Predictive Analytics in Healthcare: A Glimpse Into the Future
We all know this Hypocrite’s quote, “The disease is easier to prevent than to cure.” Indeed, taking preventive measures can save many nerve cells, money, and above all, health. However, the given phrase can be applied not only to a single patient but also to the general population. It’s much more reasonable to prevent disease outbreaks than to deal with the aftermath, given the scale of the problem.
Predictions in healthcare are impossible without information and the right healthtech solution. Historical and real-time data have to be recorded and analyzed to be used for further forecasting. But what paths do they travel to become suitable for these purposes? What’s the cost of data processing failures, and how do they affect prediction accuracy in the context of healthcare?
Key Highlights
Remote patient monitoring reduces the risk of potential readmissions, which may cause heavy fines for hospitals.
It’s critical to prepare and structure variables for analytical models; otherwise, you will end up with unreliable forecasts.
Employing cybersecurity measures and blockchain technologies is the only way to ensure safe interaction with big data.
Unlike mathematical formulas that generate basic predictions, ML models may capture complex, non-linear patterns in healthcare data.
That’s the topic we’d like to touch upon in this blog post: predictive analytics in healthcare. How it helps the industry to move forward, consider some examples of predictive analytics in healthcare, and which pitfalls may occur on the way to accurate predictions.
What Is Predictive Analytics in Healthcare?
What is predictive analytics in general? If we turn to Deloitte, it defines predictive analytics as a subtype of data analytics for the creation of predictions about something unknown in the future. Put simply, the forecasts are built upon historical and real-time data.
In healthcare, predictive analytics allows us to anticipate future trends by leveraging diverse healthcare information from various sources, including Electronic Health Records (EHRs), patient registries, surveys, health insurance claims, and more. Also, it helps to maintain HIPAA compliance through medical and billing data analysis and detect potential fraud. Before any of these capabilities can function reliably in a clinical environment, the underlying data infrastructure needs to be evaluated against actual AI requirements. AI readiness assessment for healthcare organizations helps identify whether the data sources, integration layers, and governance policies are mature enough to support predictive models that inform real clinical decisions.
But how exactly can we use the tool, what are the pros and cons of predictive analytics in healthcare, and what issues can it help to address? Let’s explore some examples in the following section.
Let’s consider one simple example of utilizing predictive analytics in healthcare. Say, there is a plan to design a new residential area in a megapolis. Depending on its scale, we need to know how many ambulance substations must be built in the district.
In addition, our task is to anticipate how big these substations should be — how many ambulances are to be procured and how many dispatchers should be hired. To make an informed decision and allocate our resources wisely, we should predict the number of possible calls to the emergency.
We’re not able to make a forecast out of thin air. Instead, we need a lot of different information. Statistics on emergency calls for the past period, including the difference between daytime and nighttime, the approximate average population of a similar residential area, patient waiting time, and so on. Only after gathering all this info and its thorough analysis will we be able to make the most accurate predictions and plan and allocate our resources reasonably.
Sure thing, the example is relevant not only for large-scale resource planning. Healthcare predictive analytics can also be used for capacity and resource management within a particular medical facility, its staff workload planning, equipment maintenance planning, and many more.
We all remember the start of the Coronavirus outbreak when it seemed that the entire world had come to a halt. It’s quite logical to wonder if it was possible to foresee the pandemic using predictive analytics and take preventive measures before the disease spread around the world. And the answer is yes.
For example, BlueDot, a Canadian startup developing AI and predictive analytics solutions, gave a warning about the occurrence of unknown pneumonia on Dec 30, 2019, in Wuhan, while the WHO officially declared the emergence of the virus in 9 days only.
The given example showcases that using the power of predictive analytics, we can foresee even novel viruses. But if we speak about well-researched diseases, such as measles, for example, the outbreaks can be foreseen well in advance.
Readmission Prevention
Besides spotting some possible diseases, predictive analytics can be extremely useful in minimizing patient readmission. The thing is that rehospitalization is not only unpleasant for individuals but also pretty costly for hospitals.
In the USA, for example, readmission for the same reason within 30 days may cause heavy penalties to healthcare organizations. So, not surprisingly, hospitals are seeking ways to streamline remote patient monitoring and reduce the risk of rehospitalization.
“The U.S. healthcare system spends approximately $52.4 billion annually on hospital readmissions.”
