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/PMC

DQ 5 -1 Responses

When it comes to " A Patient-Driven Adaptive Prediction Technique" the ncbi article related to providing a prediction of necessary clinical decision risks (Jiang, et al., 2012). There were two prediction models used that scored from 0 – 100 by utilizing the Patients at Risk for Re-Hospitalization (PARR) or the Combined Predictive Model. The PARR score determined the chance of the patient’s readmission into the hospital. This is important since it would cost the hospital money for readmitting. The Combined Predictive Model was to determine an entire populations. The results give resources and determine the level of interventions to help the community. These technologies are to assist the health care professional to learn when intervention and what level of intervention is needed for that patient, to prevent a readmission.

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 (el).

Patient driven adaptive technologies has the potential to improve clinical decision-making for personalized risk estimation of patients (Jiang, Boxwala, El-Kareh, Kim, & Ohno-Machado, 2012). Within the health care industry, data is driving new discoveries, reimbursement, outcomes, systems design as well as the decisional making process. Patient data sources are used to perform clinical interpretation and evidence based reporting having the potential to minimize risk, promote health and encourage patient engagement in their care across the care continuum. In cancer care, quality care has been challenged by the multifactorial nature of cancer disease and patient systems. Patient driven adaptive technologies is driving the fight against cancer and is promising in the development of new drug discoveries and clinical trials (Taglang & Jackson, 2016). Clinical decision making can potentially mean the difference between toxic and effective cancer treatment choices, thus affecting patient quality of life and longevity. Patient safety can be improved with the detection of early adverse drug event using spontaneous reporting systems database such as the Food and Drug Administration's Adverse Event Reporting System. The use of patient driven adaptive technologies has the potential to shift from providing standard care to providing a personalized approach in care of that patient (Jiang, Boxwala, El-Kareh, Kim, & Ohno-Machado, 2012).

References

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. doi:10.1136/amiajnl-2011-000751

Taglang, G., & Jackson, D. B. (2016). Use of “big data” in drug discovery and clinical trials. Gynecologic Oncology, 141(1), 17-23. doi:10.1016/j.ygyno.2016.02.022

Complexity in decisions involving multiple factors and variability in interpretation of data motivate the development of computerized techniques to assist humans in decision-making.Because the goal of predictive models is to estimate outcomes in new patients (who may or may not be similar to the patients used to develop the model), a critical challenge in prognostic research is to determine what evidence beyond validation is needed before practitioners can confidently apply a model to their patients. This is important to determine a patient's individual risk.As each model is constructed using different features, parameters, and samples, specific models may work best for certain subgroups of individuals. For example, many calculators and charts use the Framingham model to estimate cardiovascular disease (CVD) risk.These models work well, but may underestimate the CVD risk in patients with diabetes. illustrates a case in which a patient can get significantly different CVD risk scores from different online risk estimation calculators. (Jiang, et, al., 2012).

In this research, they address the problem of selecting the most appropriate model for assessing the risk for a particular patient. they developed an algorithm for online model selection based on the CI of predictions so that clinicians can choose the model at the point of care for their patients.

Reference:

Jiang, X., Boxwala, A. A., El-Kareh, R., Kim, J., & Ohno-Machado, L. (2012, June). A patient-driven adaptive prediction technique to improve personalized risk estimation for clinical decision support. Retrieved October 24, 2017, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3392846/

In many organizations you will find that they are using Patient-Driven Adaptive Prediction Techniques. These technologies have shown to really help in decision making as well as improve in quality of the delivery of care. Many healthcare decisions that are made might be difficult and therefore which is why you will see a computer-based decision-aid sometimes used. These aids help a patient's preferences and values, as well as present treatment choices along with benefits and adverse side effects. After evaluating what the patient preferences are, we can then compare that to the standard care that we offer and implement what works best in favor of the patient.
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. doi:10.1136/amiajnl-2011-000751

Based on the article, the applications were developed to aid in medical decision support and be portable for patients to use on the go (Jiang et al., 2012). The usage has a lot to do with statistics and formulas that has been modified into the healthcare setting. This effort to attempt to increase health promotion makes it easier to target and pinpoint health disparities without even necessitating subjective data. Sometimes patients tend to conceal certain information that could be vital to providers and healthcare personal. These innovations are very cutting edge but it does not replace getting information first hand from the patient, whether it be the truth or lie or withheld information. New services that have been witnessed firsthand would be the new telehealth options. It is a new innovation that allows patients to eliminate going to an emergency room and the convenience of talking to a physician from anywhere with internet or wifi connection. The application has a list of common issues that can be addressed via telehealth. If the patient has serious complications not listed on the app then the patient should have common sense to go to an emergency department.

References:

Jiang et al., 2012. US National Library of Medicine National Institutes of Health. A patient-driven adaptive prediction technique to improve personalized risk estimation for clinical decision support. Retrieved from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3392846/