Expertise as a domain of epistemics in intensive care shift-handovers

2021 ◽  
pp. 146144562110168
Author(s):  
Paulien Harms ◽  
Tom Koole ◽  
Ninke Stukker ◽  
Jaap Tulleken
Keyword(s):  
Intensive Care ◽  
Medical Information ◽  
Professional Competence ◽  
Factual Knowledge ◽  
Clinical Impression ◽  
Knowing How ◽  
Clinical Procedures ◽  
Resident Physicians ◽  
Horizontal Expertise ◽  
Knowledge Gradient

This paper examines how expertise is treated as a separable domain of epistemics by looking at simulated intensive care shift-handovers between resident physicians. In these handovers, medical information about a patient is transferred from an outgoing physician (OP) to an incoming physician (IP). These handovers contain different interactional activities, such as discussing the patient identifiers, giving a clinical impression, and discussing tasks and focus points. We found that with respect to (factual) knowledge about the patient, the OPs display an orientation to a knowledge imbalance, but with respect to (clinical) procedures, reasoning, and activities, they display an orientation to a knowledge balance. We use ‘expertise’ to refer to this latter type of knowledge. ‘Expertise’ differs from, and adds to, how knowledge is often treated in epistemics in that it is concerned with professional competence or ‘knowing how’. In terms of epistemics, the participants in the handovers orient to a steep epistemic or knowledge gradient when it concerns the patient, while simultaneously displaying an orientation to a horizontal expertise gradient.

A Comprehensive Model for Medical Information Processing

1983 ◽  
Vol 22 (03) ◽  
pp. 124-130 ◽  
Author(s):  
J. H. Bemmel
Keyword(s):  
Intensive Care ◽  
Information Processing ◽  
Medical Information ◽  
Computer Applications ◽  
Human Interaction ◽  
Comprehensive Model ◽  
Computers In Medicine ◽  
Medical Information Systems ◽  
Research And Education ◽  
The Many

At first sight, the many applications of computers in medicine—from payroll and registration systems to computerized tomography, intensive care and diagnostics—do make a rather chaotic impression. The purpose of this article is to propose a scheme or working model for putting medical information systems in order. The model comprises six »levels of complexity«, running parallel to dependence on human interaction. Several examples are treated to illustrate the scheme. The reason why certain computer applications are more frequently used than others is analyzed. It has to be strongly considered that the differences in complexity and dependence on human involvement are not accidental but fundamental. This has consequences for research and education which are also discussed.

A promising predictor of mortality— A cohort study (Preprint)

2021 ◽  
Author(s):  
Ji Ha Ling
Keyword(s):  
Intensive Care ◽  
Poor Prognosis ◽  
Medical Information ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Hazards Model ◽  
Clinical Biomarker ◽  
Secondary Result

UNSTRUCTURED Severe inflammation leads to poor prognosis for intensive care unit hospitalized patients. The is a biomarker used to monitor inflammation and immune response, which can predict poor prognosis of various diseases. However, it is unclear whether NLR is associated with all-cause mortality in ICU patients. This study investigated the correlation between MLR and ICU results. Extract clinical data from Medical Information Mart for Intensive Care III (MIMIC-III) database, which contains health data of more than 50,000 patients. The main result was 30-day mortality, and the secondary result was 90-day mortality. Use the Cox proportional hazards model to reveal the association between MLR and results. Multivariable analyses were used to control for confounders. NLR is a promising clinical biomarker, which can be used as a available predictor of ICU mortality.

A machine learning-based model for predicting intensive care unit length of stay in patients with cardiac surgery (Preprint)

2021 ◽  
Author(s):  
Nianyue Wu ◽  
Siru Liu ◽  
Haotian Zhang ◽  
Xiaomin Hou ◽  
Ping Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Length Of Stay ◽  
Confidence Interval ◽  
Medical Information ◽  
Lasso Regression ◽  
Icu Length Of Stay ◽  
Data Source ◽  
The Mean ◽  
Selection Operator

