A recent predictive analytics trial focused on multiple sclerosis succeeded in predicting the disease at least eight months before it was diagnosed through traditional methods. How about the benefits of predictive analytics in healthcare?

The trial studied four billion data points with high levels of precision. The power of this approach was demonstrated in a series of pilot studies conducted by IQuity, an American data analysis company.

Recent research in predictive and prescriptive analytics suggests that by inputting large data sets drawn from health care claims or electronic medical records, sophisticated algorithms can identify patterns that provide meaningful diagnostic information for patients with a wide range of conditions.

These technologies can uncover hidden risks in a population by detecting disease, correcting misdiagnosis, and monitoring disease progression.

While these pilot studies have focused on claims analytics and social determinants data, several resources can nurture predictive analytics. Real-time patient information can be integrated into embedded predictions, Fitbits numbers, and mobile apps that track weight, blood pressure, and sleep patterns.

When this level of exploration is accumulated over health care claims, electronic medical records or other proxy factors can offer physicians a more comprehensive view of the health picture.

Research suggests that early diagnosis leads to better outcomes and substantial cost savings. Predictive platforms analyze multiple longitudinal datasets from hundreds or thousands of patients to develop tools that find patterns and thus facilitate early diagnosis or anticipation of negative results.

Using these approaches also helps monitor diagnosed patients and assess how the disease state progresses in real-time and the person’s response to a particular treatment.

What is Predictive Analytics in Healthcare? A Broader Perspective

In healthcare, data and reporting have always played an important role. Electronic medical records, digital health records, and health surveys – doctors, nurses, pharmacists, and hospital staff have long relied on this information to make diagnoses and decisions.

In turn, the industry will need more than just essential patient intelligence and data to stay abreast with the growing patient base and scientific demands. People now have higher expectations from healthcare institutions, and in the same vein, medical research is constantly releasing discovery after discovery.

To remain competitive, healthcare professionals must evolve and embrace advanced data analytics to access otherwise unavailable insights to treat patients proactively.

More than simply answering “what’s up?”, predictive analytics in healthcare seeks answers to the question “what’s going to happen?”

This advanced analytics subcategory uses historical data to build and train machine learning models to forecast outcomes. And these predictions can lead to significant improvements in patient care and experience, hospital management, and process improvement.

May Clinic resorts to machine learning for optimizing its resource utilization, including services, supplies, and staff. It is also used to cut costs and improve patient care and safety. Its models identify opportunities for performance improvement and guide the selection of appropriate laboratory tests based on the best expected patient outcome, reducing the number of costly and unnecessary tests.

  • The Michigan Center for Integrative Research in Critical Care strives to test machine learning prowess in building models capable of predicting critical care events like internal bleeding.
  • By exploiting data streaming from ICU machines, including pulse-ox, blood pressure and temperature, the models may one day allow caregivers to monitor a patient’s condition continuously and proactively intervene when needed.
  • Indiana University Health department counts on machine learning for the prediction and minimization of the central line-associated bloodstream infections (CLABSIs) incidence that can be life-threatening. By predicting the risk of CLABSI, care teams can intervene and decrease the number of incidences of infection.
  • Beth Israel Deaconess Medical Center (BIDMC) leverages machine learning to drive a sepsis monitoring system that stratifies patients by risk scores. Machine learning is also helping BIDMC improve the accuracy of a breast cancer diagnosis on pathology images.
  • Deaconess Health uses machine learning to monitor patients with opioid prescriptions and identify those who may be at risk of drug abuse.

Benefits of Predictive Analytics in Healthcare

The fact of the matter is that the current expansion of advanced analytics in healthcare reflects just the tip of the iceberg. There are immense benefits of predictive analytics in healthcare. And there are going to be countless more shortly. The list below covers some of the most basic ones.

     1. Expediting Operational Management for Better Commercial Decision Making

Predictive analysis allows restructuring of the configuration of Business Intelligence strategies, facilitating access to large amounts of data. Real-time reporting is relatively new, but it is slowly making its way into the healthcare industry, helping make well-informed decisions.

     2. Fine-tuning Diagnosis and Treatment in Primary Care

The new analytics-based on Artificial Intelligence allows prognoses and data to be evaluated so that professionals in the sector can find answers to certain incurable diseases. As a consequence, the global mortality rate would progressively decline.

     3. Greater Insights to Improve Treatment of Risk Groups

The increasing digitization of electronic health records and statutory pre-performance reporting requirements provides valuable data sets for the health insights of certain social groups.

     4. Optimal Patient to Staff Ratios

Valuable data, such as seasonal forms of pain, data on the reputation of patients, as well as data from various health conditions, can confirm the pain of the exchange of patients and people.

