Post-Fall Care Nursing Algorithm
Submitted by Keisha Lovence DNP, RN
Falls that occur in the older adult population are of widespread concern since they can result in significant morbidity. Falls are the most common reportable incident in hospitals, and as such they pose a major risk, not only to patients, but also to the healthcare organization itself in terms of financial burden and risk of litigation 2,5. Nursing assessment is an important but often lacking piece of the puzzle when managing falls, since rapid and appropriate nursing assessment, including physical assessment and communication of findings to other professionals, aids optimal and timely management of the patient. Whilst best practice guidelines have been developed and are in place in the majority of institutions to support nurses in the event of falls 7, the guidelines can be difficult to implement, and the extent to which nurses adhere to guidelines remains unclear. However, when algorithms are developed from best practice guidelines, they can be useful tools that facilitate simple and concise nursing assessment. The aim of this article is to introduce a new post-fall care algorithm that can be used to guide nurses’ assessment and management of older adult inpatients in the immediate post-fall period.
Falls are a national concern for health professionals and organizations since they can result in significant morbidity (including permanent injury), consequent increased length of hospital stay, and in some cases even mortality 1. A review of observational studies in acute care hospitals shows that fall rates range from 1.3 to 8.9 falls/1,000 patient days and that higher rates occur in units that focus on care of the elderly, neurology, and rehabilitation 5. In 2010, falls occurring in older adults cost the U.S. health care system $30 billion in direct medical costs 6, and falls continue to be the most frequently reported incident in hospitals. Nurses are often the primary care givers to older adults who fall, and not infrequently they either bear witness to a fall or provide the initial assessment of a patient post fall. It is therefore important that immediate post-fall nursing assessment is evidence-based and standardized so that the highest standard of post-fall care can be given to these individuals, and so that nurses communicate the most important information with one another and with other interdisciplinary team members in the post-fall period (Weinberg et al., 2011). When team members communicate clearly – both verbal and via clear documentation - then each team member can appropriately and efficiently plan their care for the individual. This process of care can ultimately improve patient care post-fall, reduce length of stay, and speed up planning of placement, e.g., home versus a rehabilitation facility.
The American Geriatrics Society and British Geriatrics Society (AGS/BGS) falls prevention algorithm is used to guide stepwise evaluation and management of patients at risk from falling. The guidelines are an explicit description of an ordered sequence of steps to be taken in patient care under specific circumstances, the observations that should be made, the decisions to be considered, and the actions to be taken to manage older people who present with recurrent falls, difficulty walking, or in the emergency department after an acute fall. Although fall prevention algorithms are well developed, currently there are few post-fall assessment algorithms and their use in clinical practice is limited. This article will introduce a new post-fall clinical algorithm that can be used in hospital to improve nursing care of this vulnerable group of patients.
Why an algorithm?
Algorithms are often developed from evidence-based clinical practice guidelines and are useful tools that can help facilitate the application of research into 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 (Jablonski et al., 2011, Margolis, 1983). They can be valuable teaching aids when addressing any performance deficits, since their visual flow is easily comprehensible and a highly effective tool for learning and teaching adherence to best practice guidelines. The development of a clinical algorithm first begins with the identification of a need, followed by basic research to ensure accuracy and completeness of information sources (which may include policies, articles, current evidencebased practice, and observation of the clinical practice). A list of all potential decision-points and interventions is created in order to then formulate a draft of the algorithm. The algorithm must then be tested in both didiactic and clinical situations to ensure both currency and accuracy, and the algorithm needs to be audited and updated as clinical practice evolves.
There are many benefits to be gained from using algorithms to guide clinical practice. Algorithms can be used as teaching tools in a variety of situations and can help inexperienced nurses learn and problem solve, troubleshoot, and improve decision-making skills; they are also useful in competency-based orientation programs since they focus on psychomotor skills. They provide more experienced nurses with a quick review of critical problems, they can be visually attractive and easy to understand, and they are a useful way of providing cross-training needs, especially for high-risk situations that require rapid clinical judgment. In addition, they provide an opportunity for exchange of information between professionals and facilitate interdisciplinary and collaborative teamwork.
