The technology landscape is full of solutions that can parse historical data, but modern businesses increasingly want to predict the future. What are the risks? What are the opportunities? Where will the market – and revenue – be five years from now? Using predictive analytics, organizations can take steps toward answering these future-focused questions.

Predictive analytics is a statistical technique that analyzes current and historical data to produce forecasting results. For instance, data analysis can determine associations between various behavior factors. From these associations, a variety of models can then be designed. Using a specific set of conditions for each model, businesses can gain valuable insights that lead to better decision-making.

How can predictive analytics be leveraged in healthcare?

The healthcare sector has a long history of keeping one eye on the horizon. Considering recent advancements in Artificial Intelligence (AI), many people have wondered how predictive analysis can benefit the medical space. With big-data analytics, AI and machine-learning models can be a key resource to improve healthcare services in high-risk areas. For example, a combination of AI and predictive analytics can help data scientists gain deeper insights into disparate factors affecting human health. 

Consider just a few areas where AI can improve population healthcare management:

1. Predicting chronic and infectious diseases
   Predictive analytics can help healthcare providers predict, with high probability, illness due to a variety of data points like the patient’s birth location, work location, life-style habits, and local environmental conditions. With this information in hand, providers can then determine a patient’s risk of chronic disease and take preventative action. The sector is already seeing results in this area: the rates of some chronic diseases like diabetes, congestive heart failure, and chronic obstructive pulmonary disease (COPD) are in decline.
   
2. Streamlining patient throughput and workflows
   AI-powered predictive analytics can rationalize resource allocations by forecasting variations in patient flow. These forecasts can ensure suitable bed allocations, reorganize staff to enhance patient care, and optimize healthcare facility employment rates by extrapolating needs from patient data.
   
3. Predicting hospital readmissions
   With big data analytics, it is possible to predict extreme epidemic conditions using machine learning and high computational power. Contagious diseases can even be predicted with various data sources such as weather reports, reported cases, population density, economic profile, etc.
   
4. Analyzing patient deterioration
   Through predictive factor analysis, data scientists can understand and monitor disease progression, or predict adverse side effects of medications or treatment, to provide patients with the right intervention at the right time.

Today, health data comes from multiple sources – medical notes, electronic recordings from medical devices, laboratory images, physical examinations, and even smart devices that capture and track a variety of health data. Leveraging AI, these data can be continuously assessed and analyzed to provide healthcare practitioners and researchers boundless resources to improve diagnoses and treatments.

Machine learning has become a mature technology. Combined with AI, it can provide powerful, actionable insights. This advancement in technology, combined with modern data sets, can be utilized in population health management to achieve the ultimate goal – better health outcomes.

References:

1. https://www.predictiveanalyticstoday.com/predictive-modeling/
2. https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-020-02658-5/figures/2

Kalyani Vuppalapati
Technical Lead, Data Analytics & AI, Wipro Limited.

Kalyani is an expert in multiple analytical tools and data mining methodologies. She has also worked on various analytics projects in Manufacturing, Technology, Energy and Utilities, and Healthcare domains. Her experience includes data analysis, proposing analytical solutions, generating dynamic reports, and other data preparation components like data pre-processing, profiling, cleansing, validation, and transformation.