Correlate: A PACS- and EHR-integrated Tool Leveraging Natural Language Processing to Provide Automated Clinical Follow-up
| Autor: | John Mongan, Joseph Mesterhazy, Mark D. Kovacs, David E. Avrin, Thomas H. Urbania |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
business.industry
MEDLINE Continuity of Patient Care computer.software_genre 030218 nuclear medicine & medical imaging Systems Integration 03 medical and health sciences Radiology Information Systems 0302 clinical medicine Picture archiving and communication system Software Electronic health record 030220 oncology & carcinogenesis Electronic Health Records Humans System integration Medicine Radiology Nuclear Medicine and imaging Artificial intelligence Radiology information systems business computer Natural language processing Natural Language Processing |
| Zdroj: | RadioGraphics. 37:1451-1460 |
| ISSN: | 1527-1323 0271-5333 |
| Popis: | A major challenge for radiologists is obtaining meaningful clinical follow-up information for even a small percentage of cases encountered and dictated. Traditional methods, such as keeping medical record number follow-up lists, discussing cases with rounding clinical teams, and discussing cases at tumor boards, are effective at keeping radiologists informed of clinical outcomes but are time intensive and provide follow-up for a small subset of cases. To this end, the authors developed a picture archiving and communication system-accessible electronic health record (EHR)-integrated program called Correlate, which allows the user to easily enter free-text search queries regarding desired clinical follow-up information, with minimal interruption to the workflow. The program uses natural language processing (NLP) to process the query and parse relevant future clinical data from the EHR. Results are ordered in terms of clinical relevance, and the user is e-mailed a link to results when these are available for viewing. A customizable personal database of queries and results is also maintained for convenient future access. Correlate aids radiologists in efficiently obtaining useful clinical follow-up information that can improve patient care, help keep radiologists integrated with other specialties and referring physicians, and provide valuable experiential learning. The authors briefly review the history of automated clinical follow-up tools and discuss the design and function of the Correlate program, which uses NLP to perform intelligent prospective searches of the EHR. |
| Databáze: | OpenAIRE |
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