Scoping review on the economic aspects of machine learning applications in healthcare.

Background: The development and use of artificial intelligence and machine learning technologies in healthcare have increased, prompting a need for evidence on their safety and value. Economic evaluations support healthcare decision-making and resource allocation. This scoping review aimed to map and synthesize current approaches to evaluating the economic aspects of machine learning based technologies implemented in healthcare.

The climate impacts of healthcare digitalization: A scoping review.

Objective: The rapid digitalization of healthcare has implications for its carbon footprint. The goal of this scoping review was to identify how digitalization is proceeding in healthcare and the mechanisms through which it can affect the climate impacts of healthcare.

Methods: The scoping review was conducted following PRISMA guidelines and utilized the databases Web of Science and PubMed to identify literature on the climate impacts of digitalization in healthcare.

Supervised machine learning applied in nursing notes for identifying the need of childhood cancer patients for psychosocial support.

Introduction: Childhood cancer survivors have a higher risk of mental health and adaptive problems compared with their siblings, for example. Assessing the need for psychosocial support is essential for prevention. This project aims to investigate the use of supervised machine learning in the form of text classification in identifying childhood cancer patients needing psychosocial support from nursing notes when at least 1 year had passed from their cancer diagnosis.

Toward equitable documentation: Evaluating ChatGPT's role in identifying and rephrasing stigmatizing language in electronic health records.

Stigmatizing language in electronic health records (EHRs) harms clinician and patient relationships, reinforcing health disparities. To assess ChatGPT's ability to reduce stigmatizing language in clinical notes. We analyzed 140 clinical notes and 150 stigmatizing examples from 2 urban hospitals.

Stigmatizing and Positive Language in Birth Clinical Notes Associated With Race and Ethnicity.

Importance: Language used in clinical documentation can reflect biases, potentially contributing to health disparities. Understanding associations between patient race and ethnicity and documentation of stigmatizing and positive language in clinical notes is crucial for addressing health disparities and improving patient care.

Objective: To examine associations of race and ethnicity with stigmatizing and positive language documentation in clinical notes from hospital birth admission.

Identifying stigmatizing and positive/preferred language in obstetric clinical notes using natural language processing.

Objective: To identify stigmatizing language in obstetric clinical notes using natural language processing (NLP).

Materials And Methods: We analyzed electronic health records from birth admissions in the Northeast United States in 2017. We annotated 1771 clinical notes to generate the initial gold standard dataset.

A risk identification model for detection of patients at risk of antidepressant discontinuation.

Purpose: Between 30 and 68% of patients prematurely discontinue their antidepressant treatment, posing significant risks to patient safety and healthcare outcomes. Online healthcare forums have the potential to offer a rich and unique source of data, revealing dimensions of antidepressant discontinuation that may not be captured by conventional data sources.

Methods: We analyzed 891 patient narratives from the online healthcare forum, "askapatient.

Exploring the Documentation of Delirium in Patients After Cardiac Surgery: A Retrospective Patient Record Study.

Delirium is a common disorder for patients after cardiac surgery. Its manifestation and care can be examined through EHRs. The aim of this retrospective, comparative, and descriptive patient record study was to describe the documentation of delirium symptoms in the EHRs of patients who have undergone cardiac surgery and to explore how the documentation evolved between two periods (2005-2009 and 2015-2020).

Electronic Health Records as Information Source in Assessment of the Effectiveness of Delivered Care - A Pilot Study.

Effectiveness is a key element of high quality health services. The aim of this pilot study was to explore the potential of electronic health records (EHR) as an information source for assessing the effectiveness of nursing care by investigating the appearance of nursing processes in the documentation of care. Deductive and inductive content analysis were used in a manual annotation of ten patients' EHRs.

Identifying nursing sensitive indicators from electronic health records in acute cardiac care-Towards intelligent automated assessment of care quality.

Aim: The aim of this study is to explore the potential of using electronic health records for assessment of nursing care quality through nursing-sensitive indicators in acute cardiac care.

Background: Nursing care quality is a multifaceted phenomenon, making a holistic assessment of it difficult. Quality assessment systems in acute cardiac care units could benefit from big data-based solutions that automatically extract and help interpret data from electronic health records.

Using machine learning to predict subsequent events after EMS non-conveyance decisions.

