Samples / NURS FPX 4045 / Assessment 4
Informatics and Nursing-Sensitive Quality Indicators
NURS FPX 4045 · Assessment 4
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Hi! I'm and this audio training tutorial on nursing-sensitive quality indicators is designed to teach you about one nursing-sensitive quality indicator — patient falls without injuries. Patient safety is the foundation of professional nursing practice and guides everything we do as nurses. This nursing-sensitive quality indicator is only one example of how we can use data to measure our nursing practice and how our practice impacts patient safety. This tutorial will help you understand what this indicator is and why it is important; how data is collected and what it is used for, and how each of you plays a role in providing high-quality and safe patient care. The NDNQI (National Database of Nursing Quality Indicators) was created in 1998 by the American Nurses Association to collect data to measure nursing sensitive quality indicators (Chen et al., 2022).
Nursing-Sensitive Quality Indicators and Patient Falls Without Injury
Indicators that reflect the quality of care provided by nurses to patients are identified as “nursing-sensitive quality indicators.” While other indicators may indirectly reflect the quality of care provided by nurses to patients, nursing-sensitive quality indicators are measures of the effect of nursing care on a patient’s outcomes, including structure and process of providing nursing care (Kim et al., 2022). Some examples of nursing-sensitive quality indicators are pressure ulcer (decubitus) prevalence, catheter-associated urinary tract infections, nurse to patient ratios, and patient falls. The identification, measurement, and monitoring of nursing-sensitive quality indicators helps provide identification of opportunities to improve the quality of patient care by strengthening nursing care and, in turn, improving patient safety and patient outcome.
Patient falls with no injury is an example of a process indicator in the NDNQI framework; it is an indication of the process used by nurses to prevent falls in patients. Nurses who consistently conduct fall risk assessments using consistent validated tools for assessing fall risks, such as the Morse Fall Scale and use individualized fall prevention protocols, are affecting the patient falls with no injury indicator. Studies by McVey et al. (2022) have shown that implementation of formal structured nurse-led fall prevention programs has resulted in reductions in fall rates of up to 35% in hospitals. Measuring and reporting on patient falls with no injury is necessary for quality improvement efforts as they can help to identify gaps in nursing assessment, communication, and patient education before a fall results in serious injury to a patient.
The Interdisciplinary Team's Role in Collecting and Reporting Quality Indicator Data
It requires collaboration among the whole health care team, not just nursing staff, to collect accurate falls quality data. Physicians provide fall-related risk factor documentation in the electronic medical record; pharmacists review the use of medications that can contribute to an increased risk for falls, such as narcotics and sedatives; and physical therapists evaluate a person's ability to move about and can recommend the use of assistive devices to facilitate mobility (Peng et al., 2023). Information technology professionals support the EHR that houses and aggregates fall event data and Quality Improvement Coordinators analyze the trends and make the results available to the nursing leadership. According to the data provided by the NDNQI, the average fall rate for acute care hospitals for the past year is approximately 3.44 falls per 1,000 patient days, demonstrating the continued need for interdisciplinary collaborative engagement (Takase, 2022).
The interdisciplinary team's contribution to data collection is incredible, as each member contributes a separate data point to the total picture of the patient and how at risk the patient is for falling. For example, when physical therapists document mobility assessments in the EHR, those assessments are then used in scoring the patient's risk of falling that nursing will utilize for their daily care planning. Additionally, because pharmacists provide documented reviews of the patient's medications in the EHR, nurses can receive alerts in real-time if a high-fall-risk medication is present, allowing for immediate interventions (Schoberer et al., 2022). The combination of these data streams provides a complete view, across an entire organization, of their performance in preventing patient falls and enables accurate data for nursing to report quality and improvement initiatives.
