Predictive analytics in healthcare:
top use cases & adoption tips

Predictive analytics in healthcare: top use cases & adoption tips

March 5, 2024

Predictive analytics’ market stats

of healthcare leaders in the US have adopted predictive analytics tools


expected CAGR of the global healthcare predictive analytics market by 2030

Next Move Strategy Consulting

Top use cases for predictive analytics in healthcare

The numerous benefits of predictive analytics for healthcare organizations, individual patients, and the population make it one of the most used types of analytics in the industry.

Personalizing care

Predictive analytics helps healthcare professionals spot trends in patients’ medical records and genomic information that are impossible to detect manually, helping to understand patient conditions better. Predictive modeling, in particular, allows forecasting the course of the disease, enabling medical professionals to avoid health risks like adverse reactions to medicine, genetically determined resistance to treatment, and failure to adhere to the regimen. This way, clinicians receive actionable insights that help them optimize patients’ treatment plans according to their physical and psychological needs.

Improved health outcomes
medicineTherapy planningGenomic considerationsClinical considerationsSocial determinants of healthPredictive
analyticsRisk prediction or diagnosisGenomic considerationsClinical considerationsImproving personalized care

Scheme title: How to combine precision medicine and predictive analytics for personalized care
Data source: — Precision Medicine, AI, and the Future of Personalized Health Care

Avoiding readmissions

According to Medicare’s Hospital Readmission Reduction program, healthcare organizations are subject to penalties in case of patient readmission within 30 days after receiving care. Therefore, avoiding readmission will improve patient satisfaction and save healthcare providers’ money.

Predictive analytics can identify patients with a high risk of readmission after a particular treatment. Predictive analytical tools usually use EHR data to report which factors are most likely to increase patients' risk of readmission within 30 days. Care providers can use this information to make more appropriate clinical decisions to avoid readmission, like scheduling more frequent follow-up visits, optimizing discharge instructions, and prescribing additional medication.

Reduced pressure on hospital staff and increased revenue

Developing new treatments

Medical researchers are turning to predictive modeling and analytics to augment traditional clinical study processes. One example of a predictive modeling application is silico testing. This method involves using extensive quantities of data from multiple sources as a basis for a predictive model assessing the therapy’s impact on patients. As a result, the findings can lower the risks for study participants and reduce the treatment research costs for researchers and manufacturers.

Faster treatment rollout
A. Risk detectionHistorical health recordsDemographic infoElectroencephalogram signalsPredictDisease incidence rates10Heart
diseaseSleep disordersB. Sequential event predictionVisit 1Visit 2Visit 3ModelModel

Scheme title: Individualized predictive modeling for in silico clinical trials
Data source: — Artificial Intelligence for In Silico Clinical Trials: A Review

Enhancing care for patients with chronic conditions

Another common use of predictive analytics in healthcare is to help clinicians make better decisions when providing long-term, palliative, and other types of care for patients with chronic diseases. Predictive models help identify patients at risk of developing certain complications or adverse drug reactions and recommend adjusting their treatment plans to avoid adverse patient outcomes. Data analytics can also help spot patients likely to develop chronic conditions in the first place and suggest an early intervention. A scientific paper published in the Journal of Healthcare Engineering shows that using sophisticated machine learning algorithms such as convolutional neural networks (CNN) and K-nearest neighbor (KNN) can improve the accuracy of chronic disease prediction to 96%.

Improved quality of life for the chronically ill

Scheme title: Comparison of accuracies of Naïve Bayes, decision tree, logistic regression, and CNN & KNN algorithms
Data source: — Identification and Prediction of Chronic Diseases Using Machine Learning Approach

Streamlining inventory management

With predictive analytics tools, clinicians can discover specific trends in resource allocations and foresee the facility’s upcoming needs. This can enable healthcare administrators to buy the required medical goods or relocate them on time to prevent stockouts. Besides, hospitals can employ predictive analytics to determine what resources are prone to be out-of-stock faster than others, relying on trends among the population or seasonal needs. This knowledge helps implement better strategies for saving money, keeping healthcare costs affordable for patients, and reducing medical waste.

Optimized resource allocation and usage

Improving patient engagement

Predictive analytics offers healthcare providers better insights into patients and their needs. For example, Community Health Network from Indianapolis, Indiana, uses predictive analytics models to reduce no-shows. Their analytical tools help identify no-show risk factors among the demographic determinants of the patients and develop special workflows to make sure patients confirm their appointments. Healthcare data analytics can also help pinpoint which line of conduct would resonate with certain patients or patient groups. Using this information, providers can design patient engagement strategies, offer more personalized care, and improve patient outcomes.

