Narrative information is vital to health care, because it enables physicians to synthesize the raw facts and provide a context and interpretation for them. Electronic medical record systems contain a wealth of clinical data, but typically lack the clinical narrative found in paper records, e.g., the patient history and progress notes. Numerous barriers prevent the timely acquisition of narrative data, and most computer systems are unable to use such information productively. Current approaches offer a tradeoff, capture of rich clinical data that lacks structure (using transcription services or speech technology), versus entry of structured data that lacks flexibility and expressiveness (using template systems). Natural language processing can integrate these approaches by allowing physicians full freedom of expression while producing structured documents that preserve the richness and enable further computer processing. This proposal seeks to capture and structure narrative in the online medical record in order to improve entry time, completeness, information content and consistency of clinical documentation.
The specific aims of this proposal are: 1) Maintain the continuity of the medical record; a lengthy medical record requires significant time to review and digest. Many facts from past narratives remain true in the present or persist with minor changes. By automatically bringing these facts forward into the current narrative, the system can reduce the time to enter the document, and improve the completeness of documentation by maintaining continuity of what is known about a patient; 2) Integrate the medical record: Electronic medical records contain a vast amount of data. However, most of these data are raw facts. By helping the physician to connect, interpret and summarize these facts, the system can improve the usefulness of the information in the record, and reduce the time to enter documents by performing some syntheses automatically; and 3) Harmonize the medical record; the multidisciplinary nature of health care creates the potential for the differing perspectives and interpretations in the medical record, and even contradictions. By bringing possible discrepancies to the attention of the physician, the system can help resolve the inconsistencies.
| Hyun, Sookyung; Shapiro, Jason S; Melton, Genevieve et al. (2009) Iterative evaluation of the Health Level 7--Logical Observation Identifiers Names and Codes Clinical Document Ontology for representing clinical document names: a case report. J Am Med Inform Assoc 16:395-9 |
| Hyun, Sookyung; Johnson, Stephen B; Stetson, Peter D et al. (2009) Development and evaluation of nursing user interface screens using multiple methods. J Biomed Inform 42:1004-12 |
| Stetson, Peter D; Morrison, Frances P; Bakken, Suzanne et al. (2008) Preliminary development of the physician documentation quality instrument. J Am Med Inform Assoc 15:534-41 |
| Johnson, Stephen B; Bakken, Suzanne; Dine, Daniel et al. (2008) An electronic health record based on structured narrative. J Am Med Inform Assoc 15:54-64 |
| Wrenn, Jesse O; Stetson, Peter D; Johnson, Stephen B (2007) An unsupervised machine learning approach to segmentation of clinician-entered free text. AMIA Annu Symp Proc :811-5 |
| Hyun, Sookyung; Bakken, Suzanne; Johnson, Stephen B (2006) Markup of temporal information in electronic health records. Stud Health Technol Inform 122:907-8 |
| Zhou, Li; Parsons, Simon; Hripcsak, George (2006) Handling implicit and uncertain temporal information in medical text. AMIA Annu Symp Proc :1158 |
| Hyun, Sookyung; Ventura, Rosemary; Johnson, Stephen B et al. (2006) Is the Health Level 7/LOINC document ontology adequate for representing nursing documents? Stud Health Technol Inform 122:527-31 |
| Zhou, Li; Melton, Genevieve B; Parsons, Simon et al. (2006) A temporal constraint structure for extracting temporal information from clinical narrative. J Biomed Inform 39:424-39 |
| Bright, Tiffani J; Bakken, Suzanne; Johnson, Stephen B (2006) Heuristic evaluation of eNote: an electronic notes system. AMIA Annu Symp Proc :864 |
Showing the most recent 10 out of 17 publications
