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Technological advancements provided healthcare with numerous opportunities of improving efficiency and the quality of the provided care. Data-driven decision-making (DDDM) in healthcare refers to using facts, metrics, and statistical data to make an informed treatment plan. One of the ways DDDM is utilized is the patients’ databases. Databases store crucial patient information and also research evidence that can help nurses improve patient care. The most crucial database that nurses frequently use is the Electronic Health Records (EHR), which contains patients’ biodata that nurses can use to improve clinical outcomes (Sedig et al., 2017). Hence, working, analyzing, and in some cases designing EHRs is an essential skill for a modern nurse practitioner.
After accurate gathering of data, a further analysis that will lead to findings that are valid and reliable is needed. Information technology and Computer-Assisted Qualitative Data Analysis tools (CAQDAS) make it easy to analyze the massive amounts of data generated with accuracy eliminating the human errors associated with such volumes of data. CAQDAS include software such as NVivo, Atlas.ti, MAXQDA, among others, and SPSS to analyze quantitative data.
Data-driven decision making models help enhance the delivery of health services, increase overall population health, and cut the cost of healthcare. One of the primary uses of health informatics in nursing is to improve clinical outcomes among patients (Sedig et al., 2017). Information technology ensures that research findings are used to design evidence-based interventions. Health Information Technology helps improve evidence-based interventions by improving communication between nurses and patients. Since all patient information is available and updates on the EHR, nurses can predict clinical outcomes by analyzing patients’ biodata.
Another example of using DDDM in improving healthcare outcomes and efficiency can be seen in the article by Hagopian et al. (2017). The research evaluates the combination of factors such as sensitivity to changes in functional analysis (FA) conditions and the existence of self-restraint. Authors utilized forty-nine datasets that were being issued up until 2015 to create a model for subtyping automatically reinforced self-injurious behavior (SIB).
Subtype-1 SIB was quite comparable to socially-strengthened SIB regarding the reaction to treatment than it was to Subtype-2 SIB. Subtype classification is derived mainly from the level of differentiation in the FA. The level of distinction in the FA can be viewed as an index of the sensitivity of SIB to changing environmental conditions. The sensitivity of SIB to interruption by alternate reinforcement apparent in the context of the FA was later visible in the context of treatment—Subtype 1 seemed to be extremely sensitive, while Subtype-2 SIB looked to be unresponsive. Subtype-1 SIB are increasingly successfully treated as outpatients and do not require hospitalization, while subtype -2 SIB required hospitalization. There is little research done on subtype-3 (Hagopian et al., 2017). Subtype-1 SIB can be treated with reinforcement alone; thereby, they can easily be controlled. Subtype -2 SIB cannot be treated with support alone; therefore, they represent a class of people that cannot be easily controlled. Hence, in the future, more studies should be done on self-restraint subtype-3.
This is an example of how data-driven decision-making can be used to classify and categorize self-injurious behavior. DDDM in healthcare contributes to increased precision and efficiency of patient care. Besides that, some approaches became more common, such as EHRs, which also help eliminate errors and the safe time of healthcare workers. Overall, technological advancements simplify operations on various aspects of life, including healthcare provision. Therefore, applying models and frameworks based on innovations is a beneficial thing, which drives progress and improves efficiency.
References
Hagopian, L. P., Rooker, G. W., Zarcone, J. R., Bonner, A. C., & Arevalo, A. R. (2017). Further analysis of subtypes of automatically reinforced SIB: A replication and quantitative analysis of published datasets. Journal of Applied Behavior Analysis, 50, 48–66.
Sedig, K., Naimi, A., & Haggerty, N. (2017). Aligning information technologies with evidence-based healthcare activities: A design and evaluation framework. Human Technology, 13(2), 180–215.
Zaccagnini, M., & Pechacek, J. M. (2019). The doctor of nursing practice essentials: A new model for advanced practice nursing. Jones & Bartlett Learning.
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