Risks and Rewards      One potential benefit of using big data within our clinical system is the ability of systems to analyze patterns to build and assess a

Risks and Rewards      One potential benefit of using big data within our clinical system is the ability of systems to analyze patterns to build and assess a

Discussion Big Data Risks and Rewards

model to generate predictions of new observations, otherwise known as predictive capability (Wang et al., 2018).  Within our healthcare organization, we utilize a function that uses predictive capability known as the Modified Early Warning Score, or MEWS, to predict a patient’s potential for decline.  The MEWS system analyzes a patient’s vital signs trends with algorithms for the regular heart rate, blood pressure, temperature, respiration rate, and level of consciousness.  Once documented, the algorithm assigns a score to the patient based on any deviations from the norm within the algorithm; if the patient receives a score of 6 or greater, a MEWS warning is generated. Our hospital Emergency Response Team is paged to assess the patient in person.  The MEWS system can benefit patient care because it allows nurses a second set of eyes and a fresh perspective, especially on units responsible for patient ratios upwards of 1:8. It allows early recognition of a patient’s potential decline and needs a higher level of care.

On the other hand, there is an increased risk with big data directly rated to patient care and the overall cost of healthcare.  There is evidence that the use of predictive capability in healthcare can cause patients to receive additional unnecessary testing and transfer to higher levels of care and cause further complications during their hospital stay.  When patients are negatively affected by algorithms with predictive capability, the cost of healthcare can also increase.  According to Househ et al. (2019), Big Data in healthcare is not all that is cracked up to be, and while the potential to be good is there, it often leads to misguided medical decisions by healthcare providers.  I have personally seen this with the Modified Early Warning Score in use in my institution.  The MEWS system realized a patient with abnormal vital signs and an altered level of consciousness but that was not abnormal for this particular patient due to their history of chronic renal failure and other comorbidities but what the system ended up doing was creating more work for the nurses and costs for the patient because the physician was alerted by our emergency response team that the patient was maybe declining.

A strategy I have after reading the article Big Data Means Big Potential, Challenges for Nurse Execs (Thew, 2016) is for nursing staff, especially chief nursing executives and administrators, to become involved in the process and recognize what types of big data are beneficial to their organization.  One specific big data capability the hospitals in the eastern region of my state saw as beneficial is a program to be able to share patient medical records even with different electronic health record systems.  Sharing patient’s medical record information and sharing information with our patients is a considerable benefit between different hospital systems. It goes along with the goals of HITECH, or the Health Information Technology for Economic and Clinical Health Act (Glassman, 2017).  It allows for staff to safely integrate patient records and engage them in their care by ensuring accuracy.  Being directly involved with the integration of big data that positively impacts patient care and staff, chief nursing executives can build a rapport as advocates within their health system.

References

Glassman, K. (2017). Using data in nursing practice. American Nurse Today, 12(11), 45-47.  Retrieved from https://www.americannursetoday.com/wp-content/uploads/2017/11/ant11-Data-1030.pdf