Health Analytics - Clustrex Data

Integrations

Integrated With 30+ EHR, EMR & Practice Management Solutions

Reach Us: +1 (445) 447-3120

Developed inbound and outbound HL7 interfaces to enable seamless and reliable data exchange with Epic environments.
Worked directly with Epic Vendor Services and utilized the Hyperspace desktop sandbox for interface development, testing, and workflow validation.
Built a SMART on FHIR application that exchanges patient demographics and procedure information with external systems and retrieves clinical reports back into Epic.
Worked with client-authorized Epic FHIR APIs and Share Everywhere workflows to securely access patient records, appointments, and clinical documents.
Implemented structured ingestion processes for Epic-generated CSV exports, ensuring accurate movement of EMR and PM data across operational systems.
Supported providers and administrative teams by simplifying data sharing and interoperability tasks without requiring changes to their existing Epic setup.

Worked with AthenaOne APIs, FHIR, HL7, and SMART on FHIR workflows to enable secure data exchange across clinical and mobile applications.
Built automated CSV-based pipelines that synchronize appointments, encounters, medications, demographics, charges, and financial transactions with downstream systems.
Developed a custom Athena PM Chrome Extension for caller identification and instant patient-profile loading, enabling front-desk staff to efficiently support patient requests related to refills, scheduling, and billing.
Implemented scalable reporting foundations using Snowflake tables covering patients, providers, departments, encounters, and financial data.
Build online appointment booking capability with Athena API, Clustrex can enabled your practice online appointment booking via your website, Whatsapp, Phone number.

Worked with eCW using FHIR and standard APIs to retrieve patient and encounter data and programmatically update clinical notes to streamline data management and reduce manual effort.
Works with HL7 interface that enabled AI enabled Physician Scribe application to push SOAP notes seamlessly into eCliniclWorks EMR system.
Processed clinical notes and documents, ensuring proper decoding, formatting, and upload back into eCW while maintaining HIPAA compliance.
Troubleshot data inconsistencies, missing information, and API errors, working closely with eCW support to resolve issues efficiently.
Managed end-to-end workflows for handling critical issues, including corrupted or incomplete clinical documents, ensuring continuous data availability for clinical systems.
Prioritized and tracked EMR issues systematically to maintain accurate, timely, and reliable patient and encounter information across connected applications.
Coordinated with vendor teams to implement fixes and workarounds, ensuring smooth operations while safeguarding patient data.

Worked with ModMed to access key practice data, including encounters, visit summaries, appointments, practitioners, locations, and charge items.
Supported clinical documents using CCDA (HL7 v3) parsing and PDF extraction for accurate record-keeping.
Built secure workflows that retrieve data through ModMed APIs with FHIR capabilities and automated extraction scripts.
Processed transaction details from Production Summary Reports to ensure accurate operational and billing information.

Connected with Nextech Select using its secure practice-management APIs to work with key operational data, including locations, appointments, patients, practitioners, claims, and payment reconciliation.
Implemented the platform’s secure token-based sign-in process to ensure authorized access to practice data.
Set up clear workflows that retrieve each type of information through its dedicated API route, such as locations, appointments, patients, claims, and payments.

Connected with Veradigm’s FHIR-based APIs to access practice and clinical data, including locations, providers, appointments, encounters, charges, transactions, and patient records.
Used secure token-based authentication and user validation to ensure authorized access to practice information.
Retrieved updated data using ChangeDTTM filters to keep schedules, records, and financial details in sync.
Worked with available API endpoints to manage appointments, patient demographics, provider and location information, and financial data.

Worked extensively with CCDA (HL7 v3) clinical documents, including handling non-standard formats used by different practices.
Processed full XML-based clinical records to extract and organize key patient information.
Built workflows that clean, standardize, and load clinical data into centralized datamarts and datalakes to support reporting, analytics, and care-related processes.

Integrated key practice data from AdvancedMD, including visits, encounters, insurance records, patient information, appointments, and historical transactions.
Set up secure, authorized access to ensure only approved systems can retrieve and send data.
Built structured workflows to keep clinical, scheduling, and financial information consistent and accurate across connected systems.

Connected with Allscripts using FHIR-based APIs for practice-management and clinical data, including locations, providers, appointments, encounters, charges, transactions, and patient details.
Used secure token-based authentication and ChangeDTTM filters for authorized and updated data retrieval.
Leveraged available endpoints to manage appointments, demographics, provider/location information, and financial records.

