Behavioral Heuristics in Coronary-Artery Bypass Graft Surgery

Olenski, Andrew; Zimerman, André; Coussens, Stephen; Jena, Anupam B.

doi:10.1056/nejmc1911289

Cited by 61 publications

(48 citation statements)

References 4 publications

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“…Resource allocation should be based on concepts similar to those of disposition decisions, because short-term prognosis and the patients' preferences and goals of care are the cornerstones of vitally important choices. "Left digit bias" (e.g., patients admitted 2 weeks after their 80th birthday were less likely to undergo bypass surgery than patients admitted 2 weeks before [29]) must affect neither disposition nor resource allocation. Whereas disposition decisions should ideally be independent of available resources, they pose an even greater challenge in times of resource scarcity due to COVID-19.…”

Section: Resource Allocation During the Covid-19 Pandemicmentioning

confidence: 99%

Age, comorbidity, frailty status: effects on disposition and resource allocation during the COVID-19 pandemic

Nickel¹,

Rueegg²,

Pargger³

et al. 2020

Swiss Med Wkly

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Section: Resource Allocation During the Covid-19 Pandemicmentioning

confidence: 99%

Age, comorbidity, frailty status: effects on disposition and resource allocation during the COVID-19 pandemic

Nickel¹,

Rueegg²,

Pargger³

et al. 2020

Swiss Med Wkly

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“…Findings from this study are consistent with previous studies demonstrating low SSC guideline concordance, associations between physician risk tolerance and variable clinical practices, and deviations in clinical decision-making associated with cognitive bias and choice architecture in other clinical conditions and circumstances. 26,27 Adding to the existing literature, our ndings also suggest response time may be an important predictor, marker, or endpoint for evaluating intentional and inadvertent choice architecture effects. Although associations between response time and cognitive errors or bias remain inferential, there are credible theoretical constructs that support mechanisms by which response time may impact risk of guideline discordance.…”

Section: Discussionmentioning

confidence: 67%

The Impact of Choice Architecture on Clinical Decision-Making in Sepsis: A Proof of Concept Study

Mansoori¹,

Clark²,

Havranek³

et al. 2020

Preprint

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Background: Despite improved survival and increasing hospital mandates, discordance with sepsis resuscitation guidelines is common. We sought to determine whether choice architecture can promote faster or slower decision-making among physicians and the associated risk of guideline-discordance in sepsis.Methods: We conducted an electronic, survey-based time-to-event analysis using a sepsis clinical vignette and multivariable Cox proportional hazards regression. Respondents included physicians from multiple specialties and levels of training at an academic tertiary-care hospital and academic safety-net hospital. Respondents were randomized to one of three distinct answer sets: control (6 options with no time limit), time constraint (10 seconds, intended to promote faster thinking) or choice overload (24 options, intended to promote slower thinking). The primary outcomes were response time and discordance with Surviving Sepsis Campaign 2016 fluid resuscitation guidelines, adjusting for physician characteristics. Physician risk tolerance and predisposition towards intuitive or analytical thinking were assessed for effect modification.Results: 189 of 624 (30.3%) physicians completed the survey. Total response time was lower in time constraint (45.8s, IQR 38.3s-56.6s, P<0.001) and higher in choice overload (94.2s, IQR 73.0s-142.6s, P=0.005) groups compared to control (71.5s, IQR 52.6s-100.6s). In contrast, relative hazard for guideline discordance was increased in time constraint (3.38, 1.97-5.79, P<0.001) and decreased in choice overload (0.52, 0.30-0.93, P=0.03) groups dependent on Cognitive Reflection Test (7.87, 1.80-34.44, P=0.006) and risk tolerance scores (JPI-RTS 2.00, 1.05-3.84, P=0.04 and MFS 0.42, 0.20-0.88, P=0.02), respectively.Conclusions: Choice architecture may impact clinical decisions and guideline discordance in sepsis, warranting further investigation in real-world contexts.

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“…Early in their disease course, a critically ill patient may demonstrate subtle changes in multiple vital signs, which may be difficult to recognize individually, but meaningful in the aggregate. We are primed to recognize that a heart rate above 100 or a respiratory rate above 20 is abnormal because these numbers are salient 21,22 . Comparatively, a heart rate of 95 combined with a respiratory rate of 18 might be equally predictive of illness, which may be easy for a clinician to miss, but will not elude a regression.…”

Section: Discussionmentioning

confidence: 99%

Deep‐learning approaches to identify critically Ill patients at emergency department triage using limited information

et al. 2020

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Study objective Triage quickly identifies critically ill patients, facilitating timely interventions. Many emergency departments (EDs) use emergency severity index (ESI) or abnormal vital sign triggers to guide triage. However, both use fixed thresholds, and false activations are costly. Prior approaches using machinelearning have relied on information that is often unavailable during the triage process. We examined whether deep‐learning approaches could identify critically ill patients only using data immediately available at triage. Methods We conducted a retrospective, cross‐sectional study at an urban tertiary care center, from January 1, 2012–January 1, 2020. De‐identified triage information included structured (age, sex, initial vital signs) and textual (chief complaint) data, with critical illness (mortality or ICU admission within 24 hours) as the outcome. Four progressively complex deep‐learning models were trained and applied to triage information from all patients. We compared the accuracy of the models against ESI as the standard diagnostic test, using area under the receiver‐operator curve (AUC). Results A total of 445,925 patients were included, with 60,901 (13.7%) critically ill. Vital sign thresholds identified critically ill patients with AUC 0.521 (95% confidence interval [CI] = 0.519–0.522), and ESI <3 demonstrated AUC 0.672 (95% CI = 0.671–0.674), logistic regression classified patients with AUC 0.803 (95% CI = 0.802–0.804), 2‐layer neural network with structured data with AUC 0.811 (95% CI = 0.807–0.815), gradient tree boosting with AUC 0.820 (95% CI = 0.818–0.821), and the neural network model with textual data with AUC 0.851 (95% CI = 0.849–0.852). All successive increases in AUC were statistically significant. Conclusion Deep‐learning techniques represent a promising method of augmenting triage, even with limited information. Further research is needed to determine if improved predictions yield clinical and operational benefits.

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Behavioral Heuristics in Coronary-Artery Bypass Graft Surgery

Cited by 61 publications

References 4 publications

Age, comorbidity, frailty status: effects on disposition and resource allocation during the COVID-19 pandemic

Age, comorbidity, frailty status: effects on disposition and resource allocation during the COVID-19 pandemic

The Impact of Choice Architecture on Clinical Decision-Making in Sepsis: A Proof of Concept Study

Deep‐learning approaches to identify critically Ill patients at emergency department triage using limited information

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