Healthcare to the Power of X - Assessing Disease Risk with Predictive Analytics

Predictive analytics does just what the name says – it uses historical data to predict outcomes, using machine learning, AI, statistical models and data analysis to find patterns in the data. In healthcare, predictive analytics has the biggest hidden potential in disease risk analysis. It has been projected that by 2040, over 642 million globally would suffer from diabetes, and on the current trend of diagnosis and detection, over 47% of those cases could go undiagnosed, leading to other complications and higher medical costs. A predictive analytics algorithm to identify patients with a high risk of developing diabetes could help healthcare providers provide preventive interventions. 

The benefits of disease risk analysis go beyond patient outcomes. Healthcare providers also get to plan their operations and resource allocation around accurate forecasts and stay prepared to meet any new circumstance efficiently and at optimum costs. From pandemics to congenital heart disease, predictive analysis could help preempt the issue before it manifests, also improving the overall health outlook of the community. Let’s take a look at how exactly healthcare predictive modeling could make a difference in any healthcare setting almost instantly.

Benefits of Predictive Analytics for healthcare providers

• Early detection

In the US, five chronic diseases account for 75% of all healthcare spending: cancer, cardiovascular disease (CVD), diabetes, obesity and kidney disease. Using patient data like medication history, environmental factors and lifestyle choices, predictive analytics (PA) models can identify high-risk patients and risk factors, allowing medical professionals to recommend preventive care plans to lessen the likelihood of chronic disease development. Experts say early detection is already reducing the rates of occurrence of diabetes and congestive heart failure in the US. 

• Personalized treatment

Predictive analytics can also optimize treatment plans for any given patient based on factors like genetic predisposition, medical history and lifestyle choices. These treatment plans would have the best outcomes with minimal side-effects, calculated based on the predictive models derived from historic data of other patients. A great example is the use of PA to determine the optimal chemotherapy regimen based on the patient’s genomics to minimize side effects and maximize efficacy.

• Operational efficiency

Predictive models can also be employed to help optimize operational efficiency in ways that are hard to identify. The analytic model uses historical data like patient admissions, resource utilization and outbreak trends to provide a reliable forecast that helps facilities plan ahead for any circumstance. For instance, PA can be used to predict future admission rates, allowing administrators to allocate resources to face the anticipated activity. This helps cut costs in an efficient manner. PA can also be used to predict future outbreaks, so providers can take preventive measures in advance.

Concerns about Predictive Analytics

While the benefits of Predictive analytics are profound, there are also some valid concerns that healthtech experts are working to circumnavigate.

Data security and privacy 

The Health Insurance Portability and Accountability Act (HIPAA) places strict privacy regulations on patient data, medical and personal. This could make it difficult for health tech professionals to train their predictive models, as they must ensure patient data confidentiality and compliance with all the legal requirements of the state.

Difficulty of integration

Another equally imposing hurdle is the integration process itself. Existing healthcare systems may need expensive updates on their end to ensure interoperability and compatibility. Training healthcare professionals to adopt these predictive models may also be difficult. 

Lack of model interpretability

The algorithms behind some predictive models can be quite complex, and may lack interpretability for the physician. This poses a major challenge in adoption, as healthcare providers must feel comfortable enough to adopt the solution with minimal training.

Model bias

Another major pitfall is biased training data. When predictive models are trained on biased training data, or have biased algorithms in the first place, this could skew predictions and will prove inefficient in the long-term. This highlights the importance of having unbiased and highly secure medical data available to healthtech to train effective predictive analytics models.

Conclusion

Despite the various barriers to adopting advanced healthcare analytics, it is clear that the future of healthcare will depend on such predictive models and their insights. The first step to such a future is in the building of the infrastructure for this – from robust data and data security measures to scalable and interoperable models that can be universally adopted. Once Health Information Exchanges and decision support systems become commonplace, healthcare could become highly accessible, efficient and life-affirming.

About us:

Reveal HealthTech provides specialized engineering, clinical model, and strategy support to healthcare organizations. With decades of expertise in designing predictive models for disease risk assessment, we work with healthcare providers to integrate analytic models into their existing EHRs.