Worked with HL7 v2 feeds to process and organize ADT and SIU messages for patient records and scheduling.
Supported accurate updates to demographics, appointments, insurance details, and guarantor information.
Ensured smooth synchronization of practice-management workflows across live healthcare systems through consistent, reliable data handling.

Processed EMR and PM data through structured CSV workflows to keep patient, appointment, order, and insurance information updated.
Used HL7-based processes to handle patients, appointments, practitioners, encounters, claims, insurance records, and payments.
Worked through secure file-source access for both file-driven and HL7-driven integrations.

Integrated with CollaborateMD using a blend of API calls, CSV retrieval, and file-based HL7 ingestion.
Supported patients, appointments, claims, insurance, payments, and encounter data.
Worked with HL7 message types such as ADT, SIU, DFT, and ACK, while API endpoints provided access to schedules, demographics, balances, charge history, and patient search.

Used HL7 v2 PM workflows to ingest patient, appointment, and financial messages, with supporting CSV files for charges and transactions.
Processed HL7-format files covering patients, appointments, practitioners, encounters, claims, insurance, and payments—ensuring the information is organized and ready for smooth day-to-day practice operations.

Built automated workflows that extract EMR and PM data from Dentrix, reducing manual effort and speeding up the entire data-ingestion process.

Published an app in the Practice Fusion Marketplace to retrieve patient and encounter data through approved integration workflows to streamline data access and reduce manual work.

Processed CCDA (HL7 v3) clinical documents and procedure CSV exports for structured extraction of patient and procedural information to improve workflow efficiency and data accuracy.

Implemented CCDA-based integration with Eyefinity as part of the PYA Analytics workflow to streamline clinical data access and reporting.

Built ETL pipelines to process patient demographics, encounters, and related workflows, along with automated web-based data extraction to accelerate data intake and improve workflow efficiency.

Implemented ETL pipelines for demographics and encounters, supported by automated web data extraction to streamline ingestion and reduce manual workload.

Created ETL workflows for demographics, encounters, and related processes, complemented by automated web extraction for data collection, helping streamline and speed up data intake.

Configured Cerner Developer Apps and worked within the Cerner developer console to set up and prepare integrations for FHIR-based workflows, ensuring smooth onboarding for future data exchange.

Implemented ETL pipelines handling patient demographics, encounters, and related workflows, supported by web-based automated extraction resulting in faster processing and more reliable data availability.

Built ETL processes for patient demographics and encounter data, using automated web extraction to support end-to-end ingestion resulting in reduced manual effort and improved operational efficiency.

Developed Selenium-driven automation to extract EMR and PM data from PrognoCIS, reducing manual work and speeding up data ingestion to improve turnaround time and overall workflow productivity.

Integrated with MyHelo APIs to access clinical and practice-management data, retrieving encounter IDs, updated visit information, and historical transactions through dedicated endpoints to streamline workflows and improve data reliability for connected systems.

Implemented HL7 v2 data feeds to reliably ingest patient, appointment, and financial information from MedEvolve ensuring accurate and consistent data across connected systems.

Extracted EMR procedure data, processed PM CSV files, and organized clinical and administrative information into a structured format—helping practices streamline reporting and reduce manual data handling.

Used Selenium-based automation to extract daily patient encounters, visit summaries, and clinical information from the Sevocity desktop system, reducing manual effort and ensuring consistent, timely data availability.

Case Study

1. Healthcare Analytics for Practices

Full AWS architecture and setup provisioning EC2, EBS, ECS/Fargate, Route 53, S3+Glacier, Athena, IAM, MFA, Secrets Manager, VPC, ALB, Workspace, Lambda, API Gateway, SFTP, SES, Systems Manager, Cloudwatch, SecurityHub, OpenVPN, KMS, Certificate Manager.
Large scale ETL ( Extract, Transform, Load ) of data across several US based healthcare practices using Talend / Postgres, across different data formats : CCDA/HL7, CSV. Full-scale migration of Data and Application from another cloud provider.
Processing daily patient data for over 25+ US based healthcare practices Flow/Stream based processing using Apache Nifi, Drill and Talend ETL for cross-format data, MirthConnect, AWS Lambda, SQS, AWS RDS, Cloudwatch. Automation scripts in Unix Shell scripts, Python. Data Analysis and visualization using Tableau.
Mirth Connect / NextGen Connect Healthcare data processing and Building Applications on top of the data layer.
Value delivered:
Technology Expertise, Cost Savings, Timezone support, Teamwork, HIPAA certified.
Challenges:
Variety of Data Interoperability with many systems Strict Regulatory Compliance Domain Knowledge depth.

