Decision-making process in health care can be difficult, confusing, and involving if appropriate tools and interventions are not properly harnessed. Computerized prognostic and predictive techniques have provided an important tool in patient care decision making in complex situations where multiple factors and variability in interpretation exist (Jiang et al., 2012). Predictive models are programs created and driven by data for purposes of addressing individual risk, learning, and health status. In medical practice, predictive models are often used in minimizing risks through automation of drug treatment patterns in electronic health records, prediction of renal transplantation outcomes, estimation of the success of assisted reproductive techniques, and making prognoses for patients undergoing certain procedures. For instance, consistent prescriptions and follow-up treatment can foster patient's accountability of self-management thereby minimizing the risk of advancement of his condition or re-hospitalization (Cuffel et al., 2002).
Predictive technologies are important tools for risk assessment and identification of patients who are at high risk of certain conditions (Vogenberg, 2009). Consequently, appropriate and timely preventive interventions can be implemented hence minimizing risk and provides the opportunity for cost assessment and management. These technologies, if properly applied can help lower overall healthcare costs as well as promote adherence to evidence-based medicine thereby promoting health and patient involvement in decision making (Vogenberg, 2009). Consistent medication leads to an improvement of patients’ health as adherence to the prescription schedule enhances patients’ awareness of the usage habit, thus increasing commitment. In the process, it improves the patient's health since the patients are in a position to understand what is required of him to manage his health.
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On the other hand, prognostic models can be used to guide health care policy, determine eligibility study of patients for new treatment interventions, and assist in the selection of appropriate tests and therapies, thus promoting patient involvement in decision making (Jiang et al., 2012). In addition, prognostic models can be used to emphasize health and desirable outcomes.
In conclusion, by providing accurate and detailed information to patients on their health condition, patients are empowered to make informed treatment decisions. For instance, if a prognostic model estimates a patient’s survival time as four months, the patient can make the decision to forgo costly chemotherapies and instead prefer end-of-life care interventions (Vogenberg, 2009). As a result, patients are drawn into participating in their own care decision-making processes.
References
Cuffel, B. J., Held, M., & Goldman, W. (2002). Predictive models and the effectiveness of strategies for improving outpatient follow-up under managed care. Psychiatric Services , 53 (11), 1438-1443. doi:10.1176/appi.ps.53.11.1438
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
Vogenberg, F. R. (2009). Predictive and prognostic models: Implications for healthcare decision-making in a modern recession. American Health & Drug Benefits , 2 (6), 218-222. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4106488/
