Improve care, patient outcomes and overall organization performance by utilizing predictive analytics. From predicting trends and anticipating needs to managing the spread of disease we can help your organization transition from reactive to proactive with predictive analytics.
Predicting the trends of RAF based on EHR and claims data. See the case study
Leverage predictive analytics to forecast health outcomes, empowering healthcare providers to deliver timely and personalized care for better patient results.
Enhance diagnostic accuracy and speed with predictive analytics, providing healthcare professionals with powerful tools to assist in identifying and treating conditions.
Optimize financial planning and control costs with predictive analytics, providing accurate expense forecasts to drive smarter budgeting decisions.
Maximize revenue and streamline operations with predictive analytics, enabling precise healthcare reimbursement forecasts and reducing financial uncertainty.
Reduce hospital readmission rates and improve patient care with predictive analytics, accurately assessing and mitigating readmission risks.
Boost patient engagement and retention with predictive analytics, delivering personalized experiences that foster loyalty and enhance health outcomes.
Proactively address patient needs with predictive analytics, providing early warnings of patient declines to ensure timely and effective interventions.
case study highlight
Ensure accurate RAF scores reflecting all conditions, prompt capture of chronic/episodic conditions, timely clinician assessments, and compliant, comprehensive data for precise reimbursement.
Implement the core elements of a solution necessary to solve a specific challenge
Understand the business problem, user needs, and key objectives. Validate the product concept and define the vision.
Product strategy + prioritized features
Define the product or service to solve the problem. Define technical architecture, user flows, wireframes, and visual design.
Project roadmap, technical specs, design assets, prototype
Develop the MVP through iterative sprints, incorporating user feedback and testing. Deliver core functionality to meet business goals.
MVP ready for market test & user validation
Post-launch, focus on scaling and optimizing based on user feedback and analytics. Add users and commercialize the product.
New features, enhancement, business growth
Seamlessly integrate data from various healthcare sources, ensuring comprehensive and accurate datasets for analysis.
Implement robust security measures and comply with healthcare regulations to protect patient information and ensure confidentiality.
Enable real-time data processing capabilities to provide timely insights and support immediate decision-making in clinical settings.
Design the software to handle growing amounts of data and increased user demands without compromising performance.
Develop and continuously refine predictive models to ensure high accuracy and reliability in forecasting health outcomes and risks.
Ensure the software is compatible with existing healthcare systems and standards to facilitate seamless data exchange and collaboration.
Provide detailed and easily interpretable reports and visualizations to help healthcare professionals make informed decisions.
This component is responsible for collecting and aggregating data from various sources such as electronic health records (EHRs), medical devices, and external databases, ensuring that the data is accurate, complete, and up-to-date.
This module cleans, transforms, and preprocesses raw data to make it suitable for analysis, handling tasks such as missing data imputation, normalization, and feature extraction.
This core component develops, trains, and deploys machine learning models that can predict health outcomes, risks, and trends based on historical and real-time data.
This component provides an interactive and user-friendly interface for healthcare professionals to access, visualize, and interpret predictive analytics insights through charts, graphs, and real-time dashboards.
This module generates alerts and notifications based on predictive analytics results, sending real-time updates to healthcare providers when significant changes or risks are detected in patient data.
This crucial component ensures that all data handling and processing activities comply with healthcare regulations and standards, such as HIPAA, and includes robust security measures to protect patient data and maintain privacy.
Our team excels at uncovering predictive insights by actively engaging with users and understanding their intuitions. We build our solutions around real user needs, ensuring that our software is intuitive and effective.
We have a unique ability to frame the right questions and find precise answers, ensuring that our predictive analytics solutions address the specific challenges faced by healthcare providers.
Our expertise lies in seamlessly integrating predictive models into practical applications, ensuring that the solutions we develop are not only theoretical but are also actionable and valuable in real-world healthcare settings.
“I have worked with many technology teams during my career, and Digital Scientists is one of the best. They take the time to understand the customers’ needs, deliver innovative solutions, are always professional, and work with your team as a true partner to achieve success.”
Amy severino
“I have worked with many technology teams during my career, and Digital Scientists is one of the best. They take the time to understand the customers’ needs, deliver innovative solutions, are always professional, and work with your team as a true partner to achieve success.”
Amy severino
How to get Started
Launch a new service or product in as little as 8 weeks
Consulting services for technology integration in Value-Based Care.
An evaluation to guide critical business strategies and decisions.
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Predictive analytics in healthcare uses data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data, helping to improve patient care and operational efficiency.
Predictive analytics can identify at-risk patients, forecast disease progression, and suggest personalized treatment plans, enabling proactive interventions and enhancing patient outcomes.
Data from electronic health records (EHRs), wearable devices, patient surveys, lab results, and other healthcare databases are commonly used to create comprehensive predictive models.
By analyzing patient data and identifying risk factors for readmission, predictive analytics allows healthcare providers to implement targeted interventions and follow-up care, reducing the likelihood of patients returning to the hospital.
Challenges include data privacy and security concerns, integrating data from multiple sources, ensuring data accuracy and quality, and gaining acceptance from healthcare providers.
Yes, predictive analytics can forecast healthcare expenses, optimize resource allocation, and identify cost-saving opportunities, helping healthcare organizations manage budgets more effectively.