2. Patient Mobile App for Clinics

Clustrex built a mobile app that enables patients to book, reschedule, cancel appointments with their clinic, view their medical records for each visit, update their profile information, view the payment balances and request their medication refills.
Specialities:
Ophthalmology, Pain Management, and more.
On the backend, the clinic staff can handle patient requests through a well defined workflow. All the patient records are made available integrating with the practice EMR and Practice management systems.
Mobile App is built with React Native targeted for Apple and Android platforms. Web backend is based on Python, AWS Lambda, RDS Postgresql. Data lakes is built on a HIPAA compliant data infrastructure.
Value delivered:
Improved patient experience, self-services workflows, and streamlining practice operations.

3. Clinical Trial Patient Database

Clustrex built a web application for an US based client, creating a database of patient prospects with their demographics information. Based on any specified filter criteria, select a list of prospects for a particular campaign/study. Bulk import of prospect information is enabled. Agencies can login and provide new patient prospects for Clinical Trial study.
Technologies used:
React JS, Python, AWS Lambda, and Postgresql.
Value Delivered:
Ability to quickly zero-in on a prospect list for Clinical Trials.

4. Pharma Analytics: Drug Switch Benchmarking

Introduction:
In the dynamic field of pharmaceutical analytics, understanding the patterns and impacts of drug switches is crucial for optimizing treatment strategies and improving patient outcomes. This case study focuses on developing and applying the Drug Switch Benchmark Dashboard, a data-driven tool designed to monitor and analyze drug switch trends over time.
Objective:
The primary goal of the dashboard is to track drug switches within a rolling 12-month window, ensuring that the analysis reflects the most recent trends in medication usage. Additionally, the dashboard incorporates data from the past 24 months to accurately identify new drug starts, providing a comprehensive view of both new prescriptions and switch behaviors.
Methodology:
- 1. Data Collection:
  - The dashboard aggregates data from electronic health records (EHRs) from multiple vendors.
  - A 24-month data window is utilized to capture new drug initiations and historical medication patterns.
- 2. Data Processing:
  - Patients are identified based on their prescription history, focusing on transitions from a previous drug to a current drug.
  - The analysis filters out incomplete data.
  - Patients with no treatment in the past 12 months are considered new starts.
- 3. Dashboard Design:
  - Interactive visualizations display switch rates, trends, and patient demographics.
  - Metrics include switch frequency, time to switch, and switch outcomes (e.g., efficacy, adherence).
Results:
The dashboard revealed key insights into drug switch behavior:
- Switch Rates: A noticeable trend of increasing switches in certain therapeutic areas, suggesting shifts in clinical guidelines or patient preferences.
- New Starts: The 24-month data window successfully identified new drug initiations, providing a baseline for comparing switch patterns.
- Impact Analysis: Correlation analyses showed that drug switches were often influenced by factors such as adverse events, cost considerations, and changes in clinical practice.
Conclusion:
The Drug Switch Benchmark Dashboard proved to be an effective tool for monitoring and analyzing medication switches over time. By leveraging a rolling 12-month window and a comprehensive 24-month data collection strategy, the dashboard offers valuable insights that support informed decision-making in pharmaceutical management and healthcare policy.

5. SOC 2 Compliance Implementation for Healthcare Data Security

Project Highlights:
To meet stringent healthcare security and enterprise compliance standards, the company implemented SOC 2 compliance to protect sensitive patient data, ensure HIPAA-aligned security measures, and establish audit-ready policies for regulatory adherence. This initiative enhanced data protection, access control, and continuous monitoring to safeguard healthcare information systems.
Technologies Used:
- AWS IAM & Security Groups for controlled access to electronic health records (EHRs).
- CloudTrail & CloudWatch for logging and real-time monitoring of healthcare applications.
- SIEM Solutions for proactive security event management and threat detection.
- Encryption (AES-256, TLS 1.2+) to secure patient data in transit and at rest.
- Automated Compliance Audits to ensure ongoing alignment with SOC 2 and HIPAA.
Challenges Faced:
- Defining and documenting security policies that align with SOC 2 and HIPAA requirements.
- Managing role-based access controls for sensitive healthcare data across multiple cloud environments.
- Implementing continuous monitoring to detect and mitigate security threats in real time.
- Ensuring compliance across third-party integrations, including healthcare SaaS providers.
Value Delivered:
- Successfully achieved SOC 2 Type II certification, reinforcing compliance with healthcare security standards.
- Strengthened cloud security posture with 24/7 monitoring and automated threat detection.
- Reduced compliance risk by automating security controls and access management.
- Enhanced incident response readiness with a structured framework for healthcare data breaches.
- Ensured data integrity and confidentiality for electronic health records (EHRs), improving patient trust.
This SOC 2 compliance implementation ensures that healthcare providers, insurers, and technology partners can confidently manage sensitive patient data while meeting regulatory standards and industry best practices.

