What is Predictive Analytics in Healthcare? Examples & Benefits

Healthcare, like any other industry today, is changing.

Most industry verticals are digitally transforming their work environments. Whether we talk about Healthcare, Retail, manufacturing, oil and gas, education, or any other service sector, they all become more technologically advanced daily. Similarly, the healthcare sector is on the way to fully adopting predictive analytics in their work processes.

So, what is Predictive Analytics in Healthcare?

Predictive analytics in Healthcare means predicting the possibilities of the future. It is done by using past information about the patient, understanding their pattern of getting sick, and telling them the possibility of having a disease they can get in the future.

Healthcare is getting advanced with the development of the latest tools and technologies, which help them manage a lot of data, perform certain medical practices, Operate patients with robotic equipment, etc.

Today, everything is all about data. Many software programs in the market help simplify data for healthcare organizations. Here, predictive analytics plays a crucial role. Predictive analytics also helps in managing data with ease. Data accessibility at the fingertips can transform the way Healthcare works. Through predictive analytics –

• Doctors can help patients more effectively by quickly getting their data, where they can check the patient’s past medical history. This allows them to track their disease pattern and offer solutions accordingly.
• Well-polished data helps personalize each patient’s treatment, which means a fast chance of recovery.
• Hospitals can also effectively manage their staff and equipment based on patient data.
• Lastly, with the help of data, researchers can discover new treatments and medicines for the disease.

Moreover, with the help of data analytics, we can transform healthcare data into actionable insights, which can help healthcare professionals in –

• New Drug Discoveries
• Disease Prediction and Prevention
• Support for Clinical Decisions
• Faster and More Precise Diagnoses
• Improved Success Rates for Surgeries and Medications
• Streamlined Hospital Administration
• More Accurate Health Insurance Pricing
• Personalized Treatment for Patients
• Effective Resource Allocation
• Fraud Detection and Prevention
• Better Risk Management
• Innovation in Treatments

Predictive analytics is the future of Healthcare, and it has many other benefits that can ease medical processes. In this blog, we will discuss everything about predictive analytics in Healthcare like –

• What are Predictive Analytics in Healthcare
• Predictive Analytics in Healthcare Examples
• Predictive Analytics in healthcare benefits

What are Predictive Analytics in Healthcare?

Predictive analytics means looking at old health records to spot patterns that could help predict future health issues. This can involve estimating an individual’s illness, the effectiveness of a particular treatment, or even the chances of having the same disease.

Predictive analytics is crucial in identifying potential health risks and preventing diseases before they become severe, contributing to public health.

This analysis allows doctors to select better treatment options and plan individual care according to the patient’s needs. Through the predictive approach, doctors can discover the complications involved in specific patient procedures and how to overcome them. This not only helps in providing better care but also makes the whole healthcare system work more smoothly.

These insights help healthcare organizations to make better and more informed decisions. For example, it assists doctors in

• Identifying diseases
• Helping hospitals organize resources effectively and
• Having a more profound knowledge of the clients of insurance

Healthcare businesses can leverage data effectively by employing predictive analytics tools that help reduce costs and improve patient satisfaction. This enhances healthcare facilities and services’ effectiveness, reliability, and cost. It benefits almost all aspects, including diagnosis or resource management improvements.

Considering how effectively predictive analytics improves patients’ lives, more and more healthcare organizations are adopting it. It has become one of the most rapidly adopted technologies in the healthcare sector.

Predictive Analytics in Healthcare Examples

Let’s discuss examples of predictive analytics in Healthcare to understand better how it works and why it has become so popular worldwide.

1. Early Detection of Diseases

Predictive analytics can detect potential signs of disease from patients’ records of previous diseases, lab reports, and other aspects of life. Most diseases, like cancer and heart diseases, are diagnosed later, meaning that the survival threat is higher and, hence, the level of treatment is also higher.

Predictive analytics tools can use information collected from a patient’s history, daily habits, and genetic profile to detect signs of deterioration.

For example, IBM Watson Health targets risk analytics on patients’ electronic records to determine those most likely to be at risk of diabetes or cancer.

Research has indicated that such early detection does enhance the survival rate by a considerable percentage.

2. Personalized Treatment Plans

Predictive analytics incorporate patient and genetic data to help accurately predict patient treatment. Most conventional therapies are not the same for everyone, which leads to improper treatment and side effects. Predictive analytics enables the creation of personalized treatment plans, which can significantly improve patient outcomes and reduce the risk of adverse effects.
Another way it can be beneficial is that, for instance, treatments can be predicted based on detailed information about the patient, such as genes.

For instance, the application of genomics in oncology creates effective patient treatment strategies for cancers.

On this aspect, a study by the American Association for Cancer Research showed that using predictive analytics to analyze genomic data enhanced the rate of targeted therapy treatment responses by up to 50%.

3. Predicting Hospital Readmissions

Predictive analytics tools make it easy to identify which patients are likely to be readmitted to the hospital so that necessary measures can be taken earlier. Predictive tools can identify patients at high risk of readmission and suggest preventive measures based on that risk.

For example, the Centers for Medicare & Medicaid Services (CMS) uses predictive models to recognize high-risk patients.

A trial conducted by the Agency for Healthcare Research and Quality, AHRQ, showed that hospitals that implemented analytical tools to decrease readmissions realized a decrease of approximately 30%, which also reduced expenses.

4. Predicting Hospital Readmissions

Predictive analytics tools make it easy to identify which patients are likely to be readmitted to the hospital so that necessary measures can be taken earlier.

For example, the Centers for Medicare & Medicaid Services (CMS) uses predictive models that analyze patient data, including demographics, medical history, and social determinants of health, to recognize high-risk patients and provide targeted interventions to prevent readmissions.

A trial conducted by the Agency for Healthcare Research and Quality, AHRQ, showed that hospitals that implemented analytical tools to decrease readmissions realized a decrease of approximately 15 percent, reducing many expenses.

5. Optimizing Resource Allocation

Based on the research, healthcare facilities leverage big data analytics to predict admissions and resource utilization. Inadequate resource management in hospitals is a common challenge. This often leads to congested wards, underutilized staff, and deteriorating equipment.

Finally, predictive analytics can help predict patients’ admissions and measure efficient resource use.

For example, the tool deployed at the Cleveland Clinic enables the organization to project the number of patients it expects to attend the hospital/clinic and, therefore, the number of personnel to deploy at a given time.

For instance, Gundersen Health System utilized artificial intelligence-driven predictive analytics to achieve a 9% improvement in room utilization.

6. Managing Chronic Conditions

Applications like predictive analytics help treat chronic diseases by identifying possible complications. Diabetes is a chronic illness, and conditions associated with the disease need some level of maintenance over time. Sometimes, an individual suffers a rude shock when a complication arises, and it can cause their death also.

One benefit is that predictive analytics can foresee and closely monitor complications, allowing for early treatment.

The American Diabetes Association later discovered that the prediction tools can lower diabetes hospitalization incidences by 20 to 30 percent. Many diabetic patients benefit from this. It also helps manage other chronic diseases that are common among people and require extensive care.

Unity Point Heath discovered that predictive analytics tools can reduce hospital admissions by 40% when used to manage diabetes.

7. Drug Development

In clinical research and development, big data predictive analytics helps to quicken the process of identifying new medicines by studying test results.

For instance, Pfizer applies business intelligence to screen drug candidates and modify the development model.

See Pfizer’s case, where it was noted that predictive models reduced the drug discovery phase by a quarter. Consequently, such models fast-track the clock on a new treatment.

8. Enhancing Patient Safety

Advanced predictive analytics are used to carefully examine patient data to predict and prevent potential adverse events. These models can track and forecast unfavorable occurrences like medical mistakes or operation complications.

The FDA uses predictive analytics to ensure safer drug consumption by monitoring and analyzing adverse drug events.

In addition, a paper in JAMA Network Open indicated that hospitals that applied predictive analytics to patient safety issues recorded a 40% reduction in adverse outcomes.

Interesting Read – Power BI Analytics Dashboard For Patient Management

9. Predicting Suicide Attempts

Depression is a significant issue in the US, where there are about 14 deaths by suicide per 100,000 people annually. According to the data from the CDC, more than 49,000 individuals in the U. S. died by suicide in 2022, and approximately 1.7 million people attempted it the previous year.

