A generative AI copilot is transforming clinical decision-making at Max Healthcare, where doctors and administrators were previously unable to properly use longitudinal patient data. They were constrained by siloed systems, fragmented data, and manual reporting. Accessing longitudinal patient data or identifying trends meant navigating multiple interfaces and waiting on analytics teams. With healthcare outcomes on the line, Max needed a faster, smarter way to derive longitudinal patient data insights.

Partnering with GoML, Max Healthcare implemented a gen AI copilot powered by Claude 3.5 via AWS Bedrock. The system enables medical teams to ask natural language questions, analyze real-time diagnosis patterns, and track the entire longitudinal patient journey through a secure and intuitive interface. Designed for compliance and scalability, this solution is now central to how Max delivers precision care, faster.

The problem: slow insights from fragmented patient longitudinal data

At Max Healthcare, clinicians and analysts struggled to extract insights from vast volumes of patient data stored across multiple sources. Accessing clinical findings from longitudinal patient data often required backend intervention, leading to long delays and fragmented workflows. There was no easy way to map patient journeys or track vitals and diagnoses over time, making it difficult to proactively manage chronic conditions or identify emerging health risks.  

Identifying diagnosis trends across demographics required time-consuming manual analysis, with little room for real-time decision-making. Additionally, the absence of an intuitive interface meant clinicians had to rely heavily on engineering teams, further widening the gap between data and action. As the volume of digital health records grew, it became clear that traditional systems could no longer support modern clinical demands.

The solution: Generative AI copilot for real-time longitudinal patient data and decision support

To address these pain points, GoML designed and built a generative AI copilot, leveraging Claude 3.5 on AWS Bedrock for natural language understanding and reasoning. GoML integrated the copilot with Max Healthcare’s data ecosystem using our in-house AI solutions for healthcare designed to simplify data sharing between EHR systems.  

“Max Healthcare’s AI Copilot wasn’t just built for automation, it’s a decision-making engine that puts the right insight in the right hands at the right time,” said Prashanna Rao, Head of Engineering at GoML.  

The copilot enables clinicians to instantly query structured and semi-structured patient longitudinal data using plain English prompts. It offers seamless access to diagnosis trends, cohort analysis, and longitudinal patient histories, all through a lightweight, secure interface built on Streamlit.

Always-on clinical intelligence with Gen AI

Conversational copilot‍

NLP-powered interface lets clinicians retrieve insights like “List diabetic patients over 40 with elevated HbA1c in the past year” in seconds.

Diagnosis trend analysis

AI identifies high-risk diagnosis patterns across time, location, and age groups, enabling early intervention strategies.

Longitudinal patient journey tracking

Visual timeline of each patient’s vitals, diagnoses, and treatments supports proactive, context-aware care planning.

Built for compliance and scale

Full integration with AWS infrastructure ensures HIPAA-grade data security, masking, and anonymization. IAM and governance layers restrict access based on role and need.

Intelligent, secure, and scalable architecture

Data Processing Layer

• Patient data ingested from EHRs and clinical systems

• Preprocessing, validation, masking, and anonymization

• Stored in Amazon S3 Data Lake

Intel Layer

• ETL transformations into structured datasets

• Claude 3.5 and GoML fine-tuned models applied on EC2

• Secure access with audit logs and compliance controls

Consumption Layer

• Queries executed via AWS Athena and EC2

• Visualizations powered by Streamlit

• Role-based dashboards for doctors, analysts, and administrators

The impact: faster decisions based on longitudinal patient data

GoML’s generative AI copilot has significantly improved how Max Healthcare engages with patient longitudinal data and delivers care. With faster access to insights and reduced dependence on engineering, clinicians now make real-time decisions backed by comprehensive patient data and population-level trends.

“GoML helped us shift from reactive to proactive care,” said a Max Healthcare clinical leader. “We now have instant access to the data that matters, and it’s transforming how we treat patients.”

FAQs

What is longitudinal patient data?

Longitudinal patient data refers to the series of observations and data recorded for a specific patient over time. The longitudinal patient data includes structured, semi-structured, and unstructured data.  

What is the challenge with longitudinal patient data?

Often, parts of the longitudinal patient data are stored in different systems. Some of this data may not even be available based on individuals’ locations and treatment histories. The primary challenge is that this fragmentation makes it difficult for doctors to get a complete picture of the patient state based on their history.

What are the common pitfalls for healthcare providers while building AI for longitudinal patient data?

• Building dashboards without natural language capabilities

• Overlooking clinician usability in AI tools

• Ignoring data masking and healthcare compliance layers

How can healthcare providers effectively use AI for longitudinal patient data?

• Start with high-frequency use cases like cohort analysis and longitudinal care

• Invest in infrastructure that balances security and speed

• Choose AI partners who understand both LLMs and regulated healthcare systems

Want to build a generative AI copilot for your patients’ longitudinal data?

Let GoML help you unlock real-time clinical intelligence and drive better outcomes with scalable AI infrastructure.