AI Predictive Analytics in Healthcare: Benefits & Use Cases

Healthcare has been experiencing a "Data Revolution" that is changing the way data about a patient's status, their laboratory results, or the performance of an output from a wearable device, as well as from clinical trials, generates an overwhelming amount of data that no one's resources will be able to manage fast enough to respond to the volume of data generated from patients. 

Due to this, healthcare providers are using AI Predictive Analytics solutions to gain historical data to respond to how services will be delivered; for example, hospitals can anticipate patients who will become critically ill (decrease in health), payers can limit unnecessary claims being filed, and pharmaceutical companies can conduct research to determine drugs to be marketed faster.

The broad-based applicability and benefit created from using predictive analytics by these organizations will be described in this paper in terms of the many benefits of using AI Predictive Analytics solutions and reference to several examples of current organizations currently using predictive analytics; this paper will also illustrate how these organizations have created a long-term, sustainable competitive advantage over their competitors in delivering services to patients and the use of predictive analytics.

Key Benefits of AI Predictive Analytics in Healthcare

AI predictive analytics in healthcare delivers value across three broad dimensions: clinical outcomes, operational efficiency, and financial performance. Understanding these benefits is the first step toward building a data-driven care strategy.

1. Earlier Detection of High-Risk Patients

One of the most significant advances has been made by using AI predictive analytics in healthcare: By being able to predict at-risk patients earlier on, before their conditions worsen, healthcare providers can help reduce avoidable admissions to hospitals and improve outcomes for those who suffer from chronic illnesses.

Traditional risk scoring tools have generally used a few variables and had static thresholds to calculate the risk score, whereas AI models use hundreds of different variables, including vital signs, medication history, social determinants of health (SDOH), and historical patterns of readmittance, to calculate a dynamic risk score that is updated continuously with new data flowing into the model. 

2. Reduced Operational Costs

Healthcare providers operate with a small profit margin and can quickly accumulate wasted costs. AI predictive analytics allows administrators to predict how many patients will be seen over the course of a shift, season, or department so that they can adequately staff their departments and manage resources. 

Predictive maintenance models identify pieces of equipment that are likely to fail so that plans can be made for their repair before that equipment's failure occurs and disrupts patient care. Supply chain algorithms identify spikes in demand and help reduce the amount of product ordered in advance.

3. Improved Diagnostic Accuracy

Around the world, many patients suffer preventable injuries due to diagnostic errors. Predictive modeling using AI may help in diagnosis by identifying elements within imaging studies, laboratory tests, and clinical documentation that human practitioners working under time constraints do not see. 

By integrating predictive modeling into existing practice rather than replacing the clinician's judgment, predictive modeling has consistently increased accuracy in diagnosis within radiology, pathology, and primary care triage.

High-Impact Use Cases of AI Predictive Analytics in Healthcare

The real proof of value sits in specific deployments. Here are the use cases seeing the strongest results across health systems, insurers, and life sciences organizations.

1. Predicting Hospital Readmissions

The readmission of patients into an acute care setting within 30 days post-discharge has been identified as one of the most significant indicators of health quality and is one of the major cost drivers in the health care system. Through the use of AI predictive analytics in health care, hospitals can be able to score every discharge using both structured and unstructured data found within electronic health record systems. 

Once the patients have been classified as high-risk, they may be enrolled in post-discharge follow-up programs, assigned care navigators, or connected with remote monitoring devices. Health care organizations using this strategy have achieved readmission reductions of 15% to 25% within targeted populations.

2. Early Sepsis Detection

Sepsis is among the top causes of in-hospital deaths. The length of time it takes to treat an individual with sepsis is the most significant factor in whether that person will survive. Utilizing AI to create models that can recognize the physical signs of sepsis based on vital signs, laboratory values, and nursing documentation will help identify patients with sepsis earlier than traditional methods would provide. When the alerts generated by these models are sent to the appropriate members of the healthcare team at the right time, there will be a decrease in sepsis-related mortality. 

3. Mental Health Risk Stratification

The signals of mental health conditions often go unnoticed by clinicians due to being dispersed throughout numerous clinical visits, making it difficult to diagnose them accurately. By using Artificial Intelligence Predictive Analytics within the field of healthcare, providers can track trends associated with a patient's primary care visits, their previous medication prescriptions, and their written clinical notes in order to identify patients who may be at risk of experiencing depression, anxiety, or suicide. 

Once identified, these patients are offered an opportunity for outreach, referrals to therapy, and the review of prescribed medications prior to experiencing a severe episode, which are both clinically superior options and result in considerable financial savings compared to treating patients with an emergency or inpatient psychiatric episode.

4. Drug Development and Clinical Trial Optimization

The pharmaceutical industry uses AI Predictive Analytics Solutions to speed up the drug discovery process and enhance the design of clinical trials. Predictive modeling techniques hire AI developers to rapidly screen libraries of compounds for listing, based on predicted success rates of the candidate compounds, and to identify specific subgroups of patients who will benefit most from a given treatment. 

In the context of clinical trials, AI can identify sites that are likely to underperform with respect to patient enrollment, estimate the likelihood of patients dropping out from a clinical trial program, and monitor patient safety in near real-time. Collectively, these tools are reducing the costs associated with bringing new therapies to market, as well as the time it takes for a new therapy to be brought to market.

Final Words

Using AI predictive analytics in the healthcare field is more than just an idea for the future; it is already an effective operational tool and is already being utilized at scale. The majority of healthcare organizations are utilizing this capability to enhance their operational effectiveness and improve patient outcomes while reducing prices.

The organizations that are experiencing the greatest success using AI predictive analytics have made a commitment to invest in AI for the long term as a foundational capability, instead of as a quick technology purchase. 

To achieve this, organizations need to have AI consulting services, and they can also ensure they have a solid foundation for their data infrastructure and partner with organizations that have expertise in developing AI predictive analytics solutions, can work with clinicians and clinical workflows to build appropriate models, and have hired AI development professionals who have both technical and domain-specific experience with healthcare. 

There are patterns in the data that your organization produces each day that can save lives and decrease the amount of waste produced by your organization. Identifying and utilizing those patterns through AI predictive analytics will enable you to create more effective, efficient, and productive processes.