Enhanced population health and quality of life

Accelerating insurance reimbursements

Healthcare organizations can employ predictive analytics tools to analyze claims and understand the likelihood of them being fulfilled or declined, find out which data is missing, and how to improve the documentation before sending it to the insurer. This helps optimize claim submission and fulfillment processes, helps fight fraud, and yields higher insurance payments.

Healthcare insurance fraud prevention

Anticipating epidemics

Nature Communications has recently published a study that proves the feasibility of predictive analytics for accurate estimation and forecasting of respiratory diseases for locations without sentinel surveillance. Metapopulation models that had already been successfully used to prognose outbreaks of influenza, dengue, malaria, and COVID, were coupled with other analytical tools and employed to forecast when and where additional surveillance will be necessary for epidemic prevention.

Reduced pressure on the ER and improved population health

How we help providers adopt predictive analytics software

Our specialists create stand-alone predictive analytics solutions or add analytical capabilities to the existing healthcare software.

How we help providers adopt predictive analytics software

We identify healthcare providers’ business needs, objectives, user expectations, and concerns. Then, our specialists assess the technical environment and define functional and non-functional requirements for future analytical solutions.

We analyze the current data management workflows and explore available data sources in the healthcare organization and public databases to ensure they meet project goals.

Our experts design the solution’s architecture and define the implementation strategy, predictive algorithms, and the suitable tech stack. At this stage, we also set the timeline and budget for the project.

We process, cleanse, annotate, and transform data from various sources, define the evaluation criteria for the analytical solution and then develop it in line with the implementation strategy.

Our experts integrate the solution with other health systems, deploy it to the production environment, and transfer the technical and business documentation to ensure the solution’s correct operation and maintenance.

Our team carries out model retraining and functional enhancements based on user feedback and new data from the production, as well as supports the system’s operability under heavy loads.

Ready to successfully set up an analytical solution?

Talk to us

Adoption challenges & how to solve them

When implementing analytical software, healthcare providers often face challenges related to technology, infrastructure, security, and employees’ resistance to change. As an experienced implementation partner, Itransition helps healthcare organizations overcome these and other challenges.


Possible solution


Healthcare providers often deal with large volumes of inconsistently structured data. Incorrectly transferred and aggregated big data can lead to errors, duplicates, and, ultimately, faulty predictions.


Possible solution

Implementation partners should help healthcare providers build centralized data storage. Another essential practice is seamless integration between all their health systems, including EHR, CRM, LIS, wearables, and other sources that feed data to the analytical software, to ensure uninterrupted and accurate data collection.


Data privacy remains a concern when it comes to collaboration between organizations or data exchange within one health system. Besides, population health management studies require data anonymization to ensure its safe use by any organization.


Possible solution

To retain the suitability of collected data for predictive modeling while complying with HIPAA regulations, healthcare organizations should:

  • carefully consider the legal side of the agreement with a vendor regarding the non-disclosure procedure and penalties for its breach
  • identify a person who will be responsible for the transmitted data and its obfuscation before transmission
  • ensure that all staff members involved in data handling receive proper HIPAA training
  • establish clear protocols for data encryption during transmission and storage

There are many other components to keeping data safe and secure, therefore it's best to discuss every unique case with professional data security consultants.


Some clinicians worry that doctors will rely too much on findings provided by analytical applications, and point to many biases the predictive algorithms have that can skew the results and potentially harm the patients.


Possible solution

Healthcare organizations should develop a comprehensive analytics adoption strategy, determining its place in the decision-making and the data it will process and produce. Afterward, you need to conduct staff training, clarifying which tasks the system can handle and which are still the responsibility of medical professionals.

The future of predictive analytics

More and more healthcare providers use predictive analytics to enhance clinical decision-making, optimize resource usage, and manage population health. AI-powered analytical software can help anticipate health risks at large scale, like pandemics, and take action faster and more efficiently than ever before. Its boundless potential enables data scientists, healthcare organizations, and software developers to augment analytical solutions further, opening new opportunities for its use. However, all healthcare industry players must strategically govern their data to keep it relevant, properly formatted, and error-free. Additionally, healthcare organizations must have a fully interoperable IT ecosystem with unobstructed data flow between their systems to use predictive analytics software to its full potential.

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