Why is Predictive Analytics important in healthcare?
In the realm of healthcare, data and reports have always played a significant role. Medical charts, electronic health records, health surveys—doctors, nurses, pharmacists, and hospital personnel have long relied on these to base their diagnosis and make their decisions.
But a fast-paced industry will need more than just the basic patient intelligence and visualizations to keep up with the growing patient base and scientific demands. Patients now have higher expectations from healthcare institutions and in the same vein, medical research is constantly releasing discovery after discovery. To stay competitive, medical professionals must evolve to adopting advanced data analyses to be able to proactively treat patients with insights that are otherwise not available with basic data analytics.
More than just answering “what happened?”, predictive analytics in healthcare seeks to get answers to the question “what will happen?”. This subcategory of advanced analytics uses historical data to create and train machine learning models to forecast outcomes. And these predictions can make significant improvements in terms of patient care and experience, hospital management, and process improvement.
Read the infographic below and learn how institutions can effectively use machine learning capabilities and data analysis for predictive analytics in the healthcare sector.
Download the infographic here.
What are the uses of Predictive Analytics in healthcare?
1. Hospital readmission analytics
There’s nothing like getting discharged from a hospital only to return just a short time later. Not only is this terribly inconvenient, but it’s costly as well—for both the patient and provider. In fact, the yearly cost for readmissions is $26 billion for payers and $41.3 billion for hospitals.
Readmission is a situation that can be avoided though, once the hospital irons out a few things: making sure that the patient isn’t discharged too early, that they are discharged to a location with adequate recovery facilities (if needed), and that the necessary follow-through and patient compliance are enforced.
While these strategies are all well and good, healthcare analytics offers a more efficient alternative. Using historical data, a machine learning algorithm can identify the factors that contribute the most to readmissions and in turn, detect patients that are at high-risk of readmission.
By adopting a proactive instead of a reactive strategy, medical professionals can do early intervention to decrease the risk of readmissions. They can recommend the appropriate care plans and educate those at risk, improving patient care and outcomes while decreasing healthcare costs.
2. Appointment cancellation analytics
Another administrative hindrance that offers more than just mild inconvenience is appointment cancellation. There are many reasons why patients end up missing or cancelling their reservation: financial constraints, long wait times, poor medical literacy, and transportation issues, among others.
As such, no-shows are becoming a pressing issue for hospitals and clinics, with a 23% appointment cancellation rate globally. What’s more, missed appointments are costly for medical professionals, with surgeons losing up to $500 per missed visit. After all, time is money, especially in the healthcare industry.
Data analytics for healthcare, especially predictive models, can help organizations better manage—and hopefully reduce—appointment cancellations. Patients at risk of missing their appointment can be detected ahead of time, based on socioeconomic status and other influencing factors. To take it a step further, analytics platforms can also help set up auto reminders and proactive rescheduling for patients with incoming appointments.
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3. Sepsis risk analytics
Sepsis causes the hospitalization of about 1 million individuals in the US each year and is one of the top 10 diseases leading to mortality. As with most diseases, treatment is quite costly too. In fact, sepsis is one of the most expensive reasons for hospitalization, with the US spending $20 billion on hospital care for patients diagnosed with sepsis.
These are some of the reasons why hospitals find it important to improve sepsis care. And because the disease is tricky to spot and often requires urgent medical attention and treatment, early detection is key to optimization. This is where healthcare predictive analytics comes in.
Hospitals can use advanced data modeling to determine the factors and detect patterns that contribute to sepsis. These insights can be used to enable proactive care: doctors can easily identify patients that are likely to have sepsis and send them for more tests to confirm the diagnosis. This can help reduce the risk of mortality and morbidity, plus help reduce costs for hospitals and clinics.
4. Diabetes risk analytics
Like sepsis, this illness tends to have a big financial impact on both the patient and the provider, mostly because it’s so prevalent. To illustrate this fact: diabetes is the 7th leading cause of death in the US and it affects 9% of the population, with 95% of that segment having type 2 diabetes.
As a result, the cost of diagnosed diabetes reaches a whopping $327 billion, medical costs totals $90 billion, and the disease costs $237 billion worth in reduced productivity.
Diabetes has no cure, but it can go into remission. This doesn’t mean one is completely cured though, which makes it much more important to adopt a preventative, data-driven approach when it comes to the disease. With predictive analytics, medical practitioners can predict a patient’s risk of acquiring type 2 diabetes. Extra care and interventions can be given to those at risk, reducing the occurrence of diabetes and related complications.
The state of Analytics in healthcare today
The road to analytics maturity involves more than just incorporating predictive analytics, but it’s a good start. In fact, for most healthcare companies, it’s already considered to be a huge leap. While almost 80% of executives believe in the importance of advanced analytics, only 31% of hospitals have incorporated the technology for more than a year.
That fact is a missed opportunity in itself. Just imagine the many possibilities predictive models and machine learning can offer the industry. The scenarios listed above are just some of the many examples. There is still so much room for optimization: automating administrative tasks, prediction for other illnesses, analyzing patient feedback, and more.
Hospitals, clinics, and pharmaceuticals shouldn’t be satisfied with just basic reporting and dashboards, not when they can get predictive insights, with the same data, on an advanced analytics platform.
If you’re curious to know more about healthcare use cases and see how healthcare analytics works on Analance, we would be happy to give you a demo.