From predicting medical issues before they start to providing better treatment programs for patients, predictive analytics are poised to revolutionize the healthcare industry.

Using an evidence-based approach when it comes to health management is nothing new for medical professionals. The ever-present medical charts, filing cabinets full of patient histories and terabytes of digital records are prime examples of doctors’ reliance on past knowledge to make current diagnoses. Despite the volume and value of this data, however, the current means of accessing, analyzing and employing it carries some significant limitations.

One of the most glaring is that while the information that’s collected from a patient is extremely useful for diagnosing and treating that particular person, there’s no standardized, efficient way to use that same information to help patients in similar conditions. Instead, doctors must depend on memory and medical books to piece together symptoms, treatments, and outcomes.
Healthcare institutions must be able to meet growing patient expectations, but even the most capable and dedicated physician has trouble keeping up with the latest research while comparing thousands of conditions and cures. Fortunately, predictive analytics (PA) applied to healthcare potentially offers substantial improvements.

Predictive Analytics Defined

Predictive analytics uses technology and statistical methods to search through massive amounts of data, in order to analyze and predict outcomes for individual patients. The information processed typically includes data from past treatment outcomes, individual symptoms and the latest peer-reviewed medical research and data sources. Healthcare predictions can range from responses to medications to hospital readmission rates. Examples include predicting infections, determining the likelihood of disease, helping a physician with a diagnosis and even predicting future health.

Predictive analytics in the medical world can be more accurately understood as prescriptive analytics. This kind of analysis not only provides possibilities when it comes to diagnoses but also assists healthcare providers with treatments and monitoring patient outcomes. Now, anonymous patient data can be turned into big data, transforming raw medical information into a web of interconnected symptoms, conditions, risk factors, treatments and outcomes.

Although it shares many similarities with conventional statistics, a key difference between predictive analytics and traditional stats is that PA predictions are made for specific individuals and designed to find distinct answers rather than draw broad conclusions regarding groups of people. They’re also learning systems, with PA algorithms becoming increasingly reliable as more data is added and processed.

Potential Applications of Predictive Analytics

Predictive analytics shows promise across the healthcare spectrum. Most notably, healthcare professionals will have an increased ability to home in on specific symptoms and make more accurate diagnoses based not only on an individual patient’s information but also that of similar patients. By using these predictive algorithms, doctors can determine the likelihood of a diagnosis and the chances of success for various treatments. Medical staff can use these extra insights to come to highly informed conclusions regarding their patient’s needs and provide more targeted care.

Healthcare providers will be able to track post-operational recovery of patients after they’ve been discharged from the hospital. Patients who are not progressing as expected can be scheduled to undergo a follow-up appointment before significant deterioration occurs. In addition, many diseases can be ameliorated with early intervention, and predictive analytics can allow physicians to identify at-risk patients even earlier, allowing for positive lifestyle changes to be made.

Predictive analytics is also poised to transform and improve the relationship between healthcare providers and their patients. With increased access to reliable, actionable health data, patients can play a more active role in their own care. Doctors will adopt a more advisory function, helping patients understand the data and providing recommendations.

Roadblocks

There are a number of challenges to overcome before the use of PA in healthcare becomes routine. Most of these are simple, practical challenges that stem from insufficient technological infrastructure. At the top of the list is organizations’ need for adequate data warehousing capabilities as well as the computing hardware to run the required applications.

Considering the range of tools, algorithms, open-source routines and third-party vendor offerings, integration and visualization present particularly challenging obstacles. These technology-based issues affect point solutions but are especially detrimental to comprehensive platforms that are tied into multiple departments and data silos.

Staffing and resourcing may also obstruct the full realization of predictive analytics benefits. Organizations will need to train and/or hire personnel and ensure that the staff is leaning on software to make such sensitive decisions. These changes will have to be cultivated throughout the medical community, from doctors, nurses and other medical staff to admission, reception and back-office personnel like medical billers.

Another problem is that more data does not necessarily guarantee more insight. Predictive analytics is most effective when there is a specific focus rather than a quest for a global solution. Specificity means improved performance and accuracy of the algorithm, more reliable predictions and increased efficacy of any associated intervention. Users will have to know which questions to ask to receive solid answers.

Overall, predictive analytics in healthcare can revolutionize personalized medicine, but there are still some steep hills to climb before the industry will see widespread use. These tools aren’t meant to replace the expertise or judgment of healthcare professionals. Instead, physicians can use predictive analytics to create the most effective treatment plans for their patients, leading to better outcomes and a healthier population.