Nursing

Robot-assisted minimally invasive esophagectomy (RAMIE): tips and tricks from the bedside assistant view-expert experiences.

The role of bedside assistants in robot-assisted minimally invasive esophagectomy is important. It includes knowledge of the procedure, knowledge of the da Vinci Surgical System, skills in laparoscopy, and good communicative skills. An experienced bedside assistant will likely improve efficiency and safety of robot-assisted minimally invasive esophagectomy.

Transforming clinical data into wisdom: Artificial intelligence implications for nurse leaders.

Machine Learning Classifier Models Can Identify Acute Respiratory Distress Syndrome Phenotypes Using Readily Available Clinical Data.

Two distinct phenotypes of acute respiratory distress syndrome (ARDS) with differential clinical outcomes and responses to randomly assigned treatmen...

Robot-Assisted Total Pancreatectomy With Autologous Islet Cell Transplantation: Perioperative Nursing Considerations.

Chronic pancreatitis (CP) is an inflammatory disease of the pancreas that causes pain and gastrointestinal problems in patients. Robot-assisted total ...

Machine intelligence for early targeted precision management and response to outbreaks of respiratory infections.

OBJECTIVES: To evaluate the utility of machine learning (ML) for the management of Medicare beneficiaries at risk of severe respiratory infections in ...

Enhancing Quality of Patients Care and Improving Patient Experience in China with Assistance of Artificial Intelligence.

Improving health of Chinese people has become national strategy according to the . Patient experience evaluation examines health care service from per...

Artificial intelligence for retinopathy of prematurity.

PURPOSE OF REVIEW: In this article, we review the current state of artificial intelligence applications in retinopathy of prematurity (ROP) and provid...

Adding Continuous Vital Sign Information to Static Clinical Data Improves the Prediction of Length of Stay After Intubation: A Data-Driven Machine Learning Approach.

BACKGROUND: Bedside monitors in the ICU routinely measure and collect patients' physiologic data in real time to continuously assess the health status...

Constructing Inpatient Pressure Injury Prediction Models Using Machine Learning Techniques.

The incidence rate of pressure injury is a critical nursing quality indicator in clinic care; consequently, factors causing pressure injury are divers...

A Survey of Nurses' Need for Care Robots in Children's Hospitals: Combining Robot-Care, Game-Care, and Edu-Care.

The purpose of this study was to investigate nurses' need for care robots in children's hospitals and to help develop care robots that can be used by ...

Detecting falls and estimation of daily habits with depth images using machine learning algorithms.

Different approaches have been proposed in the literature to detect the fall of an elderly person. In this paper, we propose a fall detection method b...

Fall Detector Adapted to Nursing Home Needs through an Optical-Flow based CNN.

Fall detection in specialized homes for the elderly is challenging. Vision-based fall detection solutions have a significant advantage over sensor-bas...

Investigating the Barriers to Physician Adoption of an Artificial Intelligence- Based Decision Support System in Emergency Care: An Interpretative Qualitative Study.

The development of artificial intelligence (AI) systems to support diagnostic decision-making is rapidly expanding in health care. However, important ...

Integrating Socially Assistive Robots into Japanese Nursing Care.

This paper presents experiences of integrating assistive robots in Japanese nursing care through semi-structured interviews and site observations at t...

Artificial Intelligence and Nursing: The Future Is Now.

As systems evolve over time, their natural tendency is to become increasingly more complex. Studies in the field of complex systems have generated new...

Acceptability and Perceived Utility of Telemedical Consultation during Cardiac Arrest Resuscitation. A Multicenter Survey.

Many clinicians who participate in or lead in-hospital cardiac arrest (IHCA) resuscitations lack confidence for this task or worry about errors. Well...

Introducing artificial intelligence in acute psychiatric inpatient care: qualitative study of its use to conduct nursing observations.

BACKGROUND: All patients admitted to an acute inpatient mental health unit must have nursing observations carried out at night either hourly or every ...

Can machine learning optimize the efficiency of the operating room in the era of COVID-19?

The cancellation of large numbers of surgical procedures because of the coronavirus disease 2019 (COVID-19) pandemic has drastically extended wait lis...

Artificial Intelligence Forecasting Census and Supporting Early Decisions.

Matching resources to demand is a daily challenge for hospital leadership. In interdisciplinary collaboration, nurse leaders and data scientists colla...

Postimplementation Evaluation of a Machine Learning-Based Deterioration Risk Alert to Enhance Sepsis Outcome Improvements.

Machine learning-based early warning systems (EWSs) can detect clinical deterioration more accurately than point-score tools. In patients with sepsis,...