The impact of adopting EHRs: how losing connectivity affects clinical reasoning
Bibliographic record
Abstract
CONTEXT: As electronic health records (EHRs) are adopted by teaching hospitals, educators must examine how this change impacts trainee development. OBJECTIVES: We investigate this influence by studying clinician experiences of a hospital's move from paper charts to an EHR. We ask: how does each chart modality present conceptions of time and data interconnections? How do these conceptions affect clinical reasoning? METHODS: This two-phase, longitudinal study employed constructivist grounded theory. Data were collected at a paediatric teaching hospital before (Phase 1), during and after (Phase 2) the transition from a paper chart to an EHR system. Data collection consisted of field observations (146 hours involving 300 health care providers, 22 patients and 32 patient family members), think-aloud (n = 13) and think-after (n = 11) sessions, interviews (n = 39) and document retrieval (n = 392). Theories of rhetorical genre studies and visual rhetoric informed analysis. RESULTS: In the paper flowsheet, clinicians recorded and viewed patient data in chronologically organised displays that emphasised data interconnections. In the EHR flowsheet, clinicians viewed and recorded individual data points that were largely chronologically and contextually isolated. Clinicians reported that this change resulted in: (i) not knowing the patient's evolving status; (ii) increased cognitive workload, and (iii) loss of clinical reasoning support mechanisms. CONCLUSIONS: Understanding how patient data are interconnected is essential to clinical reasoning. The use of EHRs supports this goal because the EHR is a tool for collecting dispersed data; however, these collections often deconstruct data interconnections. Where the paper flowsheet emphasises chronology and interconnectedness, the EHR flowsheet emphasises individual data values that are largely independent of time and other patient data. To prepare trainees to work with EHRs, the ways of thinking and acting that were implicitly learned through the use of paper charts must be made explicit. To support clinical reasoning, medical educators should provide lessons in connectivity – the chronologically framed data interconnections upon which clinicians rely to provide patient care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.587 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".