Health informatics is essential to healthcare as it provides effectual data collection, analysis, and management within the healthcare system. Efforts related to informatics can cultivate improved/positive clinical outcomes by reducing errors, improving collaboration among providers, facilitating better decision-making, and, ultimately, improving overall healthcare quality and cost-effectiveness (Javaid et al., 2024). Essentially, informatics uses technology to simplify medical processes and provide access to critical patient information across different healthcare settings.
As a nurse with the Missouri Dept. of Mental Health, I am task with providing comprehensive healthcare services to more than 30 consumers (patients) with various functional and intellectual disabilities. Our consumers are housed in one of seven different group homes. Most will remain long term, while others may graduate (so to speak) to independent supervised living (ISL) facilities with assistance form direct care staff (DCS). Our department has yet to transition to EHRs, although the effort is underway and slated for implementation in quarter one of 2026, making record keeping and data tracking all the more difficult to facilitate.
Our facility deals with many challenges, perhaps our greatest is fall prevention, most of our consumers either suffer from functional disabilities that put them at risk for falls, or are prescribed medications regimens that put them at even greater risk like antidepressants, anticonvulsants, antihypertensives, sedatives, and other medications (Jung et al., 2021). This scenario examines the ways in which informatics can assist the facility in reducing the number of falls and better identifying those at risk.
Describe the data that could be used and how the data might be collected and accessed
Information that would be valuable includes consumer statistics, data about each consumer that elevates their risk for falls, i.e., age and physical function, health conditions such as diabetes, heart disease, and osteoporosis, environmental factors, cognitive impairment, mental health illness, certain medications, and acute illness. Information related to the consumers health history, environmental factors, and staff training could also be used to develop predictive tools to identify at risk consumers and alert clinicians to changing variables that either heighten or reduce the consumer’s risk for falls. These alerts could be utilized to determine or adjust each consumer’s level of supervision and better identify those at risk rather than simply utilizing hand written fall risk assessments that are only updated periodically.
The vast majority of the data could be collected directly from the consumer’s health record, with information updated as new medications are prescribed, or when consumers undergo procedures that put them at greater risk, or suffer injuries that limit mobility or diminish their perception. For our consumers, the data may also include information on staffing, as some consumers experience behavioral issues depending on who is providing care or supervision from one shift to the next. All of these factors can help develop a roadmap that can lead care givers toward identifying at risk consumers and alerting care givers to changes that might increase the consumers risk in real time. By analyzing large datasets of resident information, informatics can identify patterns and predict which residents are at higher risk of falls, allowing for targeted interventions (O’Connor et al., 2022).
How would a nurse leader use clinical reasoning and judgment in the formation of knowledge from this experience?
The nurse leader would use his/her clinical reasoning and judgment to initiate protocols based on the available data to help reduce inpatient falls, and to identify those at risk. The nurse would analyze the information available and communicate the needs of the consumer with healthcare staff, direct care staff, and behavioral health managers to develop individual care plans that can either be long-term goals or acute care plans depending on those factors that increase or reduce the consumers risk for fall. Consistent evaluation of the data is key as factors that contribute to falls can change day to day. Complacency is the biggest issue, because our consumers are life long residents it is easy to assume their risk rather than maintaining real time data that can change from one moment to the next. Sometimes our close relationships with our patients can blind us to changing factors that should be noted and considered.
References:
Javaid, M., Haleem, A., & Singh, R. P. (2024). Health informatics to enhance the healthcare industry’s culture: An extensive analysis of its features, contributions, applications and limitations. Informatics and Health, 1(2), 123-148.
Jung, Y. S., Suh, D., Choi, H. S., Park, H. D., Jung, S. Y., & Suh, D. C. (2022). Risk of fall-related injuries associated with antidepressant use in elderly patients: A nationwide matched cohort study. International journal of environmental research and public health, 19(4), 2298.
O’Connor, S., Gasteiger, N., Stanmore, E., Wong, D. C., & Lee, J. J. (2022). Artificial intelligence for falls management in older adult care: A scoping review of nurses’ role. Journal of nursing management, 30(8), 3787–3801. https://doi.org/10.1111/jonm.13853
