In this issue - Benefits and disbenefits of computerised medical records (CMR): clinical, patient-centred, implementation and sociotechnical factors

Lusignan, Simon de

doi:10.14236/jhi.v23i1.864

Cited by 1 publication

(2 citation statements)

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“…This data mining method proved feasible, enabling large rapid data extraction, with sufficiently high reliability and accuracy in order to facilitate academic studies and clinical trials (1,2). We achieved 96.1% sensitivity with a recovery rate in the range from 95.6 to 101.1% for extracted structured data.…”

Section: Discussionmentioning

confidence: 95%

“…Implementation of electronic medical records (EMRs) in the healthcare system has the primary ambition to facilitate registration and sharing of patient data between different healthcare units and maintain backup information for health providers (1)(2)(3). With the growth and global adoption of EMR systems in medical healthcare, massive amounts of information have become available, motivating exploration of alternative methods to make full use of these large datasets (4).…”

Section: Introductionmentioning

confidence: 99%

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Automated data extraction of electronic medical records: Validity of data mining to construct research databases for eligibility in gastroenterological clinical trials

Joseph

Lindblad

Zaker

et al. 2022

ujms

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Background: Electronic medical records (EMRs) are adopted for storing patient-related healthcare information. Using data mining techniques, it is possible to make use of and derive benefit from this massive amount of data effectively. We aimed to evaluate validity of data extracted by the Customized eXtraction Program (CXP). Methods: The CXP extracts and structures data in rapid standardised processes. The CXP was programmed to extract TNFα-native active ulcerative colitis (UC) patients from EMRs using defined International Classification of Disease-10 (ICD-10) codes. Extracted data were read in parallel with manual assessment of the EMR to compare with CXP-extracted data. Results: From the complete EMR set, 2,802 patients with code K51 (UC) were extracted. Then, CXP extracted 332 patients according to inclusion and exclusion criteria. Of these, 97.5% were correctly identified, resulting in a final set of 320 cases eligible for the study. When comparing CXP-extracted data against manually assessed EMRs, the recovery rate was 95.6–101.1% over the years with 96.1% weighted average sensitivity. Conclusion: Utilisation of the CXP software can be considered as an effective way to extract relevant EMR data without significant errors. Hence, by extracting from EMRs, CXP accurately identifies patients and has the capacity to facilitate research studies and clinical trials by finding patients with the requested code as well as funnel down itemised individuals according to specified inclusion and exclusion criteria. Beyond this, medical procedures and laboratory data can rapidly be retrieved from the EMRs to create tailored databases of extracted material for immediate use in clinical trials.

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Section: Discussionmentioning

confidence: 95%