By Ainsley Lawrence, Writer, Researcher
LinkedIn: Ainsley Lawrence
X: @AinsleyLawrenc3
In our data-centric world, predictive analytics has emerged as a groundbreaking technology, providing powerful insights that can be used to revolutionize preventive medicine. With the proportion of people aged 65 and older increasing at a fast rate and with nearly half the American population suffering from at least one chronic illness, preventative medicine has become a critical element in patient care.
The insights provided by predictive analytics can help close care gaps by:
- Helping to slow disease progression and aid in early detection
- Lowering readmission rates
- Reducing the risk of complications
- Helping people avoid the onset of illness
By leveraging predictive technology, healthcare organizations can change the future of preventive medicine, significantly improving patient outcomes and organizational efficiencies.
How Predictive Analytics in Healthcare Works
Predictive analytics is a process that evaluates current and historical data to predict future outcomes using AI algorithms, machine learning, statistical modeling, and data mining. Using these technologies, healthcare professionals can gain insights from patterns and trends in patient data that enable them to make more informed decisions regarding patient care and treatment.
There are various kinds of data that healthcare organizations can use for predictive analytics, and the more rich and diverse the data, the more comprehensive an understanding they can gain of a patient’s risks and potential health outcomes. These various types of data can include:
- Genomic and genetic data
- Behavioral and lifestyle data
- Environmental and social data
- Geographical data
- Clinical data
- Biometric data
When using predictive analytics, there are four primary stages involved:
- Data collection: Gathering the data from various sources
- Preprocessing: Data is “cleaned” to ensure it is relevant and accurate
- Modeling: Algorithms analyze the data to make recommendations and predictions
- Results interpretation: Healthcare professionals use those predictions and recommendations to make decisions regarding patient care.
It’s worth noting that not only does predictive analytics provide insights that can be utilized to enhance patient care, but also to improve operational efficiencies. For example, some of the patterns detected in the data can help organizations identify when certain operational processes aren’t working, allowing them to make better decisions about how to run things. This can help save money and ensure better resource allocation.
What Role Does Predictive Analytics Play in Preventive Medicine?
Predictive analytics powered by AI allows for newer solutions and more effective prevention strategies, and as such, it plays a critical role in the future of preventive medicine.
Enhanced Patient Care
With access to so much data, healthcare professionals can use predictive analytics to significantly improve the quality of care they provide their patients. Instead of taking a one-size-fits-all approach, for example, healthcare professionals can use the insights from predictive analytics to provide more personalized treatment options that are unique to each patient’s individual needs.
Readmission Prevention
Many patients end up needing to return to the hospital due to a lack of care that fully addressed their needs the first time around. Predictive analytics can reduce readmission rates, however, by identifying patients who are at risk for readmission. This allows doctors to create more thorough and tailored care plans and proactively address follow-up needs and discharge protocols, minimizing the likelihood of a return visit.
Early Disease Detection and Management
Perhaps one of the most important applications of predictive analytics in healthcare is its use in early disease detection and intervention. Using various types of patient data, models can identify patterns and trends that can indicate when a patient is at risk of developing a serious disease, such as cardiovascular disease. This then allows doctors to take proactive measures to prevent the development of the disease or to at least slow the progression of the disease using disease management treatment plans.
This is particularly useful in cancer screening. Predictive analytics algorithms and models can analyze data such as lifestyle, genetics, and biomarkers to determine when a patient is at risk for developing cancer. Early cancer detection can significantly improve outcomes and survival rates.
Predictive Analytics: Challenges and Considerations
While predictive analytics is a powerful tool that can revolutionize the healthcare industry, it doesn’t come without its challenges.
For starters, to support the high data demands that come with implementing predictive analytics, healthcare organizations will need to adopt a more robust and scalable network infrastructure, such as dark fiber. Dark fiber is ideal when utilizing predictive analytics as it provides high bandwidth capabilities. Not only that, but dark fiber is also a good choice when security needs to be tight, which is the case when dealing with high volumes of sensitive patient data.
On that note, the other challenge when dealing with predictive analytics is cybersecurity. When collecting, sharing, and storing large quantities of private information, strong cybersecurity protocols are paramount. A more robust network like dark fiber can help, but additional measures should be taken.
Today, cybersecurity threats are abundant, and include advanced phishing attacks, ransomware, password-based attacks, and IoT and medical device hacks. To prevent these issues from occurring, it’s critical that healthcare organizations not only adopt network monitoring tools, end-to-end encryption, device hardening protocols, and multifactor authentication, but also conduct training to increase cybersecurity awareness among staff.
In many cases, cybersecurity incidents occur due to internal human errors that put data at risk. The more healthcare staff are educated on how to properly handle systems dealing with sensitive data, the more organizations can prevent breaches that put their business and their patients at risk.
Final Thoughts
The future of preventative medicine looks promising with advances in predictive analytics. Not only will this technology significantly improve patient outcomes, but there are numerous other benefits that can improve healthcare settings, including mitigating financial risks, better resource allocation, lower costs, and overall better business outcomes. Predictive analytics impacts just about every facet of modern medicine, enabling a more proactive and predictive healthcare system.