How the Health Care Organization Uses Nursing-Sensitive Quality Indicators
Nursing-sensitive quality indicators, such as the number of patients who fall but do not become injured, are one means by which healthcare organizations drive ongoing quality improvement both at a unit level and an organization-wide level. Hospitals use data collected about patient falls obtained through electronic health records (EHRs) to report aggregated patient fall data monthly to the National Database for Nursing Quality Indicators (NDNQI). NDNQI then provides benchmarked national, regional, and hospital type comparisons against similarly situated hospitals nationwide. Nursing leadership can close the gaps in the quality of clinical patient care provided to patients; for instance, if a particular medical-surgical unit's fall rate exceeds the NDNQI national average, the medical-surgical unit will be able to identify the gap and implement targeted interventions (Gonçalves et al., 2023). These interventions may include implementing hourly rounding protocols, changing bed alarm audit protocols, or refreshing nurse education materials related to fall prevention.
Nursing departments in healthcare organizations provide aggregate quality indicator data to their nursing staff by utilizing various methods of communication. Unit-level fall data is regularly reported to the nursing staff in multiple ways, including huddles, staff meetings, or dashboard displays located in each nursing station to provide visibility to the data at the point of care. Research conducted by Ojo and Thiamwong (2022) found that healthcare organizations that regularly provided their frontline nursing staff with fall-related data and utilized the data to guide their unit-based quality improvement initiatives demonstrated statistically significant reductions in both the number and severity of patient falls. When individual nurses can link their clinical actions to measurable quality-related outcomes, the organization empowers that individual nurse to understand their significant direct impact on patient safety and organizational performance.
Evidence-Based Practice Guidelines and Patient Care Technologies
The evidence-based guidelines for fall prevention, which are included in the "patient falls without injury" quality indicator, provide specific standards that nurses must adhere to when providing care to help minimize risk and improve patient safety. One of the guidelines states that nurses must perform a validated fall risk assessment (such as the Morse Fall Scale or STRATIFY tool) when a patient is admitted, at every shift change, and after any change in condition (Melnyk et al., 2023). Based upon the risk scores derived from validated fall risk assessments, EHR systems will prompt nurses to complete assessments and will generate individualized fall prevention care plans. In addition, these protocols use embedded technology to ensure that fall prevention strategies are consistently administered to every patient and that the implementation of strategies does not solely rely on nurse memory or judgment.
Patient Care Technologies (EHR-based systems) such as bed alarm management systems, nurse call light tracking, and fall prevention care plan templates are designed to assist nurses in achieving the evidence-based guidelines associated with this quality indicator. For example, when a nurse documents that a patient is at high risk for falling within the EHR, this documentation may result in the automated activation of a bed alarm and the addition of a yellow armband and non-skid footwear for that patient. All of these notifications provide additional visual cues for all members of the health care team. Heikkilä et al. (2022) found that the use of technology-based fall prevention strategies in conjunction with nursing education resulted in a 25% decrease in fall rates compared to the standard of care. Thus, documenting fall risk assessments and interventions in the EHR is not just an administrative task, but it is a proactive measure that can impact the safety of a patient and can positively affect the quality of care provided to patients based upon evidence.
Collection and Distribution of Quality Indicator Data
Patient falls without injuries are reported in different ways mainly through the EHR (Electronic Health Record). During a fall, nurses have to do a fall event report in the EHR that describes exactly when, where, and why it happened along with any patient risk factors and what nursing interventions were in place at the time of the event. Quality improvement staff as necessary will then take this fall event report data from the EHR for each unit monthly and aggregate it into one month’s worth of fall data that is then uploaded into the NDNQI (National Database of Nursing Quality Indicators) for national benchmarking (Ng et al., 2021). All quality reporting depends upon this documentation meeting complete, timely, and truthful conditions as recorded by a nurse at the point of care.
The aggregate fall prevention data is communicated to the nursing staff and leadership through a structured process that promotes both transparency and accountability. Fall rates for the units and trends of these rates over time are typically reviewed at the monthly quality meetings, displayed on the unit’s performance dashboard, and presented during nursing huddles at the beginning of each shift. Peng et al. (2023), stated that organizations who provided direct front nursing staff with real-time access to the unit’s fall data were much more likely to sustain fall prevention improvements over time. As a new nurse, you have a key role in this process because your documentation of fall risk assessments, nursing interventions, and fall events is the raw data used to create every quality report, benchmark comparison, and to make improvements for the entire organization.
