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  • 1
    Online Resource
    Online Resource
    Association of Sociodemographic Factors With Overtriage, Undertriage, and Value of Care After Major Surgery
    Ovid Technologies (Wolters Kluwer Health) ; 2024
    In:  Annals of Surgery Open Vol. 5, No. 2 ( 2024-06), p. e429-
    Details
    In: Annals of Surgery Open, Ovid Technologies (Wolters Kluwer Health), Vol. 5, No. 2 ( 2024-06), p. e429-
    Abstract: To determine whether certain patients are vulnerable to errant triage decisions immediately after major surgery and whether there are unique sociodemographic phenotypes within overtriaged and undertriaged cohorts. Background: In a fair system, overtriage of low-acuity patients to intensive care units (ICUs) and undertriage of high-acuity patients to general wards would affect all sociodemographic subgroups equally. Methods: This multicenter, longitudinal cohort study of hospital admissions immediately after major surgery compared hospital mortality and value of care (risk-adjusted mortality/total costs) across 4 cohorts: overtriage (N = 660), risk-matched overtriage controls admitted to general wards (N = 3077), undertriage (N = 2335), and risk-matched undertriage controls admitted to ICUs (N = 4774). K-means clustering identified sociodemographic phenotypes within overtriage and undertriage cohorts. Results: Compared with controls, overtriaged admissions had a predominance of male patients (56.2% vs 43.1%, P 〈 0.001) and commercial insurance (6.4% vs 2.5%, P 〈 0.001); undertriaged admissions had a predominance of Black patients (28.4% vs 24.4%, P 〈 0.001) and greater socioeconomic deprivation. Overtriage was associated with increased total direct costs [$16.2K ($11.4K–$23.5K) vs $14.1K ($9.1K–$20.7K), P 〈 0.001] and low value of care; undertriage was associated with increased hospital mortality (1.5% vs 0.7%, P = 0.002) and hospice care (2.2% vs 0.6%, P 〈 0.001) and low value of care. Unique sociodemographic phenotypes within both overtriage and undertriage cohorts had similar outcomes and value of care, suggesting that triage decisions, rather than patient characteristics, drive outcomes and value of care. Conclusions: Postoperative triage decisions should ensure equality across sociodemographic groups by anchoring triage decisions to objective patient acuity assessments, circumventing cognitive shortcuts and mitigating bias.
    Type of Medium: Online Resource
    ISSN: 2691-3593
    URL: Article
    DOI: 10.1097/AS9.0000000000000429
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2024
    detail.hit.zdb_id: 3079102-9
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  • 2
    Online Resource
    Online Resource
    Federated learning for preserving data privacy in collaborative healthcare research
    SAGE Publications ; 2022
    In:  DIGITAL HEALTH Vol. 8 ( 2022-01), p. 205520762211344-
    Details
    In: DIGITAL HEALTH, SAGE Publications, Vol. 8 ( 2022-01), p. 205520762211344-
    Abstract: Generalizability, external validity, and reproducibility are high priorities for artificial intelligence applications in healthcare. Traditional approaches to addressing these elements involve sharing patient data between institutions or practice settings, which can compromise data privacy (individuals’ right to prevent the sharing and disclosure of information about themselves) and data security (simultaneously preserving confidentiality, accuracy, fidelity, and availability of data). This article describes insights from real-world implementation of federated learning techniques that offer opportunities to maintain both data privacy and availability via collaborative machine learning that shares knowledge, not data. Local models are trained separately on local data. As they train, they send local model updates (e.g. coefficients or gradients) for consolidation into a global model. In some use cases, global models outperform local models on new, previously unseen local datasets, suggesting that collaborative learning from a greater number of examples, including a greater number of rare cases, may improve predictive performance. Even when sharing model updates rather than data, privacy leakage can occur when adversaries perform property or membership inference attacks which can be used to ascertain information about the training set. Emerging techniques mitigate risk from adversarial attacks, allowing investigators to maintain both data privacy and availability in collaborative healthcare research. When data heterogeneity between participating centers is high, personalized algorithms may offer greater generalizability by improving performance on data from centers with proportionately smaller training sample sizes. Properly applied, federated learning has the potential to optimize the reproducibility and performance of collaborative learning while preserving data security and privacy.
    Type of Medium: Online Resource
    ISSN: 2055-2076 , 2055-2076
    URL: Article
    DOI: 10.1177/20552076221134455
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2022
    detail.hit.zdb_id: 2819396-9
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  • 3
    Online Resource
    Online Resource
    Building an automated, machine learning-enabled platform for predicting post-operative complications
    IOP Publishing ; 2023
    In:  Physiological Measurement Vol. 44, No. 2 ( 2023-02-01), p. 024001-
    Details
    In: Physiological Measurement, IOP Publishing, Vol. 44, No. 2 ( 2023-02-01), p. 024001-
    Abstract: Objective . In 2019, the University of Florida College of Medicine launched the MySurgeryRisk algorithm to predict eight major post-operative complications using automatically extracted data from the electronic health record. Approach . This project was developed in parallel with our Intelligent Critical Care Center and represents a culmination of efforts to build an efficient and accurate model for data processing and predictive analytics. Main Results and Significance . This paper discusses how our model was constructed and improved upon. We highlight the consolidation of the database, processing of fixed and time-series physiologic measurements, development and training of predictive models, and expansion of those models into different aspects of patient assessment and treatment. We end by discussing future directions of the model.