Wearable devices in healthcare have proven to be incredibly beneficial for this end. Specifically, they may monitor critical well-being conditions like heart rate, blood pressure, temperature, etc., to spot at-risk patients. As such, doctors may adjust treatment plans to prevent the likelihood of readmission. You see, predictive analytics becomes a win-win option for a patient and a hospital alike.
One of the greatest benefits of predictive analytics in healthcare is that it can also help to foresee diseases’ onset, in the absence of even minor symptoms. We’d like to mention the research project of the University of Massachusetts. It states that they launched their deep learning model, able to predict Alzheimer’s onset several years before symptoms’ manifestation.
What is predictive modeling in healthcare?
Predictive modeling in healthcare is all about using AI-driven tools to analyze huge datasets and process patients’ historical variables. This helps spot possible changes in their health conditions and forecast what may happen based on those indicators.
As such, healthcare providers can detect serious disease symptoms even before they clearly appear. Needless to say, how it elevates preventive care.
How can this be possible? Scientists used the health indicators of patients with Alzheimer’s disease and their medical tests before diagnosis and trained the model on them. Taking note of this example, just imagine how many other diseases, including even deadly ones, can be foreseen and even prevented with the help of historical and real-time data!
Unfortunately, the one-size-fits-all approach has not been invented yet. All patients have different characteristics, anamneses, reactions to medications, and contraindications.
Therefore, each patient requires a personalized treatment approach, and a doctor should take into account all patient specifics, not only to cure but also to do no harm when making prescriptions. With the help of healthcare predictive analytics software, doctors can track patient health indicators and adjust treatments accordingly.
No-Show Rate Minimization
Did you know that in the USA, the average no-show rate is approximately 23%? This is a fairly high number, especially given the already overloaded healthcare system. To make an appointment, patients may need to wait for a long time on average. Let alone that, missed visits negatively affect clinic revenue.
What if it were possible to detect patients at high risk of no-shows and minimize this rate? Predictive analytics assists here as well. Specifically, with AI algorithms in place, hospitals may analyze patients’ previous appointment history and assess the frequency of rescheduling or cancellation.
Moreover, AI is capable of taking predictive analytics to new heights by providing insights about factors such as patient profiles (age, location, health insurance types, etc.). For example, if an individual’s current insurance plan does not cover the cost of the requested service, AI classifies the patient as being at high risk of a no-show.
It all sounds pretty promising, but what if the person is actually going to visit the clinic this time anyway? Well, there is a solution. Here, GenAI tools serve as a helping hand. They automatically create and send reminders that include insurance coverage information, asking the patient to confirm the visit.
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Last but not least, an important reason to adopt predictive analytics in healthcare is to adjust insurance packages. Here again, AI-powered tools do the heavy lifting. ML algorithms, for example, process huge datasets pretty fast and with high accuracy.
As such, insurers may analyze variables on patient records to review their previous treatment history and forecast the type of coverage they are likely to require. Robotic process automation, on the other hand, notably streamlines and speeds up a plenty of manual tasks from data entry and document handling to compliance checking.
This way, insurance companies can adjust their packages for particular groups of people, making coverage more reasonable for both insurers and patients.
How do healthcare insurers use predictive analytics to assess patient risk?
Insurance companies may analyze historical claims data to detect high-cost coverage services in the future. In addition, predictive analytics assists in identifying at-risk patients and those with chronic diseases, leading to better package optimization that can be beneficial both for clients and the business.
Predictive Modeling in Healthcare: Steps for Accurate Forecasting
Now, let’s move on directly to the predictive modeling process. One does not simply make predictions without appropriate and thorough preparation, especially if you work with big data. Variables must be found, processed, and validated to be suitable for further usage.
Here’s a brief overview of the stages we go through before we leverage our healthcare data for forecasting.
1. Data Sources Definition
Depending on the goal we pursue when using predictive analytics in healthcare, we determine the necessary sources from which we’ll extract our data. For example, if we need to predict the level of flu cases for the next season, we may require a register containing information on the incidence of the population in past years. This contains general information on incidence, not just flu, EHRs, as well as metrics from medical equipment analyses.
2. Data Modeling
At this stage, we are finalizing the requirements for the ETL process and the prediction itself. In other words, we thoroughly work out the selected sources, choose the columns we’ll work with, and identify only the data that we need. In this particular case, we select only information on flu incidence and the results of examinations of people with this diagnosis.
It’s important to understand that modeling is an iterative process, and we can revisit it at any subsequent stage. This could be triggered by the emergence of new data or the discovery of inaccurately provided data.
