Healthcare providers have employed Monte Carlo simulations and regression models to forecast healthcare myriads over the years. Nonetheless, healthcare providers want to improve the care of critically ill patients by implementing predictive analytics in their practices.
Predictive technologies may now acquire, organize, and analyze patient health data on a wide scale thanks to improvements in information technology in healthcare. Elders and chronically ill patients are already benefiting from the technology, which allows them to avoid going to the hospital and receive the best possible care right at home.
The global healthcare predictive analytics market is expected to grow from US$ 14.0 billion in 2019 to more than US$ 50 billion by the end of 2024. Experts also predict a startling 28 percent compound annual growth rate (CAGR) for the market during the projection period.
How is Predictive Analytics Important to the Healthcare Industry?
HIPAA compliance means a series of tasks and processes a healthcare organization has to follow to abide by the HIPAA regulations.
If an organization is violating HIPAA guidelines, individuals can report it to the Office for Civil Rights. This might cause the organization to pay a penalty. The penalty amount depends on the intention and the seriousness of the incident. However, it ranges from $100-$50,000 for every breach of rules.
However, disclosing medical details intentionally is a criminal offense and can lead to imprisonment.
What is the Purpose of HIPAA Compliance Rules?
Let’s have a look at some of the most notable use cases that demonstrate the importance of predictive analytics in healthcare business, as evidenced by the fact that it has a huge future potential.
1. Self-Harm Prevention
Predictive analytics in healthcare can assist patients in avoiding serious events. Healthcare providers can prevent a patient from self-harming by analyzing trends and detecting the episodes.Kaiser Permanente and the Mental Health Research Network collaborated on a study in 2018 that combined electronic health records (EHRs) and a questionnaire with predictive analytics technologies.The study was able to accurately identify patients who were willing or planning to injure themselves because to this integration.
2. Reducing readmissions to hospitals
Hospitals and other care providers can use healthcare predictive analytics to identify patients who require hospital admissions, and in some circumstances, if the patient is expected to be readmitted, the care providers can manage the facilities to accommodate them.In some circumstances, a patient wishes to be admitted to the hospital regardless of their medical condition. In such cases, predictive analytics can identify such patients and limit their hospital admissions.
The Texas Hospital demonstrated a real-time use case of this scenario, where the hospital combined HER with real-time predictive analytics and reduced patient readmissions by 5%.
According to Dr. Ethan Halm, MD, MPH, Professor of Internal Medicine and Clinical Sciences at UT Southwestern and Chief of the Division of General Internal Medicine, the data automatically assist hospitals in identifying high-risk patients. They require readmission as soon as they are admitted to the hospital for the first time.
3. Personalized Care and Precision Medicine
Precision medicine has sprung into the healthcare sector since former US President Barack Obama delivered his State of the Union address. Researchers from all over the world have dedicated their lives to genomic research and the development of tailored treatments for genetic illnesses.
Predicting the course of disease associated to genetic markers has become a possibility with the help of healthcare data. Using genetic research data, predictive analytics has also been useful in battling chronic diseases like diabetes and cancer, as well as ailments like food poisoning.
Commissioner of the Food and Drug Administration (FDA) Scott Gottlieb, MD, highlighted the FDA’s Center for Drug Evaluation and Research (CDERuse )’s of predictive modelling to predict clinical outcomes in precision medicine clinical trials. CDER’s research team used predictive modelling to help with clinical trial design, dose optimization, and product safety prediction.
The Advantages of Predictive Analytics in Healthcare
Predictive analytics models in healthcare have been successfully implemented in the use cases stated above. However, what are the advantages of these models for the healthcare industry? Let’s take a closer look at the advantages.
1. Management of Chronic Diseases and Preventive Care
Risk scoring and threshold alerts can help hospitals make better decisions using predictive analytics techniques like remote patient monitoring and machine learning. The data is then integrated into a cloud-based personalized communication system that reminds individuals to refill their medicines or seek medical treatment.
Furthermore, to check in with patients managing long-term health difficulties, a text, email, or phone call might be prompted. These are the kinds of seamless patient experiences that promote improved adherence to care pathways and, as a result, improve health outcomes.
2. Pattern Analysis and Patient Scheduling
Predictive analytics models using artificial intelligence can help predict the highs and lows of a care provider’s facility. This allows for more efficient patient scheduling and minimizes facility bottlenecks. Predictive analytics can assist hospitals in booking appointments on any given day, providing for a buffer time to attend to emergency visits, by analyzing seasonality, usual patterns of incoming patients, and the competence of the care facility.
3. Identifying Patients Who Are at Risk
Hospitals can use predictive analytics models to identify certain risk indicators in their patients. For example, based on age, coexisting chronic condition, medication adherence, and previous treatments, the model can identify a diabetic patient who requires hospitalization. Predicting the likelihood of chronic disease can prompt care professionals to intervene early, minimizing emergency room visits. Predictive analytics also assists healthcare providers in providing tailored care by allowing them to reach out to patients rather than waiting for them to come to the hospital.
4. Improved Operations Reduced Patient Leakage
The hospital management faces the difficult challenge of rescheduling patients in the event of cancelled appointments, latecomers, or no-shows. Predictive analytics solutions can assist hospitals in deciphering patterns by evaluating data gathered from patients’ whereabouts or the nature of their appointments. As a result, hospitals are able to arrange operations ahead of time so that they have adequate buffers to handle an emergency or appoint a specialist for a specific therapy, resulting in less patient leakage.
The fundamental challenge that arises as firms continue to construct new data platforms and modernize existing analytics tools is: how can you get the most value out of your big data investment? Predictive modelling is largely responsible for the solution to that query.
This is why, at ResolveData, we use the greatest predictive analytical tools for healthcare, as well as cross-validation methods and selection algorithms, to identify key variables and generate reliable data-driven future forecasts. This enables businesses to better manage their resources, mitigate risks, and forecast the outcomes of their activities.
Connect with our experts today to have a better understanding of how predictive analytics deployment may help your healthcare business develop exponentially.
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