Predictive Analytics In Healthcare – 10 Use Cases and Real-World Examples

The technological revolution in every global industry like healthcare is due to machine learning and artificial intelligence. It transforms preventative medication and treatment of diseases between the doctors and patients. These technologies help healthcare providers to collate data and to determine potential health dangers. It would also assist in refining treatment plans and maximize outcomes in patients.

Predictive Analytics Market for Healthcare is Poised for Growth on a Global Scale. Yet in 2022, its market worth was $11.7 billion. The CAGR of the market of the product is projected to be around 24.4% between 2023 and 2030. The urgency to improve the outcomes and reduce the cost of delivering health care services has necessitated such an unprecedented development.

Predictive analytics for healthcare is extremely vital because today people aspire to receive cost-effective, effective, and personalized treatment programs. Their sophisticated methodology assists healthcare facilities to develop customized treatments and to cope with growing needs effectively. With this in mind, the article brings 10 examples of predictive analytics in healthcare to demonstrate how technology shapes healthcare and the role that analytics play in making some important decisions.

• Understanding the Manifold Benefits of Predictive Analytics in Healthcare

In a nutshell, predictive­ analytics in healthcare is applying advanced data analysis with regard to previous health records. This aims at looking for meaningful features contained in this data that can help healthcare experts forecast future health events and their results with precision. Through the use of complex algorithms and sophisticated analytical methods, healthcare providers are able to identify potential health risks early before the development of diseases and anticipate patient reactions towards various interventions.

Here are some of the most remarkable benefits of predictive analytics in healthcare:

• Through analysis of information about patients, for example, medical history, diagnosis, or treatment outcome, predictive analytics helps healthcare providers customize intervention and treatment plans according to individual needs of particular patients.
• As such, predictive analytics in healthcare also supports personalized approaches which makes the outcomes higher and helps healthcare efficiency increase.
• It offers a practical way through which healthcare providers can forecast health complications likely to affect chronic disease patients. It is through this process that the right action can be taken immediately thereby averting the dire consequences.
• This also enables hospitals and various healthcare facilities to efficiently manage their resources such as predicting the number of patient admissions, allocating beds to their optimal levels, coordinating the staff and medical supply distribution at the right times.
• In addition, predictive­ analytics is crucial in making a diagnosis more precise. This assists in finding out the disease is earlier and crafting specialized preventive strategies.
• Predictive­ analytics assists healthcare providers to make decisions not only based on their experience but also by using real data. It promotes effective patient care, efficient hospital operations, and smart use of resources.

Predictive analytics in healthcare helps in making healthcare services at its very best. Predictive analytics is getting more sophisticated by day with our improving power of data analysis.

• Top 10 Use Cases of Using Predictive Analytics in Healthcare

The healthcare sector is being influenced by predictive analytics for healthcare in many ways. Predictive analytics transforms healthcare through improved health outcomes and better resource allocation for patients. Here are ten predictive analytics in healthcare examples that offer the most value to healthcare providers:

1. Predictive Analytics Prevent Patient’s Readmission

Hospitals’ readmissions is a problem which costs Medicare about two billion dollars every year. The Hospital Readmission Reduction program under Medicare has focused on the issue of readmissions and 82% of participating hospitals have been penalized for the increasing readmission rates.

Healthcare predictive analytics is used to identify patients at risk and implement special follow ups to prevent readmission after successful discharge.

For instance, there were predictive analytic models used in the healthcare assessments of UnityPoint Health where it assessed a readmission risk score for every patient. The senior physician used it correctly to anticipate and avert the patient’s hospitalization within the first thirty days by treating the symptoms earlier on. After an 18-month process utilizing predictive analytics, UnityPoint Health reduced all-cause readmissions by nearly 40%.

The mentioned cases illustrate the use of predictive analytics in the provision of medical care by reducing costs, improving medical health outcomes, and relieving medical care institutions.

2. Healthcare Predictive Analytics Enhance Cybersecurity

The Healthcare Data Breach Report (2014), from the HIPAA, indicates that cyberattacks on healthcare are a prominent issue. For example, such ransomware attacks as they were identified by the report showed that information was always stolen before the encryption process took place. Furthermore, there were 62 breaches of the health industry during April 2021. Of these 7 of them contained more than one hundred thousand records each.

