Implementing practice-linked pre-visit electronic journals in primary care: patient and physician use and satisfaction

2010-09-06T03:33:25-07:00

Electronic health records (EHRs) and EHR-connected patient portals offer patient–provider collaboration tools for visit-based care. During a randomized controlled trial, primary care patients completed pre-visit electronic journals (eJournals) containing EHR-based medication, allergies, and diabetes (study arm 1) or health maintenance, personal history, and family history (study arm 2) topics to share with their provider. Assessment with surveys and usage data showed that among 2027 patients invited to complete an eJournal, 70.3% submitted one and 71.1% of submitters had one opened by their provider. Surveyed patients reported they felt more prepared for the visit (55.9%) and their provider had more accurate information about them (58.0%). More arm 1 versus arm 2 providers reported that eJournals were visit-time neutral (100% vs 53%; p<0.013), helpful to patients in visit preparation (66% vs 20%; p=0.082), and would recommend them to colleagues (78% vs 22%; p=0.0143). eJournal integration into practice warrants further study.

Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications

2010-09-06T03:33:25-07:00

We aim to build and evaluate an open-source natural language processing system for information extraction from electronic medical record clinical free-text. We describe and evaluate our system, the clinical Text Analysis and Knowledge Extraction System (cTAKES), released open-source at http://www.ohnlp.org. The cTAKES builds on existing open-source technologies—the Unstructured Information Management Architecture framework and OpenNLP natural language processing toolkit. Its components, specifically trained for the clinical domain, create rich linguistic and semantic annotations. Performance of individual components: sentence boundary detector accuracy=0.949; tokenizer accuracy=0.949; part-of-speech tagger accuracy=0.936; shallow parser F-score=0.924; named entity recognizer and system-level evaluation F-score=0.715 for exact and 0.824 for overlapping spans, and accuracy for concept mapping, negation, and status attributes for exact and overlapping spans of 0.957, 0.943, 0.859, and 0.580, 0.939, and 0.839, respectively. Overall performance is discussed against five applications. The cTAKES annotations are the foundation for methods and modules for higher-level semantic processing of clinical free-text.

Journal of the American Medical Informatics Association Application of information technology

Implementing practice-linked pre-visit electronic journals in primary care: patient and physician use and satisfaction

Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications