Patient-Driven Adaptive Prediction Technique

Patient Driven Adaptive Predictive Technique

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Patient-Driven Adaptive Predictive Technique

Patient-driven adaptive predictive techniques are a technology, which is a data-driven approach in addressing the medical condition of patients. According to Manaktala (2015), it utilizes the individuals’ confidential interval (CI) to decide on the most appropriate model to use to assess an individual patient’s clinical condition. This approach used is compared to other different strategies such as the ideal model, best model to achieve the data of a patient.

However, the technology helps in reducing the risk because it provides detailed medical history and condition of patients to physicians or clinicians. In this case, clinicians understand medical risks and condition of patients therefore; appropriate care and medical attention would be taken with minimal risk. It would also ensure that the right medications are provided to avoid further complication of patients. As stated by Jiang, Boxwala, Robert, Kim, and Lucila (2012) it helps in the estimation of the outcome in new patients and therefore, helps in addressing the critical condition. The data is important in clinical decision making, and with patient-driven adaptive prediction techniques, important medical history of patients could easily be gathered and used for the purpose of treatment. It, therefore, helps in minimizing risks since clinicians have prior knowledge of patients’ condition.

It is also important to note that the technique ensures that data both historical and present is available for medical use. This makes it easy for the medical practitioner to provide medical treatment or healthcare services to patients CITATION Jia12 \l 1033 (Jiang, Boxwala, Robert, Kim, & Lucila, 2012). With enough detailed information of patients, it is faster and easier to understand what a patient suffers from and therefore, prescription and treatment become easy. A patient also can search for his or her medical information and seek relevant drugs or prescription from a physician. This, therefore, promotes health care and also encourages patients to engage in their own care because they can easily understand their medical condition through the available data.

References

BIBLIOGRAPHY Jiang, X., Boxwala, A., Robert, E.-K., Kim, J., & Lucila, O.-M. (2012). A patient-driven adaptive prediction technique to improve personalized risk estimation for clinical decision support.  Journal of the American Medical Informatics Association, 19 (1), 137-144.

Manaktala, S. (2015). An Intelligent Risk Detection Model to Improve Decision Efficiency . Journal of patient data and Information management , 12-34.