Predicting Disease Risk for Patients using the Lakehouse Approach

Predicting Disease Risk for Patients using the Lakehouse Approach

Introduction:

Welcome to the world of precision health, where healthcare is tailored to each individual’s unique needs. Gone are the days of generic interventions, as the healthcare system has evolved to focus on targeted patient care. At Databricks, we are committed to delivering precision health through our Lakehouse Platform, which integrates all types of data with real-time frequency and tooling for the full machine learning (ML) lifecycle. From personalized care management programs to identifying high-risk pregnancies, our platform empowers healthcare organizations to provide tailored care that improves patient outcomes. With our latest Solution Accelerator, we enable the prediction of patient risk and the measurement of quality care, revolutionizing the way healthcare is delivered. Join us on this journey towards a future of personalized and effective healthcare.

Full Article: Predicting Disease Risk for Patients using the Lakehouse Approach

Databricks Empowers Precision Health with its Lakehouse Platform

All healthcare is personal. Individuals have different underlying genetic predispositions, environmental exposures, and past medical histories, not to mention different propensities to engage and respond to treatment and programs.

Precision health goes beyond personalized medicine and encompasses approaches that occur outside the setting of a doctor’s office or hospital, such as disease prevention and health promotion activities, according to the CDC. The traditional approach of applying interventions based on a “one size fits some” model is changing as the healthcare system moves towards targeted patient care.

Databricks, a leading technology company, is at the forefront of delivering precision health through its Lakehouse Platform. This platform seamlessly integrates all types of data in real-time frequency, enabling organizations to leverage the full potential of machine learning (ML) for healthcare applications. From personalized care management programs based on web clicks and member engagement data to behavioral apps that incorporate streaming continuous glucose monitoring data, patients are benefiting from care that is tailored to their individual needs.

Patient Risk Scoring: A Step Towards Precision Care

Databricks offers a Solution Accelerator that provides a quickstart to predict patient risk and measure the quality of care. The process begins with a robust set of synthetically-generated electronic medical data stored in the OMOP 5.3 Common Data Model. To ensure the accuracy of predictions, Databricks incorporates parameters such as the target cohort, the outcome, the observation window, and the risk window into the design process.

Following OHDSI best practices for patient-level risk scoring, Databricks defines the target and outcome cohorts based on the specified parameters. The platform considers a pre-defined number of comorbidities, comorbidity history, and demographic features, which are added to the feature store. Databricks then utilizes AutoML to train a classifier that predicts the probability of the outcome, such as emergency room re-admission.

Incorporating Quality Measures for Enhanced Care

In addition to risk prediction, Databricks helps organizations incorporate quality measures into their healthcare workflows. Risk and quality are interconnected, and both payers and providers are interested in providing appropriate care in the right settings to reduce waste, increase efficiency, and manage costs.

Databricks enables organizations to determine the probability of an Emergency Room (ER) visit for individuals with Congestive Heart Failure (CHF) while using quality measures to manage this population effectively. The United States Agency for Healthcare Research and Quality (AHRQ) has developed a Preventative Indicator Quality Measure for measuring an Affordable Care Organization’s (ACO) effectiveness in managing CHF patients.

AHRQ’s CHF measure assigns a value between 0 and 1 based on the appropriate inpatient vs. outpatient handling of CHF patients, with 0 indicating appropriate management. This measure provides valuable insights for ACOs and payers in identifying unnecessary costs and assessing the quality of care provided.

Furthermore, AHRQ’s CHF measure can be utilized to encourage members to seek care in high-performing ACOs. Payers can incentivize this behavior through Next-Best-Action outreaches, guiding members towards high-quality care. Additionally, payers can create high-performing narrow networks specifically designed for individuals with CHF to ensure they receive optimal care.

Conclusion

Databricks’ Lakehouse Platform is revolutionizing precision health by integrating all types of data and enabling organizations to deliver personalized care. Through patient risk scoring and incorporating quality measures, Databricks empowers healthcare providers to deliver targeted interventions, improve patient outcomes, and drive efficiency in the healthcare system. With Databricks’ innovative solutions, the future of precision health looks promising.

Summary: Predicting Disease Risk for Patients using the Lakehouse Approach

Precision health is revolutionizing healthcare by tailoring treatment and care to individual patients. This approach goes beyond personalized medicine and encompasses disease prevention and health promotion activities. Databricks is working towards delivering precision health through its Lakehouse Platform, which integrates various types of data in real-time. By combining omics, electronic medical records, and social determinants of health data, Databricks enables accurate prediction of disease risk and adverse outcomes. With the ability to create personalized care management programs, behavioral apps, medication adherence reminders, and more, patients can now benefit from tailored and targeted healthcare. Databricks also offers a Solution Accelerator for predicting patient risk and measuring quality of care, incorporating risk prediction and quality measures to optimize patient management and reduce costs.

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