One major disadvantage of clinical algorithms is that nurses are sometimes non-compliant; they may not use the algorithm as a reference which might hinder critical thinking (or reinforce erroneous thinking), and when nurses do use it they may feel less empowered to using their own clinical judgment, which might compromise complex situations where critical thinking beyond the algorithm is required. An algorithm should be re-evaluated when in clinical practice, and further development of an algorithm should focus on its efficacy and by testing the clinical algorithm as part of clinical education.
The current clinical algorithm produced by AGS/BGS (2011) uses a multi-component approach for assessing falls. One subcomponent of this algorithm is assessment; the guidelines dictate that health care providers should perform a detailed assessment of: a) gait, balance, and mobility levels and lower extremity joint function; b) neurological function, including cognitive evaluation, lower extremity peripheral nerves, proprioception, reflexes, and tests of cortical, extrapyramidal and cerebellar function; c) muscle strength (lower extremities); d) cardiovascular status, including heart rate and rhythm, postural pulse, blood pressure, and, if appropriate, heart rate and blood pressure responses to carotid sinus stimulation; e) assessment of visual acuity; and f) examination of the feet and footwear.
Our post-fall algorithm
Sunnybrook Health Sciences Centre is a general hospital in Toronto and one of two major trauma units in the city. Since falls were noted to be occurring, we developed and implemented an algorithm to help nurses deliver care in the post-fall phase (see Figure 1); the development methodology will not be discussed here but can be found in another publication. The general scheme of the algorithm is as follows: the post-fall algorithm begins with a decision diamond that requires the nurse to determine if loss of consciousness has occurred and, if so, the nurse must immediately check circulation, airway, and breathing and call rapid response as needed. If no loss of consciousness has occurred then the next step is to determine whether serious injury has occurred; in this case, serious injury is defined as an injury involving the neck or spine, or any other major trauma. The attending nurse should not move the patient, but should call for assistance from another nurse and immediately notify a physician. At this point a head-to-toe assessment is performed to obtain baseline information, including neurologic, cardiac, musculoskeletal, and integument assessment. The cardiac assessment requires the nurse to perform a baseline set of orthostatic vitals, including blood pressure, heart rate, oxygen saturations, temperature, and telemetry (if available). Neurologic assessment includes blood sugar and assessment of Glasgow coma scale (pupils, speech, sensation, and level of consciousness). The musculoskeletal system should be assessed for any deformities, pain, swelling, weakness, strength, and range of motion, and the should be assessed for any abrasions, lacerations, obvious bleeding, and/or hematomas.
If there is no head trauma then vital signs should be taken every eight hours for the next 24 hours and then reassessed; if minor head trauma or head injury has occurred then neurologic vitals should occur every hour for at least four hours, then every eight hours for the first 24 hours prior to reassessment, or as indicated by the doctor or nurse practitioner. It is during the 24 hour reassessment period that the doctor or the nurse practitioner determines if further assessment should occur, thus termination of the protocol.
Communication of findings
After initial assessment, the nurse gathers the physical assessment findings as well as any other relevant information, such as past medical history, medications, any recent laboratory results, injury risk factors, and the situation-background-assessment-recommendation (SBAR), which is recorded in the nursing notes. This is then communicated to the physician or nurse practitioner and the nurse should determine if any testing or medication holds are indicated. Post fall, it is important to ensure that all fall indicators are in place for the nursing staff, and care indicators should be clearly visible on care visibility, the Kardex, and the communication white board at the patient’s bedside. Post fall, a modified Fulmer SPPICES tool must be completed to determine all the risk factors associated with the fall episode, and the fall should be disclosed to the family along with review of the “Fall Prevention Tips” pamphlet; this will ensure future safe practice with respect to ambulation. This is especially important since if there are no issues with mobility, the nurse is expected to continue with mobility care practice. It is important that the fall episode is documented in the nurses’ SBAR report and reported to the team leader so that the fall, and any concerns, can be discussed in the shift report and during interprofessional rounds. The nurse is responsible for informing physiotherapy and occupational therapy (PT/OT) so that an OT/PT assessment can be undertaken, and this should be followed-up on to ensure that it has happened. Twelve hours after the post-fall incident the nurse is responsible for performing a full physical examination to evaluate and document injury due to fall episode.
