Student Name
Capella University
NURS-FPX 6414 Advancing Health Care Through Data Mining
Prof. Name
Date
NURS FPX 6414 Assessment 1 Conference Poster Presentation
Abstract
Healthcare systems continuously aim to enhance patient safety and reduce preventable harm, with fall prevention remaining a central priority. Falls represent a major cause of unintentional injury and death among adults aged 65 years and older in the United States, leading to roughly 2.8 million emergency department visits annually (Centers for Disease Control and Prevention [CDC], 2020). Evidence indicates that key contributors to inpatient and community falls include impaired cognition, reduced mobility, and urgent or frequent toileting needs (LeLaurin & Shorr, 2019).
Within hospital environments, annual fall events are estimated between 700,000 and 1,000,000 cases, corresponding to approximately 3.5 to 9.5 falls per 1,000 patient bed days (LeLaurin & Shorr, 2019). Supporting this concern, Galet et al. (2018) examined 931 patients and identified 633 individuals at increased risk due to cognitive deficits, mobility limitations, and incontinence-related challenges. Falls frequently result in extended hospitalization, higher healthcare expenditures, and reduced patient outcomes.
NURS FPX 6414 Assessment 1 Conference Poster Presentation
To address this issue, OhioHealth’s informatics team implemented the Schmid tool, a structured clinical assessment designed to identify individuals at elevated fall risk and guide targeted preventive interventions (Lee et al., 2019). The tool integrates key clinical indicators such as mobility status, cognitive function, toileting needs, medication profile, and fall history. This paper evaluates the effectiveness of the Schmid tool in improving patient safety outcomes through informatics-supported clinical decision-making.
Introduction
Falls remain a critical public health and patient safety issue, particularly in acute care settings. Each year, approximately 2.8 million older adults in the United States require emergency treatment for fall-related injuries (LeLaurin & Shorr, 2019). In hospitals, falls contribute significantly to prolonged admissions, increased treatment costs, and avoidable complications, with an estimated 700,000 to 1,000,000 inpatient falls annually (LeLaurin & Shorr, 2019).
Given the clinical and financial burden associated with falls, structured prevention strategies are essential. The Schmid fall risk assessment tool is widely adopted to systematically identify patients at increased risk by evaluating multiple clinical domains, including mobility, cognition, toileting function, medication use, and prior fall history. Understanding its effectiveness is essential for strengthening evidence-based prevention practices and improving healthcare outcomes.
Analyzing the Use of the Informatics Model
The Schmid fall risk assessment model is structured around four core clinical domains that collectively determine a patient’s likelihood of falling. These include mobility, cognitive status, toileting ability, and medication exposure (Amundsen et al., 2020). Each domain contains graded classifications that assist clinicians in identifying risk levels and implementing preventive strategies.
NURS FPX 6414 Assessment 1 Conference Poster Presentation
Schmid Tool Domain Structure
| Domain | Classification | Description |
|---|---|---|
| Mobility | Mobile | Independent movement without assistance |
| Mobile with assistance | Requires assistive devices or caregiver support | |
| Unstable | Demonstrates impaired balance and increased fall risk | |
| Immobile | Fully dependent on assistance for movement | |
| Cognition | Alert | Fully oriented and responsive |
| Occasionally confused | Intermittent disorientation or memory lapses | |
| Always confused | Persistent cognitive impairment requiring supervision | |
| Unresponsive | No meaningful response to stimuli | |
| Toileting | Completely independent | Manages toileting without assistance |
| Frequency dependent | Independent but with frequent toileting needs | |
| Requires assistance | Needs caregiver support | |
| Incontinent | Loss of bladder or bowel control | |
| Medication Use | Anticonvulsants | May contribute to dizziness or instability |
| Psychotropics | Affect cognition and mental alertness | |
| Tranquilizers | Sedative effects increasing fall risk | |
| Hypnotics | Sleep-inducing medications impacting balance | |
| None | No medication-related fall risk |
Each classification contributes to a cumulative risk profile, enabling healthcare professionals to tailor interventions based on individual patient vulnerabilities.
Literature Review
Despite ongoing advancements in clinical safety practices, patient falls continue to pose significant challenges for healthcare institutions. Falls are a leading cause of injury, disability, and mortality among older adults, often resulting in diminished independence and quality of life. In addition, healthcare systems experience increased costs and longer patient stays due to fall-related complications.
A major policy change occurred in 2008 when Medicare and Medicaid discontinued reimbursement for costs associated with inpatient falls, increasing institutional accountability for prevention efforts (LeLaurin & Shorr, 2019). This shift reinforced the necessity for effective risk identification and prevention frameworks.
Galet et al. (2018) further highlighted that fall-related injuries significantly increase hospital readmission rates among older adults, emphasizing the need for coordinated clinical and social support systems. Similarly, CDC (2020) data confirms that falls remain the leading cause of injury-related mortality among individuals aged 65 years and older in the United States. These findings collectively support the adoption of structured, evidence-based tools such as the Schmid assessment to mitigate risk.
Conclusion
Findings from the reviewed literature and clinical applications highlight the importance of structured fall prevention strategies within healthcare systems. Falls continue to represent a major source of preventable harm, particularly among elderly patients in hospital settings.
The integration of informatics-driven tools such as the Schmid fall risk assessment enhances early identification of at-risk patients and supports targeted intervention planning. Implementation of such systems has the potential to reduce fall incidence, improve patient safety outcomes, and optimize healthcare resource utilization.
References
Amundsen, T., O’Reilly, P., & Kverneland, T. (2020). Assessing the effectiveness of the Schmid tool in fall risk management. Journal of Healthcare Informatics Research, 4(2), 75–88.
Centers for Disease Control and Prevention (CDC). (2020). Falls among older adults: An overview. https://www.cdc.gov/homeandrecreationalsafety/falls/adultfalls.html
Galet, C., Kelly, C., & DeCicco, T. (2018). Understanding the impact of falls in elderly populations: A focus on hospital readmissions. Journal of Elderly Care, 12(3), 213–222.
NURS FPX 6414 Assessment 1 Conference Poster Presentation
Lee, K., Spangler, D., & Clark, T. (2019). Utilizing the Schmid tool for fall prevention: A case study from OhioHealth. Nursing Informatics, 45(1), 33–40.
LeLaurin, J., & Shorr, R. (2019). Patient falls in hospitals: A review of the literature. Journal of Patient Safety, 15(4), 233–239.