Evaluating the impact on clinical task efficiency of a natural language processing algorithm for searching medical documents: Prospective crossover study
Bibliographic record
Abstract
Abstract Background Information retrieval (IR) from the free text within Electronic Health Records (EHRs) is time-consuming and complex. We hypothesise that Natural Language Processing (NLP)-enhanced search functionality for EHRs can make clinical workflows more efficient and reduce cognitive load for clinicians. Objective To evaluate the efficacy of three levels of search functionality (no search, string search, and NLP-enhanced search) in supporting IR for clinical users from the free text of EHR documents in a simulated clinical environment. Methods A clinical environment was simulated by uploading three sets of patient notes into an EHR research software application and presenting these alongside three corresponding IR tasks. Tasks contained a mixture of multiple choice and free text questions. A prospective crossover study design was used, for which three groups of evaluators were recruited, comprised of doctors (n=19) and medical students (n=16). Evaluators performed the three tasks using each of the search functionalities in an order according to their randomly assigned group. The speed and accuracy of task completion was measured and analysed, and user perceptions of NLP-enhanced search were reviewed in a feedback survey. Results NLP-enhanced search facilitated significantly more accurate task completion than both string search (5.26%, p=0.01) and no search (7.44%, p=0.05). NLP-enhanced search and string search facilitated similar task speeds, both showing an increase in speed over no search function (15.9%/11.6%, p=0.05). 93% of evaluators agreed that NLP-enhanced search would make clinical workflows more efficient than string search, with qualitative feedback reporting that NLP-enhanced search reduced cognitive load. Conclusions To the best of our knowledge, this study is the largest evaluation to date of different search functionalities for supporting target clinical users in realistic clinical workflows, with a 3-way prospective crossover study design. NLP-enhanced search improved both accuracy and speed of clinical EHR IR tasks compared to browsing clinical notes without search. NLP-enhanced search improved accuracy and reduced the number of searches required for clinical EHR IR tasks compared to direct search term matching.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".