Copyright
ELSEVIER (Source: Critical Care Clinics)
Source: Critical Care Clinics - May 24, 2023 Category: Intensive Care Source Type: research

Contributors
GREGORY S. MARTIN, MD, MSC (Source: Critical Care Clinics)
Source: Critical Care Clinics - May 24, 2023 Category: Intensive Care Source Type: research

Contents
Hannah Wunsch (Source: Critical Care Clinics)
Source: Critical Care Clinics - May 24, 2023 Category: Intensive Care Source Type: research

Forthcoming Issues
Data Science in Critical Care (Source: Critical Care Clinics)
Source: Critical Care Clinics - May 24, 2023 Category: Intensive Care Source Type: research

Machine Learning of Physiologic Waveforms and Electronic Health Record Data
Perioperative morbidity and mortality are significantly associated with both static and dynamic perioperative factors. The studies investigating static perioperative factors have been reported; however, there are a limited number of previous studies and data sets analyzing dynamic perioperative factors, including physiologic waveforms, despite its clinical importance. To fill the gap, the authors introduce a novel large size perioperative data set: Machine Learning Of physiologic waveforms and electronic health Record Data (MLORD) data set. They also provide a concise tutorial on machine learning to illustrate predictive m...
Source: Critical Care Clinics - May 18, 2023 Category: Intensive Care Authors: Sungsoo Kim, Sohee Kwon, Mia K. Markey, Alan C. Bovik, Maxime Cannesson Source Type: research

Predictive Modeling Using Artificial Intelligence and Machine Learning Algorithms on Electronic Health Record Data
The rapid adoption of electronic health record (EHR) systems in US hospitals from 2008 to 2014 produced novel data elements for analysis. Concurrent innovations in computing architecture and machine learning (ML) algorithms have made rapid consumption of health data feasible and a powerful engine for clinical innovation. In critical care research, the net convergence of these trends has resulted in an exponential increase in outcome prediction research. In the following article, we explore the history of outcome prediction in the intensive care unit (ICU), the growing use of EHR data, and the rise of artificial intelligenc...
Source: Critical Care Clinics - April 26, 2023 Category: Intensive Care Authors: Michael J. Patton, Vincent X. Liu Source Type: research

Making the Improbable Possible: Generalizing Models Designed for a Syndrome-Based, Heterogeneous Patient Landscape
Syndromic conditions, such as sepsis, are commonly encountered in the intensive care unit. Although these conditions are easy for clinicians to grasp, these conditions may limit the performance of machine-learning algorithms. Individual hospital practice patterns may limit external generalizability. Data missingness is another barrier to optimal algorithm performance and various strategies exist to mitigate this. Recent advances in data science, such as transfer learning, conformal prediction, and continual learning, may improve generalizability of machine-learning algorithms in critically ill patients. Randomized trials w...
Source: Critical Care Clinics - April 26, 2023 Category: Intensive Care Authors: Joshua Pei Le, Supreeth Prajwal Shashikumar, Atul Malhotra, Shamim Nemati, Gabriel Wardi Source Type: research

How Electronic Medical Record Integration Can Support More Efficient Critical Care Clinical Trials
Large volumes of data are collected on critically ill patients, and using data science to extract information from the electronic medical record (EMR) and to inform the design of clinical trials represents a new opportunity in critical care research. Using improved methods of phenotyping critical illnesses, subject identification and enrollment, and targeted treatment group assignment alongside newer trial designs such as adaptive platform trials can increase efficiency while lowering costs. Some tools such as the EMR to automate data collection are already in use. Refinement of data science approaches in critical illness ...
Source: Critical Care Clinics - April 26, 2023 Category: Intensive Care Authors: Ankita Agarwal, Joseph Marion, Paul Nagy, Matthew Robinson, Allan Walkey, Jonathan Sevransky Source Type: research

Implementing Artificial Intelligence
This article provides an overview of the most useful artificial intelligence algorithms developed in critical care, followed by a comprehensive outline of the benefits and limitations. We begin by describing how nurses and physicians might be aided by these new technologies. We then move to the possible changes in clinical guidelines with personalized medicine that will allow tailored therapies and probably will increase the quality of the care provided to patients. Finally, we describe how artificial intelligence models can unleash researchers ’ minds by proposing new strategies, by increasing the quality of clinical pr...
Source: Critical Care Clinics - April 26, 2023 Category: Intensive Care Authors: Pier Francesco Caruso, Massimiliano Greco, Claudia Ebm, Giovanni Angelotti, Maurizio Cecconi Source Type: research

