Identifying Symptoms of Delirium from Clinical Narratives Using Natural Language Processing.

Delirium is an acute decline or fluctuation in attention, awareness, or other cognitive function that can lead to serious adverse outcomes. Despite the severe outcomes, delirium is frequently unrecognized and uncoded in patients' electronic health records (EHRs) due to its transient and diverse nature. Natural language processing (NLP), a key technology that extracts medical concepts from clinical narratives, has shown great potential in studies of delirium outcomes and symptoms.

Unveiling Fall Risk Factors: Nurse-Driven Corpus Development for Natural Language Processing.

Hospital-acquired falls are a continuing clinical concern. The emergence of advanced analytical methods, including NLP, has created opportunities to leverage nurse-generated data, such as clinical notes, to better address the problem of falls. In this nurse-driven study, we employed an iterative process for expert manual annotation of RNs clinical notes to enable the training and testing of an NLP pipeline to extract factors related to falls.

Quantifying Health Outcome Disparity in Invasive Methicillin-Resistant Staphylococcus aureus Infection using Fairness Algorithms on Real-World Data.

This study quantifies health outcome disparities in invasive Methicillin-Resistant Staphylococcus aureus (MRSA) infections by leveraging a novel artificial intelligence (AI) fairness algorithm, the Fairness-Aware Causal paThs (FACTS) decomposition, and applying it to real-world electronic health record (EHR) data. We spatiotemporally linked 9 years of EHRs from a large healthcare provider in Florida, USA, with contextual social determinants of health (SDoH). We first created a causal structure graph connecting SDoH with individual clinical measurements before/upon diagnosis of invasive MRSA infection, treatments, side effects, and outcomes; then, we applied FACTS to quantify outcome potential disparities of different causal pathways including SDoH, clinical and demographic variables.

Clinical Prediction Models for Hospital-Induced Delirium Using Structured and Unstructured Electronic Health Record Data: Protocol for a Development and Validation Study.

Background: Hospital-induced delirium is one of the most common and costly iatrogenic conditions, and its incidence is predicted to increase as the population of the United States ages. An academic and clinical interdisciplinary systems approach is needed to reduce the frequency and impact of hospital-induced delirium.

Objective: The long-term goal of our research is to enhance the safety of hospitalized older adults by reducing iatrogenic conditions through an effective learning health system.

Optimizing Dynamic Antibiotic Treatment Strategies against Invasive Methicillin-Resistant Infections using Causal Survival Forests and G-Formula on Statewide Electronic Health Record Data.

Developing models for individualized, time-varying treatment optimization from observational data with large variable spaces, e.g., electronic health records (EHR), is problematic because of inherent, complex bias that can change over time.

Application of a practice-based approach in variable selection for a prediction model development study of hospital-induced delirium.

Background: Prognostic models of hospital-induced delirium, that include potential predisposing and precipitating factors, may be used to identify vulnerable patients and inform the implementation of tailored preventive interventions. It is recommended that, in prediction model development studies, candidate predictors are selected on the basis of existing knowledge, including knowledge from clinical practice. The purpose of this article is to describe the process of identifying and operationalizing candidate predictors of hospital-induced delirium for application in a prediction model development study using a practice-based approach.

Risk of bias in prognostic models of hospital-induced delirium for medical-surgical units: A systematic review.

Purpose: The purpose of this systematic review was to assess risk of bias in existing prognostic models of hospital-induced delirium for medical-surgical units.

Methods: APA PsycInfo, CINAHL, MEDLINE, and Web of Science Core Collection were searched on July 8, 2022, to identify original studies which developed and validated prognostic models of hospital-induced delirium for adult patients who were hospitalized in medical-surgical units. The Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies was used for data extraction.

Validation of the tablet-based Turkish-PAINReportIt® for lung cancer patients after thoracotomy in Turkey.

Background: Digital pain assessment is advantageous and timely for healthcare priorities in Turkey. However, a multi-dimensional, tablet-based pain assessment tool is not available in the Turkish language.