Healthcare facilities can introduce specific measures in growth, leading to a large number of patients using the treatment route. Predictive analytics can help them allocate better resources, including human resources.

     5. Provision of Premium Quality, Patient-Centered Services

Predictive analysis can also help address the health disadvantages. For the first time, people who observe a person’s health, patient fluctuations, and the amount of the person’s and the patient’s compensation can be used to identify disagreements and the need for improvement in the provision of premium quality patient-centered services.

     6. Improved Treatment Targeting

Predictive analytics allows for better identification of side effects and treatment complications. It is hard to accomplish using conventional methods because some drugs and treatments work in some groups of patients and others do not.

Several factors come into play here. Predictive analytics can help analyze data on side effects and develop insights, contexts, and models that can help predict better outcomes and help GPs find the right treatments to treat the disease.

     7. Risk Forecasting

Predictive analysis also helps predict health risks, such as the likelihood of death during surgery, based on personal history and pre-existing conditions. It may also indicate the possibility of hospitalization in patients with diseases such as diabetes. In an ongoing study at the University of Pennsylvania, septic shock can be diagnosed in patients 12 hours before it occurs.

18 Examples of Predictive Analytics in Healthcare

Every passing day adds to the examples of predictive analytics in healthcare as the world continues to acknowledge and embrace the significance of advanced AI tools in healthcare.

In the passage below, we have compiled a list of the top 19 predictive analytics use cases in healthcare that are evolving the perspective and practices of the healthcare industry for good.

     1. Detecting the Initial Signs of Deterioration Under Patient’s Condition in Intensive Care

Intensive care is one area that requires quick decisions and constant attention to the patient’s condition. Since intensive care units are often overcrowded with critically ill patients (especially under the peak of the COVID-19 pandemic) and the lack of specialists in intensive care, the quality of treatment usually decreases.

Because the vital signals of each patient are constantly monitored, this data can be used in predictive analysis. Predictive algorithms can effectively identify patients at high risk of deterioration within the next 60 minutes. It allows the response team to act promptly and prevent or minimize the impact of the crisis.

     2. Biosensors for Monitoring in ICU

Another effective use of predictive analysis in the intensive care unit is its use in remote treatment. Tele-ICUs are only available for biosensors that collect patient data and assist in predictive analytics, which analyze that data and help teams respond effectively to impaired patient conditions.

The use of predictive analytics reduces response times, enables more efficient care delivery, increases unit capacity, and provides a way to ensure the safety of healthcare professionals.

     3. Risk Scoring for Chronic Illnesses

Six out of ten American adults suffer from chronic incurable or permanent illnesses. Some of them are constantly in danger of complications. Continuous analysis of patient status data is necessary to determine the possibility of such complications over time correctly.

This is where predictive health analytics using big data comes into action. By analyzing laboratory results, patient-generated lifestyle data, and biometric data, such a system can assign an individual a specific risk assessment, indicating the possibility of complications soon. It is even more likely to detect the first signs of deterioration and inform his doctor.

     4. Improved Predictive Care for Patients at Risk

In addition to chronic patients, other risk groups of patients may benefit from predictive treatment. This is especially true for the elderly and patients recently released from the hospital after invasive treatments.

The benefits of telehealth and predictive analytics allow these patients to avoid adverse events or get help as soon as possible during a crisis.

Thanks to the elaboration of historical data, the software can also predict a fall in the case of an elderly patient and save him from possible trauma and readmission to the hospital.

     5. Suicide and Self-Harm Prevention in Patients

Mental health problems deserve the same attention as other chronic disorders. Suicide, self-harm, and other violent tendencies may seem random and unprovocative, but predictive algorithms can detect specific patterns.

Professional help can prevent a psychological crisis even in the most vulnerable patients at the right time. Predictive analysis can be used to improve patients’ quality of life and save their lives.

     6. Slashing Hospital Readmission Rates

Although the hospitalization reduction program has taken steps to reduce the unplanned 30-day patent readmission, it is still happening across the country. In 2018, the average adult readmission rate was 14%, of which 20% was related to one of four conditions: sepsis, heart failure, diabetes, and COPD.

Predictive analysis can help identify, alert, and provide better preventive care to patients at high risk of readmission.

     7. Genetics Research-based Predictions

Genetic abnormalities are found in at least 10% of adults. Capturing some of them early on can help you deal with them and prevent complications later in life. However, the analysis of genetic information is a complicated process because the human genome is a complex system.

Predictive analytics can be used to analyze a person’s genetic data and compare it with a database of possible deficiencies and related diseases. In addition, it can be used in the neonatal phase to alert parents about any possible abnormality in their child’s health.

     8. Search for Latest Treatments and Precision Medicine

In addition to patient care, predictive analytics can be used extensively in health research. Algorithms can accurately predict a person’s response to a drug or treatment plan based on their data (genetic information, medical history, etc.) and the responses of previously studied patient groups.

