The Effect of Health Information Technology on Health Care Provider Communication: A Mixed-Method Protocol
Bibliographic record
Abstract
BACKGROUND: Communication failures between physicians and nurses are one of the most common causes of adverse events for hospitalized patients, as well as a major root cause of all sentinel events. Communication technology (ie, the electronic medical record, computerized provider order entry, email, and pagers), which is a component of health information technology (HIT), may help reduce some communication failures but increase others because of an inadequate understanding of how communication technology is used. Increasing use of health information and communication technologies is likely to affect communication between nurses and physicians. OBJECTIVE: The purpose of this study is to describe, in detail, how health information and communication technologies facilitate or hinder communication between nurses and physicians with the ultimate goal of identifying how we can optimize the use of these technologies to support effective communication. Effective communication is the process of developing shared understanding between communicators by establishing, testing, and maintaining relationships. Our theoretical model, based in communication and sociology theories, describes how health information and communication technologies affect communication through communication practices (ie, use of rich media; the location and availability of computers) and work relationships (ie, hierarchies and team stability). Therefore we seek to (1) identify the range of health information and communication technologies used in a national sample of medical-surgical acute care units, (2) describe communication practices and work relationships that may be influenced by health information and communication technologies in these same settings, and (3) explore how differences in health information and communication technologies, communication practices, and work relationships between physicians and nurses influence communication. METHODS: This 4-year study uses a sequential mixed-methods design, beginning with a quantitative survey followed by a two-part qualitative phase. Survey results from aim 1 will provide a detailed assessment of health information and communication technologies in use and help identify sites with variation in health information and communication technologies for the qualitative phase of the study. In aim 2, we will conduct telephone interviews with hospital personnel in up to 8 hospitals to gather in-depth information about communication practices and work relationships on medical-surgical units. In aim 3, we will collect data in 4 hospitals (selected from telephone interview results) via observation, shadowing, focus groups, and artifacts to learn how health information and communication technologies, communication practices, and work relationships affect communication. RESULTS: Results from aim 1 will be published in 2016. Results from aims 2 and 3 will be published in subsequent years. CONCLUSIONS: As the majority of US hospitals do not yet have HIT fully implemented, results from our study will inform future development and implementation of health information and communication technologies to support effective communication between nurses and physicians.
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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 | 0.046 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| 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".