Predictive Analytics In Healthcare: 7 Examples and Risks

What is predictive analytics in healthcare?

According to Deloitte, predictive analytics can be described as a branch of advanced analytics, which is used to make predictions about unknown future activities.

Unlike traditional approaches to analytics that simply present processed information from the past, predictive analytics uses historical and real-time data to forecast future events and identify trends in patient care. To achieve this, predictive analytics employs a variety of techniques including data mining, statistics, AI, and ML. Simply put, predictive analytics solutions answer the question: what can possibly happen next?

What can predictive analytics accomplish in healthcare?

In healthcare, predictive analytics solutions rely on big data and artificial intelligence. Predictive analytics in healthcare aggregates vast amounts of patient data incoming from electronic health records (EHR), insurance claims, administrative paperwork, medical imaging, etc. and processes it searching for patterns. With predictive analytics healthcare providers can figure out:

Which diseases patients are likely to develop?
How will they respond to different treatments?
Will they be a no-show in their next medical appointment?
Will they return to the hospital within 30 days of discharge?

Here’s what Vinnie Ramesh, CTO and Co-founder of Wellframe, says about predictive analytics: “Combining these new datasets with the existing sciences of epidemiology and clinical medicine allows us to accelerate progress in understanding the relationships between external factors and human biology—ultimately resulting in enhanced reengineering of clinical pathways and truly personalized care.”

Benefits of predictive analytics in healthcare

Here are some examples of how healthcare leaders can benefit from predictive analytics:

Reducing costs on appointment no show and readmission penalties
Speeding up administrative tasks such as discharge procedures and insurance claims submission
Preventing ransom and other cyberattacks by analyzing ongoing transactions and assigning risk scores
Proactively preparing for upcoming population health trends
Acquiring new patients through personalized campaigns

7 examples of predictive analytics in healthcare

Predictive analytics is transforming the medical field in multiple ways. Here are seven applications that offer the most value to healthcare providers:

Preventing readmissions
Managing population health
Enhancing cybersecurity
Increasing patient engagement and outreach
Speeding up insurance claims submission
Predicting suicide attempts
Forecasting appointment no-shows

1. Preventing readmission

The costs of hospital readmission are rather high. According to HealthcareDive, Medicare spends around $26 billion annually on readmissions. Additionally, hospitals are subject to heavy penalties under Medicare’s Hospital Readmission Reduction Program, which adds financial incentives to combat readmission. As research shows, 82% of hospitals in this program received this type of penalty. Healthcare predictive analytics can identify patients with traits indicating a high possibility of readmission so that doctors can allocate additional resources for follow-ups and personalize discharge protocols to prevent a quick turnaround. How predictive analytics in healthcare reduces hospital readmission UnityPoint Health, a network of healthcare facilities, wanted to understand why patients are being readmitted. They simply asked them “Why do you think you are back?” The answers varied from not having the means to buy medication to not being able to book follow-up appointments. UnityPoint Health aggregated these answers into a predictive model that would assign a readmission risk score to every visiting patient. A family physician, Patricia Newland, used the UnityPoint algorithm to prevent the readmission of one of her patients. The tool predicted this patient would experience symptoms during the next 13 to 18 days after discharge. Newland shared the results with her patient and instructed her to call the clinic as soon as the described symptoms appear. Sure enough, the patient called during the predicted time frame. Newland, who had already allocated time and resources, was able to see the patient on the same day and change her medication, thereby preventing readmission. UnityPoint Health could reduce patient all-causes readmission by 40% within 18 months of deploying the predictive analytics tool. Another example comes from Utah-based Health Catalyst, which provides data and analytics technology to the healthcare sector. The company developed a predictive health analytics solution to reduce readmissions. If you want to deploy their software, your data scientists will collaborate with the vendor to integrate your data into the program and establish a dashboard where medical staff can log in to see the likelihood of admitted patients developing a central line-associated bloodstream infection (CLABSI), which would ensure readmission. Thanks to Health Catalyst, the University of Kansas Health System reduced their all-causes readmission by 39% .

