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.

Experts' Perspectives on Use of Fast Healthcare Interoperable Resources for Computerized Clinical Decision Support.

Barriers to improving the US healthcare system include a lack of interoperability across digital health information and delays in seeking preventative and recommended care. Interoperability can be seen as the lynch pin to reducing fragmentation and improving outcomes related to digital health systems. The prevailing standard for information exchange to enable interoperability is the Health Level Seven International Fast Healthcare Interoperable Resources standard.

Management of Fall Risk Among Older Adults in Diverse Primary Care Settings.

Objectives: Falls are persistent among community-dwelling older adults despite existing prevention guidelines. We described how urban and rural primary care staff and older adults manage fall risk and factors important to integration of computerized clinical decision support (CCDS).

Methods: Interviews, contextual inquiries, and workflow observations were analyzed using content analysis and synthesized into a journey map.

Usability Testing of an Interoperable Computerized Clinical Decision Support Tool for Fall Risk Management in Primary Care.

Background: Falls are a widespread and persistent problem for community-dwelling older adults. Use of fall prevention guidelines in the primary care setting has been suboptimal. Interoperable computerized clinical decision support systems have the potential to increase engagement with fall risk management at scale.

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.

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.

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.