title: Extraction of electronic health record data in a hospital setting: comparison of automatic and semi-automatic methods using anti-TNF therapy as model.
abstract: There is limited experience and methods for extractions of drug therapy data from electronic health records (EHR) in the hospital setting. We have therefore developed and evaluated completeness and consistency of an automatic versus a semi-automatic extraction procedure applied on prescribing and administration of the TNF inhibitor infliximab using a hospital EHR system in Karolinska University Hospital, Sweden. Using two different extraction methods (automatic and semi-automatic), all administered infusions of infliximab between 2007 and 2010 were extracted from a database linked to the EHR system. Extracted data included encrypted personal identity number (PIN), date of birth, sex, time of prescription/administration, healthcare units, prescribed/administered dose and time of admission/discharge. The primary diagnosis (ICD-10) for the treatment with infliximab was extracted by linking infliximab infusions to their corresponding treatment episode. A total of 13,590 infusions of infliximab were administered during the period of 2007 to 2010. Of those were 13,531 (99.6%) possible to link to a corresponding treatment episode, and a primary diagnosis was extracted for 13,530 infusions. Information on encrypted PIN, date of birth, time of prescription/administration, time of admission/discharge and healthcare unit was complete. Information about sex was missing in one patient only. Calculable information about dosage was extracted for 13,300 (98.3%) of all linked infusions. This methodological study showed the potential to extract drug therapy data in a hospital setting. The semi-automatic procedure produced an almost complete pattern of demographics, diagnoses and dosages for the treatment with infliximab.
authors: Public Healthcare Services Committee Administration, Stockholm County Council, Stockholm, Sweden. thomas.cars@sll.se
authors: Thomas
authors: T
authors: Cars
authors: Björn
authors: B
authors: Wettermark
authors: Rickard E
authors: RE
authors: Malmström
authors: Gunnar
authors: G
authors: Ekeving
authors: Bo
authors: B
authors: Vikström
authors: Ulf
authors: U
authors: Bergman
authors: Martin
authors: M
authors: Neovius
authors: Bo
authors: B
authors: Ringertz
authors: Lars L
authors: LL
authors: Gustafsson
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000911:Antibodies, Monoclonal / Q000008:administration & dosage*; D002648:Child; D002675:Child, Preschool; D016208:Databases, Factual; D004363:Drug Utilization; D057286:Electronic Health Records / Q000458:organization & administration* / Q000706:statistics & numerical data; D005260:Female; D006785:Hospitals, University / Q000458:organization & administration* / Q000706:statistics & numerical data; D006801:Humans; D000069285:Infliximab; D008297:Male; D008498:Medical Record Linkage / Q000379:methods*; D016347:Medical Records Systems, Computerized / Q000458:organization & administration / Q000706:statistics & numerical data; D008875:Middle Aged; D013548:Sweden; D014409:Tumor Necrosis Factor-alpha / Q000037:antagonists & inhibitors*; D055815:Young Adult
chemical_list: D000911:Antibodies, Monoclonal; D014409:Tumor Necrosis Factor-alpha; D000069285:Infliximab