Every industry is trying to tap into artificial intelligence’s (AI) powers and collect and activate data for better performance and outcomes. In healthcare, the promise of AI is particularly essential for tracking time, predicting changes in health status, monitoring progress, and providing comprehensive but cost-effective care—all things that will support you and help you be more efficient at your job. One area of research and implementation that is transforming the way healthcare is delivered is predictive analytics. Machine learning algorithms can synthesize large quantities of real-time and historical data and offer information providers need to make informed decisions quickly and accurately.
What Is Predictive Analytics?
Predictive analytics uses data to make educated predictions about future trends or outcomes. We encounter this machine learning constantly. When Netflix anticipates what you want to watch next, that’s predictive analytics. Using past financial statements and metrics, business leaders look into the future to predict sales, revenue, and expenses for fiscal years. Manufacturing companies use predictive analytics to anticipate when a piece of equipment will need to be serviced or replaced, and marketing departments use this kind of data to plan campaigns and other content relevant to customers’ needs and concerns.
Several techniques are available for analyzing data for prediction. The choice of model depends on the data type and the analysis’s goal.
- Regression Analysis: This model tracks and anticipates how an action or variable will affect the outcome. Regression analysis asks “what if” one or a variety of factors changes. For example, will the color of the paint increase or decrease car sales? If the in-patient hospital stay is shortened by two days, how does it affect healing?
- Decision Trees: Visualize choices as a tree. The branches are decisions that could be made at any junction. The leaves are the outcomes. Predictive analytics helps to determine which decisions will lead to the desired outcomes. For example, what is the best treatment for this particular patient experiencing these symptoms?
- Neural Networks: This method is for particularly complex questions with many variables. This technology can synthesize information from texts, images, and reports to find anomalies and make diagnoses. Because the human body is such a complex system, neural networks that can input data from a variety of sources and in a variety of formats will be helpful for creating personalized treatment plans.
- Classification Models: These models separate data into two binary groups. The answers will be yes/ no, true/false, or other dual outputs. Financial institutions use classifications to determine if a transaction is fraud. Healthcare systems will use data to determine which patients are at higher-than-average risk of developing specific diseases or conditions.
- Clustering models: While similar to classification models, cluster models can have infinitely more outcomes. Data is used to divide people into clusters based on defined attributes. A marketing company will develop personas to personalize its outreach to each group. Patients may be grouped by diagnosis, genetic markers, or age in healthcare. Grouping like this can surface patterns that are difficult to notice in larger, more diverse groups.
- Time Series Models: Data is plotted by time in these models. Customer support services may anticipate what times of day or days of the week they will receive the most calls and schedule additional staff at those times. Time Series Models also predict how long wounds will take to heal or forecast how quickly intervention is necessary for a positive outcome after a traumatic event.
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What Role Does Predictive Analytics Play in Healthcare?
Predictive analytics is the closest we can get to a peek into the future. Particularly when things follow a pattern of progression, as disease frequently does, predictive analytics can consider all of the factors and influences to make better predictions than the human brain can. These models are trained on millions of data points directly related to the function they will serve. Just some of the ways predictive analytics can be used today include:
- Accurately diagnose
- Determine the best course of treatment
- Improve operational efficiency and safety
- Predict disease spread
- Anticipate the likelihood of following through on treatment
- Pinpoint risk factors
- Identify high-risk patients
- Anticipate needs or hospital readmissions
- Reduce the number of insurance claims that are denied
When used in a predictive model, the data is stripped of all personal feelings and relationships, and no external factors influence the outcome. At the same time, this technology is powerful enough to take the data from each individual, compare it against millions of other data sets, and create personalized treatment, intervention, and support recommendations.
How Predictive Analytics Makes a Difference in Medical Settings
Predictive analytics is a form of agnostic machine learning that can help users identify trends, make better decisions, and pinpoint new opportunities. Direct healthcare providers can use this data to predict long-term outcomes for patients, direct treatment decisions, and anticipate the occurrence of preventable conditions. At the administrative level, it can accurately plan for resource allocation, reduce costs, and create new efficiencies for patient care, documentation, and billing.
