Nursing and precision predictive analytics monitoring in the acute and intensive care setting: An emerging role for responding to COVID-19 and beyond

Keim‐Malpass, Jessica; Moorman, Liza P.

doi:10.1016/j.ijnsa.2021.100019

Cited by 19 publications

(20 citation statements)

References 56 publications

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“…Clinicians at the point of care must engage with risk estimates in a way that integrates within their already complex workflow. This is fundamental in the learning health system where informatics and care culture align for continuous improvement 13,25,30‐32 …”

Section: Discussionmentioning

confidence: 99%

“…These inputs are used to calculate the fold‐increase in the risk of an event of clinical deterioration. These scores are calculated every 15 min and displayed visually 15,24,25 . The models for risk score calculation are multivariable logistic regression expressions adjusted for repeated measures and use a sample and hold strategy for results like laboratory results or nurse‐entered vital signs that are not updated at the same frequency.…”

Section: Introductionmentioning

confidence: 99%

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Beyond prediction: Off‐target uses of artificial intelligence‐based predictive analytics in a learning health system

Keim‐Malpass

Moorman²,

Monfredi

et al. 2022

Learning Health Systems

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Introduction: Artificial-intelligence (AI)-based predictive analytics provide new opportunities to leverage rich sources of continuous data to improve patient care through early warning of the risk of clinical deterioration and improved situational awareness.Part of the success of predictive analytic implementation relies on integration of the analytic within complex clinical workflows. Pharmaceutical interventions have offtarget uses where a drug indication has not been formally studied for a different indication but has potential for clinical benefit. An analog has not been described in the context of AI-based predictive analytics, that is, when a predictive analytic has been trained on one outcome of interest but is used for additional applications in clinical practice. Methods:In this manuscript we present three clinical vignettes describing off-target use of AI-based predictive analytics that evolved organically through real-world practice.Results: Off-target uses included:real-time feedback about treatment effectiveness, indication of readiness to discharge, and indication of the acuity of a hospital unit. Conclusion:Such practice fits well with the learning health system goals to continuously integrate data and experience to provide.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Beyond prediction: Off‐target uses of artificial intelligence‐based predictive analytics in a learning health system

Keim‐Malpass

Moorman²,

Monfredi

et al. 2022

Learning Health Systems

Self Cite

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“…Nurses are already and will continue to be the first responders in the context of the global pandemic [49]. As the immediate antecedent of behavior [16], nurses' behavioral intention and its related factors are crucial to designing tailored interventions that foster nurses' caring behavior.…”

Section: Plos Onementioning

confidence: 99%

Factors influencing nurses’ behavioral intention toward caring for COVID-19 patients on mechanical ventilation: A cross-sectional study

Cheng

Cui

et al. 2021

PLoS ONE

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Objectives To investigate nurses’ behavioral intention toward caring for COVID-19 patients on mechanical ventilation, as well as the factors affecting their intention. Background COVID-19 patients undergoing mechanical ventilation have many care needs and pose more challenges for nurses, which might adversely affect nurses’ intention toward caring behavior. Methods A cross‐sectional study was conducted by using simple random sampling to recruit 598 nurses from five tertiary hospitals in Sichuan Province, China. The participants responded to an online questionnaire that included questions on demographic characteristics; the Attitude, Subjective Norms, and Behavioral Intention of Nurses toward Mechanically Ventilated Patients (ASIMP) questionnaire; the Nursing Professional Identity Scale (NPIS); and the Compassion Fatigue-Short Scale (CF-Short Scale). ANOVA, Spearman correlation analysis, and multiple linear regression were performed to analyze the data. Results The mean total behavioral intention score was 179.46 (± 14.83) out of a total score of 189.00, which represented a high level of intention toward caring for patients on mechanical ventilation. Multiple linear regression revealed that subjective norms (β = 0.390, P<0.001), perceived behavioral control (β = 0.149, P<0.001), professional identity (β = 0.101, P = 0.009), and compassion fatigue (β = 0.088 P = 0.024) were significant predictors of nurses’ behavioral intention. Conclusions Most nurses have a positive behavioral intention to care for COVID-19 patients undergoing mechanical ventilation. The findings in this study provide some insight for developing effective and tailored strategies to promote nurses’ behavioral intention toward caring for ventilated patients under the pandemic situation.

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“…The outputs that generate CoMET, which are the products of logistic regression, are instantaneous metrics of 'cardiorespiratory' and 'cardiovascular instability', and the output display charts the trajectory of this instability over the prior 3 hours to allow identification of patients with a deteriorating trajectory. [11][12][13][14] A potential shortcoming of this however is that, while excellent at reflecting patient trajectory now, i.e. based on the prior 3 hours of data, the CoMET score is unable to reflect the cumulative burden of illness sustained by the patient throughout their hospital stay.…”

Section: Introductionmentioning

confidence: 99%

“…The outputs that generate CoMET, which are the products of logistic regression, are instantaneous metrics of ‘cardiorespiratory’ and ‘cardiovascular instability’, and the output display charts the trajectory of this instability over the prior 3 hours to allow identification of patients with a deteriorating trajectory. 11-14…”

Section: Introductionmentioning

confidence: 99%

Predictive analytics that reflect disease burden – the cumulative COMET score

Monfredi

Andris

Lake

et al. 2022

Preprint

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Predictive analytics tools variably take into account data from the electronic medical record, lab tests, nursing charted vital signs and continuous cardiorespiratory monitoring data to deliver an instantaneous score that indicates patient risk or instability. Few, if any, of these tools reflect the risk to a patient accumulated over the course of an entire hospital stay. This approach fails to best utilize all of the collated data regarding the risk or instability sustained by the patient, and hence fails to fully characterize this to optimize the ability of treating clinicians to maximize the chances of a favorable outcome. We have built on our instantaneous CoMET predictive analytics score to generate the cumulative CoMET score (cCOMET), which sums all of the instantaneous CoMET scores throughout a hospital admission relative to a baseline expected risk unique to that patient. We have shown that higher cCOMET scores predict mortality, but not length of stay, and that higher baseline CoMET scores predict higher cCoMET scores at discharge/death. cCoMET scores were higher in males in our cohort, and added information to the final CoMET when it came to the prediction of death. In summary, if one is going to go to the trouble and expense of performing repeated measures when performing predictive analytics calculations, we have shown that including all of these measures in a cumulative way adds data to instantaneous predictive analytics, and could improve the ability of clinicians to predict deterioration, and improve patient outcomes in so doing.

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Nursing and precision predictive analytics monitoring in the acute and intensive care setting: An emerging role for responding to COVID-19 and beyond

Cited by 19 publications

References 56 publications

Beyond prediction: Off‐target uses of artificial intelligence‐based predictive analytics in a learning health system

Beyond prediction: Off‐target uses of artificial intelligence‐based predictive analytics in a learning health system

Factors influencing nurses’ behavioral intention toward caring for COVID-19 patients on mechanical ventilation: A cross-sectional study

Predictive analytics that reflect disease burden – the cumulative COMET score

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