Triggering of systolic arterial pressure alarms using statistics‐based versus threshold alarms*

Connor, Chris; Gohil, B.; Harrison, Michael J.

doi:10.1111/j.1365-2044.2008.05738.x

Cited by 12 publications

(2 citation statements)

References 19 publications

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“…Similar severity ranking showed success in increasing alert acceptance rate by 50%, despite a 60% increase in alert events [35]. Other studies attempting to reduce noise, however, achieved limited or mixed results [36][37][38][39][40][41][42][43][44][45].…”

Section: Background and Significancementioning

confidence: 70%

“…The best models showed that by allowing 30 seconds of delay, false alarms can be better distinguished from true alarms; the best models were able to achieve 80% reduction in false alarms, missing 1% of true alarms. Studies focusing on pulse oximetry to reduce peripheral capillary oxygen saturation (SpO 2 ) false alarms, intracranial pressure alarms, and general vital sign monitoring alarms found mixed results ranging from 25% to 47% in alarm reduction, with 0% to 5% false-negative rates [42][43][44][45]. A more recent study showed that, by increasing delayed time within 3 min for alarms with physiologic monitoring waveforms, as well as including electrocardiography, SpO 2 , and arterial blood pressure, an ML model can achieve slightly better performance but fails to stably generalize to unseen data [52].…”

Section: Principal Findingsmentioning

confidence: 99%

See 1 more Smart Citation

Development, Implementation, and Evaluation of a Personalized Machine Learning Algorithm for Clinical Decision Support: Case Study With Shingles Vaccination

Ji¹,

Chokshi²,

Hegde³

et al. 2020

J Med Internet Res

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Background Although clinical decision support (CDS) alerts are effective reminders of best practices, their effectiveness is blunted by clinicians who fail to respond to an overabundance of inappropriate alerts. An electronic health record (EHR)–integrated machine learning (ML) algorithm is a potentially powerful tool to increase the signal-to-noise ratio of CDS alerts and positively impact the clinician’s interaction with these alerts in general. Objective This study aimed to describe the development and implementation of an ML-based signal-to-noise optimization system (SmartCDS) to increase the signal of alerts by decreasing the volume of low-value herpes zoster (shingles) vaccination alerts. Methods We built and deployed SmartCDS, which builds personalized user activity profiles to suppress shingles vaccination alerts unlikely to yield a clinician’s interaction. We extracted all records of shingles alerts from January 2017 to March 2019 from our EHR system, including 327,737 encounters, 780 providers, and 144,438 patients. Results During the 6 weeks of pilot deployment, the SmartCDS system suppressed an average of 43.67% (15,425/35,315) potential shingles alerts (appointments) and maintained stable counts of weekly shingles vaccination orders (326.3 with system active vs 331.3 in the control group; P=.38) and weekly user-alert interactions (1118.3 with system active vs 1166.3 in the control group; P=.20). Conclusions All key statistics remained stable while the system was turned on. Although the results are promising, the characteristics of the system can be subject to future data shifts, which require automated logging and monitoring. We demonstrated that an automated, ML-based method and data architecture to suppress alerts are feasible without detriment to overall order rates. This work is the first alert suppression ML-based model deployed in practice and serves as foundational work in encounter-level customization of alert display to maximize effectiveness.

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Section: Background and Significancementioning

confidence: 70%

Section: Principal Findingsmentioning

confidence: 99%

Development, Implementation, and Evaluation of a Personalized Machine Learning Algorithm for Clinical Decision Support: Case Study With Shingles Vaccination

Ji¹,

Chokshi²,

Hegde³

et al. 2020

J Med Internet Res

View full text Add to dashboard Cite

show abstract

Melodic algorithms for pulse oximetry to allow audible discrimination of abnormal systolic blood pressures

Chima

Ortega

Connor

2014

J Clin Monit Comput

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An anesthesiologist must remain vigilant of the patient's clinical status, incorporating many independent physiological measurements. Oxygen saturation and heart rate are represented by continuous audible tones generated by the pulse oximeter, a mandated monitoring device. Other important clinical parameters--notably blood pressure--lack any audible representation beyond arbitrarily-configured threshold alarms. Attempts to introduce further continuous audible tones have apparently foundered; the complexity and interaction of these tones have exceeded the ability of clinicians to interpret them. Instead, we manipulate the tonal and rhythmic structure of the accepted pulse oximeter tone pattern melodically. Three melodic algorithms were developed to apply tonal and rhythmic variations to the continuous pulse oximeter tone, dependent on the systolic blood pressure. The algorithms distort the original audible pattern minimally, to facilitate comprehension of both the underlying pattern and the applied variations. A panel of anesthesia practitioners (attending anesthesiologists, residents and nurse anesthetists) assessed these algorithms in characterizing perturbations in cardiopulmonary status. Twelve scenarios, incorporating combinations of oxygen desaturation, bradycardia, tachycardia, hypotension and hypertension, were tested. A rhythmic variation in which additional auditory information was conveyed only at halftime intervals, with every other "beat" of the pulse oximeter, was strongly favored. The respondents also strongly favored the use of musical chords over single tones. Given three algorithms of tones embedded in the pulse oximeter signal, anesthesiologists preferred a melodic tone to signal a significant change in blood pressure.

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Reduction of clinically irrelevant alarms in patient monitoring by adaptive time delays

Schmid

Goepfert

Franz

et al. 2015

J Clin Monit Comput

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The problem of high rates of false alarms in patient monitoring in anesthesiology and intensive care medicine is well known but remains unsolved. False alarms desensitize the medical staff, leading to ignored true alarms and reduced quality of patient care. A database of intra-operative monitoring data was analyzed to find characteristic alarm patterns. The original data were re-evaluated to find relevant events and to rate the severity of these events. Based on this analysis an adaptive time delay was developed that individually delays the alarms depending on the grade of threshold deviation. The conventional threshold algorithm led to 4893 alarms. 3515 (71.84 %) of these alarms were annotated as clinically irrelevant. In total 81.0 % of all clinically irrelevant alarms were caused by only mild and/or brief threshold violations. We implemented the new algorithm for selected parameters. These parameters equipped with adaptive validation delays led to 1729 alarms. 931 (53.85 %) alarms were annotated as clinically irrelevant. 632 alarms indicated the 645 clinically relevant events. The positive predictive value of occurring alarms improved from 28.16 % (conventional algorithm) to 46.15 % (new algorithm). 13 events were missed. The false positive alarm reduction rate of the algorithm ranged from 33 to 86.75 %. The overall reduction was 73.51 %. The implementation of this algorithm may be able to suppress a large percentage of false alarms. The effect of this approach has not been demonstrated but shows promise for reducing alarm fatigue. Its safety needs to be proven in a prospective study.

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Triggering of systolic arterial pressure alarms using statistics‐based versus threshold alarms*

Cited by 12 publications

References 19 publications

Development, Implementation, and Evaluation of a Personalized Machine Learning Algorithm for Clinical Decision Support: Case Study With Shingles Vaccination

Development, Implementation, and Evaluation of a Personalized Machine Learning Algorithm for Clinical Decision Support: Case Study With Shingles Vaccination

Melodic algorithms for pulse oximetry to allow audible discrimination of abnormal systolic blood pressures

Reduction of clinically irrelevant alarms in patient monitoring by adaptive time delays

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