252 research outputs found
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Dynamic Clinical Data Mining: Search Engine-Based Decision Support
The research world is undergoing a transformation into one in which data, on massive levels, is freely shared. In the clinical world, the capture of data on a consistent basis has only recently begun. We propose an operational vision for a digitally based care system that incorporates data-based clinical decision making. The system would aggregate individual patient electronic medical data in the course of care; query a universal, de-identified clinical database using modified search engine technology in real time; identify prior cases of sufficient similarity as to be instructive to the case at hand; and populate the individual patient's electronic medical record with pertinent decision support material such as suggested interventions and prognosis, based on prior outcomes. Every individual's course, including subsequent outcomes, would then further populate the population database to create a feedback loop to benefit the care of future patients
An artificial intelligence tool to predict fluid requirement in the intensive care unit: a proof-of-concept study
The Use of a Formative Pedagogy Lens to Enhance and Maintain Virtual Supervisory Relationships:Appreciative Inquiry and Critical Review
BACKGROUND: Virtual supervisory relationships provide an infrastructure for flexible learning, global accessibility, and outreach, connecting individuals worldwide. The surge in web-based educational activities in recent years provides an opportunity to understand the attributes of an effective supervisor-student or mentor-student relationship. OBJECTIVE: The aim of this study is to compare the published literature (through a critical review) with our collective experiences (using small-scale appreciative inquiry [AI]) in an effort to structure and identify the dilemmas and opportunities for virtual supervisory and mentoring relationships, both in terms of stakeholder attributes and skills as well as providing instructional recommendations to enhance virtual learning. METHODS: A critical review of the literature was conducted followed by an AI of reflections by the authors. The AI questions were derived from the 4D AI framework. RESULTS: Despite the multitude of differences between face-to-face and web-based supervision and mentoring, four key dilemmas seem to influence the experiences of stakeholders involved in virtual learning: informal discourses and approachability of mentors; effective virtual communication strategies; authenticity, trust, and work ethics; and sense of self and cultural considerations. CONCLUSIONS: Virtual mentorship or supervision can be as equally rewarding as an in-person relationship. However, its successful implementation requires active acknowledgment of learners’ needs and careful consideration to develop effective and mutually beneficial student-educator relationships
Withholding or withdrawing invasive interventions may not accelerate time to death among dying ICU patients
We considered observational data available from the MIMIC-III open-access ICU
database and collected within a study period between year 2002 up to 2011. If a
patient had multiple admissions to the ICU during the 30 days before death,
only the first stay was analyzed, leading to a final set of 6,436 unique ICU
admissions during the study period. We tested two hypotheses: (i)
administration of invasive intervention during the ICU stay immediately
preceding end-of-life would decrease over the study time period and (ii)
time-to-death from ICU admission would also decrease, due to the decrease in
invasive intervention administration. To investigate the latter hypothesis, we
performed a subgroups analysis by considering patients with lowest and highest
severity. To do so, we stratified the patients based on their SAPS I scores,
and we considered patients within the first and the third tertiles of the
score. We then assessed differences in trends within these groups between years
2002-05 vs. 2008-11.
Comparing the period 2002-2005 vs. 2008-2011, we found a reduction in
endotracheal ventilation among patients who died within 30 days of ICU
admission (120.8 vs. 68.5 hours for the lowest severity patients, p<0.001; 47.7
vs. 46.0 hours for the highest severity patients, p=0.004). This is explained
in part by an increase in the use of non-invasive ventilation. Comparing the
period 2002-2005 vs. 2008-2011, we found a reduction in the use of vasopressors
and inotropes among patients with the lowest severity who died within 30 days
of ICU admission (41.8 vs. 36.2 hours, p<0.001) but not among those with the
highest severity. Despite a reduction in the use of invasive interventions, we
did not find a reduction in the time to death between 2002-2005 vs. 2008-2011
(7.8 days vs. 8.2 days for the lowest severity patients, p=0.32; 2.1 days vs.
2.0 days for the highest severity patients, p=0.74)
Evaluating the Impact of Social Determinants on Health Prediction in the Intensive Care Unit
Social determinants of health (SDOH) -- the conditions in which people live,
grow, and age -- play a crucial role in a person's health and well-being. There
is a large, compelling body of evidence in population health studies showing
that a wide range of SDOH is strongly correlated with health outcomes. Yet, a
majority of the risk prediction models based on electronic health records (EHR)
do not incorporate a comprehensive set of SDOH features as they are often noisy
or simply unavailable. Our work links a publicly available EHR database,
MIMIC-IV, to well-documented SDOH features. We investigate the impact of such
features on common EHR prediction tasks across different patient populations.
We find that community-level SDOH features do not improve model performance for
a general patient population, but can improve data-limited model fairness for
specific subpopulations. We also demonstrate that SDOH features are vital for
conducting thorough audits of algorithmic biases beyond protective attributes.
We hope the new integrated EHR-SDOH database will enable studies on the
relationship between community health and individual outcomes and provide new
benchmarks to study algorithmic biases beyond race, gender, and age
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