Paving Path for Healthcare Advancements with Predictive Analytics
With the emergence of new diseases, it has become imperative for researchers to accelerate the solutions, patterns, and predictability of such diseases with the help of predictive analysis.
As the COVID 19 outbreak greeted the world, the healthcare industry scrambled to take necessary actions that can curb the spread of the Coronavirus. By using various tools of technological advancements, the frontline workers are predicting the future of the world with COVID 19. Researchers all around the world are promptly relying on new technologies to develop the vaccine and draw patterns, which would enable them to find a solution. However, the use of Predictive analytics is not new to the healthcare industry. The COVID 19 outbreak has just highlighted the imperativeness for diagnosis and treatment of diseases. Researchers and healthcare workers have applied predictive analytics to get more information about diseases such as diabetes and cancer.
Hence, soon, the use of Predictive analytics will be robust, especially for identifying the outbreak, cause, symptoms, diagnosis, and treatment of a particular disease. Its application will enable health care workers to make sound decisions for diseases that are emerging and re-emerging.
What is Predictive Analytics?
Predictive Analytics is a branch of advanced analytics that is used in making predictions of the future by observing the activities of the past. It is governed by data modeling, data mining, statistics, artificial intelligence, and big data analysis by evaluating the historical data and predicting future outcomes.
Predictive analytics uses the application of both, algorithms, which is a set of rules and processes developed into a formula for a possible solution and unsupervised learning, where the machine or the analytics would not know what it is looking for and present the researcher with a new set of patterns and processes unidentified by the researcher, with a possibility that this pattern would hold significance for a unique outcome.
Applications of Predictive Analytics in Healthcare
In a healthcare infrastructure, the patient’s data becomes compelling for diagnosing and providing a plausible line of treatment for that particular disease. Any disease is accompanied by symptoms that identify with many other diseases. With the new advancements in laboratory testing and the identification of new diseases, it becomes daunting for any health care professionals to keep the plethora of disease’ data in a structured format and to keep a tab for the progression of such diseases. Hence the use of predictive analytics becomes imperative.
- Mapping COVID 19 and other infectious diseases- Many Healthcare workers and researchers are now using Predictive Analytics for mapping the spread of COVID 19 and other infectious diseases, by using predictive models. This enables them to measure the impact, the future trends of the effectiveness and contagiousness regarding, and the effective management necessary for that particular disease.
- Chronic Disease Management- With the help of Predictive Analysis, the patient’s data, comorbidities, and medications determining the life expectancy of the patient can be assessed. This enables healthcare professionals to make decisions that balance treatments and risks. With the help of a comprehensive decision-making tool, the outcome of the disease can be altered. Earlier detection of diseases by predictive analytics would be insightful in avoiding long term treatment, which is often cost-effective and would enable the prevention of such diseases by identifying risk factors associated with it.
- Maintaining Healthcare Infrastructure– Disease outbreak is an unpredictable phenomenon, one that renders human beings at the mercy of healthcare services. With the outbreak of COVID 19, it has become evident that monitoring over the past outbreaks and vigilance over the existing healthcare infrastructure has become a necessity to fight the current disease. As many researchers are using Predictive Analytics for mapping out the spread of Coronavirus, the technology can be useful to formulate laws and policies that would enhance the field of medicine. The predictive analytics would be insightful to healthcare workers for establishing a strategical approach in countering the diseases.
For example, with the use of predictive analytics, many healthcare workers are identifying the hotspots of COVID 19, monitoring the patients, and the cantonment zones.
This type of approach would also demand the discussion of such policies at the public forum so that more informed decisions can be taken to curb down a particular disease. It will also provide the anticipation of potential consequences.
- Managing a Healthcare Infrastructure– With the help of Predictive analytics, the patient pattern within a healthcare set up can also be maintained. Using predictive analytics, the health care professionals would be able to identify the arrival pattern of the patient seeking treatment for a particular disease, thus maintaining the staff flow in emergency wards, inpatient, and outpatient departments.
- New Drug Advancements- In the Pharmaceutical Industry, researchers are everyday testing and supplementing traditional trials with the help of predictive analysis. With the uncertainty over the effectiveness of traditional drug testing and regular evaluation of new drugs, Predictive analytics would enable researchers to incorporate individual physiology and genetics for drug-metabolizing enzymes, in identifying the patient subgroups that would require dose adjustments. This would especially effective in diseases like Alzheimer’s and Parkinson’s, where no definitive treatment or therapies are discovered yet.
Challenges associated with Predictive Analytics
Predictive Analytics capitalizes on data and algorithms, and every algorithm is associated with human intelligence. Hence one of the biggest challenges while using Predictive Analytics is the reflection of algorithmic biases. Algorithmic biases appear when the technology reflects the prejudices and biases of humans, which can be conscious and unconscious. It can likewise happen during coding, selecting, or training the algorithm. Lack of regulation over algorithm biases would, therefore, have a high chance of altering the desired results.
Another issue that has been of utmost concern amongst many patients is an infringement of privacy. As a plethora of data is received every day, from the patient’s history to the treatment of a particular disease, with the leakage of the available patient’s data to other web portals due to security infringement has pushed many activists to demand strong privacy laws.