This implies that, on average, every 11 minutes, a person dies from suicide. Also, the male gender was confirmed to have a suicide rate that was over three times that of the female gender.

This can be addressed through predictive analytics, in which the possibility of a repeat attempt is anticipated based on the patient’s behavior, medical history, and social and economic factors.

Scientists have developed a method that can work with patient’s records and estimate the likelihood of a suicide attempt. It remained operational in the background for 11 months to analyze patients into eight risk categories.

The highest-risk group was also noted to have made over one-third of the attempts for clients, leading to closer monitoring recommendations.

This study highlights how predictive tools can help in identifying and monitoring individuals at risk of suicide, encouraging more effective intervention and support.

Benefits of Predictive Analytics in Healthcare

Predictive analytics presents numerous advantages that help cure health issues with the proper treatment at the right time. Here’s how it makes a difference:

1. Enhanced Hospital Management and Operational Efficiency

This form of analytics can go a long way in enhancing how health facilities are managed. It helps avoid schedule conflicts and delays in handling the claims, and as a result, it helps make everything go smoothly and without hitches.

This technology can also forecast patient traffic, helping to schedule appointments more effectively and avoid long waits. It makes it easier for hospitals, insurance companies, and patients to work together on claims, speeding up the process.

Furthermore, automating routine tasks with predictive analytics saves healthcare workers time by reducing their workload while maintaining quality healthcare provision.

2. Disease Prevention and Early Detection

Another significant benefit of using predictive analytics in Healthcare is that it is easy to prevent upcoming diseases by taking the proper precautions at the right time. Doctors and insurance companies use intelligent algorithms to determine the likelihood of a patient developing an illness.

They can step in early with treatment plans to help patients stay healthy when they spot a risk.

These tools can also monitor the signs of disease and treat the patient before the disease progresses to a level that necessitates costly treatments. Doctors even suggest ways for patients to save money on early treatment.

3. Proactive Care

Similarly, using predictive analysis, doctors can quickly identify health issues or complications after surgery at an early stage. This acts as an early warning alarm, allowing them to treat diseases before they become serious.

Healthcare is shifting towards a proactive approach, where doctors use patient data to predict issues early and take preventive measures to deal with them.

Patients are distinguished by their history; nurses or doctors observe specific patterns in patient data and treat them accordingly. This method results in better care and less spending, which benefits everyone, including the patient, the physician, and the insurance company.

4. Reducing Healthcare Costs

Trends in Healthcare indicate that healthcare costs continue to increase, straining patients’ pockets. Predictive analytics identify patients with poor health outcomes who can be given proper care, contributing to healthcare cost savings.

For example, a study in The New England Journal of Medicine reported that using predictive models in patient care management programs reduced healthcare costs for high-risk patients by 12%.

5. Stopping Healthcare Fraud

Healthcare fraud is a big problem. It includes things like

• Charging too much
• Billing for services that are not required.
• Making fake claims and
• Bribing doctors.

They are charging too much, meaning a healthcare provider will charge more for a procedure than the amount required for the same treatment. Apart from this, there are service providers who overcharge their clients, and sometimes, they render services that the clients never needed and or never requested in the first place. This is unethical.

Bribery involves paying doctors to send patients, prescribe certain drugs, or use specific medical devices. Fake claims include identity fraud, where unauthorized individuals conduct medical procedures and have a licensed doctor sign them.

Predictive analytics helps stop healthcare fraud by finding unusual patterns and spotting suspicious activities early, preventing significant losses.

Conclusion

Predictive analytics transforms the healthcare industry by enabling early disease detection, personalized treatment plans, and improved operational efficiency. Its benefits, including improved patient outcomes, cost reduction, and enhanced patient satisfaction, make it a crucial tool for modern Healthcare.

Despite challenges such as data privacy and regulatory compliance, the future of predictive analytics in Healthcare looks promising, with ongoing advancements driving further innovations.

SPEC INDIA is highly specialized in offering healthcare software solutions, ranging from state-of-the-art predictive analytics software to improving the quality of patient care. We develop predictive analytics software designed to assist healthcare organizations in discovering current and future health risks, readmission rates, and future patient outcome probabilities, making healthcare delivery more practical and cost-effective.