During the ETL stage, we proceed directly to raw health data extraction from the required sources, processing and filtering, and loading into our chosen storage for subsequent prediction-building. The quality of the process and the built data architecture will determine the system’s forecasting capabilities. That’s why this step is of paramount importance.
4. Data Validation
This stage involves verifying the data already loaded into the storage. We check the quality of the transformed data, its consistency, and whether it corresponds to the intervals of acceptable values.
5. Data Enrichment
At the enrichment stage, we have the opportunity to expand our dataset by adding extra columns. This becomes possible through the use of special tools, such as LLM (Large Language Model). For example, a doctor leaves notes after a patient’s visit. An LLM model is capable of analyzing handwritten text and providing an assessment of the patient’s condition in the range of 0-10, and this value can be used in the prediction itself.
6. Testing
The validation stage entails only checking the data format, whereas testing helps verify the entire flow. For example, we added new data to the ETL process and thereby slightly altered it. In such a case, tests that previously had worked successfully may have failed, which indicated that something went awry in the flow itself.
ML Model Training & Prediction Building
After we’ve prepared our healthcare data and made sure that they are clean, consistent, and suitable for further usage, we may start to select the machine learning model, train it on them, and build predictions. Below, there is a table of major steps for prediction building.
Step
Description
ML Model Selection
Choose the right machine learning model based on the data type, desired outcome, and complexity. Options include Linear Regression for simple predictions, Decision Trees for non-linear data, and Neural Networks for complex patterns like image recognition.
Data Preprocessing
Prepare data by normalizing numeric inputs, imputing missing values, and encoding categorical data to ensure it is in a format suitable for modeling.
Training the ML Model
Adjust model parameters through cross-validation and regularization to prevent overfitting and ensure it performs well on new data.
Prediction Building
Generate predictions using the trained model on new data, apply appropriate thresholds for binary outcomes, and evaluate probabilistic outputs for decision-making under uncertainty.
Model Evaluation
Use accuracy, precision, recall, ROC-AUC, and F1 Score to evaluate the model’s performance and ensure its reliability for healthcare applications.
Continuous Learning and Model Updating
Incorporate new data through online learning or apply transfer learning to keep the model updated and relevant for current medical challenges.
Forecast Precision in Danger: Main Risks of Predictive Analytics in Healthcare
As always, it’s easier said than done. All the steps of predictive analytics in healthcare mentioned above sound quite simple, but it’s just in words. There are so many factors that may affect the accuracy of predictions, starting from the engineer’s ineptitude and ending with business rules alterations.
And while the future of predictive analytics tends to be quite promising in healthcare, this opportunity can only be realized by being prepared to address potential critical issues. Below, let’s take a look at some of the most common pitfalls and the best possible solutions to overcome them.
Poor Raw Data Quality & Heterogeneity
Problem
Let’s explore the example of medical equipment for blood testing. A sensor that detects hemoglobin levels had malfunctioned, which was not immediately detected by the laboratory staff. Consequently, due to the faulty equipment, we receive incorrect results, which are then loaded into the database. Thus, since our source data are of low quality, it’s quite naive to expect accurate predictions on their basis.
Solution
If the equipment returns extreme values, for example, 0 or 1000, which is completely unacceptable when we are talking about hemoglobin levels, then we can easily detect such anomalies at the validation stage and conduct filtering immediately.
Of course, it’s a bit more complicated if the equipment returns results within the normal range. For example, over the past week, the hemoglobin levels of all patients were 139 and 140, which are in the normal range, but the equipment did not return any other values at all. In this case, we can use Artificial Intelligence to power an anomaly detector, which will help identify points that don’t fall into the statistical norm range for this sample.
Alterations in Initial Sources
Problem
Let’s continue discussing the hemoglobin indicator with the following example. Say, in the database containing equipment analysis results, the firmware was updated. As a result, the column that was previously called hemoglobin was transformed into Hemoglobin, meaning that the lowercase letter was changed to uppercase. The ETL, expecting the column name “hemoglobin”, cannot find the necessary column in the source and logically fails to process this data further.
Solution
To detect such discrepancies promptly, it’s necessary to set up alerts that will notify us if an error occurs. In our ETL pipeline, we will also need to change the column name so that the process continues smoothly and without a glitch.
Data Privacy and Security
Problem
Given the huge datasets healthcare organizations process, they are a tempting target for cybercriminals. Overall, clinics collect these variables from multiple sources, such as appointment and scheduling systems, electronic health records, and wearable devices, which makes them particularly vulnerable.
