Name
Abstract | ||
|---|---|---|
Objective Clinician progress notes are an important record for care and communication, but there is a perception that electronic notes take too long to write and may not accurately reflect the patient encounter, threatening quality of care. Automatic speech recognition (ASR) has the potential to improve clinical documentation process; however, ASR inaccuracy and editing time are barriers to wider use. We hypothesized that automatic text processing technologies could decrease editing time and improve note quality. To inform the development of these technologies, we studied how physicians create clinical notes using ASR and analyzed note content that is revised or added during asynchronous editing. Materials and Method We analyzed a corpus of 649 dictated clinical notes from 9 physicians. Notes were dictated during rounds to portable devices, automatically transcribed, and edited later at the physician's convenience. Comparing ASR transcripts and the final edited notes, we identified the word sequences edited by physicians and categorized the edits by length and content. Results We found that 40% of the words in the final notes were added by physicians while editing: 6% corresponded to short edits associated with error correction and format changes, and 34% were associated with longer edits. Short error correction edits that affect note accuracy are estimated to be less than 3% of the words in the dictated notes. Longer edits primarily involved insertion of material associated with clinical data or assessment and plans. The longer edits improve note completeness; some could be handled with verbalized commands in dictation. Conclusion Process interventions to reduce ASR documentation burden, whether related to technology or the dictation/editing workflow, should apply a portfolio of solutions to address all categories of required edits. Improved processes could reduce an important barrier to broader use of ASR by clinicians and improve note quality. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1055/s-0038-1673417 | APPLIED CLINICAL INFORMATICS |
Keywords | DocType | Volume |
electronic health records and systems,clinical documentation and communications,natural language processing,notes,workflow | Journal | 9 |
Issue | ISSN | Citations |
4 | 1869-0327 | 0 |
PageRank | References | Authors |
0.34 | 15 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kevin J Lybarger | 1 | 0 | 0.34 |
| Mari Ostendorf | 2 | 2462 | 348.75 |
| Eve A. Riskin | 3 | 362 | 47.22 |
| Thomas H. Payne | 4 | 12 | 6.39 |
| Andrew A White | 5 | 7 | 2.22 |
| Meliha Yetisgen-Yildiz | 6 | 328 | 34.25 |