About the Client

The client is a healthcare provider that offers medical services and care to a broad patient base. They manage extensive clinical and operational data to ensure high-quality care but faced challenges in integrating their systems.

Project Overview

The provider faced issues with fragmented data between their EHR system and CRM. Clinical data was stored in the EHR, while patient communication and appointment details were managed in the CRM. This disjointed data structure hindered their ability to use predictive analytics to enhance patient care and operational efficiency.

Challenges

Fragmented Patient Data

The EHR system held patient medical history, diagnoses, lab results, and treatment records.
The CRM captured appointment schedules, follow-up communications, and patient satisfaction data. 
The lack of integration between these systems made it difficult for healthcare teams to make proactive and data-driven decisions.

Limited Predictive Capabilities

The provider wanted to leverage data to predict patient risks, improve treatment outcomes, and optimize follow-up care, but the fragmented data limited their ability to do so.

Solution

We integrated data from the EHR and CRM systems into a centralized platform using Azure Data Factory for orchestrating data ingestion and Databricks for data processing, transformation, and storage. Predictive analytics were applied using MLlib within Databricks, providing actionable insights to enhance patient care and enable more data-driven, proactive decision-making by healthcare teams.

#1. Data Integration Implementation

Data Extraction & Transformation

Goal: Seamlessly extract and transform data from the EHR and CRM systems to create a unified data source.
Outcome: Using Azure Data Factory, we efficiently ingested and transformed disparate data into a centralized platform, ensuring comprehensive data availability for further processing.

Centralized Data Storage

Goal: Store integrated data in a scalable and reliable environment that supports real-time analytics and transaction integrity.
Outcome: Leveraging Databricks and Delta Lake, we provided scalable storage with ACID transaction support, enabling secure, unified data storage that supports efficient data analysis and reporting.

Data Unification

Goal: Achieve a single source of truth by unifying EHR and CRM data into a cohesive platform.
Outcome: The integration of data into Databricks facilitated a holistic view of patient information, ensuring that healthcare teams had access to accurate and real-time data for decision-making.

#2. Predictive Analytics Implementation

Patient Deterioration Prediction

Goal: Predict patient health deterioration by analyzing EHR data (e.g., lab results, medical history) and CRM data (e.g., missed appointments, communication frequency).
Outcome: Enabled early intervention by flagging at-risk patients, helping healthcare teams adjust treatment plans.

Chronic Disease Progression

Goal: Forecast the progression of chronic conditions (e.g., diabetes, heart disease) using medical history, treatment records, and patient engagement data.
Outcome: Allowed clinicians to tailor treatment plans and prevent complications, improving long-term outcomes.

No-Show Risk for Appointments

Goal: Predict the likelihood of patients missing their scheduled appointments based on previous behavior and engagement metrics from the CRM.
Outcome: Reduced missed appointments by sending timely reminders and rescheduling at-risk patients.

Readmission Risk Prediction

Goal: Identify patients at high risk of readmission post-discharge by analyzing clinical data and post-care engagement.
Outcome: Reduced readmissions by enabling more focused post-discharge follow-ups and personalized care.

#3. Data Visualization Implementation

Tableau Dashboards

Goal: Develop interactive dashboards to provide healthcare teams with real-time insights into patient risk profiles, high-risk flags for readmission, and health deterioration forecasts.
Outcome: Enabled healthcare teams to efficiently prioritize patient care by accessing real-time, actionable data through Tableau dashboards.

Tools & Technologies Used

Data Integration

Azure Data Factory for extracting and transforming data from the EHR and CRM systems.
Databricks with Delta Lake to unify and store the integrated data, providing scalable storage with ACID transaction support.

Predictive Analytics

Apache Spark within Databricks to process large datasets and perform data analysis.
MLlib (Databricks’ machine learning library) for building machine learning models that generated predictive insights.

Data Visualization

Tableau for building interactive dashboards to display real-time predictions and patient risk profiles to clinical teams.

Results

18% Reduction in Readmission Rates

Reduced readmission rates by 18% through timely interventions and personalized care plans.

20% Reduction in Complications

Early treatment adjustments led to a 20% reduction in complications.

30% Decrease in Missed Appointments

Proactive actions led to a significant reduction in missed appointments.

Chronic Disease Management

Improved care plans for chronic disease patients lowered hospitalization rates.

Team Composition

Project Manager: 1
Data Engineers: 2
Data Scientists: 2
Tableau Developer: 1

Conclusion

By integrating EHR and CRM data using Azure Data Factory and Databricks, and applying predictive analytics with MLlib, the healthcare provider was able to make proactive, data-driven decisions that significantly improved patient care. Tableau dashboards further empowered teams to take real-time actions, leading to better patient outcomes and optimized healthcare operations 

Testimonials

Our Slam Book

Tony Lehtimaki

DIRECTOR - AMEOS

Spain

Very professional, accurate and efficient team despite all the changes I had them do. I look forward to working with them again.

Antoine de Bausset

CEO - BEESPOKE

France

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MARKETING & SALES HEAD - VARMORA

Gujarat

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DIRECTOR - COVERTEK CERAMICA

Gujarat

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Enhancing Patient Care with Integrated Predictive Analytics

Industry

Healthcare

Project Type

Location

Timeline

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