Solution
The best way for providers to protect their systems is to employ strong cybersecurity measures like data encryption, strong authentication, and role-based access. Plus, conduct regular penetration tests to spot any possible vulnerabilities.
Additionally, it is worth leveraging a blockchain-based intrusion detection system to double down on data protection. You see, blockchain provides tamper-proof security logs and decentralized verification, making it possible to identify suspicious activities.
The outcome of predictive analytics is based on the data that the trained algorithm has learned from. Any algorithmic bias can negatively affect a model’s performance, thus impacting the fairness of analytics.
Consider you aim to detect patients with a high risk of heart attack, but you unintentionally trained your algorithm mostly on male symptom patterns. As a result, the model fails to accurately identify heart attack risks in female patients. This biased prediction could lead to unpleasant consequences, such as potentially delayed diagnoses.
Solution
One good option to prevent such issues is to regularly train algorithms on diverse groups of patient data. For example, by including symptom patterns from both males and females, the model learns to recognize a broader range of heart attack indicators. As such, systemic bias can be notably reduced.
However, let’s confess that identifying algorithmic unfairness can be pretty challenging. And one of the effective ways to address it is to involve humans in the process. They will constantly monitor the data algorithms used for analytics and may make necessary adjustment to maintain fair outcomes.
What are the biggest challenges when applying predictive analytics in healthcare?
Poor data quality, alterations in initial data sources, security breaches, and algorithmic bias are among the core pitfalls healthcare providers face in predictive analytics. These factors may lead to many unfavorable outcomes, such as heavy fines due to data security issues, inaccurate predictions, unfair decision-making, and reduced quality of patient care.
On top of that, a robust analytical solution can be extremely costly and challenging to employ. Specifically, given the lack of expertise.
ML for Predictive Analytics in Healthcare
As a rule, predictive analytics in healthcare can’t do without machine learning. However, it’s possible to use mathematical statistics formulas to create predictions. But if we speak about such a complex domain as healthcare, there are tasks that can’t be done with formulas only, and we need to leverage one of the ML models.
When is a formula enough? Say, we want to predict the number of COVID patients for the next month in one particular hospital. We take figures for previous months and, with the help of special tools like Facebook Prophet, for instance, gain the result. Obviously, the prediction will not be as accurate as we could count on using ML, but if we need only a recommendation, that’ll do.
Higher Prediction Accuracy as ML’s Greatest Benefit
Relying only on formulas that predict trends, we can’t consider third-party factors that may affect the final result. For example, we need to calculate the number of patients that must be vaccinated to decrease the measles case rate. We understand that there is a dependency of incidence on vaccinated patients. Therefore, we train our ML model on the vaccination data for previous years and make a prediction, and the level of accuracy will be quite high.
As always, there is a caveat we can’t fail to mention. Measles is well-studied and quite stable, which can’t be said about influenza or, moreover, COVID. New strains emerge from time to time, and making predictions considering these diseases is not an easy task to tackle.
ML Pitfalls Worth Mentioning
1. ML Model Selection
There are numerous ML models that can be used for predictive analytics in healthcare. However, it’s challenging to foresee how this or that model would work, particularly in your case. Therefore, the most suitable ML model can be selected only through trial and error, no matter how sad it may be.
2. Picking the Right Metrics
Incorrect metrics selection, their excessive or insufficient number, and the wrong definition of dependencies between them – all these factors affect prediction accuracy. This can be resolved with the help of specific tools, such as scatter plots and big data analysis.
Sometimes, no matter how perfectly your ML model corresponds to your aims and how well it may have worked on your data. If something that we discussed above about preparation steps goes wrong, ML will be powerless. Therefore, to make everything run like clockwork, it’s necessary to pay due attention to the preparation phase, not only to ML algorithms.
To Wrap It Up
Predictive analytics, especially using big data, helps us expand opportunities and mitigate related risks in healthcare. With the right approach, we are empowered to manage population health, decrease the likelihood of irrational resource allocation, take preventive measures in case of high probability of disease outbreaks, and much more. But we should always keep in mind that the key phrase here is “the right approach”.
Without thorough preparation, strategizing, and consideration of numerous intricacies of data manipulation, predictive analytics in healthcare may turn out to be not just a useless tool but also a quite dangerous one if your forecasts are far away from a decent level of accuracy.
Velvetech’s team has vast experience in healthcare software development, complemented by a proven proficiency in data analytics. Reach out to us, and we’ll assist you in extracting maximum value from your data for your healthcare project!
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