BACKGROUND The intensive care unit (ICU) length of stay is significant to evaluate the effect of cardiac surgical treatment inpatient. OBJECTIVE This research aims to accurately predict the ICU length of stay in patients with cardiac surgery. Methods: We used machine learning methods to construct the model, and the medical information mart for intensive care (MIMIC IV) database was used as the data source. A total of 7,567 patients were enrolled and the mean length of stay in the ICU was 3.12 days. A total of 126 predictors were included, and 44 important predictors were screened by least absolute shrinkage and selection operator (Lasso) regression. METHODS We used machine learning methods to construct the model, and the medical information mart for intensive care (MIMIC IV) database was used as the data source. A total of 7,567 patients were enrolled and the mean length of stay in the ICU was 3.12 days. A total of 126 predictors were included, and 44 important predictors were screened by least absolute shrinkage and selection operator (Lasso) regression. RESULTS The mean accuracy are 0.603 (95% confidence interval (CI): [0.602-0.604]), 0.687 (95% confidence interval (CI): [0.687-0.688]) and 0.688 (95% confidence interval (CI): [0.687-0.689]) for the logistic regression (LR) with all variables, the gradient boosted decision tree (GBDT) with important variables and the GBDT with all variables respectively. CONCLUSIONS The GBDT model with important predictors partly overestimated patients whose length of stay was less than 3 days and underestimated patients whose length of stay was longer than 3 days. But the better prediction performance of GBDT facilitates early intervention of ICU patients with a long period of hospitalization.

scholarly journals The training of neonatologists and the paradigms implied in their relationship with the parents of babies in the Neonatal Intensive Care Unit

2014 ◽  
Vol 32 (1) ◽  
pp. 11-16 ◽  
Author(s):  
Ethel Cukierkorn Battikha ◽  
Maria Teresa de M. Carvalho ◽  
Benjamin Israel Kopelman
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Neonatal Intensive Care Unit ◽  
Professional Practice ◽  
Neonatal Intensive Care ◽  
Structured Interview ◽  
Public Institutions ◽  
Training And Education ◽  
Resident Physicians ◽  
Child's Death

Objective: To analyze and to interpret the psychological repercussions generated by the presence of parents in the Neonatal Intensive Care Unit for residents in Neonatology. Methods: Study based on the psychoanalytic theory, involving a methodological interface with qualitative surveys in Health Sciences. Twenty resident physicians in Neonatology, from five public institutions of São Paulo state, responded to a single semi-structured interview. Based on several readings of the material, achieving the core of emergent meanings that would be significant to the object of the survey, six categories were elected for analysis and interpretation: parents' staying at the Neonatal Intensive Care Unit and its effects on the neonatologists' professional practice; communication of the diagnosis and what parents should know; impasses between parents and doctors when the diagnosis is being communicated; doctor's identification with parents; communication of the child's death and their participation in the interview. Results: The interpretation of the categories provided an understanding of the psychic mechanisms mobilized in doctors in their relationships with the children's parents, showing that the residents experience anguish and suffering when they provide medical care and during their training process, and also that they lack psychological support to handle these feelings. Conclusions: There is a need of intervention in neonatologists training and education, which may favor the elaboration of daily experiences in the Unit, providing a less anguishing and defensive way out for young doctors, especially in their relationship with patients and parents.

scholarly journals Association Between Oxygen Partial Pressure Trajectories and Short-Term Outcomes in Patients With Hemorrhagic Brain Injury

2021 ◽  
Vol 8 ◽  
Author(s):  
Guolong Cai ◽  
Weizhe Ru ◽  
Qianghong Xu ◽  
Jiong Wu ◽  
Shijin Gong ◽  
...  
Keyword(s):  
Intensive Care ◽  
Brain Injury ◽  
Hospital Mortality ◽  
Oxygen Partial Pressure ◽  
Partial Pressure ◽  
Medical Information ◽  
Logistic Models ◽  
Neurological Outcomes ◽  
Risk Of Death ◽  
Logistic Analysis

