[Recommended]Predictive Modeling as a Preventive Technology in the Health Sector

Predictive Modeling as a Preventive Technology in the Health Sector Running head: Predictive modeling 1 Predictive modeling 7 Predictive Modeling as a Preventive Technology in…

Predictive Modeling as a Preventive Technology in the Health Sector
Running head: Predictive modeling 1
Predictive modeling 7
Predictive Modeling as a Preventive Technology in the Health Sector
Topic area
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.
Research question
What impact does predictive modelling on creating successful treatment plan for patients with diagnosis of Alzheimer’s disease?
Alzheimer’s disease is one mental problem that mostly affects the elderly population. This disease is characterized by a progressive memory loss leading to impaired cognitive functions. The condition is usually chronic hence affect the patient for a long time. Patients with, therefore, disease have a reduced quality of life coupled with continuous treatments. Use of predictive modeling technology in the management of this patient can play a significant role in creating a successful treatment plan for the condition. Predictive modeling technology has several benefits for both the patient and the provider of healthcare.
Predictive analysis or modeling in healthcare improves the accuracy of diagnosis. For patients with Alzheimer’s disease, using the predictive modeling technology help the primary care physicians to make an accurate diagnosis of the bug regarding the staging and presence of other comorbid condition such as dementia. To achieve this, the clinicians use the predictive algorithm provided by this model to assess for the stage of the disease and the presence of other conditions (Wang, Kung, & Byrd, 2018). As a result, the patient is usually screened using this model before being discharged home to promote patient safety and quality of life.
Predictive modeling technology play a significant role in preventive disease medicine and public health. In this case, this analytic technology helps care providers to prevent diseases and their complications from advancing by identifying populations at risk (Ritchie et al., 2015). Alzheimer’s disease patients are at risk of developing other mental condition such as dementia or promoting to the severely chronic stage that will acutely reduce the health status and life of patients. Therefore, primary care physicians use this model to screen the Alzheimer’s disease patients under their care for possible complications or deterioration. Knowledge gained about the disease using this analytic model is therefore used to implement safety precautions such as lifestyle changes that promote disease recovery. The model, thus, transforms the care of Alzheimer’s disease from curative to preventive since prevention is better than cure (Wang, Kung, & Byrd, 2018).
Predictive modeling technology in mental health provides physicians and psychiatrists with information on how to provide care for the individual patient. For a patient with Alzheimer’s disease that was diagnosed with the algorithm as per this model, the exact treatment methods required are also identified. This is different from the usual plan of care that is drawn from standard symptoms and treatment. The patient will be given specific drugs to meet their health needs. Also due to predictive analysis on the type of effective medications, the hospital can use this information to purchase only the required drugs from the pharmaceutical stores to prevent wastage and shortages of medicines (Bhagwat et al., 2018). Therefore, medication for Alzheimer’s disease will be readily available for use.
Predictive modeling in healthcare also has a direct impact on patients with Alzheimer’s disease. The use of the algorithm in diagnosis and prescription of treatment results in patients getting the drugs that are only effective or active against their condition. Use of specific medications to treat this condition increases patient knowledge on how to promote the effectiveness of these medications (Bhagwat et al., 2018). Patients will be aware of potential complications and learn how to prevent such from occurring. This is, in turn, support evidence-based practice where patient-centered care is practiced. As a result, quality and safe care is practice hence promoting better health outcomes for the patients.
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.
References
Bhagwat, N., Viviano, J. D., Voineskos, A. N., Chakravarty, M. M., & Alzheimer’s Disease Neuroimaging Initiative. (2018). Modeling and prediction of clinical symptom trajectories in Alzheimer’s disease using longitudinal data. PLoS computational biology, 14(9), e1006376.
Breuker, D., Matzner, M., Delfmann, P., & Becker, J. (2016). Comprehensible Predictive Models for Business Processes. MIS Quarterly, 40(4), 1009-1034.
Ritchie, C. W., Molinuevo, J. L., Truyen, L., Satlin, A., Van der Geyten, S., & Lovestone, S. (2016). Development of interventions for the secondary prevention of Alzheimer’s dementia: the European Prevention of Alzheimer’s Dementia (EPAD) project. The Lancet Psychiatry, 3(2), 179-186.
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.
Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and
potential benefits for healthcare organizations. Technological Forecasting and Social
Change, 126, 3-13.
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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