Predictive Analytics in Healthcare: A Pathway to Personalized Patient Care

Mar 12, 2019

healthcare data analytics

Every industry today is transformed virtually by predictive analytics and the healthcare industry is no exception. Predictive analytics in healthcare is advancing patient care significantly by offering detailed insights into the medical history of patients. However, there are many healthcare organizations that are yet to tap into the full potential of predictive analytics in healthcare. Deploying predictive capabilities in extracting actionable, forward-looking insights from the growing pool of data is still a daunting task for many healthcare companies.

However, realizing the power of predictive analytics in healthcare is highly essential as predictive analytics platforms can help healthcare companies to transform medical wisdom into real-world insights. Leveraging predictive analytics in healthcare can help companies to move into the new era of medicine. Consequently, this can improve hospital care, help in the management of chronic diseases, and enhance supply chain processes.

At Quantzig, we understand the difference that leveraging predictive analytics solutions can create in the healthcare industry. And to help companies in the healthcare sector stay ahead of the curve, our team of analytics experts has highlighted the ways in which predictive analytics can improve healthcare and also have explained a few use cases for predictive analytics in healthcare.

Are you looking for ways to gain consolidated insights to mitigate risks and resolution of issues for medical cost management and improved patient care? Get in touch with our experts now!

How Can Predictive Analytics Improve Healthcare?

#1: Improve Public Health and Preventive Medicine

With the intervention of predictive analytics models and predictive algorithms, many diseases can be treated or ameliorated. This technology in the realm of genomics can enable the physicians to identify patients that are at risk and make changes in their lifestyle to avoid future risks. Furthermore, this can change disease patterns significantly. Also, predictive analytics can play a crucial role in shifting towards well-living.

#2: Provide Necessary Answers to Physicians for Better Patient Care

Machine learning and predictive models can help doctors to decide exact treatments for the patients. Targeted treatments, accurate diagnoses, and personalized care can result in better treatments and can naturally lead to better patient outcomes.

#3: Assist in Predictions for Insurance Costs

If the data of patients’ behavioral patterns are fed into the predictive analytic algorithm, it can help in assessing and predicting future medical costs. Companies working in collaboration with insurance providers can align their databases to build predictive analytics models and health plans. Predictive analytics technologies can also be used by healthcare companies to identify the insurance providers that may offer effective policies to serve the specific needs of the patients.

We offer a portfolio of healthcare analytics solutions and domain expertise that include the integration of clinical trials, electronic medical records, physician notes, hospital records, and pharmacy data to help drive better decisions, reduce the cost of care and improve payment accuracy. To gain more insights into our healthcare analytics solutions, request a free proposal today.

#4: Pharmaceutical Companies Can Leverage Predictive Analytics to Improve their Services

Predictive analytics models can help pharmaceutical companies to develop medications even for the smaller groups. It can help in discovering the side effects of the medicines, thereby serving patients with the least side effect causing medicines and health care services.

#5: Improve the patients’ Quality of Life

With the increasing use of predictive analytics in healthcare, the quality of life of patients can improve significantly. This can help patients receive accurate treatments and suitable medications. Additionally, leveraging predictive analytics in healthcare can change a patient to an informed consumer who can collaboratively work with the doctors to seek better treatments. Furthermore, predictive analytics can make the patients aware of personal health risks due to alerts generated by predictive models and genome analysis.

Use Cases for Predictive Analytics in Healthcare

#1: Risk Scores for Chronic Diseases

In the world of health management, predictions and prevention go hand-in-hand. Predictive analytics in healthcare can help in creating risk scores based on various data fetched from claims, biometric systems, lab reports, health records, and social determinants. This score can provide actionable insights into patient needs, which in turn, can help enhance patient care. Also, predictive analytics in healthcare can help in identifying risks of developing any chronic disease at the early stage.

#2: Avoid Patient Re-Admission

Predictive analytics in healthcare can deploy care coordination strategies and enhance transitions of care. Also, it can warn healthcare providers when the risk factors of a patient signal a high likelihood of readmission within 30-days. Furthermore, this can help in designing discharge planning protocols that can prevent speedy returns to the hospital.

#3: Stay Ahead of Patient Deterioration

Patients face several potential threats in the hospital like infection or unforeseen downturn due to some clinical conditions. Machine learning and predictive analytics in healthcare can help healthcare providers to react as soon as possible to identify future deterioration and changes in patient’s vitals. This can further, help in improving patient care.

Wondering how to reduce operational costs, increase profit margins, and address challenges in the healthcare industry? Request a free demo to know how our analytics solutions can help.

#4: Forestall Appointments

There are incidences of unexpected gaps in the daily calendar. This can impact the financial outcome of any healthcare organization. But predictive analytics in healthcare can help in filling these gaps and can improve the workflow. By using predictive analytics in healthcare, healthcare providers can effectively identify patients that are likely to miss an appointment. This can help in improving patient service, reducing revenue loss, and speeding patient care. 

#5: Predict Patterns of Patient Utilization

By leveraging predictive analytics in healthcare, it is easy to forecast the situation when the clinic is about to get busy, require more staff for patient care and emergency units. Also, it can predict the flow of patients in a specific time period to indicate the need for more beds and physicians to keep patient wait time low. This can result in raising patient satisfaction.

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