2. Managing population health

This is another prominent healthcare predictive analytics example, which covers three aspects. Spotting chronic diseases with predictive analytics for healthcare Predictive analytics in healthcare helps medical institutions identify people who are at risk of developing chronic conditions and give them preventive care before the disease progresses. This type of analytics assigns scores to patients based on a variety of factors, including demographics, disabilities, age, and past patterns of care. Diabetes Care published a study demonstrating that predictive analytics models for healthcare can determine a five to ten years life expectancy for older adults with diabetes, enabling doctors to craft customized treatment plans. Identifying public health trends with predictive analytics Additionally, predictive analytics in the healthcare industry helps identify potential population health trends. The Lancet Public Health journal published a study that used predictive analytics to uncover health trends. This study found that unless alcohol consumption patterns will change in the US, alcohol-related liver diseases will rise, causing deaths. Detecting disease outbreaks with predictive healthcare analytics When speaking of outbreak predictions, one can’t help but ask, “could predictive analytics have foreseen the COVID-19 pandemic?” The answer is yes. BlueDot, a Canadian company building predictive analytics and AI solutions, issued a warning about the rise of unfamiliar pneumonia cases in Wuhan on December 30, 2019. Only nine days later, the World Health Organization released an official statement declaring the novel coronavirus emergence. To this day, predictive analytics in healthcare helps authorities and ordinary people to have a view on the pandemic. For instance, a research team at the University of Texas Health Science Center at Houston (UTHealth) developed a predictive analytics-based tool for COVID-19 tracking. This program produces and maintains a public health dashboard that shows current and future trends of the virus. Shreela Sharma, Ph.D., and a member of the UTHealth research team, outlined the benefits of this predictive analytics tool: “The dashboard identifies the current hot spots, predicts future spread both at the state and county level, and houses relevant public health resources. It can effectively inform decision-makers across Texas to help mitigate the spread of COVID-19.”

3. Enhancing cybersecurity

According to the HIPAA Healthcare Data Breach Report, cyberattacks are frequent in the healthcare sector. The report revealed that most ransomware attacks involved data theft before encryption. April 2021 sustained 62 data breaches in the healthcare sector. Seven of them resulted in compromising over 100.000 medical records each.

Cybersecurity predictive analytics in healthcare can positively contribute to this situation. Healthcare organizations can use predictive analytics coupled with artificial intelligence solutions for the medical sector to calculate risk scores for different online transactions in real-time and respond to events based on their scores. For example, during a login event, the system will grant access to a low-risk process, and block a high-risk process or challenge it for multi-factor authentication. Furthermore, predictive modeling in healthcare can monitor data access and sharing to identify any changes in patterns, which could indicate an intrusion. Predictive analytics in cybersecurity comes in two high-level types. Each has many subtypes of its own:

Vulnerability-based solutions are searching for weaknesses in the healthcare system that can be exploited. These vulnerabilities range from misconfigurations to Common Vulnerabilities and Exposures (CVEs) that were not patched.
Threat-focused platforms that look for potential threats.

Saif Abed, CEO of Clinical Cyber Defense Systems, advises healthcare clinics to consider outcome-based variants of both types. Here is what he said, “I recommend healthcare providers focus on outcomes-based analytics: solutions that can correlate technical findings with actual clinical and business metrics. If a solution can chart, forecast and predict whether patient harm will happen or whether services will be shut down, then it’s a solution able to capture the attention of hospital executives at the highest levels irrespective – of their technical acumen.”

4. Increasing patient engagement and outreach

To receive quality care, patients need to actively participate in their well-being. Another benefit is that studying patient engagement behavior can help spot when patients are becoming non-compliant. Having this information, doctors can be proactive in their approach instead of waiting until non-compliance reflects on patients’ health. Medical facilities can use predictive analytics in healthcare to engage patients and strengthen their relationships with physicians. These tools can help create patient profiles, send customized messages, and craft strategies that would be more impactful on each individual. Lillian Dittrick, Fellow of the Society of Actuaries, said on the usage of predictive analytics: “When we use predictive models to look at all the variables, it helps us prioritize those patients who are really going to be receptive to changing something in their lifestyles, such as nutrition or exercise.” Predictive healthcare analytics enable targeted marketing Hospitals and pharmacies can use predictive algorithms to analyze patient data and identify dynamic customer personas with their distinct preferences and patterns. Afterwards, physicians can relate to these personas while planning targeted outreach messaging on, for instance, treatment adherence and drug efficacy. The marketing department can use these personas to craft their email campaigns.

5. Speeding up insurance claims submission

Health insurance can also benefit from predictive analytics. Such tools can help hospitals prepare insurance claims faster while minimizing errors. One example comes from Apixio. Headquartered in California, this company builds analytics-powered tools for healthcare. Apixio developed software that helps hospital coders to identify the correct codes for insurance claims. Those codes will determine how much insurance companies need to pay. Traditionally, hospital coders sift through vast amounts of information to find the right codes. Apixio’s tool mines patient medical records for relevant data and presents hospital coders with the selected pieces that will help them determine the best code options.