Predictive analytics designed to identify trends across populations can help providers identify at-risk groups and provide intervention services to reverse the trends. Public health workers can target the right audience for their classes, treatments, and other services. As insurance companies move toward value-based care models, data will lead to better cost savings efforts.
As essential as it is for hospitals and healthcare systems to prevent readmissions and avoid financial penalties, predictive analytics can spot patients with the highest risk factors for readmission. Providers can create a treatment and follow-up plan to address these issues and proactively lower readmission rates with this information.
These are just a few uses for predictive analytics in healthcare settings. The use cases will continue to expand as the systems become better trained and the data more precise.
What Should Practitioners Know about Predictive Analytics?
Your electronic health records (EHR) serve as the base data for predictive analytics. This is another reason medical databases should be cloud-based, complete, and accurate. EHRs that analyze the data will synthesize information in the individual records, surveys, and large data sets about public health and disease registries. Because predictive analytics is trained on large data models, it can make trends-based predictions on long-term health outcomes.
To start using predictive analytics in your practice or facility, follow these steps.
- Define your goals: Because of the vast quantities of data available through large data models and EHRs, you’ll need to decide which metrics are most important to track and report. It’s easy to get overwhelmed by the amount of data collected and extracted about patients and populations. To best use your predictive analytics technology, determine what metrics you want to quickly access and build your dashboards and collection methods around those points.
- Teach your team: Who is best suited to collect and record data points? Who should have access to patient records to perform analyses and update treatment plans? You will need to ensure all team members understand what data needs to be collected, how to document it correctly, and how it will be used to make better decisions. For polychronic patients, the team could often consist of providers at many offices or facilities.
- Prepare your data: Collect historic and real-time data that supports your goals. Ensure that all data is collected and recorded using the same methods. Standardizing processes will make the analysis more accurate and useful.
- Choose your technology: Some enhanced EHR systems have built-in predictive analytics. Consider what is available in your current system and if it meets your needs. The system should enable you to intervene early for at-risk patients, maximize employee time, reduce negative outcomes, and save money for the facility and the patient. If your current EHR does not do this, consider if you need a newer system or additional technology to help you reach your goals.
- Model and refine outcomes: Healthcare is incredibly nuanced. As such, out-of-the-box solutions can be tailored to different needs, but you must decide how to examine the data and present recommendations. The time you spend fine-tuning the data, configurations, parameters, and selections will result in a more refined and effective analysis.
- Action the data: Predictive analytics is just part of the equation. Once you have the data, plan to put that information into practice. This may involve offering health education in specific communities, sending additional appointment reminders to those likely to miss, setting alerts for billing mistakes, or creating personalized treatment plans.
- Maintain compliance: Data points should be tokenized or anonymized to protect private patient health information, as you must do with any other system. Regularly monitor the predictions and recommendations for signs of bias or data breaches.
What Is Coming Next?
As with all technology, predictive analytics becomes more advanced and robust daily. Companies working in healthcare technology are applying these advancements to improve patient outcomes, save money, and become more efficient. Staffing shortages are still a concern, and advancements such as these will enable healthcare workers to continue to provide high-quality care when staff and resources are tight.
The systems themselves will continue to become better predictors of outcomes as more data points are used to train the models. More inputs mean more data to draw from. For global or regional uses, predictive analytics may soon be able to extract data from social media and the Internet of Medical Things to detect disease outbreaks and epidemics earlier. Based on behavioral data and digital fingerprints, psychiatric care can use predictive analytics to identify at-risk individuals. Our wearable health devices could also play a part in collecting better data for analysis and leading to more accurate treatment planning.
Whether you are a direct care provider determining the treatment needs of a patient recovering from surgery, an administrator scheduling your workforce or buying equipment, non-medical staff preparing billing documents, or a public health advocate, predictive analytics should be part of your EHR system and workflow to improve your accuracy and efficiency. Incorporating the benefits of big data sets into your job and making decisions that consider predictions about what will happen in the future allows you to be more targeted daily.
