Predictive analytics tools allow physicians to put patient characteristics into algorithms that predict a patient’s likelihood of getting certain diseases. Physicians can then use these predictions to hone their judgments and diagnose patients more accurately.

These algorithms can also help doctors optimize treatments, reducing the chance of unwanted side effects. The results are better outcomes and reduced costs.

Better Patient Care

Whether it’s an early warning score in a general ward or automated alerts identifying patients at risk for cardiac arrest, predictive analytics in healthcare is helping healthcare organizations turn data into forward-looking insights that support better patient care.

For example, a predictive model can help determine which patients are most likely to experience complications during surgery. This allows healthcare professionals to proactively monitor these patients and start them on the appropriate treatment path to prevent potentially life-threatening problems.

An advanced predictive analytics system can also identify patients on track to develop sepsis 12 hours ahead of time so that they can be spotted and treated sooner. In addition, a medical home network used predictive analytics to target outreach to at-risk patients during an outbreak of COVID-19, resulting in fewer patient complications.

However, some ethicists are concerned that predictive analytics could reduce human judgment and decision-making. Predictive analytics models must be built with the proper safeguards and balanced with accepted ethical standards, including intervention points when a human decision is more critical than the machine’s assessment.

Improved Utilization Management

When applied to healthcare, predictive analytics helps prevent and manage medical problems rather than simply reacting to them. This is possible by identifying patterns from various sources, such as national data, EHR data, biometric data and claims information on a local or patient level.

Predictive analytics tools can help identify and forecast peak utilization times so healthcare professionals can make changes to ensure patients receive the necessary care. A clinical practice administrator at an oncology infusion center used predictive analytics to discover that mid-day appointment times created unsustainable utilization spikes. It maintained the appointment rate by altering specific scheduling procedures while reducing workloads.

Predictive analytics can also help healthcare organizations detect potential fraud. For example, it uses predictive analytics to detect anomalous patterns of behavior that might indicate a potential credit card fraud scheme. It has also been used by Lenovo to understand warranty claims better, resulting in 10 to 15 percent reductions in warranty costs.

Increased Patient Satisfaction

Patient satisfaction entails considering the common causes of problems affecting the healthcare experience. For instance, extended waiting periods can significantly contribute to patient dissatisfaction, whether in the waiting room, for appointments, or for test results. Communication gaps, such as inadequate explanations of medical conditions, treatment options, or medication instructions between healthcare providers and patients, can lead to frustration, low retention, and dissatisfaction.  

The same holds when healthcare services are fragmented and lack coordination and continuity among different providers or healthcare settings. Any breaches of privacy can also erode trust. To increase patient satisfaction, embracing predictive analytics is a worth it investment. Predictive analytics provide data-driven analysis.

Data-driven analysis can reveal unknown correlations, insights, and hidden patterns that would be difficult to discover through any other means. This reveals new opportunities to improve services, increase productivity, and cut costs.

For example, predictive analytics can identify fraudulent healthcare schemes such as individuals obtaining subsidized prescription pills and selling them on the black market, doctors and hospitals billing for a service that isn’t covered by insurance, a doctor prescribing an unnecessary procedure to get additional Medicare payment, and more. This allows healthcare providers to catch these problems before they become too serious.

Additionally, using data to detect patterns can help reduce patient readmission rates and other operational improvement efficiencies. For instance, one hospital used predictive analytics to spot trends, prevent operating room delays, and reduce the number of canceled surgeries, saving them an estimated $6 million annually.

Reduced Readmissions

Predictive analytics in healthcare helps to keep patient care on track, reduce hospital readmissions and lower overall costs. The technology helps identify patients likely to exceed the normal length of stay by monitoring data inputs such as claims information, prescriptions and medical records. It can also be used to identify patients on a trajectory to suffer from a certain event, such as a septic shock, enabling clinicians to start early interventions and prevent the deterioration of the patient’s condition.

Similarly, it can be used to predict which patients are likely to be readmitted after a hospital stay and provide them with appropriate post-hospitalization care. This reduces readmission rates, saves money and preserves resources for new patients.

Using predictive analytics to identify high-risk patients can improve outcomes and help healthcare organizations comply with value-based reimbursement models. These models can identify patients who may require additional or more intensive treatment, resulting in better results for the individual and lower costs for the organization. They can also be used to identify cohorts exposed to a disease outbreak, which can help mitigate the spread of risk.

Lower Costs

Predictive analytics can replace many low-risk, routine decision-making tasks that would otherwise require human intervention. This can free up employees for high-value or higher-risk strategic jobs. Examples include generating credit scores, determining insurance claim payouts and deciding whether or not to approve a new treatment for a patient.

Chronic diseases like cancer, cardiovascular disease, diabetes and obesity account for 75% of healthcare costs in the US. Using predictive analytics on national, community and individual-level data to articulate the likelihood of developing such conditions can help doctors and healthcare organizations proactively identify at-risk patients for early intervention, reducing costs and saving lives.

Similarly, predictive models can help reduce operational costs by intelligently allocating facility resources and optimizing staff schedules, identifying patients at risk of costly near-term readmission, adding intelligence to pharmaceutical and supply acquisition and management and targeting public health campaigns based on cohort demographics and reported illnesses.

Many healthcare tech tools integrate predictive analytics. In the case of physical therapy billing services, automation and predictive analytics streamline and optimize the billing process. This healthcare tool helps identify billing errors or inconsistencies, ensuring accurate and timely submission of claims.  

In clinical trials, predictive analytics helps target patient populations that meet the criteria and enroll in the study. Leveraging predictive analytics in clinical trial design and management enables researchers to optimize resource allocation, enhance patient recruitment and retention, improve data quality, and identify personalized treatment approaches. These factors collectively reduce the overall cost of conducting clinical trials while improving efficiency and outcomes.

Of course, all predictive analytics models and projects must align with privacy controls and keep information private. This fundamentally important issue must be navigated carefully, particularly as legislation and governance lag behind technology disruption.