ReviewNPJ digital medicine2019
An algorithm strategy for precise patient monitoring in a connected healthcare enterprise.
Review in NPJ digital medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- AI and Its Shifting Roles in the Therapeutic Relationship: Implications for Precision Medicine.Journal of personalized medicine · 2026Review
- Intelligent Clinical Decision Support.Sensors (Basel, Switzerland) · 2022Article
- Reducing medical device alarms by an order of magnitude: A human factors approach.Anaesthesia and intensive care · 2021Article
- Disentangling the clinical data chaos: User-centered interface system design for trauma centers.PloS one · 2021Article
- Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review.JMIR medical informatics · 2020Review
- Electrocardiographic right ventricular strain precedes hypoxic pulseless electrical activity cardiac arrests: Looking beyond pulmonary embolism.Resuscitation · 2020Article
- Generalizability of SuperAlarm via Cross-Institutional Performance Evaluation.IEEE access : practical innovations, open solutions · 2020Article
- Usefulness of Trends in Continuous Electrocardiographic Telemetry Monitoring to Predict In-Hospital Cardiac Arrest.The American journal of cardiology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
This perspective paper describes the building elements for realizing a precise patient monitoring algorithm to fundamentally address the alarm fatigue problem. Alarm fatigue is well recognized but no solution has been widely successful. Physiologic patient monitors are responsible for the lion's share of alarms at the bedside, most of which are either false or non-actionable. Algorithms on patient monitors lack precision because they fail to leverage multivariate relationship among variables monitored, to integrate rich patient clinical information from electronic health record system, and to utilize temporal patterns in data streams. Therefore, a solution to patient monitor alarm fatigue is to open the black-box of patient monitors to integrate physiologic data with clinical data from EHR under a four-element algorithm strategy to be described in this paper. This strategy will be presented in this paper in the context of its current status as described in our prior publications.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.