Purpose: To validate the Turkish-PAINReportIt® as a multi-dimensional measure of post-thoracotomy pain.

Clinical Decision Support Systems for Palliative Care Management: A Scoping Review.

Context: With the expansion of palliative care services in clinical settings, clinical decision support systems (CDSSs) have become increasingly crucial for assisting bedside nurses and other clinicians in improving the quality of care to patients with life-limiting health conditions.

Objectives: To characterize palliative care CDSSs and explore end-users' actions taken, adherence recommendations, and clinical decision time.

Methods: The CINAHL, Embase, and PubMed databases were searched from inception to September 2022.

Fall risk management using clinical decision support in a rural nursing-led primary care practice.

Background: The leading cause of injuries among older adults in the United States is unintentional falls. The American Geriatrics Society/British Geriatrics Society promote fall risk management in primary care; however, this is challenging in low-resource settings.

Local Problem: Archer Family Health Care (AFHC), an Advanced Practice Registered Nurse (APRN)-managed and federally designated rural health clinic, identified a care gap with falls adherence to guidelines for patients at higher risk for falls.

A Stress and Pain Self-management mHealth App for Adult Outpatients With Sickle Cell Disease: Protocol for a Randomized Controlled Study.

Background: This paper describes the research protocol for a randomized controlled trial of a self-management intervention for adults diagnosed with sickle cell disease (SCD). People living with SCD experience lifelong recurrent episodes of acute and chronic pain, which are exacerbated by stress.

Objective: This study aims to decrease stress and improve SCD pain control with reduced opioid use through an intervention with self-management relaxation exercises, named You Cope, We Support (YCWS).

Clinical Decision Support for Fall Prevention: Defining End-User Needs.

Background And Significance: Falls in community-dwelling older adults are common, and there is a lack of clinical decision support (CDS) to provide health care providers with effective, individualized fall prevention recommendations.

Objectives: The goal of this research is to identify end-user (primary care staff and patients) needs through a human-centered design process for a tool that will generate CDS to protect older adults from falls and injuries.

Methods: Primary care staff (primary care providers, care coordinator nurses, licensed practical nurses, and medical assistants) and community-dwelling patients aged 60 years or older associated with Brigham & Women's Hospital-affiliated primary care clinics and the University of Florida Health Archer Family Health Care primary care clinic were eligible to participate in this study.

Nutrition Support Therapies on the Medication Administration Record: Impacts on Staff Perception of Nutrition Care.

Background: It has been reported that many hospitals in the United States have fragmented and ineffective ordering, administration, documentation, and evaluation/monitoring of nutrition therapies. This paper reports on a project to investigate if perceived hospital staff awareness and documentation of nutrition support therapies (NSTs) improves by including them as part of the medication administration record (MAR).

Methods: Surveys were conducted with nursing staff, physicians, and dietitians before and after adding NSTs to the MAR to evaluate the perceived impact on the outcome of interest.

Identification of important factors in an inpatient fall risk prediction model to improve the quality of care using EHR and electronic administrative data: A machine-learning approach.

Background: Inpatient falls, many resulting in injury or death, are a serious problem in hospital settings. Existing falls risk assessment tools, such as the Morse Fall Scale, give a risk score based on a set of factors, but don't necessarily signal which factors are most important for predicting falls. Artificial intelligence (AI) methods provide an opportunity to improve predictive performance while also identifying the most important risk factors associated with hospital-acquired falls.

Fall Prevention Decision Making of Acute Care Registered Nurses.

Objective: The aim of this study was to examine acute care registered nurses' (RNs') fall prevention decision-making.

Background: The RN decision-making process related to fall prevention needs to be investigated to ensure that hospital policies align with nursing workflow and support nursing judgment.

Methods: Qualitative semistructured interviews based on the Critical Decision Method were conducted with RNs about their planning and decision making during their last 12-hour shift worked.

Language equivalence of the modified falls efficacy scale (MFES) among English- and Spanish-speaking older adults: Rasch analysis.

