NURS FPX 6414 Assessment 1 Conference Poster Presentation

Name

Capella university

NURS-FPX 6414 Advancing Health Care Through Data Mining

Prof. Name

Date

Abstract

Healthcare professionals continue to prioritize strategies that enhance patient safety, with fall prevention remaining a critical component, particularly for adults aged 65 and older. Each year, falls contribute to nearly 2.8 million emergency department visits across the United States, often resulting in severe injuries or fatalities (CDC, 2020). The risk of falling increases due to various intrinsic and extrinsic factors such as cognitive challenges, mobility limitations, and urgent toileting needs (LeLaurin & Shorr, 2019).

In inpatient settings, the frequency of falls ranges from 700,000 to 1 million incidents annually, with an average rate of 3.5 to 9.5 falls per 1,000 patient days (LeLaurin & Shorr, 2019). Galet et al. (2018) observed that a majority of hospital patients demonstrated fall-prone conditions including cognitive dysfunction, reduced mobility, and incontinence. These falls not only extend hospital stays but also elevate healthcare expenses and compromise patient recovery.

To address these concerns, OhioHealth’s informatics division introduced the Schmid tool—a structured clinical instrument designed to identify individuals at high fall risk and inform appropriate care interventions (Lee et al., 2019). The tool systematically assesses parameters like cognitive status, physical mobility, toileting requirements, medication intake, and previous fall history. This report investigates how informatics-based strategies, especially the Schmid tool, support safer clinical practices and improved health outcomes.

Application of Informatics in Fall Risk Management

Falls represent a growing challenge in hospital environments, particularly affecting older adult populations. These events result in both physical harm and increased hospitalization costs, with estimates indicating 700,000 to 1 million falls annually within U.S. healthcare systems (LeLaurin & Shorr, 2019). Accordingly, there is a critical need for proactive fall prevention systems, especially those incorporating digital tools to streamline assessments and interventions.

One of the widely implemented tools for fall risk identification is the Schmid tool. This instrument evaluates risk based on five domains: mobility, mental status, toileting independence, use of certain medications, and historical fall incidents. Developed and validated by OhioHealth, the tool empowers clinicians to score and classify patient risks effectively (Lee et al., 2019). Using informatics-based workflows, the Schmid tool offers a reliable, scalable way to facilitate early interventions and optimize patient care strategies.

Healthcare professionals apply the Schmid tool to flag vulnerable patients and initiate preventive measures accordingly. It enables staff to monitor trends, document improvements, and integrate findings into broader quality improvement initiatives. Furthermore, leveraging the tool supports compliance with safety regulations and aligns with ethical obligations to minimize preventable harm.

Evidence-Based Evaluation and Clinical Implications

Despite various advancements in hospital safety protocols, fall incidents remain pervasive and harmful. For the aging population, falls are a top contributor to injury-related deaths and long-term disability. The financial implications are also substantial, with prolonged hospitalizations increasing costs for both institutions and payers. Since 2008, the Centers for Medicare & Medicaid Services (CMS) have stopped reimbursing hospitals for fall-related injuries, reinforcing the urgency of effective fall prevention protocols (LeLaurin & Shorr, 2019).

Research consistently emphasizes the importance of implementing structured assessment tools. Galet et al. (2018) documented that elderly patients recovering from falls often experience recurrent hospital admissions and diminished quality of life. The Schmid tool, with its evidence-based framework, is uniquely positioned to mitigate these outcomes by providing actionable insights that promote early intervention and resource allocation.

Furthermore, integrating such tools into clinical routines fosters interprofessional collaboration, especially between nursing staff and informatics teams. The use of predictive algorithms and standardized assessments ensures consistent care delivery and real-time data sharing, ultimately improving safety metrics and operational efficiency.