Critical Care Clinics

No abstract text is available yet for this article.

Critical care data contain information about the most physiologically fragile patients in the hospital, who require a significant level of monitoring. However, medical devices used for patient monitoring suffer from measurement biases that have been largely underreported. 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...

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 practice, and by questioning current knowledge and understanding...

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 that the promise of AI in health care can be realized...

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...

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...

The practice of medicine is characterized by uncertainty, and the findings of randomized clinical trials (RCTs) are meant to help curb that uncertainty. Traditional RCTs, however, have many limitations. To overcome some of these limitations, new trial paradigms rooted in the origins of evidence-based medicine are beginning to disrupt the traditional mold. These new designs recognize uncertainty permeates medical decision making and aim to capitalize on modern health system infrastructure to integrate investigation as a component of care delivery...

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...

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 health-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 research, health-care delivery, and stakeholder satisfaction...

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...

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 intelligence and ML (AI) 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 implement precision medicine strategies in the critical care setting...

No abstract text is available yet for this article.

No abstract text is available yet for this article.

Critical care units-designed for concentrated and specialized care-came from multiple parallel advances in medical, surgical, and nursing techniques and training taking advantage of new therapeutic technologies. Regulatory requirements and government policy impacted design and practice. After WWII, medical practice and education promoted further specialization. Hospitals offered newer, more extreme, and specialized surgeries and anesthesia enabled more complex procedures. ICUs developed in the 1950s, providing a recovery room's level of observation and specialized nursing to serve the critically ill, whether medical or surgical...

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 staff, unremitting advances in diagnostic and therapeutic approaches, ICU technologies and informatics, and the ongoing search to best fit ICUs within greater hospital complexes...

The modern cardiothoracic intensive care unit (CTICU) developed as a result of advances in critical care, cardiology, and cardiac surgery. Patients undergoing cardiac surgery today are sicker, frailer, and have more complex cardiac and noncardiac morbidities. CTICU providers need to understand postoperative implications of different surgical procedures, complications that can occur in CTICU patients, resuscitation protocols for cardiac arrest, and diagnostic and therapeutic interventions such as transesophageal echocardiography and mechanical circulatory support...

This article gives a historical perspective of visitation in the intensive care unit (ICU) since the establishment of critical care units. Initially, visitors were not allowed because it was thought to be harmful to the patient. Despite the evidence, ICUs with open visitation have consistently been in the minority and the COVID-19 pandemic halted progress in this area. Virtual visitation was introduced during the pandemic to maintain family presence, but limited evidence suggests that this is not equivalent to in-person visitation...

In this article, the authors review the origins of palliative care within the critical care context and describe the evolution of symptom management, shared decision-making, and comfort-focused care in the ICU from the 1970s to the early 2000s. The authors also review the growth of interventional studies in the past 20 years and indicate areas for future study and quality improvement for end-of-life care among the critically ill.

Critical care pharmacy has evolved rapidly over the last 50 years to keep pace with the rapid technological and knowledge advances that have characterized critical care medicine. The modern-day critical care pharmacist is a highly trained individual well suited for the interprofessional team-based care that critical illness necessitates. Critical care pharmacists improve patient-centered outcomes and reduce health care costs through three domains: direct patient care, indirect patient care, and professional service...