Decision Support Tools within the Electronic Health Record

Rudolf, Joseph W; Dighe, Anand S.

doi:10.1016/j.cll.2019.01.001

Cited by 21 publications

(5 citation statements)

References 72 publications

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“…Especially in the diagnostic field, algorithms to support medical decision making are rapidly growing, as they are fast, accurate and cheap. Decision support tools provide a welcome opportunity to lower the work pressure of physicians in an aging population with multimorbidity [14,15]. In several regions of the Netherlands, general practitioners are supported by specialists in laboratory medicine with a service called 'reflective testing' [7,16,17].…”

Section: Discussionmentioning

confidence: 99%

Automated prediction of low ferritin concentrations using a machine learning algorithm

Kurstjens

Bel

Horst

et al. 2022

Clinical Chemistry and Laboratory Medicine (CCLM)

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Objectives Computational algorithms for the interpretation of laboratory test results can support physicians and specialists in laboratory medicine. The aim of this study was to develop, implement and evaluate a machine learning algorithm that automatically assesses the risk of low body iron storage, reflected by low ferritin plasma levels, in anemic primary care patients using a minimal set of basic laboratory tests, namely complete blood count and C-reactive protein (CRP). Methods Laboratory measurements of anemic primary care patients were used to develop and validate a machine learning algorithm. The performance of the algorithm was compared to twelve specialists in laboratory medicine from three large teaching hospitals, who predicted if patients with anemia have low ferritin levels based on laboratory test reports (complete blood count and CRP). In a second round of assessments the algorithm outcome was provided to the specialists in laboratory medicine as a decision support tool. Results Two separate algorithms to predict low ferritin concentrations were developed based on two different chemistry analyzers, with an area under the curve of the ROC of 0.92 (Siemens) and 0.90 (Roche). The specialists in laboratory medicine were less accurate in predicting low ferritin concentrations compared to the algorithms, even when knowing the output of the algorithms as support tool. Implementation of the algorithm in the laboratory system resulted in one new iron deficiency diagnosis on average per day. Conclusions Low ferritin levels in anemic patients can be accurately predicted using a machine learning algorithm based on routine laboratory test results. Moreover, implementation of the algorithm in the laboratory system reduces the number of otherwise unrecognized iron deficiencies.

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Section: Discussionmentioning

confidence: 99%

Automated prediction of low ferritin concentrations using a machine learning algorithm

Kurstjens

Bel

Horst

et al. 2022

Clinical Chemistry and Laboratory Medicine (CCLM)

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“…Ref. [ 2 ] claims that EHRs reduce pharmaceutical errors by giving medical practitioners decision-support tools including drug interaction alerts and dose suggestions. As they may be password-protected and encrypted, EHRs also offer a more secure access to patient data than conventional paper-based medical records, which lowers the risk of theft or unauthorized access.…”

Section: Introductionmentioning

confidence: 99%

Capturing Semantic Relationships in Electronic Health Records Using Knowledge Graphs: An Implementation Using MIMIC III Dataset and GraphDB

et al. 2023

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Electronic health records (EHRs) are an increasingly important source of information for healthcare professionals and researchers. However, EHRs are often fragmented, unstructured, and difficult to analyze due to the heterogeneity of the data sources and the sheer volume of information. Knowledge graphs have emerged as a powerful tool for capturing and representing complex relationships within large datasets. In this study, we explore the use of knowledge graphs to capture and represent complex relationships within EHRs. Specifically, we address the following research question: Can a knowledge graph created using the MIMIC III dataset and GraphDB effectively capture semantic relationships within EHRs and enable more efficient and accurate data analysis? We map the MIMIC III dataset to an ontology using text refinement and Protege; then, we create a knowledge graph using GraphDB and use SPARQL queries to retrieve and analyze information from the graph. Our results demonstrate that knowledge graphs can effectively capture semantic relationships within EHRs, enabling more efficient and accurate data analysis. We provide examples of how our implementation can be used to analyze patient outcomes and identify potential risk factors. Our results demonstrate that knowledge graphs are an effective tool for capturing semantic relationships within EHRs, enabling a more efficient and accurate data analysis. Our implementation provides valuable insights into patient outcomes and potential risk factors, contributing to the growing body of literature on the use of knowledge graphs in healthcare. In particular, our study highlights the potential of knowledge graphs to support decision-making and improve patient outcomes by enabling a more comprehensive and holistic analysis of EHR data. Overall, our research contributes to a better understanding of the value of knowledge graphs in healthcare and lays the foundation for further research in this area.

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“…Computerized clinical decision support (CCDS) tools for laboratory results are frequently used and comprise flagging, messages and alerts on single test results among other appliances [3,4]. However, CCDS tools based on multiple factors for interpretation of laboratory results resulting in a clinical diagnosis are sparse [5,6], but some appliances are used [3,5]: Detection of drug-laboratory test interaction [7], test ordering guidance [4] and automatic interpretation of results [8]. Interpretive comments based on a combination of different laboratory results are sparsely developed [4,[7][8][9], and almost nonexisting for rare disease result interpretation [9].…”

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confidence: 99%

“…Based on 965 workups investigated, we found that required interpretation time by the MD was reduced by 33% after introduction of the CCDS tool, from median (interquartile range) 6 (2)(3)(4)(5)(6)(7)(8)(9)(10)(11) to 4 (2-7) days. In August 2020, 25% of porphyria workups were performed with the CCDS tool, increasing to 66% in December 2020.…”

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confidence: 99%

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Computerized clinical decision support tool for diagnosing porphyria – improving efficiency in a specialized laboratory

Hansen

Ejdesgaard

Sølling

et al. 2022

Scandinavian Journal of Clinical and Laboratory Investigation

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Introduction:Uncertainty in interpreting laboratory results has been reported for 8% of tests, and may be even more common for infrequently ordered laboratory analyses, e.g. for rare diseases. We therefore aimed to evaluate whether a computerized clinical decision support (CCDS) tool can aid interpretation of specialized porphyria laboratory results, leading to faster diagnostic workup and fewer errors. Method:Our CCDS tool consisted of an algorithm made in Statistical Analysis System® (SAS) that automatically generate an Excel® file with a suggested interpretation of the laboratory results.We evaluated the efficacy and accuracy of the CCDS tool, by comparing time and interpretation before and after introduction of the CCDS. Results:Based on 965 workups investigated, we found that the required interpretation time by the medical doctor was reduced by 33% after introduction of the CCDS tool, Accuracy was 92% for diagnostic workups using the CCDS tool, compared to 88% for manual interpretation. None of the 8% (n=5) CCDS tool interpretation errors were critical. In conclusion, we proved that a CCDS tool for interpretation of laboratory results for a rare disease can be implemented safely and with a reduction in interpretation workload.

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Decision Support Tools within the Electronic Health Record

Cited by 21 publications

References 72 publications

Automated prediction of low ferritin concentrations using a machine learning algorithm

Automated prediction of low ferritin concentrations using a machine learning algorithm

Capturing Semantic Relationships in Electronic Health Records Using Knowledge Graphs: An Implementation Using MIMIC III Dataset and GraphDB

Computerized clinical decision support tool for diagnosing porphyria – improving efficiency in a specialized laboratory

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