Background: Predictors of subsequent events after Emergency Medical Services (EMS) non-conveyance decisions are still unclear, though patient safety is the priority in prehospital emergency care. The aim of this study was to find out whether machine learning can be used in this context and to identify the predictors of subsequent events based on narrative texts of electronic patient care records (ePCR).

Methods: This was a prospective cohort study of EMS patients in Finland.

Towards Automated Screening of Literature on Artificial Intelligence in Nursing.

We evaluate the performance of multiple text classification methods used to automate the screening of article abstracts in terms of their relevance to a topic of interest. The aim is to develop a system that can be first trained on a set of manually screened article abstracts before using it to identify additional articles on the same topic. Here the focus is on articles related to the topic "artificial intelligence in nursing".

Text Classification Model Explainability for Keyword Extraction - Towards Keyword-Based Summarization of Nursing Care Episodes.

Tools to automate the summarization of nursing entries in electronic health records (EHR) have the potential to support healthcare professionals to obtain a rapid overview of a patient's situation when time is limited. This study explores a keyword-based text summarization method for the nursing text that is based on machine learning model explainability for text classification models. This study aims to extract keywords and phrases that provide an intuitive overview of the content in multiple nursing entries in EHRs written during individual patients' care episodes.

Clustering Nursing Sentences - Comparing Three Sentence Embedding Methods.

In health sciences, high-quality text embeddings may augment qualitative data analysis of large amounts of text by enabling, e.g., searching and clustering of health information.

Artificial Intelligence -based technologies in nursing: A scoping literature review of the evidence.

Background: Research on technologies based on artificial intelligence in healthcare has increased during the last decade, with applications showing great potential in assisting and improving care. However, introducing these technologies into nursing can raise concerns related to data bias in the context of training algorithms and potential implications for certain populations. Little evidence exists in the extant literature regarding the efficacious application of many artificial intelligence -based health technologies used in healthcare.

How GPs can Recognize Persistent Frequent Attenders at Finnish Primary Health Care Using Electronic Patient Records.

Introduction: The proportion of patients who are frequent attenders (FAs) varies from few percent to almost 30% of all patients. A small group of patients continued to visit GPs year after year. In previous studies, it has been reported that over 15% of all 1-year FAs were persistent frequent attenders (pFAs).

How to Improve Information Technology to Support Healthcare to Address the COVID-19 Pandemic: an International Survey with Health Informatics Experts.

Objectives: To identify the ways in which healthcare information and communication technologies can be improved to address the challenges raised by the COVID-19 pandemic.

Methods: The study population included health informatics experts who had been involved with the planning, development and deployment of healthcare information and communication technologies in healthcare settings in response to the challenges presented by the COVID-19 pandemic. Data were collected via an online survey.

Neural Network and Random Forest Models in Protein Function Prediction.

Over the past decade, the demand for automated protein function prediction has increased due to the volume of newly sequenced proteins. In this paper, we address the function prediction task by developing an ensemble system automatically assigning Gene Ontology (GO) terms to the given input protein sequence. We develop an ensemble system which combines the GO predictions made by random forest (RF) and neural network (NN) classifiers.

Semantic Clustering to Augment Qualitative Content Analysis in Exploring Reasons for Emergency Department Transfer Delays.

The aim of the study was to explore emergency department transfer delays and to assess the potential of using a semantic clustering approach to augment the content analysis of transfer delay data. Data were collected over a period of 5 months from two hospitals. A set of (unique) phrases describing reasons for transfer delays (n=333) were clustered using the k-means with 1) cluster centroids initiated in an unsupervised fashion and 2) a semi-supervised version where the cluster centroids were initiated with keywords.

Assisting nurses in care documentation: from automated sentence classification to coherent document structures with subject headings.

Background: Up to 35% of nurses' working time is spent on care documentation. We describe the evaluation of a system aimed at assisting nurses in documenting patient care and potentially reducing the documentation workload. Our goal is to enable nurses to write or dictate nursing notes in a narrative manner without having to manually structure their text under subject headings.

Exploring Unsupervised Query Paraphrasing to Identify Relevant Search Phrases for a Literature Review.

Literature databases have multifaceted search options, but emerging research areas do not have an established terminology and therefore it is difficult to find relevant literature when conducting a review. This study aimed to explore if an unsupervised paraphrasing approach is useful in identifying relevant search phrases for a literature review on an emerging research topic - situational leadership in critical care. Using an initial set of 12 search phrases, the system was used to propose additional phrases, which were manually classified and further used in an expanded PubMed database search.