposes other than medical research. We describe an approach for developing, validating, and sharing reproducible phenotypes from national structured EHR in the United Kingdom with applications for translational research. Materials and Methods: We implemented a rule-based phenotyping framework, with up to 6 approaches of validation. We applied our framework to a sample of 15 million individuals in a national EHR data source (population-based primary care, all ages) linked to hospitalization and death records in England. Data comprised continuous measurements (for example, blood pressure; medication information; coded diagnoses, symptoms, procedures, and referrals), recorded using 5 controlled clinical terminologies: (1) read (primary care, subset of SNOMED-CT [Systematized Nomenclature of Medicine Clinical Terms]), (2) International Classification of Diseases–Ninth Revision and Tenth Revision (secondary care diagnoses and cause of mortality), (3) Office of Population Censuses and Surveys Classification of Surgical Operations and Procedures, Fourth Revision (hospital surgical procedures), and (4) DMþD prescription codes. Results: Using the CALIBER phenotyping framework, we created algorithms for 51 diseases, syndromes, biomarkers, and lifestyle risk factors and provide up to 6 validation approaches. The EHR phenotypes are curated in the open-access CALIBER Portal (https://www.caliberresearch.org/portal) and have been used by 40 national and international research groups in 60 peer-reviewed publications. Conclusions: We describe a UK EHR phenomics approach within the CALIBER EHR data platform with initial evidence of validity and use, as an important step toward international use of UK EHR data for health research

Managerial Strategies for Maximizing Benefits From Electronic Health Record Systems

In 2009, the U.S. government allocated $27 billion to health care agencies for electronic health records (EHRs) implementation. The increased use of EHR systems is expected to drive down health care costs and increase profits. To meet this anticipated return on investment (ROI), hospital managers need to be able to successfully design, deploy, and manage EHR systems. The purpose of this single case study was to explore organizational management strategies that hospital managers can use to ensure their investments in EHRs meet targeted ROIs and work efficiency goals. The conceptual framework for this study was based on the technology acceptance model. Primary data were collected from a criterion sample of 6 hospital managers with direct experience designing and implementing successful EHRs in a small hospital in the Northeastern United States. Secondary data were collected using public financial records available on the Internet. After cataloging and grouping the raw data, 4 emergent themes were identified: (a) training, (b) the role of organizational management strategies, (c) technological barriers, and (d) ongoing support and maintenance. Findings may contribute to social change through an increase in the quality of patient care and making health care records more accessible to doctors in isolated areas

Common data elements for secondary use of electronic health record data for clinical trial execution and serious adverse event reporting

Background: Data capture is one of the most expensive phases during the conduct of a clinical trial and the increasing use of electronic health records (EHR) offers significant savings to clinical research. To facilitate these secondary uses of routinely collected patient data, it is beneficial to know what data elements are captured in clinical trials. Therefore our aim here is to determine the most commonly used data elements in clinical trials and their availability in hospital EHR systems.Methods: Case report forms for 23 clinical trials in differing disease areas were analyzed. Through an iterative and consensus-based process of medical informatics professionals from academia and trial experts from the European pharmaceutical industry, data elements were compiled for all disease areas and with special focus on the reporting of adverse events. Afterwards, data elements were identified and statistics acquired from hospital sites providing data to the EHR4CR project.Results: The analysis identified 133 unique data elements. Fifty elements were congruent with a published data inventory for patient recruitment and 83 new elements were identified for clinical trial execution, including adverse event reporting. Demographic and laboratory elements lead the list of available elements in hospitals EHR systems. For the reporting of serious adverse events only very few elements could be identified in the patient records.Conclusions: Common data elements in clinical trials have been identified and their availability in hospital systems elucidated. Several elements, often those related to reimbursement, are frequently available whereas more specialized elements are ranked at the bottom of the data inventory list. Hospitals that want to obtain the benefits of reusing data for research from their EHR are now able to prioritize their efforts based on this common data element list.</p

Developing HL7 CDA-Based Data Warehouse for the Use of Electronic Health Record Data for Secondary Purposes

Background The growing availability of clinical and administrative data collected in electronic health records (EHRs) have led researchers and policy makers to implement data warehouses to improve the reuse of EHR data for secondary purposes. This approach can take advantages from a unique source of information that collects data from providers across multiple organizations. Moreover, the development of a data warehouse benefits from the standards adopted to exchange data provided by heterogeneous systems. Objective This article aims to design and implement a conceptual framework that semiautomatically extracts information collected in Health Level 7 Clinical Document Architecture (CDA) documents stored in an EHR and transforms them to be loaded in a target data warehouse. Results The solution adopted in this article supports the integration of the EHR as an operational data store in a data warehouse infrastructure. Moreover, data structure of EHR clinical documents and the data warehouse modeling schemas are analyzed to define a semiautomatic framework that maps the primitives of the CDA with the concepts of the dimensional model. The case study successfully tests this approach. Conclusion The proposed solution guarantees data quality using structured documents already integrated in a large-scale infrastructure, with a timely updated information flow. It ensures data integrity and consistency and has the advantage to be based on a sample size that covers a broad target population. Moreover, the use of CDAs simplifies the definition of extract, transform, and load tools through the adoption of a conceptual framework that load the information stored in the CDA in the data warehouse