Simply stated, predictive analytics is the process of studying and learning from historical data to make predictions. In the case of healthcare, this ability can have vast potential implications.
Today, healthcare organizations are gathering better and more data about patient behavior, psychology, and biometrics. Predictive analytics is helping doctors apply their knowledge and skill to a wider group of patients and at a larger scale.
Predictive analytics is helping demystify the relationship between external factors and human biology and allowing us to fathom conditions not previously possible. This practice is enhancing the reengineering of clinical pathways and personalized care.
The rapid pace at which healthcare is adopting technology has had a positive and massive impact on medical processes and practices to improve the quality of care as well as the operational aspects of providing that care.
Let’s look at a few ways predictive analytics is directly impacting care delivery. Here’s how healthcare organizations are deploying predictive analytics capabilities to extract forward-looking insights from their growing data assets:
7 High-Potential Use Cases of Predictive Analytics in Healthcare
Minimizing Operating Room Delays
The University of Chicago Medical Center used data and predictive analytics to deal with the problem of operating room delays. These delays impact physicians, patients, and families while wasting resources since it is expensive to run ORs. Since multiple teams and physicians work on each surgical case, when one procedure ends, there is a series of tasks to complete before the next one begins.
UCMC combined real-time data with event-processing algorithms to create notifications, improve workflows, and streamline the process of handoffs between operating teams. This decreased the turnover time by four minutes per room -a massive improvement in resource utilization.
Preventing Patient Deterioration
While in the hospital, patients face an array of threats to their well-being. These include the risk of developing sepsis, acquiring a hard-to-treat infection, or a downturn from their existing clinical conditions.
Predictive analytics can help physicians see an upcoming deterioration before symptoms manifest themselves to the naked eye. Having the ability to predict these conditions can help physicians change a patient’s treatment in time. The analytics-driven strategy can identify situations and prevent them from worsening.
Foreseeing and Managing Patient No-shows
Unexpected gaps in a physician’s appointment calendar can have financial ramifications for any healthcare organization. Predictive analytics can be used to identify patients at high risk of a no-show.
Capabilities such as this can help healthcare organizations improve provider satisfaction, prevent or minimize revenue losses, and offer potential open slots to other patients in urgent need.
A study from Duke University says that EHR data can reveal individuals most likely to not show up on their appointments. Providers can send additional reminders to patients who might fail to show up, offer transportation services, or offer them an alternate appointment for a suitable hour.
Reducing Readmissions
As healthcare moves toward a value-based or outcome-based costing approach, it will be imperative for healthcare organizations to minimize readmissions. Predictive analytics can allow for more definitive care delivery and diagnosis, followed by appropriate treatment of identified conditions.
For hospitals, this can mean the optimization of healthcare operations and a reduction in readmissions. Predictive technologies such as remote patient monitoring and machine learning can be combined to support decision-making in hospitals through threshold alerts and risk scoring. These methods can allow healthcare institutions to prevent readmissions and unforeseen emergency room visits.
Precision Medicine
Predictive analytics can play a key role in helping doctors find the cure to certain rare diseases at an individual level. It’s been long known to the medical community that some medicines work only on a group of people and not on others. This is because humans and complex and unique, and there are various factors inside of an individual’s DNA and how it’s expressed.
Big data and predictive analytics can help the involved parties unearth unknown correlations, patterns, and insights by examining large datasets and forming predictions on them. These practices can be broken down and applied at an individual level, allowing caregivers to come up with the right treatment plan for a specific illness- no matter how rare it is.
Preventing Self-harm and Suicide
If doctors can identify patients more likely to inflict harm on themselves early, they can take specific measures to curb such behavior. Predictive analytics can allow practitioners to identify such individuals and ensure they receive the necessary attention they need.
EHRs can be mined to detect suicide risks in patients. In 2018, the Mental Health Research Network and KP conducted a study bringing together EHR and a depression questionnaire to identify individuals at an elevated risk of suicide. Using a predictive algorithm, they found that self-harm was 200 times more likely in patients who were flagged based on the questionnaire.
Predicting Patient Utilization
Predictive analytics can help healthcare organizations foresee when they could get busy. Care sites such as emergency rooms and urgent care centers work without fixed schedules. As a result, the deployed staff at any given time varies. By knowing when to expect more patients, these departments can plan beds and care practitioners to reduce the waiting times for patients at crucial junctures.
In this way, hospitals can maintain optimal staffing levels, optimize costs, and avoid resource loss while also delivering rapid care in times of high patient influx.
Predictive Analytics and Personalized Care: the Expanding Influence
To individualize medicine and treatment, doctors need access to the right data and technologies to customize their responses. Predictive analytics can prove highly impactful in bolstering patient engagement and increasing satisfaction while optimizing costs and reducing wastage for healthcare institutions. These examples are the tip of the iceberg. As more data gets collected and technologies like wearables and IoT take root, it’s not hard to see a future where predictive analytics will drive decision-making to make healthcare operations more effective, quick, and patient-oriented.
