Artificial Intelligence (AI) and automation stand to revolutionize the healthcare industry in the near future. In fact, many of the positive changes have already begun to take effect in hospitals around the country, and new developments are happening at an increasingly rapid pace.
One particularly interesting subset of AI is the field of predictive analytics. And, like the other AI technologies, the benefits of using predictive analytics in healthcare clinics are numerous — and only starting to be realized.
Here are some of the most promising applications of predictive analytics that hospitals can get started with right away.
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AI tools employ one or more techniques to make predictions about healthcare management and patient care: logistic regression, time series analysis, and decision trees.
The logistic regression method can make predictions based on previous trends. When you have an existing data set, you can use statistical analysis to predict future data points that fit in with the trend. This can tell you the chances that a certain outcome will occur at some point in the future.
Time Series Analysis
Time series analysis uses data points at specified time intervals to track the progression of a certain event and compare those data points against a larger data set.
Think of this methodology as a flowchart including all the possible outcomes branching out from one central root. Using a series of if/else statements to account for certain criteria and possibilities, you can follow the decision tree all the way out to find its logical conclusion. That’s not to say the result is what will happen, but it’s an indication of the most likely outcome given your current trajectory.
These three methodologies for predictive analytics in healthcare can be used separately or together for a number of applications.
The following are the most common use cases where one or all of these methods can achieve huge benefits for patient care, operational efficiency, and your hospital’s bottom line.
Let’s say your hospital has admitted a patient with prior cardiac problems, diabetes, and COPD. There are likely several dozens, if not hundreds, of other patients in the hospital’s electronic health records (EHR) system with comparable diagnoses, ages, weights, and test results.
By comparing this patient’s data with the data of all the other, similar patients in the system, your physicians can get an idea of the likely trajectory of the patient’s health in the future. They may even be able to predict with some degree of accuracy the probability of a future heart attack or stroke, for example.
If patients fit a high risk profile, doctors can take early steps to prevent those problems from happening down the line.
Tracking disease progressions is one of the foremost applications for time series progression in healthcare clinics.
If a patient has a progressive disease, such as cancer or pre-diabetes, his or her test results can be compared to those of other patients with the same disease at intervals of, say, three months or one year.
With this comparison, your doctors will notice right away if a patient’s disease progression doesn’t match the trend, for better or worse. They can then take action to improve the future outlook. That might mean preparing the patient for treatment in the next stage, switching medications to see if the progression can be slowed, or any other change of strategies.
Say our example patient comes in with diabetes but has no trace yet of heart disease or renal damage. Using a combination of each methodology, doctors could predict which comorbidities are likely to show up and when, based on different treatments and changes of lifestyle factors.
Once again, this gives your physicians the ability to begin treatments early and start preparing the patient for the likely progression of their illness. In many cases, knowing the ultimate outcome — say, for example, in our diabetic patient’s case — can provide significant motivation for the patient to improve lifestyle factors that may slow or prevent that outcome.
The costs of keeping patients in the hospital longer than necessary often outweigh the benefits. Some of those costs are financial, but others are more subtle. When patients are kept too long, it diverts hospital resources away from more critical cases and keeps patients in an environment where secondary infections could happen.
Hospital readmissions can be just as costly as overstays, for the same reasons. To make matters worse, some programs such as Medicare actually penalize hospitals with hefty fees for readmissions.
One way to minimize both is to use predictive analytics to identify patients at risk of an overstay or a readmission and preemptively manage the situation.
By analyzing departmental data as well as patient data, your hospital can allocate resources or change treatments to keep recoveries on track and get patients healthier faster. This can be accomplished by devising more robust post-hospital care, conducting more intensive follow ups, or providing more resources for patients to manage their conditions outside the hospital.
It’s rare for a hospital to have a consistent, steady need for one type of resource or another. Variations in patient demographics, hospital staff, finances, equipment, and even seasons can all affect the supply and demand for resources, and sometimes hospitals can get overwhelmed when they can’t meet those needs.
Using the pattern recognition capabilities of predictive analytics, your facility can predict future fluctuations in demand and make sure resources are on hand to keep operations running smoothly.
Healthcare companies are not immune to the supply chain issues affecting most industries in recent years. With predictive analytics, however, some of these wrinkles can be smoothed over. Your supply chain operations staff may be able to find better deals on supplies by anticipating demand and purchasing early and in bulk, for example, or do a deep analysis of their supply networks to identify the most cost-efficient vendors and a purchasing schedule that will minimize waste.
Predicting which patients are most likely to have problems and meeting those needs before the problem occurs can go a long way toward ensuring better treatment outcomes and boosting the patient’s perception of the hospital.
Additionally, your administrators can use predictive analytics to identify patients likely to reschedule or no-show their appointments, so they can keep those resources on reserve to be diverted elsewhere at a moment’s notice if necessary.
Physicians can also keep a closer eye on patients who are less likely to follow up or adhere to prescribed treatment regimens, and choose the messages and interventions that would best fit their patients on an individual or population level.
As another move toward customized care, predictive analytics may be used to help patients choose the best provider for their needs based on their likely experiences and outcomes with that provider.
Even if a patient’s data fits in with the trend of similar patients, that person is still a unique individual with potentially distinctive comorbidities or other factors. This means treatment should almost never be a one-size-fits-all approach.
With predictive analytics, treatments can be customized to individual patients to maximize their likelihood for positive outcomes. The potential here goes deep; cancer treatment could, in theory, be customized to consider the patient’s background in combination with the specific type of cancer, the patient’s response to previous treatments, family history, and other variables.
Insurance claims are a notorious hassle — and significant cost — for hospital administrators. Thankfully, the claims process is also easily automated.
When you’re able to predict claims that are likely to be approved or denied, you can take actions ahead of time to fix errors, avoid denied claim fees, and give special care to perfecting claims that could result in higher payouts.