Objectives: Arterial hyperoxia is reportedly a risk factor for poor outcomes in patients with hemorrhagic brain injury (HBI). However, most previous studies have only evaluated the effects of hyperoxia using static oxygen partial pressure (PaO2) values. This study aimed to investigate the association between overall dynamic oxygenation status and HBI outcomes, using longitudinal PaO2 data.Methods: Data were extracted from the Medical Information Mart for Intensive Care III database. Longitudinal PaO2 data obtained within 72 h of admission to an intensive care unit were analyzed, using a group-based trajectory approach. In-hospital mortality was used as the primary outcomes. Multivariable logistic models were used to explore the association between PaO2 trajectory and outcomes.Results: Data of 2,028 patients with HBI were analyzed. Three PaO2 trajectory types were identified: Traj-1 (mild hyperoxia), Traj-2 (transient severe hyperoxia), and Traj-3 (persistent severe hyperoxia). The initial and maximum PaO2 of patients with Traj-2 and Traj-3 were similar and significantly higher than those of patients with Traj-1. However, PaO2 in patients with Traj-2 decreased more rapidly than in patients with Traj-3. The crude in-hospital mortality was the lowest for patients with Traj-1 and highest for patients with Traj-3 (365/1,303, 209/640, and 43/85 for Traj-1, Traj-2, and Traj-3, respectively; p < 0.001), and the mean Glasgow Coma Scale score at discharge (GCSdis) was highest for patients with Traj-1 and lowest in patients with Traj-3 (13 [7–15], 11 [6–15], and 7 [3–14] for Traj-1, Traj-2, and Traj-3, respectively; p < 0.001). The multivariable model revealed that the risk of death was higher in patients with Traj-3 than in patients with Traj-1 (odds ratio [OR]: 3.3, 95% confidence interval [CI]: 1.9–5.8) but similar for patients with Traj-1 and Traj-2. Similarly, the logistic analysis indicated the worst neurological outcomes in patients with Traj-3 (OR: 3.6, 95% CI: 2.0–6.4, relative to Traj-1), but similar neurological outcomes for patients in Traj-1 and Traj-2.Conclusion: Persistent, but not transient severe arterial hyperoxia, was associated with poor outcome in patients with HBI.

scholarly journals The Value of Admission Serological Indicators for Predicting 28-Day Mortality in Intensive Care Patients With Acute Heart Failure: Construction and Validation of a Nomogram

2021 ◽  
Vol 8 ◽  
Author(s):  
Xiaoyuan Wei ◽  
Yu Min ◽  
Jiangchuan Yu ◽  
Qianli Wang ◽  
Han Wang ◽  
...  
Keyword(s):  
Heart Failure ◽  
Intensive Care ◽  
Acute Heart Failure ◽  
Sofa Score ◽  
Medical Information ◽  
Troponin T ◽  
Adequate Treatment ◽  
Intensive Care Patients ◽  
Creatine Kinase Mb ◽  
Death Group

Background: Acute heart failure (AHF) is a severe clinical syndrome characterized as rapid onset or worsening of symptoms of chronic heart failure (CHF). Risk stratification for patients with AHF in the intensive care unit (ICU) may help clinicians to predict the 28-day mortality risk in this subpopulation and further raise the quality of care.Methods: We retrospectively reviewed and analyzed the demographic characteristics and serological indicators of patients with AHF in the Medical Information Mart for Intensive Care III (MIMIC III) (version 1.4) between June 2001 and October 2012 and our medical center between January 2019 and April 2021. The chi-squared test and the Fisher's exact test were used for comparison of qualitative variables among the AHF death group and non-death group. The clinical variables were selected by using the least absolute shrinkage and selection operator (LASSO) regression. A clinical nomogram for predicting the 28-day mortality was constructed based on the multivariate Cox proportional hazard regression analysis and further validated by the internal and external cohorts.Results: Age > 65 years [hazard ratio (HR) = 2.47], the high Sequential Organ Failure Assessment (SOFA) score (≥3 and ≤8, HR = 2.21; ≥9 and ≤20, HR = 3.29), lactic acid (Lac) (>2 mmol/l, HR = 1.40), bicarbonate (HCO3-) (>28 mmol/l, HR = 1.59), blood urea nitrogen (BUN) (>21 mg/dl, HR = 1.75), albumin (<3.5 g/dl, HR = 2.02), troponin T (TnT) (>0.04 ng/ml, HR = 4.02), and creatine kinase-MB (CK-MB) (>5 ng/ml, HR = 1.64) were the independent risk factors for predicting 28-day mortality of intensive care patients with AHF (p < 0.05). The novel nomogram was developed and validated with a promising C-index of 0.814 (95% CI: 0.754–0.882), 0.820 (95% CI: 0.721–0.897), and 0.828 (95% CI: 0.743–0.917), respectively.Conclusion: This study provides a new insight in early predicting the risk of 28-day mortality in intensive care patients with AHF. The age, the SOFA score, and serum TnT level are the leading three predictors in evaluating the short-term outcome of intensive care patients with AHF. Based on the nomogram, clinicians could better stratify patients with AHF at high risk and make adequate treatment plans.

scholarly journals Towards a decision support tool for intensive care discharge: machine learning algorithm development using electronic healthcare data from MIMIC-III and Bristol, UK

