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It has been said that predictive analysis is going to help create more efficient and effective health systems. But are there any tangible and successful results? Let’s review the benefits of predictive analytics in healthcare and how some organizations have already achieved tremendous progress.
An insurance provider in California was able to use predictive analysis to reduce the number of infants who get antibiotic treatment after birth from 11% to 0.05%. The algorithm determined that only 0.05% of the children are born with infections that require antibiotic usage.
Such implementations are by all means encouraging. Predictive analysis in healthcare is worth pursuit. In this article, we explore more benefits of predictive analytics in healthcare.
As its name suggests, it is an analytics offshoot used to help make future predictions, resulting in more informed decisions. Data is central to accurate predictions.
Several concepts need work in tandem to ensure precise predictions – Data Mining, AI (Artificial Intelligence), Machine Learning, and statistics.
The Healthcare Industry is definitely making strides regarding the use of predictive analysis and healthcare. However, a major challenge is that these applications tend to be localized to predominantly a health center, like a hospital. Where technology companies have built solutions using predictive analysis, they are not scalable globally.
To a large extent, this could be a result of the nature of the industry itself. You need to consider the unique aspects of a geographical location or population before concluding that a solution, predictive or not, is good for them.
On the other hand, a predictive analysis should help personalize healthcare. The individual patient needs to be at the center of predictive analysis models.
With all the potential benefits of predictive analytics in healthcare already out outlined above, here are some of the areas in healthcare where predictive analytics is used.
With predictive analysis, patient records can be used to create a prognosis score. The data points are defined, and a Machine Learning model is created to connect the different data points. Conclusions and predictions can then be made from insights gained. A patient’s prognosis can be predicted over six months.
Genetic screening can help determine possible diseases and plan accordingly. For example, suppose a couple is looking to have a child, using predictive analysis. In that case, their DNA samples can be used to predict the probability of the child inheriting a particular disease. The parents can then choose to go ahead or plan on what they need to ensure that the child is healthy and comfortable.
Predictive analysis can help with faster and more accurate interpretation of medical images like x-rays. An example is an AI algorithm that is being trained at the Stanford University to interpret chest x-rays to make pathologies. The ultimate goal is to use the algorithm in emergency chest care.
The following image summarizes the advantages predictive analysis can bring in healthcare. Let’s explore them further below:
Using data points like seasonal sickness patterns, patient historical data, and even data from nearby health facilities, it is possible to determine better patient-to-staff ratios. This could help healthcare facilities prepare better for emergencies, for example. Weather conditions can result in spikes in specific ailments, resulting in more patients seeking treatment. Predictive analysis can help with better allocation of resources, including human resources.
With predictive analysis, it is possible to assess loopholes in the healthcare system. For example, services provided to senior citizens. Data points like pressure injuries, staff qualifications, patient turnover, and staff-to-patient ratios can be used to establish inconsistencies in care and what needs to be improved.
There is a lot of patient data available, even from wearables. Remote patient monitoring can also help with accessing crucial patient data that can predict hospital visits, admissions, and emergencies.
For example, suppose a wearable shows that a patient’s blood pressure has been excessively high for a couple of days. In that case, their doctor can reach out to them and inquire whether they have been consistently taking their medication. They can even ask them to go in for a check-up. Proactive care could help identify underlying problems that the patient may be unaware of.
With predictive analysis, it is possible to determine treatment side effects and complications better. This has been impossible to achieve as some medications and treatments work in specific patient cohorts and not others.
This is because there are many factors at play here. Predictive analysis can help analyze data around side effects and develop insights, correlations, and patterns that can help better predict outcomes, enabling practitioners to come up with correct drugs to treat illnesses correctly.
Predictive analysis helps forecast health risks like the probability of death during surgery based on an individual patient’s medical history and existing conditions. It can also predict hospitalization likelihoods for patients with diseases like diabetes. In an ongoing study at the University of Pennsylvania, septic shock can be identified in patients 12 hours before it happens.
Predictive analytics can handle large data sets, for example, cohort data. With predictive analysis, it is possible to establish the general health of a community. You can identify the prevalence of a habit like smoking which could have adverse effects in the future.
Public health officials can then do health campaigns and, of course, prepare for the possible influx of lung diseases in a few years (as not everyone will heed the campaign’s message).
We have looked at the benefits of predictive analytics in healthcare and the various use cases. While some of the applications have not fully rolled out and are still in the experimental stage, they predict extraordinary healthcare breakthroughs.
There have been a few success stories, though. But there is a need to scale them, even if not globally yet, regionally. Predictive analysis and healthcare have to tango for more success stories to be birthed.