Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Objective: Medical laboratory data together with prescribing and hospitalisation records are three of the most used electronic health records (EHRs) for data-driven health research. In Scotland, hospitalisation, prescribing and the death register data are available nationally whereas laboratory data is captured, stored and reported from local health board systems with significant heterogeneity. For researchers or other users of this regionally curated data, working on laboratory datasets across regional cohorts requires effort and time. As part of this study, the Scottish Safe Haven Network have developed an open-source software pipeline to generate a harmonised laboratory dataset.
Methods: We obtained sample laboratory data from the four regional Safe Havens in Scotland covering people within the SHARE consented cohort. We compared the variables collected by each regional Safe Haven and mapped these to 11 FHIR and 2 Scottish-specific standardised terms (i.e., one to indicate the regional health board and a second to describe the source clinical code description) RESULTS: We compared the laboratory data and found that 180 test codes covered 98.7 % of test records performed across Scotland. Focusing on the 180 test codes, we developed a set of transformations to convert test results captured in different units to the same unit. We included both Read Codes and SNOMED CT to encode the tests within the pipeline.
Conclusion: We validated our harmonisation pipeline by comparing the results across the different regional datasets. The pipeline can be reused by researchers and/or Safe Havens to generate clean, harmonised laboratory data at a national level with minimal effort.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.jbi.2024.104771 | DOI Listing |
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!