7 Ways Predictive Analysis Can Improve Healthcare
In general, Predictive Analysis has seven major positive effects on the healthcare industry. Let’s take a look at each one individually.
Healthcare technology has dramatically improved the average life expectancy of nearly 7 billion individuals around the world in the last century. But, like with any advancement, there is a cost. Advances in healthcare have resulted in a growing knowledge base of biomedical data that doctors and clinicians can use to diagnose and treat their patients.
According to The Future State of Clinical Data Capture and Documentation, research by the American Medical Informatics Association (AMIA), worldwide healthcare data totaled over 500 petabytes in 2012 and is anticipated to reach around 25,000 petabytes by 2020.
While this gives doctors, physicians, and other medical experts a wealth of information, it also creates a problem: accessibility. Simply put, these specialists are incapable of memorizing and implementing such a big amount of information.
This is where Predictive Analysis (PA) comes into play, allowing the medical community to focus on finding innovative ways to enhance people’s health without having to manually examine a large amount of data.
Predictive Analytics tools primarily comb through massive databases of a patient’s medical history. It then separates out various ailments and diseases, as well as the most recent biological and pharmaceutical research and expert suggestions, all of which are statistically examined using advanced data analytics tools. This data is used to determine the most likely medical prognoses for a given patient.
PA assists insurance firms in determining better and more appropriate health insurance policies for their consumers, in addition to anticipating possible medical outcomes for the patient.
Broadly speaking, there are seven core positive impacts of Predictive Analysis on the healthcare sector. Let’s take a look at each one.
1. Improved accuracy of diagnosis
In the sphere of healthcare, the first and most important application of Predictive Analysis is to assist clinicians in providing more accurate diagnoses to patients far earlier.
A symptom of weight loss, for example, could be suggestive of a range of ailments and diseases. Predictive Analytics can be used instead of a doctor relying exclusively on their own knowledge. An examination of the patient’s previous medical history against a comprehensive database can assist the doctor in making a more informed diagnosis and implementing an early treatment plan.
2. The ability to diagnose & treat life-threatening diseases faster
People are becoming increasingly vulnerable to a rising range of complex, life-threatening diseases as a result of modern life. This puts a lot of pressure on the medical community to keep looking for better answers. These life-threatening disorders are frequently misdiagnosed until it is far too late for the patient to battle them and recover complete health.
Predictive Analytics, in conjunction with modern genetic sciences, can help us turn the tables by identifying patients who are at risk of severe diseases and providing them with the information they need to take preventive health steps.
3. Predictions to employers & hospitals about insurance product cost
Most businesses now work with insurance companies and hospitals to provide health insurance to their employees as part of their employee benefits package. Together, the three may perform Predictive Analysis for a company’s employees to gain a better understanding of the charges they’ll incur over the course of a fiscal year.
For example, if a company employs 800 people, 200 of whom are at high risk of being hospitalized within a year due to health issues, it may be beneficial for the company, the insurance company, and the hospital to better understand how they would work out their mutual financial obligations.
4. Predictive Analytics helps pharmaceutical companies meet the needs of the public
The pharmaceutical industry will be one of the major beneficiaries of Predictive Analysis in the near future since it will provide a clearer, more evidence-based prediction regarding diseases and disorders that will most likely affect a large number of people. Pharmaceutical companies will be able to turn their focus to creating larger quantities of medicines for certain conditions, rather than wasting their resources, efforts, and time developing medicines that will be in short supply.
For example, if the Predictive Analysis for a certain province in India indicates that the number of Dengue patients will increase next year, pharmaceutical companies will be able to prepare to face this challenge in that territory by producing more drugs to combat the outbreak.
5. Helps researchers develop better prediction models
Medical researchers can use Predictive Analysis to construct prediction models that increase their accuracy over time and rely on a smaller number of case studies. It’s vital to note that in the world of medical research, the distinction between statistical and clinical significance is crucial. This result demonstrates that large population studies can be extremely beneficial to researchers, as statistically significant discrepancies can be fatal to a clinical trial.
For example, an observational study with a large sample size may suggest that farmers who use a specific pesticide in their fields are more likely to contract a deadly disease, whereas a Predictive Analysis with a smaller sample size may yield an entirely different result. Furthermore, one of the most compelling arguments in favor of Predictive Analysis is that it can gradually improve its accuracy as more inferences and insights are acquired over time.
6. Provides physicians with detailed information about individual patients
Predictive Analysis will have a significant impact on individual patients and their doctors since it relies on the Evidence-Based Medicine (or EBM) method to offer doctors a big bank of individualized health information on the patient. On the basis of numerous statistics learned via PA, this will assist the doctor in determining the most appropriate treatment strategies for each individual. If a Predictive Analysis of a patient’s medical history indicates an alternative category of the drug to treat a chronic cough, it can save the patient money and adverse effects while also offering the doctor a greater understanding of the patient.
7. PA provides the benefit of better patient outcomes
Aside from hospitals, insurance companies, and physicians, Predictive Analysis will provide a slew of benefits to patients, including improved overall quality of life and the avoidance of squandering money on medications or treatments that may not be appropriate for them.
If a person with a complicated genetic background can get a medicine that their doctor knows would likely work best for them based on PA, they won’t have to go through the process of experimenting with different prescriptions that have worked for a bigger group. As a result, the patient will become a more knowledgeable consumer who will be able to collaborate directly with their doctor to obtain better health outcomes with fewer expenses and complications.
The application of Predictive Analysis will rise exponentially as the world becomes more data-driven. Funding agencies are showing a lot of interest in the healthcare sector right now, according to Rock Health’s Digital Health Funding Database, which shows that between 2011 and 2014, a total of $1.9 billion was invested in companies that use Predictive Analysis.
Physicians, pharmaceutical firms, healthcare agencies, organizations that provide healthcare benefits to their employees, and, last but not least, patients will all gain from this acceptance.