    Type of Medium: Online Resource
    ISSN: 0967-3334 , 1361-6579
    URL: Article
    DOI: 10.1088/1361-6579/acb4db
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2023
    detail.hit.zdb_id: 2002076-4
    SSG: 11
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  • 4
    Online Resource
    Online Resource
    Postoperative Overtriage to an Intensive Care Unit Is Associated With Low Value of Care
    Ovid Technologies (Wolters Kluwer Health) ; 2023
    In:  Annals of Surgery Vol. 277, No. 2 ( 2023-02), p. 179-185
    Details
    In: Annals of Surgery, Ovid Technologies (Wolters Kluwer Health), Vol. 277, No. 2 ( 2023-02), p. 179-185
    Abstract: We test the hypothesis that for low-acuity surgical patients, postoperative intensive care unit (ICU) admission is associated with lower value of care compared with ward admission. Background: Overtriaging low-acuity patients to ICU consumes valuable resources and may not confer better patient outcomes. Associations among postoperative overtriage, patient outcomes, costs, and value of care have not been previously reported. Methods: In this longitudinal cohort study, postoperative ICU admissions were classified as overtriaged or appropriately triaged according to machine learning-based patient acuity assessments and requirements for immediate postoperative mechanical ventilation or vasopressor support. The nearest neighbors algorithm identified risk-matched control ward admissions. The primary outcome was value of care, calculated as inverse observed-to-expected mortality ratios divided by total costs. Results: Acuity assessments had an area under the receiver operating characteristic curve of 0.92 in generating predictions for triage classifications. Of 8592 postoperative ICU admissions, 423 (4.9%) were overtriaged. These were matched with 2155 control ward admissions with similar comorbidities, incidence of emergent surgery, immediate postoperative vital signs, and do not resuscitate order placement and rescindment patterns. Compared with controls, overtraiged admissions did not have a lower incidence of any measured complications. Total costs for admission were $16.4K for overtriage and $15.9K for controls ( P =0.03). Value of care was lower for overtriaged admissions [2.9 (2.0–4.0)] compared with controls [24.2 (14.1–34.5), P 〈 0.001]. Conclusions: Low-acuity postoperative patients who were overtriaged to ICUs had increased total costs, no improvements in outcomes, and received low-value care.
    Type of Medium: Online Resource
    ISSN: 0003-4932
    URL: Article
    DOI: 10.1097/SLA.0000000000005460
    RVK:
    XA 23900
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2023
    detail.hit.zdb_id: 2002200-1
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  • 5
    Online Resource
    Online Resource
    Phenotype clustering in health care: A narrative review for clinicians
    Frontiers Media SA ; 2022
    In:  Frontiers in Artificial Intelligence Vol. 5 ( 2022-8-12)
    Details
    In: Frontiers in Artificial Intelligence, Frontiers Media SA, Vol. 5 ( 2022-8-12)
    Abstract: Human pathophysiology is occasionally too complex for unaided hypothetical-deductive reasoning and the isolated application of additive or linear statistical methods. Clustering algorithms use input data patterns and distributions to form groups of similar patients or diseases that share distinct properties. Although clinicians frequently perform tasks that may be enhanced by clustering, few receive formal training and clinician-centered literature in clustering is sparse. To add value to clinical care and research, optimal clustering practices require a thorough understanding of how to process and optimize data, select features, weigh strengths and weaknesses of different clustering methods, select the optimal clustering method, and apply clustering methods to solve problems. These concepts and our suggestions for implementing them are described in this narrative review of published literature. All clustering methods share the weakness of finding potential clusters even when natural clusters do not exist, underscoring the importance of applying data-driven techniques as well as clinical and statistical expertise to clustering analyses. When applied properly, patient and disease phenotype clustering can reveal obscured associations that can help clinicians understand disease pathophysiology, predict treatment response, and identify patients for clinical trial enrollment.
