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 semiautomatic 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.

Big Data for Health

“…Mining EHRs is a valuable tool for improving clinical knowledge and supporting clinical research, for example, in discovering phenotype information [5]. Mining local information included in EHR data has already been proven to be effective for a wide range of healthcare challenges, such as disease management support [6], [7], pharmacovigilance [8], building models for predicting health risk assessment [9], [10], enhancing knowledge about survival rates [11], [12], therapeutic recommendation [11], [13], discovering comorbidities, and building support systems for the recruitment of patients for new clinical trials [14]. Most of this work focused on the analysis of very large multidimensional longitudinal patient data collected over many years.…”

Development of reusable logic for determination of statin exposure-time from electronic health records

“…Previous methodologies for ascertaining drug exposure can be particularly challenging in inpatient hospital records systems [47], where dosage are not recorded [45,48] or where dosage recorded in EHR sources are potentially misleading [45]. By applying the modified appointment window model described above, we are able to identify proxies for intended statin-start and end dates.…”

From Wearable Sensors to Smart Implants-–Toward Pervasive and Personalized Healthcare

“…Electronic health records (EHRs) describing treatments and patient outcomes are rich but under-utilised. Mining local information included in EHR data-aware houses has already proved an effective way of managing a wide range of healthcare challenges such as supporting disease management system [66,67], pharmacovigilance [68], building models for predicting health risk assessment [69,70], communicating survival rates [71,72], making therapeutic recommendations [71,73], discovering co-morbidities and building support systems for clinical trial recruitment [74]. When longitudinal health data are sampled in a continuous fashion, meaningful and rich time-series can be collected in order to enable temporal data mining.…”