Hospital‐Based Clinicians' Use of Technology for Patient Care‐Related Communication: A National Survey
Bibliographic record
Abstract
OBJECTIVE: To characterize current use of communication technologies, including standard text messaging and secure mobile messaging applications, for patient care-related (PCR) communication. METHODS: We used a Society of Hospital Medicine database to conduct a national cross-sectional survey of hospital-based clinicians. RESULTS: We analyzed data from 620 survey respondents (adjusted response rate, 11.0%). Pagers were provided by hospitals to 495 (79.8%) of these clinicians, and 304 (49%) of the 620 reported they received PCR messages most commonly by pager. Use of standard text messaging for PCR communication was common, with 300 (52.9%) of 567 clinicians reporting receipt of standard text messages once or more per day. Overall, 21.5% (122/567) of respondents received standard text messages that included individually identifiable information, 41.3% (234/567) received messages that included some identifiable information (eg, patient initials), and 21.0% (119/567) received messages for urgent clinical issues at least once per day. About one-fourth of respondents (26.6%, 146/549) reported their organization had implemented a secure messaging application that some clinicians were using, whereas few (7.3%, 40/549) reported their organization had implemented an application that most clinicians were using. DISCUSSION: Pagers remain the technology most commonly used by hospital-based clinicians, but a majority also use standard text messaging for PCR communication, and relatively few hospitals have fully implemented secure mobile messaging applications. CONCLUSION: The wide range of technologies used suggests an evolution of methods to support communication among healthcare professionals.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".