Running head: Predictive modeling 1
Predictive modeling 5
The average human lifespan is increasing along with the world population, which poses new challenges to today’s treatment delivery methods. The health sector is capable of collecting massive amounts of data and look for best strategies to use the numbers. With the use of predictive modeling, the health sector has the potential to reduce costs of treatment, predict outbreaks of epidemics, avoid preventable diseases and improve the quality of life.
Advances in predictive modeling as a preventive technology in the health sector should be designed to help clinically integrated networks manage large, complex patient populations. One of the challenges facing providers today is that predictive modeling requires a strong data infrastructure, user engagement, staffing and other resources.
Preventive technology could help make diagnoses more accurate and treatment regimens more precise, reduce labor costs, capture data faster, sort through vast amounts of data to gain insights needed to drive better care decisions and outcomes. With the use of predictive modeling and preventive technology, from a patient-facing perspective, they could improve health literacy. These instruments combined with patient-contributed data could also help predict and fill patients’ clinical knowledge gaps. In that way, patients could more confidently manage their own health and make healthier choices on their own.
For example, the use of the fit bit arm/wrist band could be extended into an integrated system that could collect patients’ health data continuously and send this data to the cloud which will allow doctors to monitor and compare this data and react every time if the results will be disturbing. If a patient’s blood pressure increases alarmingly, the system will send an alert to the doctor who will then take action to reach the patient and administer measures to lower the pressure.
What is the need for predictive modeling, as a preventive technology, and how can it revolutionize the health field?
The significance of the study
The purpose of healthcare predictive modeling is to help doctors make data-driven decisions within seconds to improve a patients’ treatment. By using data-driven findings to predict and solve a problem before it is too late, also assess methods and treatments faster, keep better track of inventory, involve patients more in their own health and give them the tools to do so.
This is very useful with patients who have a complex medical history and suffering from multiple conditions. This tool would be able to predict, for example, who is at risk of diabetes, and thereby be advised to make use of additional screenings or weight management. For year’s gathering huge amounts of data for medical use was costly and time-consuming. With technology improving on a daily basis, it is easier to not only collect such data but also to convert it into a useable form to provide better care.
Healthcare providers had no direct incentive to share patient information with one another, which made it harder to utilize the power of predictive modeling and preventive technology. Now that more of the health sector are getting paid based on patient outcomes, they have a financial incentive to share data that can be used to improve the lives of patients while cutting costs for insurance companies. Healthcare needs to catch up with other industries that have moved from the standard regression-based methods to a more future oriented like predictive model, to improve patient outcomes while reducing spending.
Breuker, D., Matzner, M., Delfmann, P., & Becker, J. (2016). Comprehensible Predictive Models for Business Processes. MIS Quarterly, 40(4), 1009-1034.
Wagenen, J. (2017, November). Predicting-analytics- 3-Big-Data Trends-in-Healthcare. Healthtech, pp.-1-6 Retrieved from https://healthtechmagazine.net/article/2017/11/predicting-analytics-3-big-data-trends-healthcare.
Zheng, B., Zhang, J., Yoon, S. W., Lam, S. S., Khasawneh, M., & Poranki, S. (2015). Predictive modeling of hospital readmissions using metaheuristics and data mining. Expert Systems with Applications, 42(20), 7110-7120.
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