DEPARTMENTS: Guest Editorial
For decades, cancer treatment has been following the rules of evidence-based medicine supported by population data. Although many breakthroughs have been made, current treatments cannot adequately meet the increasingly recognized unique individual needs of patients. Fortunately, the advances emerging through genetics and new intelligence technology in recent years allow us a new view of cancer treatment. Of note, President Obama spoke to his Precision Medical Initiative during the March 2015 State of Union Address, an initiative that will include building a personalized health database that includes genetic information so that the cure of diseases such as cancer and diabetes may be much more likely.1 It is inspiring that the medical world is going to change from group-centered clinical decision making to individual-centered clinical decision making.
Nursing clinical practice models need to more fully reflect the new era of Precision Nursing that targets patients’ need–based nursing care. The key feature of this new care model is the identification of patient’s needs, which can be acquired from both the big data collecting, as well as recording and analyzing the individual patient’s stated preferences and needs.
To embrace the new era, nursing will need to contribute to and access personalized health databases. E-health, defined as the transfer of health resources and healthcare by electronic means,2 has shown great potential in precision nursing. In other words, developing technologies such as smartphone applications and wearable devices related to data processing have been proven to be feasible solutions. Kearney et al3 built an application running on smartphones called ASyMS (Advanced Symptom Management System) for the management of chemotherapy-related toxicity in 2009. Patients using the application are enabled to record and upload symptoms and feelings in a standard way without constraints of time and location; nurses are enabled to give accurate and consistent responses to patients’ needs accordingly. Thus, developing more e-health tools that effectively influence the form of nursing care provided should be an area that arouses high interest in nurse researchers now and in the future.
Another key point to achieving precision nursing is the utilization of the existing massive databases. Classification is one of the important methods to adopt. For example, Fahey et al4 recruited participants from EPIC (European Prospective Investigation into Cancer) as a sample. It consisted of 12 018 women with different types of cancer to characterize the eating habits of a population and to associate diet with disease. Taking one step further, it is necessary to apply appropriate data analysis technology to distinguish groups of different needs. For example, in 1 study, the information needs of cancer patients with different social demographic and clinical experience varied a lot.5 By Latent Class Analysis (LCA), a model of 5 subgroups exhibiting differences in type and extent of cancer patients’ unmet information needs was identified. For 2 subgroups with high levels of psychosocial unmet information needs, matching nursing care provided would selectively concentrate on the psychosocial issues that patients might face. While dealing with the subgroup with a high level of purely medical unmet information needs, nurses should offer them more information about medical examination results, treatment options, medications, and adverse effects. As we can see, taking the results of data analysis into consideration for nursing care can satisfy patients and reduce the burden of nursing efforts that do not match the patients’ care needs. Only after mastering advanced analysis tools can we let massive databases guide nursing care toward making greater contributions to patient outcomes.
Lastly, implementing precise interventions based on characteristics and needs of the individual patient is our final objective. The core of precision nursing is need-based nursing care, which means pointed and professional performance of nurses when facing patients with different requirements. At the same time, nurse researchers should develop more tools related to health data collection and improve the ability of data analysis to provide clinical nurses with a prospective guide. The results of data analysis should be exploited in order to guarantee the scientificity of the new nursing mode. We have a good start, although there is a long way to go, but we can expect a bright future.
My best to you,
Changrong Yuan, PhD, RN
School of Nursing
Second Military Medical University
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