    Type of Medium: Online Resource
    ISSN: 2624-8212
    URL: Article
    DOI: 10.3389/frai.2022.842306
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2957496-1
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  • 6
    Online Resource
    Online Resource
    Overtriage, Undertriage, and Value of Care after Major Surgery: An Automated, Explainable Deep Learning-Enabled Classification System
    Ovid Technologies (Wolters Kluwer Health) ; 2023
    In:  Journal of the American College of Surgeons Vol. 236, No. 2 ( 2023-02), p. 279-291
    Details
    In: Journal of the American College of Surgeons, Ovid Technologies (Wolters Kluwer Health), Vol. 236, No. 2 ( 2023-02), p. 279-291
    Abstract: In single-institution studies, overtriaging low-risk postoperative patients to ICUs has been associated with a low value of care; undertriaging high-risk postoperative patients to general wards has been associated with increased mortality and morbidity. This study tested the reproducibility of an automated postoperative triage classification system to generating an actionable, explainable decision support system. STUDY DESIGN: This longitudinal cohort study included adults undergoing inpatient surgery at two university hospitals. Triage classifications were generated by an explainable deep learning model using preoperative and intraoperative electronic health record features. Nearest neighbor algorithms identified risk-matched controls. Primary outcomes were mortality, morbidity, and value of care (inverted risk-adjusted mortality/total direct costs). RESULTS: Among 4,669 ICU admissions, 237 (5.1%) were overtriaged. Compared with 1,021 control ward admissions, overtriaged admissions had similar outcomes but higher costs ($15.9K [interquartile range $9.8K to $22.3K] vs $10.7K [$7.0K to $17.6K] , p 〈 0.001) and lower value of care (0.2 [0.1 to 0.3] vs 1.5 [0.9 to 2.2] , p 〈 0.001). Among 8,594 ward admissions, 1,029 (12.0%) were undertriaged. Compared with 2,498 control ICU admissions, undertriaged admissions had longer hospital length-of-stays (6.4 [3.4 to 12.4] vs 5.4 [2.6 to 10.4] days, p 〈 0.001); greater incidence of hospital mortality (1.7% vs 0.7%, p = 0.03), cardiac arrest (1.4% vs 0.5%, p = 0.04), and persistent acute kidney injury without renal recovery (5.2% vs 2.8%, p = 0.002); similar costs ($21.8K [$13.3K to $34.9K] vs $21.9K [$13.1K to $36.3K] ); and lower value of care (0.8 [0.5 to 1.3] vs 1.2 [0.7 to 2.0] , p 〈 0.001). CONCLUSIONs: Overtriage was associated with low value of care; undertriage was associated with both low value of care and increased mortality and morbidity. The proposed framework for generating automated postoperative triage classifications is reproducible.
    Type of Medium: Online Resource
    ISSN: 1072-7515
    URL: Article
    DOI: 10.1097/XCS.0000000000000471
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2023
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  • 7
    Online Resource
    Online Resource
    Uncertainty-aware deep learning in healthcare: A scoping review
    Public Library of Science (PLoS) ; 2022
    In:  PLOS Digital Health Vol. 1, No. 8 ( 2022-8-10), p. e0000085-
    Details
    In: PLOS Digital Health, Public Library of Science (PLoS), Vol. 1, No. 8 ( 2022-8-10), p. e0000085-
    Abstract: Mistrust is a major barrier to implementing deep learning in healthcare settings. Entrustment could be earned by conveying model certainty, or the probability that a given model output is accurate, but the use of uncertainty estimation for deep learning entrustment is largely unexplored, and there is no consensus regarding optimal methods for quantifying uncertainty. Our purpose is to critically evaluate methods for quantifying uncertainty in deep learning for healthcare applications and propose a conceptual framework for specifying certainty of deep learning predictions. We searched Embase, MEDLINE, and PubMed databases for articles relevant to study objectives, complying with PRISMA guidelines, rated study quality using validated tools, and extracted data according to modified CHARMS criteria. Among 30 included studies, 24 described medical imaging applications. All imaging model architectures used convolutional neural networks or a variation thereof. The predominant method for quantifying uncertainty was Monte Carlo dropout, producing predictions from multiple networks for which different neurons have dropped out and measuring variance across the distribution of resulting predictions. Conformal prediction offered similar strong performance in estimating uncertainty, along with ease of interpretation and application not only to deep learning but also to other machine learning approaches. Among the six articles describing non-imaging applications, model architectures and uncertainty estimation methods were heterogeneous, but predictive performance was generally strong, and uncertainty estimation was effective in comparing modeling methods. Overall, the use of model learning curves to quantify epistemic uncertainty (attributable to model parameters) was sparse. Heterogeneity in reporting methods precluded the performance of a meta-analysis. Uncertainty estimation methods have the potential to identify rare but important misclassifications made by deep learning models and compare modeling methods, which could build patient and clinician trust in deep learning applications in healthcare. Efficient maturation of this field will require standardized guidelines for reporting performance and uncertainty metrics.
    Type of Medium: Online Resource
    ISSN: 2767-3170
    URL: Article
    URL: Article
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    URL: Article
    DOI: 10.1371/journal.pdig.0000085
    DOI: 10.1371/journal.pdig.0000085.g001
    DOI: 10.1371/journal.pdig.0000085.g002
    DOI: 10.1371/journal.pdig.0000085.t001
    DOI: 10.1371/journal.pdig.0000085.s001
    DOI: 10.1371/journal.pdig.0000085.s002
    DOI: 10.1371/journal.pdig.0000085.r001
    DOI: 10.1371/journal.pdig.0000085.r002
    DOI: 10.1371/journal.pdig.0000085.r003
    Language: English
    Publisher: Public Library of Science (PLoS)
    Publication Date: 2022
    detail.hit.zdb_id: 3106944-7
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