Conclusion
Achieving patient safety in nursing is not a random occurrence; rather, it is developed via ongoing evidence-based practice that is backed by precise data. The uitility falls without injury indicator (UFI) of daily nursing functions involves completed daily nursing activity reports (DNARs), nurses' daily assessments of patient condition through a physical examination, nurses' daily documentation of patient care delivered via the nursing process, each or by completed patient care plans. The new nurse's understanding of NDNQI and how their organization collects and publishes quality data will help provide them with greater knowledge about how they contribute to evidenced-based quality improvement. By accurately documenting cases on a timely basis, the new nurse is not only doing their job correctly, but they are also being a patient advocate.
References
Chen, L., Zhang, Y., Li, C., Li, Q., & He, L. (2022). Construction of haemodialysis nursing‐sensitive quality indicators based on Donabedian theory: A Delphi method study. Nursing Open, 10(2). https://doi.org/10.1002/nop2.1349
Gonçalves, I., Mendes, D. A., Caldeira, S., Jesus, É., & Nunes, E. (2023). The primary nursing care model and inpatients’ nursing-sensitive outcomes: A systematic review and narrative synthesis of quantitative studies. International Journal of Environmental Research and Public Health, 20(3), 2391. https://doi.org/10.3390/ijerph20032391
Heikkilä, A., Lehtonen, L., & Junttila, K. (2022). Fall rates by specialties and risk factors for falls in acute hospital: A retrospective study. Journal of Clinical Nursing, 32(15-16). https://doi.org/10.1111/jocn.16594
Kim, J., Lee, E., Jung, Y., Kwon, H., & Lee, S. (2022). Patient‐level and organizational‐level factors influencing in‐hospital falls. Journal of Advanced Nursing, 78(11). https://doi.org/10.1111/jan.15254
McVey, C., von Wenckstern, T., Mills, C., Yager, L., McCauley, C., Rivera, Y., & Reed, E. (2022). Nurse-sensitive indicator quality improvement toolkit. Journal of Nursing Care Quality, 37(4), 295–299. https://doi.org/10.1097/ncq.0000000000000634
Melnyk, B. M., Hsieh, A. P., Messinger, J., Thomas, B., Connor, L., & Gallagher‐Ford, L. (2023). Budgetary investment in evidence‐based practice by chief nurses and stronger EBP cultures associated with less nursing turnover and better patient outcomes. Worldviews on Evidence-Based Nursing, 20(2), 162–171. https://doi.org/10.1111/wvn.12645
Ng, Z. Q. P., Ling, L. Y. J., Chew, H. S. J., & Lau, Y. (2021). The role of artificial intelligence in enhancing clinical nursing care: A scoping review. Journal of Nursing Management, 30(8). https://doi.org/10.1111/jonm.13425
Ojo, E. O., & Thiamwong, L. (2022). Effects of nurse-led fall prevention programs for older adults: A systematic review. Pacific Rim International Journal of Nursing Research, 26(3), 417. https://pmc.ncbi.nlm.nih.gov/articles/PMC9432804/
Peng, X., Ye, Y., Ding, X. (David), & Chandrasekaran, A. (2023). The impact of nurse staffing on turnover and quality: An empirical examination of nursing care within hospital units. Journal of Operations Management, 69(7), 1124–1152. https://doi.org/10.1002/joom.1245
Schoberer, D., Breimaier, H. E., Zuschnegg, J., Findling, T., Schaffer, S., & Archan, T. (2022). Fall prevention in hospitals and nursing homes: Clinical practice guideline. Worldviews on Evidence-Based Nursing, 19(2), 86–93. https://doi.org/10.1111/wvn.12571
Takase, M. (2022). Falls as the result of interplay between nurses, patient and the environment: Using text-mining to uncover how and why falls happen. International Journal of Nursing Sciences, 10(1), 30–37. https://doi.org/10.1016/j.ijnss.2022.12.003