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Assessment of the impact of EHR heterogeneity for clinical research through a case study of silent brain infarction.

ABSTRACT: BACKGROUND:The rapid adoption of electronic health records (EHRs) holds great promise for advancing medicine through practice-based knowledge discovery. However, the validity of EHR-based clinical research is questionable due to poor research reproducibility caused by the heterogeneity and complexity of healthcare institutions and EHR systems, the cross-disciplinary nature of the research team, and the lack of standard processes and best practices for conducting EHR-based clinical research. METHOD:We developed a data abstraction framework to standardize the process for multi-site EHR-based clinical studies aiming to enhance research reproducibility. The framework was implemented for a multi-site EHR-based research project, the ESPRESSO project, with the goal to identify individuals with silent brain infarctions (SBI) at Tufts Medical Center (TMC) and Mayo Clinic. The heterogeneity of healthcare institutions, EHR systems, documentation, and process variation in case identification was assessed quantitatively and qualitatively. RESULT:We discovered a significant variation in the patient populations, neuroimaging reporting, EHR systems, and abstraction processes across the two sites. The prevalence of SBI for patients over age 50 for TMC and Mayo is 7.4 and 12.5% respectively. There is a variation regarding neuroimaging reporting where TMC are lengthy, standardized and descriptive while Mayo's reports are short and definitive with more textual variations. Furthermore, differences in the EHR system, technology infrastructure, and data collection process were identified. CONCLUSION:The implementation of the framework identified the institutional and process variations and the heterogeneity of EHRs across the sites participating in the case study. The experiment demonstrates the necessity to have a standardized process for data abstraction when conducting EHR-based clinical studies.

SUBMITTER: Fu S

PROVIDER: S-EPMC7106829 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Assessment of the impact of EHR heterogeneity for clinical research through a case study of silent brain infarction.

Fu Sunyang S Leung Lester Y LY Raulli Anne-Olivia AO Kallmes David F DF Kinsman Kristin A KA Nelson Kristoff B KB Clark Michael S MS Luetmer Patrick H PH Kingsbury Paul R PR Kent David M DM Liu Hongfang H

BMC medical informatics and decision making 20200330 1

<h4>Background</h4>The rapid adoption of electronic health records (EHRs) holds great promise for advancing medicine through practice-based knowledge discovery. However, the validity of EHR-based clinical research is questionable due to poor research reproducibility caused by the heterogeneity and complexity of healthcare institutions and EHR systems, the cross-disciplinary nature of the research team, and the lack of standard processes and best practices for conducting EHR-based clinical resear ...[more]

PMID: 32228556

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Project description:Neuroinflammation is believed to be involved in the pathophysiological mechanisms of silent brain infarcts (SBI). However, the immunological profile of SBI has been scarcely investigated. In the context of a national research project named SILENCE, aimed at investigating clinical, biochemical and pathogenic features of SBI, we have measured the plasma profile of some inflammatory-related molecules in SBI patients (n?=?21), patients with recent lacunar infarcts (LI, n?=?28) and healthy controls (n?=?31), consecutively enrolled in four Italian centres. A panel of chemokines (MIG, CTACK, IL16, SDF1a, MCP1), growth factors (SCF, SCGFb, HGF, IL3), immunoglobulin-type adhesion molecules (ICAM1, VCAM1), proinflammatory cytokines (IL18, INFa2, MIF, IL12p40), cell surface receptors on T-cells (IL2Ra), and inductors of apoptosis (TRAIL) was assessed in plasma samples by Luminex xMAP™ technology. Immunological parameters were compared using non-parametric statistics and performance to distinguish SBI and LI was evaluated by receiver operating characteristic (ROC) analysis. Plasma levels of ICAM1 were significantly higher in both SBI and LI patients as compared to controls (SBI?LI>Ctrl). A different trend was observed for IL16 (SBI<LI>Ctrl), SCF (LI<SBI>Ctrl) and SCGFb (SBI>LI<Ctrl). SBI subjects had significantly increased levels of MIG when compared to controls (LI?SBI>Ctrl) and IL18 when compared to LI patients (Ctrl?SBI>LI). All the other immunological markers did not significantly differ among groups. According to ROC analysis, the best predictor for SBI condition was the chemokine MIG (AUC?=?0.84, sensitivity 86%, specificity 77%), while SCF had the best performance in distinguishing LI patients (AUC?=?0.84, sensitivity 86%, specificity 68%). These results confirm the involvement of inflammatory processes in cerebrovascular disorders, particularly in SBI, a very common age-related condition. The differences in plasma profile of inflammatory molecules may underlie different pathological mechanisms in SBI and LI patients.