6. Optimizing Revenue Cycle Management (RCM) in Healthcare

Objective:
Streamline and optimize the Revenue Cycle Management process to enhance financial efficiency, reduce claim denials, and improve operational transparency for healthcare organizations.
Scope:
Develop an integrated RCM solution that automates claims processing, ensures compliance, and provides actionable insights into financial operations.
Key Benefits:
- Automated claims tracking and real-time reconciliation.
- Proactive detection of underpayments and charge variances.
- Accurate and automated fee schedule management.
- Seamless integration with EMR and Practice Management Systems.
- Interactive dashboards for lifecycle monitoring and revenue insights.
Technologies Used:
- Healthcare Platforms:
  - Athenahealth, Nextech – for seamless data integration with EMR and PMS systems.
- Programming & Data Processing:
  - Python – for custom logic, data pipelines, and automation scripts.
- Data Warehousing & ETL:
  - Snowflake – for scalable data warehousing.
  - DBT, Dagster, Apache Airflow – for ETL orchestration and data transformation.
- Databases:
  - PostgreSQL – for structured data management.
- Visualization & Reporting:
  - Tableau, AWS QuickSight – for interactive dashboards and business intelligence reporting.
Value Delivered:
- Enhanced Claims Processing & Transparency: Automated claim lifecycle management and custom dashboards improved accuracy, reduced administrative burden, and expedited reimbursement cycles.
- Revenue Optimization & Compliance: Proactive monitoring of underpayments and charges ensured revenue integrity, while real-time reconciliation minimized financial discrepancies and enhanced compliance.
- Accurate Fee Schedule Management: Automated fee schedule extraction and updates eliminated manual errors, maintained compliance with payer policies, and reduced claim rejections due to outdated rates.
- Reduced Claim Denials: Advanced coding validation and reconciliation processes increased first-pass acceptance rates, resulting in faster payments and fewer denials.
- Seamless EMR/PMS Integration: Robust data pipelines enabled smooth interoperability between the RCM system and leading EMR platforms, facilitating end-to-end process automation.
- Actionable Revenue Insights: Lifecycle monitoring dashboards and interactive visualizations provided transparency into key revenue metrics and enabled informed decision-making.
Challenges Faced:
- Data Standardization: Integrating and standardizing disparate data sources from multiple EMR and PMS platforms required building robust ETL processes and reconciliation frameworks.
- Complex Fee Schedule Management: Automating the extraction and update of payer fee schedules, each with varying formats and timelines, posed significant technical challenges.
- Ensuring Data Integrity: Balancing automation with manual audits was necessary to ensure data accuracy, particularly in EOB audits and reimbursement tracking.
- Scalability of ETL Pipelines: Handling large volumes of claim and billing data while maintaining high performance required the use of modern orchestration tools like Dagster and Airflow.
- Regulatory Compliance: Maintaining compliance with healthcare regulations (HIPAA, payer-specific rules) while automating processes demanded stringent security and validation checks.

