To begin my discussion, I would like to discuss the facility in which I work as an assistant director of nurses. I spoke with my corporate nursing consultant as well as my administrator for this assignment. Reliance healthcare corporation owns several skilled nursing facilities in the state of Arkansas. Their main office has different branches that research different areas of the healthcare system in nursing homes such as payables. They look at what each nursing home spends on each item compared to their census. Then a budget is created and given to each department (nursing, housekeeping, maintenance activities, and dietary) for each month. Using big data and algorithms, this branch would be null and void. Analysis of big data by machine learning offers considerable advantages for the assimilation and evaluation of large amounts of complex healthcare data (Ngiam & Khor, 2019). There would need to be a few to manage the machine, but no longer as many employees to configure data. With the clinical problem as the focal point, the ability of machine learning to assimilate and analyze large and diverse datasets comprising different types of clinical data makes it an invaluable aid to clinicians in making decisions for the care of their patients. Using this tool, healthcare clinicians can take into consideration more pieces of evidence than they could otherwise process and remember on their own accord.

While I was doing the budget during the month my DON was out, I learned that Reliance has certain companies they allow us to order from so they can assimilate the data to configure how much we are spending in each department on each resident. The EHR (electronic health record) is also used to run reports to configure how much we should be spending on supplements and snacks for weight loss. If the residents are not consuming the supplements, corporate knows by running reports on the amounts consumed. The drawback to big data is that if not engineered correctly, big data can be ineffective. The flaw in our system is that if the algorithm words are not put in correctly all the data will be skewed. A report can be run on supplements such as Boost and how much is consumed at each administration. This shows nursing that the resident doesn’t like the supplement and to provide something else to gain weight. If the amount consumed is not put in as mL then the algorithm cannot read and produce an accurate count of who drinks how much. Training our staff in how to run and use these reports would facilitate a more positive outcome in using these reports to help residents gain weight, and also save on the facility’s budget. Big data analytics that evolved from business intelligence and decision support systems enable healthcare organizations to analyze an immense volume, variety and velocity of data across a wide range of healthcare networks to support evidence-based decision-making and action-taking (Wang et al., 2017) In the incident and accident section of the EHR, the assessments flow fluidly, but if not started efficiently then they do not flow correctly. I feel with proper training in nursing school on EHRs and how to correctly run reports, we could see a better outcome for our residents in skilled nursing facilities. Training in every department would be required in order for the facility to run seamlessly. Failure to recognize how this data interacts throughout the system has been a limitation in the types of data analytics that have been put forth. The frustration that we often have as nurse leaders in looking at this data, is [that] some of the variables we care about the most, aren’t even in the data. Nurses don’t have something that measures nursing competence, for example. We don’t have something that measures how committed the nurses are. We don’t have something that measures if the patient is really going to do the interventions, we just invested time and money into teaching/providing them (Thew, 2016). Through education in nursing schools and orientation at new nursing jobs, we should be able to combat some of these issues.

References

Ngiam, K. Y., MMBS, & Khor, I. W., PhD. (2019, May). Big data and machine learning algorithms for health-care delivery. ScienceDIrect. https://doi.org/10.1016/S1470-2045(19)30149-4Links to an external site.

Thew, J. (2016, April 19). Big data means big potential, challenges for nurse execs Links to an external site. Links to an external site.. Retrieved from https://www.healthleadersmedia.com/nursing/big-data-means-big-potential-challenges-nurse-execs

Wang, Y., Kungh, L., & Byrd, T. A. (2017). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Elsevier. https://doi.org/10.1016/j.techfore.2015.12.019Links to an external site.