The purpose of a clinical algorithm is to provide a process of sequential clinical decision-making for evidence-based practice. This post-fall care algorithm, along with educational training, can provide staff nurses with the ability to make decisions about the correct care process to use with respect with 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 provide the stimulus for appropriate training of nursing staff on how to respond to fall events. This approach may result in improved quality of care in the assessment and nursing management of geriatric patients post-fall, and we recommend its use in other institutions.
Implications for Practice
There are a number of algorithms used in falls prevention (risk assessment and prediction), but relatively few that specifically focus on post-fall management. As noted above, the AGS/BGS fall prevention algorithm includes a physical assessment component with assessment of gait, balance, mobility levels, neurological function, muscle strength (lower extremities), and cardiovascular status. Fenton et al. (2008) report a clinical algorithm that uses an assess, look, and feel (ALF) approach to assess patients post-fall. The algorithm first assesses level of consciousness, bleeding, or pain to face/head, neck, trunk, and lower extremities, and then requires to the nurse to use the ALF approach to guide when to call the physician based on the level of injury experienced by the patient. The algorithm also encourages staff to record pulse/blood pressure, neurologic observations if the head involved, and the location and type of injury on the diagram included on the algorithm sheet. While these existing clinical algorithms do assess each body system post fall, our proposed algorithm includes more specific and detailed assessment; a detailed assessment of both the neurologic and musculoskeletal systems including the GCS. Our algorithm emphasizes the neurologic assessment given that the patient may have experienced an un-witnessed fall, and therefore reporting of head injury might be unreliable. Our algorithm carries a clear indication of when reassessment should occur, and specific guidance about the information that should be communicated with the doctor or nurse practitioner at the time of the fall. There is clear transfer of accountability to other staff to ensure that appropriate interdisciplinary team members are aware of the fall event.
The post-fall algorithm can be used in clinical areas where patients are likely to experience a fall. All staff require training in the use of the algorithm and this can take time; however, given its benefits, this is likely to be time-saving and result in clinical improvements in the long run and therefore worth the effort. Implementing the post-fall care algorithm not only raises awareness of the topic of falls, but provides clear guidance for post-fall care once risk assessments and interventions to prevent falls are in place. In this way, it closes the gap between fall risk assessments, implementation of multi-factorial falls prevention plan, and assessment of injury should the patient fall.
More research is needed around the usefulness of post-fall care and the use of algorithms in practice. Further outcome data are needed on the impact that detailed assessment algorithms have on patients post-fall, i.e., does a detailed neurologic assessment post-fall influence the timing of transfer to the intensive care unit? Further research is also needed to determine the impact of early consultation with interdisciplinary team members; for example, does the algorithm result in earlier consultation with OT/PT, and how does this impact on the timing of orthopedic consultations, surgery, and placement planning?
Falls are of widespread concern, particularly in inpatients. Guidelines and algorithms have been created to prevent falls, but there are very few studies or clinical algorithms in place to specifically guide post-fall care practice. Clinical algorithms are useful because they are developed from evidence-based clinical practice guidelines, and help facilitate the application of research into practice (Jablonski et al., 2011, Margolis, 1983). Algorithms promote critical thinking and decision-making for both new and experienced nurses, and benefit from being well organized and visually appealing. An algorithm should be re-evaluated after introduction into clinical practice, and further development of an algorithm should focus on its efficacy in producing a clinical outcome and by testing the clinical algorithm in clinical education. The proposed post-fall algorithm is unique from other current algorithms in that it focuses more on specific parameters for physical assessment post-fall, and the nurse is directed to provide assessment within a span of 48 hours and connect with other interdisciplinary team members during this period. The algorithm can be used in any clinical practice environment (acute or long-term care), community, and teaching hospital settings, is easy to use, and is visually appealing. 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.
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