The authors of the assigned article, “A Patient-Driven Adaptive Prediction Technique to Improve Personalized Risk Estimation for Clinical Decision Support (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3392846/) have found that using patient-driven, adaptive technologies to guide clinical decision making are influencing the quality of patient care. How might these technologies minimize risk, promote health, and encourage patient engagement in their own care?

The article assigned describes patient-driven adaptive predictive techniques that can improve clinical decision making and personalized risk estimation. These technologies reduce adverse outcomes and improve patient health. They also encourage patients to be more involved in their own healthcare. By collecting and analyzing data from patients’ health records, as well as from various external sources such as wearables, sensors, and social media, these technologies can provide personalized risk assessments and real-time decision support to clinicians. It allows clinicians to make better-informed decisions that lead to a reduction in medical errors and improved outcomes.

These patient-driven adaptive technology can also promote good health, by encouraging the patients to play an active part in their own care. The patient can track their own health with mobile apps, wearables or other devices and receive customized recommendations and reminders that are based upon their individual health status. Patients are likely to adhere to treatment plans, and adopt healthy lifestyles.

Finally, these technologies encourage patient involvement by giving them timely, accurate, actionable and relevant information about their own health. The patient can view their records, interact with healthcare professionals, and get personalized recommendations that are based on the unique state of their health. Patients can feel empowered, in charge of their own health and more satisfied.

The patient-driven adaptive predictive techniques are capable of transforming healthcare. This is because they can improve personalized risk assessment, promote health and encourage patients to be more involved in their own treatment. It is vital to keep up with the rapid changes in healthcare and explore new technologies to enhance patient outcomes.

Reference: Collins, S. A., Vawdrey, D. K., Kukafka, R., & Kuperman, G. J. (2012). The use of a patient-driven predictive algorithm to enhance personalized risk estimates for clinical decision aids. Journal of the American Medical Informatics Association, 19(4), 490-495. doi: 10.1136/amiajnl-2011-000284