Posted by Priyansha Sinha | Last Updated: 21-Sep-18
Predictive Analytics uses historical and existing data in order to determine and figure out the probability of a particular outcome. This is peculiarly a remarkable approach when applied to the healthcare industry. In an attempt to reduce and eradicate misdiagnosis, the historical data and previous symptoms of a former patient might be utilized for the assessment of a new patient.
Not only can predictive analytics help in foretelling an event, but it can also unveil astonishing associations in data that our human brains can possibly never decode.
In medical diagnosis, the predictions can range from medications to responses to hospital readmission rates. The possible examples are determining the likelihood of a disease, assisting the physicians in the diagnosis, predicting infections from the suture applications, and even foreseeing future wellness.
The statistical methods are basically known as learning models because they can increase and grow in precision inclusive of the additional cases. There are two crucial reasons where traditional statistics differ from the predictive analytics:
- PA does not depend on the bell-shaped or normal curve.
- The predictions are specially made for the individuals and not for the whole group.
The prediction modelling encompasses techniques such as artificial intelligence to devise a prediction profile and algorithms from the past individuals. In this post, I will discuss the top few contributions of predictive analytics in the field of healthcare and medical diagnosis.
Predictive Analytics Will Help In Improving Public Health And Developing Preventive Medicines
With an early intervention, a handful of diseases can be successfully ameliorated or prevented. Predictive analytics, with the aid of genomics, can enable primary care physicians to single out at-risk patients and offer effective solutions accordingly. With that information, the patients can significantly make changes in their lifestyles and avoid risks.
It is also being assumed that the future medications might be designed on the basis of individual requirements and spot out what is beneficial for people with “similar subtypes and molecular pathways”.
Predictive Analytics Enhances The Accuracy Of Diagnoses
Physicians can leverage predictive algorithms to allow them in making more accurate diagnoses. For instance, when patients visit the ER with an issue of chest pain, it is hard to understand if the patient needs to be hospitalised.
If the doctors were somehow able to answer the questions about the patients as well as their conditions and feed the data into a system with an accurate and tested predictive algorithm, things would have been a lot easier. The technology would assess the possibilities if the patient needs to sent home or admitted for further treatment and in this way it could aid the clinical judgement of the doctors.
Note that the prediction will not replace the functionality or the judgement of the doctors but would rather assist.
Patients Will Have The Potential Advantage Of A Better Outcome With Predictive Analytics
As the use of predictive analytics increases, there will be several modifications in the lifestyles of the patients. Potentially, the patients will now receive proper medications that would specifically work for them and not be given unnecessary prescriptions just because those treatments and medications work for a majority of people. The patients will also become more aware of the personal health risks because of the potential alerts from the genome analysis, from increasing use of medical devices and apps, from predictive models devised by the physicians, and due to an improved accuracy of what data is required for accurate predictions.
A Final Note
All in all, I believe that the changes are still on its way. The healthcare industry has started to adopt predictive analytics models in their diagnosis procedures which is greatly contributing to a better treatment. What are your thoughts on the contribution of predictive analytics in the healthcare industry? Let us know by spilling out your thoughts in the comment section below.