Introduction
The use of patient-driven adaptive technologies can not only directly influence the quality of patient care but also minimize risks for patients, promote their health outcomes, and encourage their engagement in care. The reason is that these technologies provide decision-makers with a lot of systemized data regarding different treatment strategies, disease management results, patient outcomes, limitations, and other important factors.
Main Text
As a result, if a decision is made referring to the data retrieved with reference to patient-driven adaptive technologies, it is possible to minimize a variety of negative consequences for patients (Jiang, Boxwala, El-Kareh, Kim, & Ohno-Machado, 2012). Thus, these technologies can be viewed as user-friendly, and they help healthcare providers to cope with complex decisions.
Additionally, these patient-driven adaptive technologies can be applied in order to promote patients’ health because the majority of decisions made when using these techniques and tools are patient-oriented and highly effective. The expected outcome is the choice of and the focus on the most relevant diagnosis, the selection of the most efficient treatment strategy, and the improvement of a regimen among other examples. However, the determination of an individual risk in relation to these technologies was not the aim of research during many years, and Jiang et al. (2012) concentrated on examining this aspect in their study.
Conclusion
Furthermore, one of their conclusions is associated with determining a positive role of patient-driven adaptive technologies in encouraging patient engagement in their care. It is possible to expect such engagement because the use of discussed models significantly contributes to the customization of care (Jiang et al., 2012). As a result, the application of these tools potentially has a positive impact on patients and their health.
Reference
Jiang, X., Boxwala, A. A., El-Kareh, R., Kim, J., & Ohno-Machado, L. (2012). A patient-driven adaptive prediction technique to improve personalized risk estimation for clinical decision support. Journal of the American Medical Informatics Association, 19(e1), e137-e144.