Predictive Analytics in Healthcare: 10 Superb Use Cases


The predictive analysis process can be beneficial in financial success, health management of a large population, and better results for a long time in the value-based healthcare field.

As healthcare institutions develop more and more cultured and more significant data analytics proficiency, rudimentary descriptive analytics has begun to reach greater predictive heights in a very short amount of time.

In the journey towards analytics maturity, predictive analytics is one of the three steps. Still, it is a huge step forward for many healthcare organizations that can revolutionize their whole systems.

Rather than providing simple information to the customers about previous events, predictive analytics can be a massive leap towards estimating the probability of some event happening in the future based on already present past data on it.

Predictive analytics can allow financial experts, clinicians, and the administrative staff to be alert beforehand about some events that might happen in the future. Thus, they can make some knowledgeable choices regarding how to proceed in light of that.

Being a step ahead of the events that may happen in the near future can be crucial in the cases of surgery, intensive care, or emergency care, where a patient’s life could be saved based on predictive analytics, and giving an idea to the clinicians before anything goes wrong.

There always exist some high-value cases within the healthcare system that require predictive analytics. These cases can involve some real-time warnings which may require immediate action by a team.

In that scenario, payer and provider organizations can use the tool of predictive analytics to solve administrative, data security, and financial challenges, which can ultimately bring a significant improvement in consumer satisfaction and overall efficiency for both sides.

Now, let’s see how some healthcare organizations use these predictive analytics across the board to extract forward-looking, actionable insights from emerging data assets.

Risk Scoring in Case of Population Health and Chronic Disease

Predicting and preventing an event go hand in glove, especially in the realm of management of population health, where every event can be life-threatening.

In that case, organizations can pinpoint individuals with an increased risk of chronic conditions at the early stages. Predictive analytics can be beneficial for giving the best care possible to such patients in the early stages, rather than making them susceptible to life-long health problems, which can be expensive and very difficult to treat.

Risk scores can be created based on biometric data, lab testing, claims data, the social elements of health, and the data generated from patients’ health can be monumental in giving some insight into individuals. This way, they might get assistance from improved wellness activities and services.

AAMC ( The Association of American Medical Colleges) says that the stratification, identification, and management of patients with increased risks is crucial to improving cost outcomes and service quality for all reimbursement models.

Predictive analytics can be used to identify those poor patients with high-risk health outcomes in a playful manner. And those are the same patients that can benefit the most from this intervention. This trick will significantly improve risk management and taking health management towards value-based payment.

Predictive Analytics and Avoiding 30-day Hospital Readmission

Under HRRP (Medicare’s Hospital Readmissions Reduction Program), health systems and hospitals are liable to huge penalties. This will provide a financial spur to prevent unintentional returns.

Moreover, predictive analytics can warn if a patient has a high-risk readmission factor in 30 days. This strategy can be helpful in employing the right coordination strategies for providing health care.

A study from Texas Southwestern University in 2016 showed that some events if they occur during the stay of a patient in the hospital, like difficile infection, vital sign unsteadiness after discharge, or a longer stay at the hospital, indicates a higher probability of readmission within 30 days.

Predictive Analytics tools can help identify patients with a high readmission possibility for which doctors can ask for a follow-up after discharge. This can lead to an overhaul of the discharge protocols of a hospital.

Limit Patient Deterioration

While being in the hospital, there can be many potential threats to the patients in their well-being, such as sepsis development, sudden downturn due to existing medical conditions, or some harder-to-treat infection.

Predictive analytics can be helpful for quick reaction by the providers to improve patient’s vitals. This can lead to early detection of some upcoming decline. With this, providers will know before severe symptoms show themselves. Strategies of machine learning are the most helpful in predicting events in the hospital like sepsis development.

Appointment No-Shows

Due to unexpected gaps in the daily calendar, there can be financial complications for the institution, which can affect the complete workflow of a clinician.  Predictive analytics can identify patients who are likely to skip their appointments without giving them notifications. This can be a way to cut revenue losses and provide organizations a chance to offer positions to more deserving patients.

The providers can use this data for sending additional notifications to patients to reduce the risk of no show-up and offer services like transportation so that the patients do not forget to make an appointment. They may also suggest some alternative times and settings suit their custom needs.

Self-Harm and Suicide Prevention for Patients

Early documentation of persons likely to harm themselves can warrant that those patients are receiving the kind of mental healthcare they want to avoid any severe events, containing suicide. By using the predictive algorithm, teams can find if suicides and self-harm can be prevented using these techniques.

Substance abuse detection, mental health issues, psychiatric medication use, history of suicide attempts, or bad scores in depression questionnaires are the most vital indicators of the presence of intentions of self-harm in the future.

Patient Utilization Patterns

A heads-up about the busy schedule of clinics can also be given in addition to the prevention of self-harm, which can be very helpful for organizations.

The care sites which operate with flexible schedules like emergency departments or urgent care departments can vary the staff levels to handle fluctuations in patients’ inflow using these analytics. Beds can also be made available for the patients who need admission, whereas outpatients’ wards and the physician offices can make wait-time, less for patients.

The use of predictive analytics to foretell patterns of utilization would help ensure optimum staff level, leading to a reduction in wait time and improved patient satisfaction. The analysis of classic utilization rates shows that mid-day appointments spike, whereas morning and afternoon spots are filled, which becomes a burden on the capacity of the hospital.

By altering specific schedule procedures, even distribution can be created, which would reduce the significant burden on nurses and improve patient satisfaction level.

Supply Chain Management

A provider has the largest cost center in the form of a supply chain. Supply chains represent a significant opportunity for healthcare institutions to cut down unnecessary spending and increase efficiency.

Predictive analytical tools have higher demand among hospital officials in order to reduce deviation and achieve a more actionable understanding of supply utilization and ordering patterns.

Ensuring Data Security

Artificial intelligence and predictive analytics are expected to play a significant role in the field of cybersecurity, especially when cyberattacks are on the rise.

Analytical tools can help monitor data access, utilization, and sharing to help organizations issue an early warning if something changes.

Precision Medicine and Novel Therapies

Predictive analytics can help develop precision medicine and genome, which can help researchers and providers improve clinical trials and techniques of drug discovery. Silicon models are used to make trial control groups for deteriorating conditions like Huntington’s disease, Parkinson’s disease, and Alzheimer’s.

Analytics and Patient Engagement

Predictive analytics can cut waiting times and help in making targeting therapies to achieve better outcomes in patient engagement and satisfaction. This can also help in other aspects of healthcare.

Management of consumer relationship has become an important skill for insurance companies and providers looking to encourage the wellness of the customers and decrease long-term expenditure. Prediction of patient behaviors can be key in developing adherence and communication techniques.

Anthem has been using data analytics for creating compelling consumer profiles that allow the consumers to send customized messages, discover strategies, and improve the customer retention of appointments. This is based on the impact it would create on each individual. Providers are also using different behavioral patterns to generate necessary care plans and keep them engaged in their clinical and financial responsibilities.

The use of predictive analytics for informed care management and develop more and stronger motivational relations between providers and patients can bring long-term benefits and decrease disease risks.