Project description:Quantifying the value of investment in medical research can inform decision-making on the prioritisation of research programmes. Existing methodologies to estimate the rate of return of medical research are inappropriate for early-phase translational research due to censoring of health benefits and time lags. A strategy to improve the process of translational research for patient benefit has been initiated as part of the UK National Institute for Health Research (NIHR) investment in Biomedical Research Centres (BRCs) in England. By providing a platform for partnership between universities, NHS trusts and industry, successful BRCs should reduce time lags within translational research whilst also providing an impetus for local economic growth through industry collaboration. We present a novel contribution in the assessment of early-phase biomedical research by estimating the impact of the Oxford Biomedical Research Centre (OxBRC) on income and job creation following the initial NIHR investment. We adopt a macroeconomic assessment approach using Input-Output Analysis to estimate the value of medical research in terms of income and job creation during the early pathway towards translational biomedical research. Inter-industry linkages are assessed by building a model economy for the South East England region to estimate the return on investment of the OxBRC. The results from the input-output model estimate that the return on investment in biomedical research within the OxBRC is 46%. Each £1 invested in the OxBRC generates an additional £0.46 through income and job creation alone. Multiplicative employment effects following a marginal investment in the OxBRC of £98m during the period 2007-2017 result in an estimated additional 196 full time equivalent positions being created within the local economy on top of direct employment within OxBRC. Results from input-output analyses can be used to inform the prioritisation of biomedical research programmes when compared against national minimum thresholds of investment.

Project description:BackgroundPerforming cancer research relies on substantial financial investment, and contributions in time and effort from patients. It is therefore important that this research has real life impacts which are properly evaluated. The optimal approach to cancer research impact evaluation is not clear. The aim of this study was to undertake a systematic review of review articles that describe approaches to impact assessment, and to identify examples of cancer research impact evaluation within these reviews.MethodsIn total, 11 publication databases and the grey literature were searched to identify review articles addressing the topic of approaches to research impact assessment. Information was extracted on methods for data collection and analysis, impact categories and frameworks used for the purposes of evaluation. Empirical examples of impact assessments of cancer research were identified from these literature reviews. Approaches used in these examples were appraised, with a reflection on which methods would be suited to cancer research impact evaluation going forward.ResultsIn total, 40 literature reviews were identified. Important methods to collect and analyse data for impact assessments were surveys, interviews and documentary analysis. Key categories of impact spanning the reviews were summarised, and a list of frameworks commonly used for impact assessment was generated. The Payback Framework was most often described. Fourteen examples of impact evaluation for cancer research were identified. They ranged from those assessing the impact of a national, charity-funded portfolio of cancer research to the clinical practice impact of a single trial. A set of recommendations for approaching cancer research impact assessment was generated.ConclusionsImpact evaluation can demonstrate if and why conducting cancer research is worthwhile. Using a mixed methods, multi-category assessment organised within a framework, will provide a robust evaluation, but the ability to perform this type of assessment may be constrained by time and resources. Whichever approach is used, easily measured, but inappropriate metrics should be avoided. Going forward, dissemination of the results of cancer research impact assessments will allow the cancer research community to learn how to conduct these evaluations.

Dataset Information

Assessment of the impact of EHR heterogeneity for clinical research through a case study of silent brain infarction.

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Assessment of the impact of EHR heterogeneity for clinical research through a case study of silent brain infarction.

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