Data Sources

Abstract

Electronic medical records (EMRs), often also referred to as electronic health records (EHRs), are a major source of clinical data (although EMR and EHR have subtle differences). ("EHR (electronic health record) vs. EMR (electronic medical record)," [6]) EMRs are computerized medical information systems that collect, store and display patient information. They are means to create legible and organized recordings and to access clinical information about individual patients. EMRs have been described as an important tool to reduce medical errors and improve information sharing among physicians [1]. Nevertheless, there are many barriers that limit EMR adoption, varying from time, cost, security concerns and vendor trust to absence of computer skills for the physician [1]. To some extent such barriers can be lowered by using a framework for systematic EMR implementation [2]. On the other hand, expectations about using EHRs need to be tempered by practical considerations, recognizing that even those countries with relatively high rates of EHR penetration have achieved only limited successes in using EHR data for population health [7]. To what extent EMRs effectively succeed in improving quality of care and patient safety, remains a matter of debate [12,16].

Functional and Process Model on Big Data, Machine Learning, and Digital Phenotyping in Clinical Psychiatry

“…The most common barriers to data acquisition are 25 : Technical barriers (lack of interoperability). Political barriers (lack of effective data-sharing policies). Ethical barriers (lack of consensus on privacy and confidentiality). Administrative barriers (inadequate human resources to handle clinical data). …”

“…The most common barriers to data acquisition are 25 : Technical barriers (lack of interoperability). Political barriers (lack of effective data-sharing policies).…”

Clinical validation of clinical decision support systems for medication review: A scoping review

“…Most studies in our scoping review (17/30) described step 3 (prospective validation) of the validation strategy of Scheepers‐Hoeks et al 13 , 14 We reviewed clinical validation studies and therefore step 1 (technical CDSS validation) of the strategy was not within the scope of our review. None of the included studies reported all steps (2, 3 and 4) of the validation strategy and none referred to a published CDSS validation strategy.…”

“…12 A medication‐related CDSS validation strategy consists of four steps: (1) technical validation to check whether the CDSS functions as expected, (2) retrospective validation to review whether the output is clinically relevant, actionable and useful, (3) prospective validation before implementation in a real‐life EHR to check whether the CDSS fits in the workflow (e.g., timing and frequency), and finally (4) post‐implementation validation for continuous improvement. 13 , 14 The validation process can prevent irrelevant output for the clinical setting, alert fatigue or low user acceptance. Validation of a CDSS is, therefore, key for successful implementation.…”

“…Validation of a CDSS is, therefore, key for successful implementation. 13 , 14 To date, a systematic review was published in 2012 on the detection of ADEs, which found that the accuracy of ADE rules was reported in only 50% of the ADE detection system studies. 15 The rule accuracy was often poor and varied due to multiple factors including differences in validation methods.…”