Artificial intelligence and clinical deterioration

Reframing the Morbidity and Mortality Conference: The Impact of a Just Culture

ABSTRACT

Morbidity and mortality (M&M) conferences are prevalent in all fields of medicine. Historically, they arose out a desire to improve medical care. Nevertheless, the goals of M&M conferences are often poorly defined, at odds with one another, and do not support a just culture. We differentiate among the various possible goals of an M&M and review the literature for strategies that have been shown to achieve these goals. Based on the literature, we outline an ideal M&M structure within the context of just culture: The process starts with robust adverse event and near miss reporting, followed by careful case selection, excluding cases solely attributable to individual error. Prior to the M&M, the case should be openly discussed with involved members and should be reviewed using a selected framework. The goal of the M&M should be selected and clearly defined, and the presentation format and rules of conduct should all conform to the selected presentation goal. The audience should ideally be multidisciplinary and multispecialty. The M&M should conclude with concrete tasks and assigned follow-up. The entire process should be conducted in a peer review protected format within an environment promoting psychological safety. We conclude with future directions for M&Ms.

Advancing nursing practice with artificial intelligence: Enhancing preparedness for the future

AIM

This article aimed to explore the role of AI in advancing nursing practice, focusing on its impact on readiness for the future.

DESIGN AND METHODS

A position paper, the methodology comprises three key steps. First, a comprehensive literature search using specific keywords in reputable databases was conducted to gather current information on AI in nursing. Second, data extraction and synthesis from selected articles were performed. Finally, a thematic analysis identifies recurring themes to provide insights into AI's impact on future nursing practice.

RESULTS

The findings highlight the transformative role of AI in advancing nursing practice and preparing nurses for the future, including enhancing nursing practice with AI, preparing nurses for the future (AI education and training) and associated, ethical considerations and challenges. AI-enabled robotics and telehealth solutions expand the reach of nursing care, improving accessibility of healthcare services and remote monitoring capabilities of patients' health conditions.

Clinical Instability Is a Sign of Severity of Illness: A Cohort Study

OBJECTIVES

Test the hypothesis that within patient clinical instability measured by deterioration and improvement in mortality risk over 3-, 6-, 9-, and 12-hour time intervals is indicative of increasing severity of illness.

DESIGN

Analysis of electronic health data from January 1, 2018, to February 29, 2020.

SETTING

PICU and cardiac ICU at an academic children's hospital.

PATIENTS

All PICU patients. Data included descriptive information, outcome, and independent variables used in the Criticality Index-Mortality.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

There were 8,399 admissions with 312 deaths (3.7%). Mortality risk determined every three hours using the Criticality Index-Mortality, a machine learning algorithm calibrated to this hospital. Since the sample sizes were sufficiently large to expect statical differences, we also used two measures of effect size, the proportion of time deaths had greater instability than survivors, and the rank-biserial correlation, to assess the magnitude of the effect and complement our hypothesis tests. Within patient changes were compared for survivors and deaths. All comparisons of survivors versus deaths were less than 0.001. For all time intervals, two measures of effect size indicated that the differences between deaths and survivors were not clinically important. However, the within-patient maximum risk increase (clinical deterioration) and maximum risk decrease (clinical improvement) were both substantially greater in deaths than survivors for all time intervals. For deaths, the maximum risk increase ranged from 11.1% to 16.1% and the maximum decrease ranged from -7.3% to -10.0%, while the median maximum increases and decreases for survivors were all less than ± 0.1%. Both measures of effect size indicated moderate to high clinical importance. The within-patient volatility was greater than 4.5-fold greater in deaths than survivors during the first ICU day, plateauing at ICU days 4-5 at 2.5 greater volatility.

CONCLUSIONS

Episodic clinical instability measured with mortality risk is a reliable sign of increasing severity of illness. Mortality risk changes during four time intervals demonstrated deaths have greater maximum and within-patient clinical instability than survivors. This observation confirms the clinical teaching that clinical instability is a sign of severity of illness.

Application of artificial intelligence in diagnosis and treatment of colorectal cancer: A novel Prospect

In the past few decades, according to the rapid development of information technology, artificial intelligence (AI) has also made significant progress in the medical field. Colorectal cancer (CRC) is the third most diagnosed cancer worldwide, and its incidence and mortality rates are increasing yearly, especially in developing countries. This article reviews the latest progress in AI in diagnosing and treating CRC based on a systematic collection of previous literature. Most CRCs transform from polyp mutations. The computer-aided detection systems can significantly improve the polyp and adenoma detection rate by early colonoscopy screening, thereby lowering the possibility of mutating into CRC. Machine learning and bioinformatics analysis can help screen and identify more CRC biomarkers to provide the basis for non-invasive screening. The Convolutional neural networks can assist in reading histopathologic tissue images, reducing the experience difference among doctors. Various studies have shown that AI-based high-level auxiliary diagnostic systems can significantly improve the readability of medical images and help clinicians make more accurate diagnostic and therapeutic decisions. Moreover, Robotic surgery systems such as da Vinci have been more and more commonly used to treat CRC patients, according to their precise operating performance. The application of AI in neoadjuvant chemoradiotherapy has further improved the treatment and efficacy evaluation of CRC. In addition, AI represented by deep learning in gene sequencing research offers a new treatment option. All of these things have seen that AI has a promising prospect in the era of precision medicine.