Predictive analytics

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.

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Predicting the trends of RAF based on EHR and claims data. See the case study

Dukehealth -dark
Congruity Health – Black
CommuniCare – Black

Use Cases for Predictive Analytics

Health outcome prediction

Leverage predictive analytics to forecast health outcomes, empowering healthcare providers to deliver timely and personalized care for better patient results.

Assistive diagnostic

Enhance diagnostic accuracy and speed with predictive analytics, providing healthcare professionals with powerful tools to assist in identifying and treating conditions.

Expense prediction

Optimize financial planning and control costs with predictive analytics, providing accurate expense forecasts to drive smarter budgeting decisions.

Reimbursement prediction

Maximize revenue and streamline operations with predictive analytics, enabling precise healthcare reimbursement forecasts and reducing financial uncertainty.

Risk of Hospital readmission

Reduce hospital readmission rates and improve patient care with predictive analytics, accurately assessing and mitigating readmission risks.

Patient engagement and retention

Boost patient engagement and retention with predictive analytics, delivering personalized experiences that foster loyalty and enhance health outcomes.

Early warning of patient declines

Proactively address patient needs with predictive analytics, providing early warnings of patient declines to ensure timely and effective interventions.

New Risk Adjustment Factor models for reliable scoring

Ensure accurate RAF scores reflecting all conditions, prompt capture of chronic/episodic conditions, timely clinician assessments, and compliant, comprehensive data for precise reimbursement.

SEE THE CASE STUDY

Feature Requirements for Telehealth

Data Integration

Seamlessly integrate data from various healthcare sources, ensuring comprehensive and accurate datasets for analysis.

Data Security and Privacy

Implement robust security measures and comply with healthcare regulations to protect patient information and ensure confidentiality.

Real-time Processing

Enable real-time data processing capabilities to provide timely insights and support immediate decision-making in clinical settings.

Scalability

Design the software to handle growing amounts of data and increased user demands without compromising performance.

Accurate Predictive Models

Develop and continuously refine predictive models to ensure high accuracy and reliability in forecasting health outcomes and risks.

Interoperability

Ensure the software is compatible with existing healthcare systems and standards to facilitate seamless data exchange and collaboration.

Comprehensive Reporting

Provide detailed and easily interpretable reports and visualizations to help healthcare professionals make informed decisions.

Integrating Predictive Analytics into a Value Based Care Technology Architecture

  • Enhanced Patient Outcomes: By predicting patient health trajectories and identifying those at high risk for complications, healthcare providers can intervene early, personalize treatment plans, and improve overall patient outcomes.
  • Cost Efficiency: Predictive analytics helps identify potential cost-saving opportunities, optimize resource allocation, and reduce unnecessary treatments or hospitalizations, leading to more efficient use of healthcare funds.
  • Proactive care management: Predictive models enable healthcare providers to proactively manage patient care by identifying trends and potential issues before they become critical, ensuring timely and effective interventions.
  • Improved care coordination: By integrating predictive insights into the care continuum, healthcare teams can better coordinate care plans, share relevant information, and ensure that all providers are aligned on patient needs.

Predictive Analytics Solution Expertise

Data Ingestion and Integration Module

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.

Data Cleaning and Preprocessing Engine

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.

Machine Learning and Predictive Modeling Engine

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.

User Interface and Visualization Dashboard

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.

Alerting and Notification System

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.

Security and Compliance Layer

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.

Technologies
Elasticsearch
tableau
Kibana
sql
Python
PowerBI
R
Bigquery
Googleanalytics
Google Cloud H
Microsoft fabric

Predictive Analytics Experts

User-Centric Design & Research

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.

Expert Communication & Problem-Solving

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.

Seamless Operationalization

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.

How to get Started

HIPAA Compliant MVP

Launch a new service or product in as little as 8 weeks

VBC Technology Consulting

Consulting services for technology integration in Value-Based Care.

Strategic Assessment

An evaluation to guide critical business strategies and decisions.

Our latest insights

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1

What is predictive analytics in healthcare?

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.

2

How can predictive analytics improve patient outcomes?

Predictive analytics can identify at-risk patients, forecast disease progression, and suggest personalized treatment plans, enabling proactive interventions and enhancing patient outcomes.

3

What types of data are used in healthcare predictive analytics?

Data from electronic health records (EHRs), wearable devices, patient surveys, lab results, and other healthcare databases are commonly used to create comprehensive predictive models.

4

How does predictive analytics help reduce hospital readmissions?

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.

5

What are the challenges of implementing predictive analytics in healthcare?

Challenges include data privacy and security concerns, integrating data from multiple sources, ensuring data accuracy and quality, and gaining acceptance from healthcare providers.

6

Can predictive analytics assist in managing healthcare costs?

Yes, predictive analytics can forecast healthcare expenses, optimize resource allocation, and identify cost-saving opportunities, helping healthcare organizations manage budgets more effectively.