This can effectively reduce the need for hospital groups and generally simplify the research process. In addition, it provides the opportunity to restrict the focus to a patient and develop the right solution for their particular case.

     9. Improved Patient Experience

Establishing a personal relationship based on trust is as important in healthcare as choosing the right treatment plan. It may encourage patients to continue treatment, return to another study, and generally adopt healthier habits, avoiding more severe complications and problems.

Using predictive analytics to assess a person’s behavioral patterns, the system can determine the best approach. You can also choose the most suitable professional who is more likely to have a personal relationship.

     10. Efficient Supply Chain Management

The supply chain for each hospital is a complex system since the necessary supplies depend on the patient’s workload and the specific circumstances of each patient’s case.

Using predictive analytics can help the hospital make future purchasing decisions based on the data. It makes buying easier and more cost-effective because it reduces unnecessary purchases and lost equipment.

     11. Improved Staff Management

By identifying models in clinical care, a hospital can use predictive models to determine how many people should be in the hospital at a given time. It can also help to correctly identify the needs of certain professionals and allow them to take time off.

Staff optimization can significantly reduce operating costs, which can help reorganize hospital budgets and provide better patient care.

     12. Forecasting Medical Equipment Maintenance Needs

Physics is relentless – no machine can run forever because of friction and resistance. Therefore, predictive analytics has long been used in various industries to predict individual components’ possible wear and tear, currently also benefiting the healthcare industry.

For example, by analyzing sensor data on an MRI machine, a predictive analytics system can predict an error before it occurs. This may resolve the issue of partial repair or separate exchange of details.

     13. Detecting Insurance Frauds

The National Anti-Fraud Health Association estimates that financial losses from health fraud range from 3% to 10% of health care funds, the equivalent of up to 300 billion USD. Insurance companies have invested a lot of time and effort in reducing these amounts, a few of them also using predictive analytics to solve the problem on a large scale.

With enough data on false claims and mismanagement of insurance funds, customized machine learning algorithms can be developed and trained to determine whether there is abuse on time. It will help reduce lost money and deter fraudsters from future attempts.

     14. Expedited Drug Approval

The approval of new drugs in Mexico takes one to three years after their launch in the United States or the European Union. This process could be accelerated by following the lead of the US Food and Drug Administration’s Center for Drug Evaluation and Research. It uses so-called “in silico” tests, which combine modeling and simulation to predict clinical outcomes, influence clinical trial design, support evidence of effectiveness, dose optimization, predict product safety and assess potential adverse events.

     15. Total Healthcare Cost Reduction

Predictive analytics can also be used to reduce healthcare costs. It can reduce patient costs, reduce unnecessary hospitalizations when not needed, manage hospital costs for medicines and supplies, and anticipate the need for hospital staff.

     16. Preventing Human Error

The impact that human error can have on health can be fatal. Fortunately, data can help identify potential errors and prevent fatal errors by providing accurate real-time information that directs doctors to action.

     17. Overall Improved Patient Care and Personalized Treatment

The main advantage of predictive health analytics is the availability of all data types: demographic, economic, and comorbidity. This information provides physicians and healthcare professionals with valuable information to help them make informed decisions. Better, smarter, and data-driven decisions generally lead to better patient care.

For example, predictive analytics is used to improve patient outcomes. By looking at the data and outcomes of elderly patients, machine learning algorithms can be programmed to provide insight into the treatment approach that works best for each patient.

Traditionally, medicine has worked on a coherent approach. Treatments and medications are prescribed on a limited basis of information based on general population statistics regarding specific patients. However, because physicians can diagnose patients more accurately, they can determine the most effective approach to treatment tailored to the patient’s unique medical situation.

     18. Public Health Management

Predictive analysis is not only applicable at the individual level. Healthcare organizations can also use it to manage public health. Once they have data on pre-existing patient conditions, medications, and personal history, the analysis can be used to find other similar patients in a population group.

It may also help identify groups prone to a potential epidemic. In such a scenario, health professionals can immediately start looking for treatments, which increases people’s chances of survival.

Bottom Line

The future of health is based on data, and machine learning is key to activating an artificial intelligence-led health organization. In fact, predictive analytics contributes significantly to improving our prospects of bettering in almost all walks of life, as evident in these predictive analytics examples.

The effective use of machine learning will help healthcare organizations extract information from large data repositories and sources such as electronic health records (EMRs), clinical trials, and billing and requirements.

It will reduce costs, maximize revenue, improve patient outcomes and streamline operations across the healthcare industry. Healthcare organizations want actionable clinical insights to deliver the best care and consultancy while managing cost and safety. And nothing beats predictive analytics in helping them do so effectively.