ArticleJournal of the American Medical Informatics Association : JAMIA2018
LabRS: A Rosetta stone for retrospective standardization of clinical laboratory test results.
Article in Journal of the American Medical Informatics Association : JAMIA, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- lab2clean: a novel algorithm for automated cleaning of retrospective clinical laboratory results data for secondary uses.BMC medical informatics and decision making · 2024Article
- Article
- Graphical analysis of guideline adherence to detect systemwide anomalies in HIV diagnostic testing.PloS one · 2022Article
- Interactive Machine Learning for Laboratory Data Integration.Studies in health technology and informatics · 2019Article
- Managing Complexity. From Documentation to Knowledge Integration and Informed Decision Findings from the Clinical Information Systems Perspective for 2018.Yearbook of medical informatics · 2019Review
- Semantic data interoperability, digital medicine, and e-health in infectious disease management: a review.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2019Review
- Regional and Rural-Urban Differences in the Use of Direct-acting Antiviral Agents for Hepatitis C Virus: The Veteran Birth Cohort.Medical care · 2019Observational
- Article
- Automated mapping of laboratory tests to LOINC codes using noisy labels in a national electronic health record system database.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Using Heatmaps to Identify Opportunities for Optimization of Test Utilization and Care Delivery.Journal of pathology informatics · 2018Article
- Natriuretic peptide testing in veterans hospitalized with heart failure: Potential differences by sex.Heart & lung : the journal of critical careArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Objective: Clinical laboratories in the United States do not have an explicit result standard to report the 7 billion laboratory tests results they produce each year. The absence of standardized test results creates inefficiencies and ambiguities for secondary data users. We developed and tested a tool to standardize the results of laboratory tests in a large, multicenter clinical data warehouse. Methods: Laboratory records, each of which consisted of a laboratory result and a test identifier, from 27 diverse facilities were captured from 2000 through 2015. Each record underwent a standardization process to convert the original result into a format amenable to secondary data analysis. The standardization process included the correction of typos, normalization of categorical results, separation of inequalities from numbers, and conversion of numbers represented by words (eg, "million") to numerals. Quality control included expert review. Results: We obtained 1.266 × 109 laboratory records and standardized 1.252 × 109 records (98.9%). Of the unique unstandardized records (78.887 × 103), most appeared <5 times (96%, eg, typos), did not have a test identifier (47%), or belonged to an esoteric test with <100 results (2%). Overall, these 3 reasons accounted for nearly all unstandardized results (98%). Conclusion: Current results suggest that the tool is both scalable and generalizable among diverse clinical laboratories. Based on observed trends, the tool will require ongoing maintenance to stay current with new tests and result formats. Future work to develop and implement an explicit standard for test results would reduce the need to retrospectively standardize test results.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.