Advancing Continuous Predictive Analytics Monitoring

Keim‐Malpass, Jessica; Kitzmiller, Rebecca Rutherford; Skeeles-Worley, Angela; Lindberg, Curt; Clark, Matthew; Tai, Robert H.; Calland, J. Forrest; Sullivan, Kevin; Moorman, J. Randall; Anderson, Ruth A.

doi:10.1016/j.cnc.2018.02.009

Cited by 32 publications

(12 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While we recognize that scoring systems and risk scores cannot supplant physician experience, they can certainly provide valuable information to a treating provider. This is evidenced by reductions in rates of septic shock and morality associated with clinical implementation of predictive analytic tools [34,36]. Our current work also largely sidesteps this issue, as the event population is virtually pathognomonic of a failure in gestalt with resultant Fig.…”

Section: Discussionmentioning

confidence: 65%

Dynamic data in the ED predict requirement for ICU transfer following acute care admission

Glass

Hartka

Keim‐Malpass

et al. 2020

J Clin Monit Comput

Self Cite

View full text Add to dashboard Cite

Misidentification of illness severity may lead to patients being admitted to a ward bed then unexpectedly transferring to an ICU as their condition deteriorates. Our objective was to develop a predictive analytic tool to identify emergency department (ED) patients that required upgrade to an intensive or intermediate care unit (ICU or IMU) within 24 h after being admitted to an acute care floor. We conducted a single-center retrospective cohort study to identify ED patients that were admitted to an acute care unit and identified cases where the patient was upgraded to ICU or IMU within 24 h. We used data available at the time of admission to build a logistic regression model that predicts early ICU transfer. We found 42,332 patients admitted between January 2012 and December 2016. There were 496 cases (1.2%) of early ICU transfer. Case patients had 18.0-fold higher mortality (11.1% vs. 0.6%, p < 0.001) and 3.4 days longer hospital stays (5.9 vs. 2.5, p < 0.001) than those without an early transfer. Our predictive analytic model had a cross-validated area under the receiver operating characteristic of 0.70 (95% CI 0.67-0.72) and identified 10% of early ICU transfers with an alert rate of 1.6 per week (162.2 acute care admits per week, 1.9 early ICU transfers). Predictive analytic monitoring based on data available in the emergency department can identify patients that will require upgrade to ICU or IMU if admitted to acute care. Incorporating this tool into ED practice may draw attention to high-risk patients before acute care admit and allow early intervention.

show abstract

Section: Discussionmentioning

confidence: 65%

Dynamic data in the ED predict requirement for ICU transfer following acute care admission

Glass

Hartka

Keim‐Malpass

et al. 2020

J Clin Monit Comput

Self Cite

View full text Add to dashboard Cite

show abstract

“…This is not altogether unreasonable. For example, we found that large abrupt spikes in risk estimation using a single model to identify patients at risk for ICU transfer had a positive predictive value 25% for imminent acute adverse event ( 27 ). But use of only a single model is a limited approach, as there are many paths of deterioration—accurate capture of all of these paths at the same time is challenging.…”

mentioning

confidence: 99%

“…Clinicians know, although, that there are many paths to clinical deterioration. For example, the most common forms of deterioration leading to ICU transfer are respiratory instability ( 27 , 30 – 35 ), hemodynamic instability ( 30 , 33 , 34 ), sepsis ( 27 , 36 ), bleeding ( 33 , 36 ), neurologic decompensation ( 36 ), unplanned surgery ( 33 ), and acute renal failure or electrolyte abnormalities ( 36 ). The multiplicity of candidate culprit organ systems suggests that a single model is not likely to catch all the patients who are worsening ( 33 , 34 ).…”

mentioning

confidence: 99%

Early Detection of In-Patient Deterioration: One Prediction Model Does Not Fit All

Blackwell

Keim‐Malpass

Clark

et al. 2020

Critical Care Explorations

Self Cite

View full text Add to dashboard Cite

show abstract

“… 11 Further, there is little evidence to guide interventions to promote clinician acceptance and use of predictive technologies as the majority of studies focus upon model development and accuracy, not on providers' acceptance of prediction as an element of care decision making. 9 11 14 62 63 Because the success of predictive technologies such as HeRO rely on the human system for interpretation and action, 26 49 64 processes to build human capacity to interpret predictive data in the context of clinical reasoning are essential. Simple strategies designed to engage clinicians' attention and promote data communication may be foundational to helping clinicians to learn how to effectively use new tools in care delivery.…”

Section: Discussionmentioning

confidence: 99%

Diffusing an Innovation: Clinician Perceptions of Continuous Predictive Analytics Monitoring in Intensive Care

et al. 2019

Self Cite

View full text Add to dashboard Cite

Background The purpose of this article is to describe neonatal intensive care unit clinician perceptions of a continuous predictive analytics technology and how those perceptions influenced clinician adoption. Adopting and integrating new technology into care is notoriously slow and difficult; realizing expected gains remain a challenge. Methods Semistructured interviews from a cross-section of neonatal physicians (n = 14) and nurses (n = 8) from a single U.S. medical center were collected 18 months following the conclusion of the predictive monitoring technology randomized control trial. Following qualitative descriptive analysis, innovation attributes from Diffusion of Innovation Theory-guided thematic development. Results Results suggest that the combination of physical location as well as lack of integration into work flow or methods of using data in care decisionmaking may have delayed clinicians from routinely paying attention to the data. Once data were routinely collected, documented, and reported during patient rounds and patient handoffs, clinicians came to view data as another vital sign. Through clinicians' observation of senior physicians and nurses, and ongoing dialogue about data trends and patient status, clinicians learned how to integrate these data in care decision making (e.g., differential diagnosis) and came to value the technology as beneficial to care delivery. Discussion The use of newly created predictive technologies that provide early warning of illness may require implementation strategies that acknowledge the risk–benefit of treatment clinicians must balance and take advantage of existing clinician training methods.

show abstract

Advancing Continuous Predictive Analytics Monitoring

Cited by 32 publications

References 24 publications

Dynamic data in the ED predict requirement for ICU transfer following acute care admission

Dynamic data in the ED predict requirement for ICU transfer following acute care admission

Early Detection of In-Patient Deterioration: One Prediction Model Does Not Fit All

Diffusing an Innovation: Clinician Perceptions of Continuous Predictive Analytics Monitoring in Intensive Care

Contact Info

Product

Resources

About