Predictive analytics in healthcare

It may sound surprising, but providing care to patients is only one small part of a healthcare practitioner’s daily work. They must also determine a care plan, prevent health complications, vet new drugs and treatments, and ensure clinics and facilities are adequately staffed to accommodate patient needs, among many other tasks. Performing these tasks ultimately leads to better health outcomes for patients, but they are also laborious and time-consuming.

This is where predictive analytics can help. A form of machine learning and artificial intelligence (AI), predictive analytics is used to analyze available data and identify trends and patterns. The insights healthcare providers find can then help improve or accelerate a wide range of tasks within healthcare, and lead to greater efficiency, successful developments, and better patient outcomes.

What are predictive analytics in healthcare?

Predictive analytics is used within the healthcare industry to make predictions based on insights mined from historical healthcare data. Some applications of predictive analytics include forecasting the likelihood of a patient having a certain disease or condition, identifying trends, devising clinical decisions, and tracking how diseases spread.

Predictive analytics begins by sorting through provided datasets. In healthcare, this may include patient health records, medical history, facility patient logs, or clinical trial site information. From there, a statistical model is developed that confirms assumptions and is run against a data set to generate predictions for inputs.

As a basic example of predictive analytics, one program may be able to give the odds of a 55-year-old male having cardiovascular disease by considering a range of inputs, such as the patient’s cholesterol level, blood pressure, blood sugar levels, etc.

Types of predictive analytics

Broadly speaking, there are two techniques commonly used to perform predictive analysis: regression models and machine learning techniques.

Regression models

Regression models develop a mathematical equation to represent the interactions between variables. Some types of regression models include:

Discrete choice models
Linear regression models
Time series models, logistic regression
Decision tree learning
Survival or duration analysis

A key part of regression techniques is finding the model that best fits the relationship between variables.

Machine Learning

The other technique used within predictive analytics is machine learning, which is an algorithm that receives input data and predicts outputs based on statistical analysis. As the name suggests, this type of predictive analytics can “learn” and improve its predictive abilities by updating outputs as new data becomes available.

Some machine learning techniques include:

Multilayer perceptron
Neural networks
Naïve bayes
Support vector machines
Geospatial predictive modeling

Pros and cons of predictive analytics in healthcare

Are you looking to leverage predictive analytics at your organization? To help you consider your decision, we’ve dug into the pros and cons below.

Benefits of predictive analytics in healthcare

Patients often see many potential benefits when their providers use predictive analytics. For instance, predictive analytics can help doctors find which patients are at risk of complications or relapses, allowing them to adjust their care plan to prevent these scenarios. With the ability to find at-risk patients before the symptoms of certain diseases appear, predictive analytics is a valuable tool within preventative care.

In addition to improving the quality of healthcare delivered, predictive analytics also helps healthcare organizations uncover hidden opportunities in their data. This can include information that reduces healthcare costs and increases efficiency, thus saving them time and money.

Predictive analytics can also play a role in creating more personalized healthcare, which benefits patients by aiding in the creation of a care plan tailored to their specific needs. For example, predictive analytics can use the patient’s genetic data to find the best medication choice with minimal side effects. As a bonus, this process is largely automated and governed by the predictive analytics system, meaning that the practitioner can focus on providing care instead of using valuable time spent analyzing data.

Lastly, a system that employs predictive analytics empowers practitioners to make decisions with more confidence. This is largely for two reasons: first, a predictive analytics model helps remove inherent biases within the decision-making process; and second, the technology is capable of uncovering patterns and trends—and making connections—that might not have been noticeable by a human.

What are the downsides to predictive analytics in healthcare?

While there are many benefits of predictive analytics, one of its most significant pitfalls is the mismanagement of the data received. Predictive analytics offers exceptional knowledge to those who use it, but it may not be helpful if not harnessed correctly. This can then lead to companies using predictive analytics but not seeing the return on their investment that they expected.

