Clinical Algorithms and Leadership
Submitted by Keisha Lovence DNP, RN
Algorithms have been developed from evidence-based clinical practice guidelines and can help apply research to practice (Jablonski et al., 2011, Margolis, 1983). By following the step-by-step approach of an algorithm, nurses can refine their skills and have confidence in making appropriate decisions; this is particularly useful for junior or less experienced nurses. When difficulties are encountered, gaps in a particular assessment or management process are revealed as are errors in thinking about a clinical problem. The algorithms can be valuable teaching aids when addressing any performance deficits because their visual flow is easily comprehensible. They are a highly effective tool for learning and teaching adherence to best practice guidelines. The development of a clinical algorithm begins with the identification of a need followed by basic research to ensure accuracy and completeness of information sources. These sources may include policies, articles, current evidence-based practice, and observation of the clinical practice.
Clinical algorithms offer sequential clinical decision-making for evidence-based practice (Margolis, 1983). The post-fall care algorithm described here—along with educational training—can provide staff nurses with the ability to make decisions about the correct care process to use with respect to falls, and the algorithm allows for appropriate assessment, transfer of accountability, and appropriate communication with interdisciplinary members of the health care team. Implementing the algorithm can stimulate the appropriate nursing training on how to respond to falls. This approach can improve the quality-of-care in the assessment and nursing management of geriatric patients post-fall, and we recommend its use in other institutions.
Clinical leaders play an integral part in process improvement (Kramer, M., Maguire, P. & Schmalenberg, C.,2007). Here, we met with clinical leaders in risk management to evaluate the clinical data that were concerning at the institutional. We met with the unit manager and unit educators to educate them and determine times when to educate staff. Once these leaders agreed with the education times, they also assisted in tracking the proposed practice change. Educators shared audit and feedback results with staff in meeting and emails. Risk management leaders were willing to meet and share evidence of the data after implementation and were open and willing to suggest changes. Ultimately, everyone was willing to support this study, and this form of leadership assisted with the final adoption.
Clinical algorithms can support the clinical practice of nursing staff. Nurses in acute care are expected to manage care for the acutely ill. The use of clinical algorithms can guide both new and experienced nurses with critical thinking and decision-making. The algorithms should be well organized and visually appealing. An algorithm should be re-evaluated after introduction into clinical practice, and further development should focus on its efficacy in producing a clinical outcome as well as by testing it in clinical education.
Nursing leaders must be readily available to plan and meet for integration of this new practice. They should change clinical concepts in accordance with research. Further research is needed in the general area of post-fall care practice, the use of algorithms for post-fall care in nursing education, and the effectiveness and usefulness of post-fall care algorithms in clinical practice.