6. Predicting suicide attempts

Suicide is the tenth leading cause of death in the US, taking lives of 14 Americans out of 100,000 annually. To improve this sad situation, a research team at the Vanderbilt University Medical Center (VUMC) developed a predictive analytics model that uses patients’ electronic health records to forecast the likelihood of suicide attempts by particular patients. The tool was tested at VUMC for 11 months. While doctors were receiving patients, the algorithm was running in the background, predicting the risk of patients returning for attempted suicide treatment. This predictive analytics in healthcare tool classified patients into 8 groups based on their estimated risk factor. The VUMC research team noticed that the highest-risk group accounted for over 33% of all suicide attempts. The team concluded that anyone who is placed into the high-risk groups should be checked for suicidal tendencies. Colin Walsh, assistant professor of Biomedical Informatics, Medicine and Psychiatry described the benefits of such predictive models, "Today across the Medical Center, we cannot screen every patient for suicide risk in every encounter — nor should we. But we know some individuals are never screened despite factors that might put them at higher risk. This risk model is a first pass at that screening and might suggest which patients to screen further in settings where suicidality is not often discussed."

7. Forecasting appointment no-shows

The US healthcare system is losing about $150 billion per year on appointment no-shows, not to mention the associated administrative burden. For individual practitioners, the costs are averaging $200 per missed appointment. Predictive analytics in healthcare can improve cost efficiency by allowing hospitals and private clinics to forecast which patients are likely to skip appointments without prior notice. Consequently, they can prevent revenue loss and increase provider satisfaction. Examples of predictive analytics for medical appointments no-shows Researchers from Duke University developed a predictive modeling tool that analyzes patients’ EHRs to identify potential no-shows. This software captured 4819 points of no-show in the Duke health system. The research team believes the algorithm performed well because it was trained specifically for this medical clinic. They strongly recommend retraining the acquired algorithms on the local clinical data. This will yield better results than when depending on the vendor’s training. Some healthcare organizations are taking it a step further and reaching out to the patients with a potential no-show record to make sure they are fine and offer care remotely. Community Health Network partnered with CipherHealth, a health tech company headquartered in New York to deploy a data analytics solution that would reduce no-shows and facilitate patient outreach. With the new system in place, Community Health Networks can not only predict a no-show event but also reach out to the corresponding patient via their preferred means of communication (text, phone, email) to offer a brief remote consultation to satisfy the patient’s needs.

Predictive analytics surely benefits healthcare providers. But there are things to consider

Predictive analytics in healthcare offers many benefits and, as the industry amasses more and more data, the algorithms will improve in accuracy. However, these types of tools bring their own risks that medical facilities will need to address before unlocking their full potential. Deloitte describes three main risk factors associated with implementing predictive analytics solutions in healthcare:

Gaining doctors’ acceptance. Healthcare providers are finding themselves in a constant need to advance their computer skills. And with predictive analytics, they will not only need to access dashboards but also keep capturing and processing patient data. It can be a challenge to find the balance between patient care and data collection during appointments. To overcome this challenge, one can involve doctors in the algorithm development process to make sure the solution addresses their needs. As Oscar Marroquin, Chief Clinical Analytics Officer at the University of Pittsburgh Medical Center, stated, “Being able to win acceptance of your models by clinicians—who are skeptics by nature—is often a challenge. By making end-users part of the whole process, we’ve been able to overcome that in the majority of cases.
Ethics and moral hazards. One manifestation of moral hazard is that people are faster to take risks when they know they are insured. Some doctors may no longer put a lot of thought into making decisions as they believe predictive analytics is responsible for the consequences. To minimize this risk, everyone involved needs to understand that some decisions made by analytical tools are not binding but merely a suggestion. Clinicians still need to think them through and discuss with patients if necessary.
Algorithm bias and lack of regulations. There are several types of algorithm bias that can impact predictive analytics’ performance on particular datasets. What makes things harder is that there are no explicit regulations governing algorithm development. At the moment, the responsibility of producing fair tools falls on the vendor’s shoulders and healthcare facilities are counting on the vendor’s goodwill. To reduce the risk of bias, vendors can use feedback loops to improve their tools and eliminate any bias that still gets in. Also, organizations that are using predictive analytics in healthcare need to conduct regular audits to make sure the algorithms are still relevant.

The global predictive analytics in healthcare market keeps growing and is expected to hit $7.8 billion by 2025. It provides plenty of opportunities for healthcare providers and health tech companies. If you are a medical facility manager, search for ways to deploy predictive analytics at your organization and ensure doctors and executives are on board. If you are a health tech startup founder, make sure the algorithms you develop are fair and suitable for the targeted population segments.

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Predictive analytics in healthcare: 7 ways to save time and money