Client Profile:
The Department of Veterans Affairs (VA) sought to enhance patient care and improve patient perception through proactive health management strategies.
Challenge:
Addressing the limitations of reactive healthcare and improving patient outcomes by anticipating and preventing potential diseases before they occur.
Solution:
To tackle the challenge of reactive healthcare, our team introduced a pilot program for innovative time series prediction technology. This initiative aimed to forecast future diseases and provide personalized risk assessments based on patient ICD-10 diagnosis codes.
Implementation:
Sophisticated Algorithms: Leveraging sophisticated algorithms, the pilot program analyzed vast amounts of patient data to identify patterns and trends, enabling predictions of future health conditions.
Personalized Risk Assessment: By integrating patient-specific ICD 10 diagnosis codes, created tailored risk assessments for individuals. This personalized approach has the potential to proactively manage patient health by identifying high-risk individuals and implementing targeted preventive measures.
Prioritizing Problems and Pilot Implementation: Based on our research and insights, we prioritize the problems to solve by focusing on those that would have the most significant impact. By leveraging a data-driven approach, we ensured our pilot targeted the issues that, when resolved, would yield the highest value.
Integration with Existing VA Systems: Our AI-enabled product leverages and integrates seamlessly the latest offerings from AWS including SageMaker, MONAI, Postgres, and S3 to label, build, and deploy machine learning models in conjunction with Veterans data.
Results and Outcomes:
Timely Interventions: The predictive analysis tool can enable early detection of potential health issues, allowing for timely interventions that can prevent the progression of diseases.
Personalized Care Plans: By providing personalized risk assessments, the solution can allow healthcare providers to develop tailored care plans that address the specific needs of each veteran.
Enhanced Patient Engagement: Veterans are more engaged in their healthcare when they understand their risks and the steps they can take to manage their health proactively. This can lead to better adherence to treatment plans and improved overall health outcomes.
Increased Capacity: The AI solution can allow the VA to handle a larger patient population without increasing the workload on healthcare providers
Cost Savings: With the potential to prevent diseases and reduce the need for expensive treatments, the solution can help lower overall healthcare costs.
Data-Driven Decision Making: The predictive analysis tool can provide valuable insights into patient health trends and outcomes.
Conclusion:
The pilot implementation of predictive analysis technology showcases the potential to transform healthcare delivery by shifting from reactive to proactive health management. While still in the testing phase, the pilot program demonstrates promising results in forecasting future disease occurrence over a 12-, 24-, or 36-month horizon utilizing diagnoses data. Anticipating and preventing potential diseases can lead to improved patient outcomes and ultimately contribute to a healthier population.