In this sense, many healthcare organizations are turning towards cybersecurity predictive analytics as a possible solution. The integrated model of predictive assessment of transactional risks of online transactions in these organizations will also be used with artificial intelligence. For instance, the system can enable a user to submit multi-factor authentication or deny access to dangerous operations. Besides, predictive analytics health models can always monitor the accessibility and sharing of data, promptly detecting any abnormal patterns that might suggest an invasion.

In the domain of cybersecurity, healthcare predictive analytics functions across two main categories, each encompassing various subtypes:

Vulnerability-based solutions: The Common Vulnerabilities and Exposures (CVE) refer to such weaknesses in the health care system.

Threat-focused platforms: These are advance warning of threats against security which could affect the system.

3. Managing Population Health

Managing population health is a significant area where healthcare predictive analytics plays a crucial role, encompassing three key aspects:

Identifying Chronic Diseases

Predictive analytics in healthcare can help to detect and prevent chronic diseases that otherwise would be treated in these institutions. Hence, it is a method of analysis which grades the patient as per certain features like demography, disability, age, as well as their history of care.

Identifying Disease Outbreaks.

Predictive analysis showed power in detecting diseases like COVID-19. BlueDot is a Canadian firm that issued a warning about the unusual pneumonia cases in Wuhan on Dec 30, 2019. The other one was the predictive analytics tool for COVID-19 tracking by UTHealth which included a comprehensive public health dashboard showing current and forecasted pandemic spread trends.

4. Streamlining the Submission of Insurance Claims

Predictive analytics is also very useful in expediting insurance claims submissions in healthcare. These tools help hospitals in fast tracking­ insurance claims and reducing errors.

5. Analyzing Equipment Maintenance Requirements

In this regard, however, the earlier examples mostly emphasized how predictive analytics can be used in clinical settings but they do not stop at that. They extend to enhance the operations of healthcare.

For example, predictive­ analytics have been applied successfully in avionics where it can forecast maintenance requirements before the problems arise. Technicians study data from various sectors within an aircraft to replace mechanical parts preemptively. Likewise, a predictive approach can also be beneficial to healthcare operations.

Consider this: medical machines like MRI scanners deteriorate with time due to continued usage. If this information can be obtained reliably by health organizations about when such components can break down; hospital can pre plan and schedule maintenance while it is at its lowest point. In essence, this ensures very minimal disruptions to both healthcare providers and patients.

With predictive analysis, it is possible to actively monitor and analyze technical data from MRI scanner sensors remotely. This helps us detect any technical malfunctions early enough so as to rectify through replacement or repairing. In the near future, hospitals can envision a scenario where every medical device and unit of equipment will be provided with detailed digital twins that are always updated with up to date data. This will also enable forecasts of future use and maintenance needs.

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6. Preventing Patient Deterioration in ICUs and General Hospitals

It is always important for doctors and nurses to quickly identify any deterioration in a sick patient’s condition in either of the intensive-care units or the general hospital wards. This is more so in cases where immediate action is key, otherwise, it’s death. This was an issue even prior to the COVID-19 pandemics. Several countries including our own had already been overburdened with more aged people, complex surgeries, deficiency of enough intensive care experts and many more factors leading up to the recent pandemic of Corona. In the current scenario where the pandemic has intensified the condition, the health sector requires urgents technological backup for timely decisions.

Predictive software can alert when a certain patient’s vitals are likely to change significantly such that ke­eping a constant eye on a patient’s vitals can help predictive software identify those likely to require help within the next hour. Hence, caregivers have the opportunity to intervene when health starts deteriorating. Using predictive analytics in healthcare to estimate the chances of a patient dying or readmission within 48 hours of discharge from the ICU. Such information aids caregivers in making wise decisions on when to discharge patients.

Predictive algorithms are used today in settings such as tele-ICUs. This involves doctors who specialize in intensive care and critical care nurses but not the same location as the patient.

As a result, they manage to act appropriately promptly. Predictive analytics also helps to detect the earliest indications of deterioration among patients who are on general wards and could remain unrecognized for quite a long period. Following this, Philips notes that early warning systems have quickly gotten Rapid Response Teams to respond. This in turn has led to a significant reduction of incidents by 35% and hospital heart attacks by 86%.

A subtle placement of wearable biosensors on a patient’s chest has significantly improved health care providers’ abilities to detect signs of patient deterioration in advance. They are very important for patients who transit across multiple care settings in the hospital.

