Computers in the clinical encounter: a scoping review and thematic analysis
Bibliographic record
Abstract
OBJECTIVE: Patient-clinician communication has been associated with increased patient satisfaction, trust in the clinician, adherence to prescribed therapy, and various health outcomes. The impact of health information technology (HIT) on the clinical encounter in general and patient-clinician communication in particular is a growing concern. The purpose of this study was to review the current literature on HIT use during the clinical encounter to update best practices and inform the continuous development of HIT policies and educational interventions. METHODS: We conducted a literature search of four databases. After removing duplicates, reviewing titles and abstracts, performing a full-text review, and snowballing from references and citations, 51 articles were included in the analysis. We employed a qualitative thematic analysis to compare and contrast the findings across studies. RESULTS: Our analysis revealed that the use of HIT affects consultations in complex ways, impacting eye contact and gaze, information sharing, building relationships, and pauses in the conversation. Whether these impacts are positive or negative largely depends on the combination of consultation room layout, patient and clinician styles of interaction with HIT as well as each other, and the strategies and techniques employed by clinicians to integrate HIT into consultations. DISCUSSION: The in-depth insights into the impact of HIT on the clinical encounter, especially the strategies and techniques employed by clinicians to adapt to using HIT in consultations, can inform policies, educational interventions, and research. CONCLUSION: In contrast to the common negative views of HIT, it affects the clinical encounter in multiple ways. By applying identified strategies and best practices, HIT can support patient-clinician interactions rather than interfering with them.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.046 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.043 | 0.046 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".