Alarm Management Systems – Are You and Your Hospital Ready?
Bibliographic record
Abstract
Patient generated monitor alarms have been a concern for health care providers for years. Initiatives to ensure that clinicians hear an alarm or a warning signal when a patient’s condition is deteriorating include: louder tones at the patients’ bedside with concomitant warning lights outside of the patient room; centralized monitoring of the patient’s conditions at the nursing station to name a few. Another initiative includes patient alarms generated at the bedside sent to dedicated pagers. Nursing personnel assigned to care for a monitored patient carry the pager and receive the alarms when their patient’s condition is deteriorating (complementary alarm notification). The Joint Commission on Hospital Accreditation published a Prepublication Standards document establishing alarm management as a 2014 National Patient Safety Goal*. The Safety goal was to be introduced in two phases culminating in the implementation of clinical alarm management policies and procedures by 2016. Manufacturers have developed systems that replace the older pager technology with smart phone technology, leveraging existing hospital’s infrastructure such as WIFI to transmit the alarm conditions. The presentation will describe the process that the Hospital followed in implementing an Alarm Management System, including our experience with the complexity of such systems that involve alarm management servers, smart phone technology, patient monitoring practices and ultimately using the WIFI infrastructure. It is our intention to provide some lessons learned so that other institutions can avoid some of the challenges associated with implementing such technologies. *Joint Commission Perspective, July 2013, Volume 33, Issue 7
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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 | 0.000 | 0.000 |
| 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".