Background: To investigate item-level measurement properties of the Modified Falls Efficacy (MFES) Scale among English- and Spanish-speaking urban-dwelling older adults as a means to evaluate language equivalence of the tool.

Methods: Secondary analysis of survey data from 170 English (n = 83) and Spanish (n = 87) speaking older adults who reported to the emergency department of a quaternary medical center in New York City between February 2010 and August 2011. The Rasch rating scale model was used to investigate item statistics and ordering of items, item and person reliability, and model performance of the Modified Falls Efficacy Scale.

Randomized Pilot Study: A Mobile Technology-based Self-management Intervention for Sickle Cell Pain.

Little is known about the effects of self-managed relaxation interventions on pain, stress, and autonomic responses in patients with sickle cell disease (SCD). This pre-post randomized controlled pilot study was conducted to determine the feasibility of using computer tablets for relaxation intervention delivery; acceptability of study procedures; and intervention effects on pain, stress, and indicators of relaxation. The 30 research participants ranged in age from 22 years to 59 years.

A data-driven and practice-based approach to identify risk factors associated with hospital-acquired falls: Applying manual and semi- and fully-automated methods.

Background And Purpose: Electronic health record (EHR) data provides opportunities for new approaches to identify risk factors associated with iatrogenic conditions, such as hospital-acquired falls. There is a critical need to validate and translate prediction models that support fall prevention clinical decision-making in hospitals. The purpose of this study was to explore a combined data-driven and practice-based approach to identify risk factors associated with falls.

The effects of information and communication technologies on informal caregivers of persons living with dementia: A systematic review.

Introduction: Information and communication technology (ICT) has emerged as promising to support health care consumers, including informal caregivers. This systematic review seeks to evaluate the state of the science of ICT interventions on the health of informal dementia caregivers.

Methods: We searched PubMed, CINAHL, Web of Science, and PsycINFO using concepts associated with ICT, dementia, and caregiver.

Hispanic Dementia Family Caregiver's Knowledge, Experience, and Awareness of Self-Management: Foundations for Health Information Technology Interventions.

Purpose: As a first step toward developing a web-based Family-Health Information Management System intervention, we explored Hispanic dementia family caregiver's knowledge, use, and awareness of self-management principles and skills to address health and health care needs for themselves and the person with dementia (PWD).

Method: Twenty caregivers and 11 caregiver counselors attended an English or Spanish language focus group ranging from 4 to 6 participants. We conducted a directed content analysis informed by Lorig and Holman's conceptualization of self-management.

Effects of Dignity Therapy on Family Members: A Systematic Review.

Dignity therapy (DT) provides, for patients with a serious illness, a guided sharable life review through a protocolized interview and the creation of a legacy document. Evidence is mounting in support of the use of DT for patients with a serious illness; however, it is unclear whether DT has effects on family members. The purpose of this article was to provide a systematic literature review of the effects DT has on family members of patients who receive DT.

What Can We Learn about Fall Risk Factors from EHR Nursing Notes? A Text Mining Study.

Introduction: Hospital falls are a continuing clinical concern, with over one million falls occurring each year in the United States. Annually, hospital-acquired falls result in an estimated $34 billion in direct medical costs. Falls are considered largely preventable and, as a result, the Centers for Medicare and Medicaid Services have announced that fall-related injuries are no longer a reimbursable hospital cost.

Impact of the CMS No-Pay Policy on Hospital-Acquired Fall Prevention Related Practice Patterns.

Background And Objectives: In October 2008, the Centers for Medicare & Medicaid Services (CMS) stopped reimbursing hospitals for costs related to patient falls. This study aimed to examine whether the CMS no-pay policy influenced four fall prevention practices: bed alarms, sitters, room changes, and physical restraints.

Research Design And Methods: Using electronic medical record data collected from four hospitals between 2005 and 2010, this secondary observational analysis examined the associations between the CMS no-pay policy and nursing interventions and medical orders related to fall prevention.