Predictive Modeling in Healthcare

Predictive modeling in healthcare is currently being utilized in improving quality of care. It has the potential to drive future models of care by reducing costs and improving health of the patient population. Predictive modeling uses statistical models that search through information ranging from data from previous treatment outcomes to the latest medical research. These models then predict outcomes, such as potential risks or costs associated with a particular patient population. In healthcare, predictions vary from medication responses to hospital readmission rates. For instance, predicting an infection will likely determine its origin, thus helping the doctor to make a diagnosis and predict a proper course of treatment. Therefore, prediction outcomes can reward doctors, such as you, by allowing you to offer better quality care.

As a healthcare professional, you are well-trained, knowledgeable, and constantly strive to stay up with the latest medical news and research. However, even you have to admit that you do not always have the information you require right at your fingertips. Even when you have access to tons of information, you still need time to analyze the information and incorporate it within your patients’ individual case. Predictive modeling can help you by creating a prediction profile from past patients. The model will simply predict outcomes so that your patient can receive the proper care he or she requires. Recent findings show that in the United States alone, one in five patients are re-admitted to a hospital within 30 days of discharge, resulting in an additional and unnecessary expense of $17 billion annually. For this reason, most hospital facilities have started using predictive models to determine when patients can be safely released.

There are several ways in which predictive modeling can influence and help the healthcare industry:

• Reducing healthcare costs – Predictive modeling allows you to divide patients into several groups and identify which group will likely have the greatest return on investment (ROI). The model can also be used to anticipate potential costs, determine if specific interventions with a specific patient population can decrease medical costs over time, and decide among pay-for-performance programs.
• Increasing accuracy of diagnoses – By using predictive algorithms, more accurate diagnoses can be made. For instance, up until now, genes have been linked with Alzheimer’s disease (AD). If a patient comes in to receive a blood test to determine whether they have the disease and it turns out that they do, predictive modeling can be used to initiate an ongoing predictive study. In this way, after identifying the patient’s specific gene associated with AD, he or she can receive the right gene therapy for their individual case based on positive findings from data of previous patient populations. The predictive algorithms do not replace your judgments as a healthcare professional but rather assist you in offering your patients accurate diagnoses.
• Attracting new patients – Predictive modeling allows you to attract prospective patients by discovering and targeting the ones who are likely to respond to tailored care. The statistical models use information based on patients’ medical history and others factors such as age, gender, etc, which are communicated within the system. The information is then processed to highlight predictive outcomes and provide you with proper insight. Furthermore, understanding this information is not only important for attracting prospective patients but it also allows you to retain current patients and get a positive return on investment. Predictive modeling is merely an effective way to gather information, visualize it, and identify prospective patients and how to communicate with them.
• Improving patient outcomes – With predictive modeling, you can monitor patient outcomes based on specific patient factors – age, gender, primary diagnosis, and more. In turn, your patients can receive treatments that are right for them based on their individual cases. They can become more informed and work closely with you to achieve optimal health-related outcomes. Moreover, they can become more aware of potential personal health risks and make better decisions about their lifestyles and future well-being.
• Creating preventive medicine and improving public health – With early intervention, several medical conditions can be slowed down or prevented altogether. Predictive modeling allows you to identify at-risk patients and help them make healthy lifestyle changes. Moreover, genomics play a significant role in identifying groups of individuals with similar sub types and molecular pathways and creating preventive medicine, all the while improving public health.
• Cutting down medical practice costs – Predictive algorithms can be used to predict the future medical costs of a medical practice. Predictions are made based on the practice’s own data and together with insurance providers; they can synchronize databases to meet their subsequent health plans. What’s more, hospitals can use predictive modeling to cut down costs and make more cost-effective decisions by treating particular patient populations successfully. By doing so, they can increase optimal patient outcomes and gain quality accreditation.
• Increased patient satisfaction – Predictive modeling can help medical practices and hospitals improve patient relations, ultimately leading to increased patient satisfaction. The longer the duration of predictive modeling, the more accurate the predictions will become. As you gain more detailed and up-to date information, you can save your patients time from waiting and better manage it by providing them with necessary care. What’s more, happier and more satisfied patients lead to a better reputation and financial stability.
• Adapting to the latest medical news and research – When it comes to medicine, even the smallest differences are significant. Predictive modeling focuses on the significance between statistical and clinical findings. With an increasing number of medical cases, the accuracy of the models improves over time with the gathered new evidence. The models learn from the new information and adapt with changes that may have occurred in the patient population. Therefore, even the smallest changes are recorded and set for continued research, with the purpose of providing valid and reliable outcomes.
• Applying to several health-related areas – Predictive modeling does not need to be limited to chronic disorders. It can also be used in several other health-related areas such as elective processes. In such instances, the patient’s condition remains unknown; however, additional information relating to their eating habits and daily activities are recorded. By using other data such as personal health records and health monitoring tools, the patient can receive the best treatment plan based on their age, gender, body mass index, and more. This is very crucial because such effective results have the ability to drive continued participation, thus encouraging guidance and recommendations.
• Offering more personalized care – Personalized care emerges from algorithms of great certainty. However, personalized care is not necessary a simple task. It requires taking into account internal and external factors that are reliable, timely, and convenient. When the certainty of a prediction is increased, long-term health outcomes are improved. Predictive analytics is the process that refers to learning from historical data in order to make predictions about the future. In healthcare, it is evident that predictive analytics directly impacts patient care by managing chronic diseases, preventing re-admission, supporting clinical decision-making and matching patient population groups. Utilizing predictive analytics can enable optimal decisions to be made and allow for care to each patient to be personalized.

In the United States, predictive modeling has the ability to revolutionize healthcare and change the roles of several individuals. Patients can become more informed and learn to make better health-related decisions. Doctors can take on more consultant-like roles and help by advising patients in making necessary lifestyle changes. Finally, medical practices, hospitals, and insurance companies can offer more specialized and personalized approaches to care. In whatever ways predictive modeling is used, it has the potential to offer data-based solutions and inform healthcare professionals, such as you, how to accurately enhance your efforts in medicine. Such efforts will allow for better diagnoses and more targeted treatments, all of which will naturally lead to optimal outcomes and improve the cost and quality of healthcare.