The Learning Electronic Health Record
Electronic medical records (EMRs) constitute the electronic version of all medical information included in a patient ’s paper chart. The electronic health record (EHR) technology has witnessed massive expansion in developed countries and to a lesser extent in underresourced countries during the last 2 decades. We will review factors leading to this expansion, how the emergence of EHRs is affecting several healt h-care stakeholders; some of the growing pains associated with EHRs with a particular emphasis on the delivery of care to the critically ill; and ongoing developments on the path to improve the quality of researc...
Source: Critical Care Clinics - April 14, 2023 Category: Intensive Care Authors: Gilles Clermont Source Type: research

Leveraging Data Science and Novel Technologies to Develop and Implement Precision Medicine Strategies in Critical Care
Precision medicine aims to identify treatments that are most likely to result in favorable outcomes for subgroups of patients with similar clinical and biological characteristics. The gaps for the development and implementation of precision medicine strategies in the critical care setting are many, but the advent of data science and multi-omics approaches, combined with the rich data ecosystem in the intensive care unit, offer unprecedented opportunities to realize the promise of precision critical care. In this article, the authors review the data-driven and technology-based approaches being leveraged to discover and impl...
Source: Critical Care Clinics - April 7, 2023 Category: Intensive Care Authors: Lazaro N. Sanchez-Pinto, Sivasubramanium V. Bhavani, Mihir R. Atreya, Pratik Sinha Source Type: research

The Role of Data Science in Closing the Implementation Gap
Data science has the potential to greatly enhance efforts to translate evidence into practice in critical care. The intensive care unit is a data-rich environment enabling insight into both patient-level care patterns and clinician-level treatment patterns. By applying artificial intelligence to these novel data sources, implementation strategies can be tailored to individual patients, individual clinicians, and individual situations, revealing when evidence-based practices are missed and facilitating context-sensitive clinical decision support. To achieve these goals, technology developers should work closely with clinici...
Source: Critical Care Clinics - April 7, 2023 Category: Intensive Care Authors: Andrew J. King, Jeremy M. Kahn Source Type: research

Four Decades of Intensive Care Unit Design Evolution and Thoughts for the Future
Intensive care unit (ICU) design has changed since the mid –1980s. Targeting timing and incorporation of the dynamic and evolutionary processes inherent in ICU design is not possible nationally. ICU design will continue evolving to incorporate new concepts of best design evidence and practice, better understandings of the needs of patients, visitors and s taff, unremitting advances in diagnostic and therapeutic approaches, ICU technologies and informatics, and the ongoing search to best fit ICUs within greater hospital complexes. As the ideal ICU remains a moving target; the design process should include the ability for ...
Source: Critical Care Clinics - March 27, 2023 Category: Intensive Care Authors: Neil A. Halpern, Elizabeth Scruth, Michelle Rausen, Diana Anderson Source Type: research

Clinician Trust in Artificial Intelligence
Predictive analytics based on artificial intelligence (AI) offer clinicians the opportunity to leverage big data available in electronic health records (EHR) to improve clinical decision-making, and thus patient outcomes. Despite this, many barriers exist to facilitating trust between clinicians and AI-based tools, limiting its current impact. Potential solutions are available at both the local and national level. It will take a broad and diverse coalition of stakeholders, from health-care systems, EHR vendors, and clinical educators to regulators, researchers and the patient community, to help facilitate this trust so tha...
Source: Critical Care Clinics - March 27, 2023 Category: Intensive Care Authors: Juan C. Rojas, Mario Teran, Craig A. Umscheid Source Type: research

Critical Bias in Critical Care Devices
This article explores sources of bias in commonly used clinical devices, including pulse oximeters, thermometers, and sphygmomanometers. Further, it provides a framework for mitigating these biases and key principles to achieve more equitable health care delivery. (Source: Critical Care Clinics)
Source: Critical Care Clinics - March 27, 2023 Category: Intensive Care Authors: Marie-Laure Charpignon, Joseph Byers, Stephanie Cabral, Leo Anthony Celi, Chrystinne Fernandes, Jack Gallifant, Mary E. Lough, Donald Mlombwa, Lama Moukheiber, Bradley Ashley Ong, Anupol Panitchote, Wasswa William, An-Kwok Ian Wong, Lama Nazer Source Type: research