Moreover, biosensors capture essential body signals such as heart beats and breathing rates among others. They also observe specific contextual elements such as body posture, and the amount of physical activity. These instruments have one positive point which is the ability to do remote tracking hence reducing the number of subsequent personal visits related to health checks. It has helped to treat patients with COVID-19, particularly.

7. Suicide Attempt Prediction

Suicide is one of the crucial public health issues in the US, as the death rate by suicide is ranked 10th among 14 deaths by suicide per 100,000 people annually. However, a research team from VUMC has developed a predictive analytics model aimed at solving this crucial predicament. This is a model of predicting the occurrences of suicide attempts among selected individuals using the electronic health records of people.

For more than 11 months at VUMC, the predictive algorithm operated silently in the background while the doctors took care of their patients. It also helped to inform the healthcare professionals by enabling the system to predict patients who may go back and seek healthcare after committing suicide.

Colin Walsh pointed out the significance of predictive analytics in health and clinical practice for an assistant professor in Biomedical Informatics, Medicine, and Psychiatry. He noted that while it is almost impossible to ascertain such risks for every patient on every visit, the risk model serves as an essential preliminary screening. It is necessary to provide context so as to discuss suicide risk in settings where it is never done. In addition, it helps detect patients who require additional interrogation.

8. Improving Patient Engagement

Active patient involvement is vital towards effective healthcare. Predictive analytics allows to detect when the patient becomes ill and take proactive measures to assure the health of patients before their next appointment or treatment.

Predictive analytics in healthcare has enabled healthcare providers to come up with custom-made patient profiles that comprise tailored communication and strategies to enhance better patient relationships.

Lillian Dittrick, a fellow of the Society of Actuaries, recommends employing predictive models, with which it would be possible to detect responsive patients for lifestyle change. In targeted marketing, predictive analytics is used to come up with customer personas based on patient data and customize communication plans, which are tailored to their likes.

9. Minimizing Missed Appointments

The US healthcare system loses about $150 billion just from missed medical appointments and other time wasting administration endeavors. Therefore, predictive analytics is one of the best approaches for alerting the hospitals and clinics with high patient no-show likelihood and thus prevent revenue loss and improve provider satisfaction.

Building this predictive modeling tool by some researchers from Duke University on the EHRs of patients for possible noncompliance. Out of the 350,000 cases of patients not showing up for an appointment at Duke’s health system, the software identified 4,819 instances. The researchers highlighted the importance of educating AI using locally derived clinical information generating better scores compared to vendor training on its own.

CipherHealth is a health tech company based in New York. The firm worked hand in hand with Community Health Network and helped to develop an analytical solution to reduce patient no-show while increasing outreach efforts. It helps in predicting potential no-shows and also allows for remote consultations customized for every client.

10. Detecting Early Signs of Sepsis

Sepsis is a fatal disease caused by an overwhelming infection developing rapidly in the body. Therefore, predictive analytics could facilitate early detection and intervention. Predictive algorithms assist in identifying those patients who are most likely to develop sepsis as this is a deadly infection.

For instance, at the University of Pennsylvania Health System, a predictive analytics tool was used to identify possible patients with sepsis. It took into account things like vital signs, lab results, and nurse data in determining whether sepsis was going to occur. This technology reduced the hospital’s early and effective sepsis-related mortality rates.

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In Conclusion

The healthcare sector is increasingly leveraging predictive­ analytics which has enhanced efficiency in operations and improved patient care. Healthcare predictive analytics and their real-life applications demonstrate the potential of predictive analytics in shaping the future of health care predictive analytics.

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FAQ’s

How to use predictive analytics in healthcare?

Healthcare predictive analytics uses historical information to anticipate future events, such as health incidents and outcomes for individualized care and pre-emptive measures. Thus, it can identify health threats, improve patient care and efficiency in operations of healthcare facilities.

What is meant by predictive models in healthcare?

A common range of predictive models used in healthcare include logistic regression, support vector machines, decision trees, and neural networks. These models utilize the patients’ information and project what would be the result of these diseases before the actual occurrence.

What is an example of predictive analytics in healthcare?

For instance, machine learning algorithms could be used to predict patient readmissions as one predictive analytics application in healthcare. Using this model, health care providers are able to study past information regarding readmissions and notice the tendencies of repeated admissions. This helps them take preemptive measures aimed at preventing admissions in the near future.