BMJ Open ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. e025925 ◽  
Author(s):  
Christopher J McWilliams ◽  
Daniel J Lawson ◽  
Raul Santos-Rodriguez ◽  
Iain D Gilchrist ◽  
Alan Champneys ◽  
...  
Keyword(s):  
Intensive Care ◽  
Decision Support ◽  
Medical Information ◽  
Learning Algorithm ◽  
Adverse Outcomes ◽  
Royal Infirmary ◽  
Automated Method ◽  
Improved Performance ◽  
Discharge Criteria ◽  
Mimic Iii

ObjectiveThe primary objective is to develop an automated method for detecting patients that are ready for discharge from intensive care.DesignWe used two datasets of routinely collected patient data to test and improve on a set of previously proposed discharge criteria.SettingBristol Royal Infirmary general intensive care unit (GICU).PatientsTwo cohorts derived from historical datasets: 1870 intensive care patients from GICU in Bristol, and 7592 from Medical Information Mart for Intensive Care (MIMIC)-III.ResultsIn both cohorts few successfully discharged patients met all of the discharge criteria. Both a random forest and a logistic classifier, trained using multiple-source cross-validation, demonstrated improved performance over the original criteria and generalised well between the cohorts. The classifiers showed good agreement on which features were most predictive of readiness-for-discharge, and these were generally consistent with clinical experience. By weighting the discharge criteria according to feature importance from the logistic model we showed improved performance over the original criteria, while retaining good interpretability.ConclusionsOur findings indicate the feasibility of the proposed approach to ready-for-discharge classification, which could complement other risk models of specific adverse outcomes in a future decision support system. Avenues for improvement to produce a clinically useful tool are identified.

scholarly journals Utilization of Deep Learning for Subphenotype Identification in Sepsis-Associated Acute Kidney Injury

2020 ◽  
Vol 15 (11) ◽  
pp. 1557-1565 ◽  
Author(s):  
Kumardeep Chaudhary ◽  
Akhil Vaid ◽  
Áine Duffy ◽  
Ishan Paranjpe ◽  
Suraj Jaladanki ◽  
...  
Keyword(s):  
Intensive Care ◽  
Deep Learning ◽  
Medical Information ◽  
Tertiary Care ◽  
Vital Signs ◽  
Kidney Injury ◽  
The United States ◽  
Care Hospital ◽  
Electronic Health Record Data ◽  
Electronic Health

Background and objectivesSepsis-associated AKI is a heterogeneous clinical entity. We aimed to agnostically identify sepsis-associated AKI subphenotypes using deep learning on routinely collected data in electronic health records.Design, setting, participants, & measurementsWe used the Medical Information Mart for Intensive Care III database, which consists of electronic health record data from intensive care units in a tertiary care hospital in the United States. We included patients ≥18 years with sepsis who developed AKI within 48 hours of intensive care unit admission. We then used deep learning to utilize all available vital signs, laboratory measurements, and comorbidities to identify subphenotypes. Outcomes were mortality 28 days after AKI and dialysis requirement.ResultsWe identified 4001 patients with sepsis-associated AKI. We utilized 2546 combined features for K-means clustering, identifying three subphenotypes. Subphenotype 1 had 1443 patients, and subphenotype 2 had 1898 patients, whereas subphenotype 3 had 660 patients. Subphenotype 1 had the lowest proportion of liver disease and lowest Simplified Acute Physiology Score II scores compared with subphenotypes 2 and 3. The proportions of patients with CKD were similar between subphenotypes 1 and 3 (15%) but highest in subphenotype 2 (21%). Subphenotype 1 had lower median bilirubin levels, aspartate aminotransferase, and alanine aminotransferase compared with subphenotypes 2 and 3. Patients in subphenotype 1 also had lower median lactate, lactate dehydrogenase, and white blood cell count than patients in subphenotypes 2 and 3. Subphenotype 1 also had lower creatinine and BUN than subphenotypes 2 and 3. Dialysis requirement was lowest in subphenotype 1 (4% versus 7% [subphenotype 2] versus 26% [subphenotype 3]). The mortality 28 days after AKI was lowest in subphenotype 1 (23% versus 35% [subphenotype 2] versus 49% [subphenotype 3]). After adjustment, the adjusted odds ratio for mortality for subphenotype 3, with subphenotype 1 as a reference, was 1.9 (95% confidence interval, 1.5 to 2.4).ConclusionsUtilizing routinely collected laboratory variables, vital signs, and comorbidities, we were able to identify three distinct subphenotypes of sepsis-associated AKI with differing outcomes.

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