Simply using predictive analytics isn’t enough; companies need to apply this knowledge to see true improvements.

Predictive analytics in healthcare use cases

There are many applications of predictive analytics in healthcare, and detailed below are some use cases in various industries.

Risk scores

One application of predictive analytics is the creation of risk scores based on factors such as health conditions, disability status, age, gender, and living situation.

These scores help providers make more detailed assessments of patients and forecast the likelihood of a patient being at-risk for certain diseases or conditions. Risk scores also help providers determine whether a patient will benefit from a personalized approach to care or a wellness program.

Lastly, risk scores also highlight areas for future research, as they uncover areas where a patient’s needs weren’t met.

Creating and responding appropriately to risk scores also helps those enrolled in Medicare’s Hospital Readmissions Reduction Program (HRRP), which provides financial incentives for minimizing hospital readmissions. Through predictive analytics, hospital staff can receive a notification when a patient’s risk factors place them with a high probability of readmission. This can then help the hospital staff know which patients would receive help from patient care resources for follow-up care.

Drug development

Pharmaceutical companies can also benefit from predictive analytics through various modalities and in different phases of the drug discovery and development process.

Developing a drug is no small feat, with the average cost of a drug candidate reaching marketing approval being $2.6 billion, according to the Tufts Center for the Study of Drug Development. With such immense investments, any tool that can assist the drug development process and increase the likelihood of success is incredibly valuable.

Predictive analytics can help pharmaceutical scientists understand, compare, and contrast hundreds of variables to determine whether a drug composition will be successful. But creating a successful drug is half the battle; another key part is ensuring a market for it. A pharma company’s marketing team can use predictive analytics to forecast market demand, which then provides insight into whether it is worthwhile to pursue the development of a product.

Clinical trial site evaluation

When it comes to research and development, clinical trials are vital components that allow scientists to test the effectiveness and safety of their drug, product, or technique. However, the success of clinical trials revolves around the site chosen. Statistics show that more than 50% of Phase 3 clinical trials fail, and two-thirds of the investigative sites selected for clinical trials do not meet patient enrollment requirements.

Predictive analytics can help by using historical pharma data to find which factors are most influential concerning site health, and then pinpoint the sites that meet these factors. Predictive analytics can also look at a site and determine how likely it is for the clinical trial to fail if that site is chosen. With these skills, predictive analytics can help the clinical trial process run more smoothly while also increasing productivity.

Monitoring patients using medical devices

Medical devices generate significant amounts of essential and relevant data. Predictive analytics offers an opportunity to make the most of this captured data by helping practitioners better monitor their patients and make more informed clinical decisions regarding their treatment and medication.

One such application of predictive analytics within the medical device space is with Medtronic, which worked with IBM to provide real-time glucose insights for those with diabetes. This collaboration takes in various inputs such as drug administration, glucose readings, and lifestyle choices to anticipate potential data points and help the patients make better decisions about their health and well-being.

When it comes to predictive analytics, the applications are nearly limitless

Above are just some use cases of predictive analytics in healthcare, but the applications of these statistical models are nearly limitless.

Some uses stem from improving patient health outcomes, such as:

Creating personalized care plans
Analyzing patient risk factors
Preventing relapses or hospital readmissions
Finding the best treatment or drug for a patient

All these uses lead to better patient health outcomes, which can result in monetary incentives for healthcare providers.

Predictive analytics can also be used to analyze the market and determine where there is a need that drugs or medical devices can fulfill. This way, companies know which initiatives are likely to be successful and have a genuine impact on the public’s well-being.

Predictive analytics, machine learning, and AI are all integral components to the healthcare commercial intelligence created by Atlas AI. From medical and prescription claims to reference data, affiliations, expert insights, and more, this intelligence can help your organization gain a better understanding of the market and win more.

If you’re interested in seeing how healthcare commercial intelligence can help your company, start a free trial now.