7. Telehealth Extension

Objective:
The extension allows the providers to attend appointments over video call. It also helps reduce the provider's time by taking SOAP notes from the recorded call and uploading the SOAP document against the appointment’s encounter.
Scope:
Deliver a rapid platform that offers easy ways to provide and attend appointments over the call, along with taking SOAP notes and uploading them against the appointment’s encounter by converting the notes to PDF format. Thus reducing the provider’s time for the next appointment session.
Technologies Used:
- 1. Frontend:
  - HTML, CSS – for clean, responsive UI design.
  - JavaScript – for dynamic content and user interactions.
  - Chrome extension - Delivers the application as a small program to be used in the browser itself.
- 2. Backend:
  - AWS Lambda – for handling business logic and API interactions.
  - Amazon API Gateway – for managing API requests and routing.
  - Vonage Video APIs - for making video calls and recording.
  - Amazon Transcribe - for speech to text conversion.
  - OpenAI - for SOAP note generation.
- 3. Cloud Infrastructure :
  - Amazon S3 — for storing the recorded video, audio and transcribed content.
  - Amazon Lambda and ECR - for serving backend on demand.
Value Delivered:
- Enabling video-based consultations: The extension enhances virtual care by enabling providers to conduct appointments via video calls.
- Automatic SOAP note generation: Converts provider-patient conversations to text format and extracts the SOAP notes.
- Minimizing documentation workload: Converts the generated SOAP notes to a properly formatted SOAP document and uploads against the appointment’s encounter.
- Improving provider efficiency and patient focus: Providers can concentrate on clinical decisions rather than administrative tasks. Allows providers to handle more appointments per day without compromising quality.
Challenges Faced:
- Vonage Video API Integration: Integrated Vonage video APIs into the platform and implemented polling mechanisms to reliably fetch recorded video sessions.
- Speech-to-Text Optimization: Evaluated and adopted a more accurate speech-to-text model, selecting AWS Transcribe for better transcription quality and language handling.
- Cost-Effective Backend Deployment: Deployed the backend application as an AWS Lambda using ECR images to reduce infrastructure costs, replacing the earlier ECS-based setup.
- PDF Generation in Serverless Lambda Environment: Encountered limitations in handling file I/O and PDF generation within the Lambda environment, making storage and uploading to encounters challenging.

8. Speech to text - Medical Scribe Extension

Objective:
To automate clinical note-taking by capturing live audio during doctor-patient appointments and extracting answers to predefined medical questions. This tool acts as a virtual scribe, reducing the need for manual note-taking and enhancing clinical workflow efficiency.
Scope:
Build a Chrome Extension that records audio from live medical consultations, uploads it to Amazon S3, transcribes it using Amazon Transcribe, and processes the transcription with a large language model (LLM). The LLM identifies and extracts answers to predefined clinical questions, presenting the structured output within the same interface to assist providers in real-time or post-visit documentation.
Technologies Used:
- 1. Frontend:
  - HTML, CSS – For designing a clean, intuitive interface within the Chrome extension.
  - JavaScript – Manages audio recording and backend interactions.
  - Chrome Extension – Delivers seamless, browser-based functionality for immediate use during appointments.
- 2. Backend:
  - API Gateway – Secures and routes API requests to appropriate Lambda functions.
  - Lambda Authorizer – Ensures authenticated and authorized access to backend services.
  - Scribe Extension Backend (Lambda) – Handles audio uploads, transcription triggering, and coordination with the LLM for extracting structured answers.
- 3. Cloud Infrastructure:
  - Amazon S3 – Stores appointment audio files and their corresponding transcriptions.
  - Amazon Transcribe – Accurately converts audio into text using speech-to-text processing.
  - OpenAI / LLM – Extracts answers to predefined clinical questions from the transcribed conversation, replacing the need for a human scribe.
Value Delivered:
- Hands-free clinical documentation: Providers can capture and review appointment notes directly from their browser.
- High-quality transcription: Ensures accurate text generation from spoken conversations using Amazon Transcribe.
- Answer extraction from LLM: Uses an LLM to identify and summarize key clinical points in response to predefined questions.
- Reduced administrative burden: Automates the documentation process, freeing providers to focus on patient care.
Challenges Faced:
- Latency in transcription workflow: Managed smooth audio upload and processing to avoid blocking user interaction.
- Secure data handling: Implemented secure, compliant audio storage and transmission using pre-signed S3 URLs and IAM policies.
- Lambda optimization for LLM calls: Tuned Lambda functions for performance and cost-efficiency while invoking the LLM.
- Maintaining UI responsiveness: Maintained a responsive user experience while handling multi-step processing involving audio, transcription, and summarization.

Clustrex Data Private Limited

Integrations

Epic

AthenaHealth

eClinicalWorks

ModMed

Nextech

Veradigm

MDIntelleSys

AdvancedMD

Allscripts

DuxWare

Nextgen

CollaborateMD

OpenPM

Dentrix

Practicefusion

EyeMD EMR

Eyefinity

PatientNow

Aesthetic Record

CentralReach

Cerner

DrChrono

inSync

PrognoCIS

myhELO

MedEvolve

MedInformatix

Sevocity

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