DEV Community: Kalyani Bansod

How We Built an Ambient AI Clinical Documentation Pipeline (and Saved Doctors 8+ Hours a Week)

Kalyani Bansod — Tue, 28 Apr 2026 08:55:07 +0000

A doctor sees 20–30 patients a day.

After each visit, they spend 15–20 minutes typing notes into an EHR system — not treating patients, just filling forms. By the end of the week, that’s 8+ hours of pure documentation overhead.

This is not a workflow problem. It’s a crisis. Physician burnout is real, and the paperwork is a massive driver. At Prolifics, we work with healthcare clients modernizing their infrastructure, and this problem kept coming up — every single time.

So we built a pipeline to fix it: an ambient AI scribe that listens to the doctor-patient conversation and auto-generates a structured clinical note directly into the EHR.
Here’s exactly how we did it — architecture, tools, tradeoffs, and results.

The Real Problem (It’s Not Just “Too Much Typing”)
Before we touch any code, let’s be clear about what’s actually broken.
EHR systems like Epic and Cerner are powerful but clinician-unfriendly.
They’re built for compliance, not usability. A doctor has to manually:
•Select the right SOAP note template
•Type symptoms, history, assessment, and plan
•Attach ICD-10 codes
•Sign off before moving to the next patient
Meanwhile the patient is sitting there watching them stare at a screen.

“I went to medical school to help people — not to become a data entry clerk.”
— A physician at one of our client health systems
This quote stuck with us. It’s the problem statement in one sentence.

The Architecture: Four Layers You Need to Get Right
A production ambient documentation system isn’t just “speech-to-text + GPT.” It’s a pipeline with four distinct technical layers:
[Microphone Input]
↓
[ASR — Automatic Speech Recognition]
↓
[Speaker Diarization — Who Said What]
↓
[Clinical NLP + Named Entity Recognition]
↓
[LLM Summarization → SOAP Note]
↓
[FHIR API → EHR (Epic / Cerner)]
Let’s walk through each one.

Layer 1: ASR — Automatic Speech Recognition
We evaluated three options:
Tool Accuracy HIPAA BAA Latency
Whisper (OpenAI) Very High No (self-hosted only) Medium
Azure Speech High Yes (with config) Low
AWS Transcribe Medical High Yes (native) Low
We went with AWS Transcribe Medical for production because it’s purpose-built for clinical vocabulary — it handles terms like “metformin,” “ejection fraction,” and “CABG” without custom vocabulary tuning.
import boto3

transcribe = boto3.client('transcribe', region_name='us-east-1')

def start_transcription(audio_s3_uri: str, job_name: str):
transcribe.start_medical_transcription_job(
MedicalTranscriptionJobName=job_name,
Media={'MediaFileUri': audio_s3_uri},
MediaFormat='mp4',
LanguageCode='en-US',
Specialty='PRIMARYCARE',
Type='DICTATION',
OutputBucketName='your-hipaa-bucket'
)
For real-time use cases (in-room), we streamed audio directly using the AWS Transcribe Medical streaming API via websockets — latency under 300ms in testing.

Layer 2: Speaker Diarization
Raw transcript is useless without knowing who said what. The doctor and patient speak differently, and mixing their words into a single block breaks downstream NLP.
We used pyannote.audio (open-source, self-hosted) for speaker segmentation:
from pyannote.audio import Pipeline

pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization")
diarization = pipeline("encounter_audio.wav")

for turn, _, speaker in diarization.itertracks(yield_label=True):
print(f"{speaker}: [{turn.start:.1f}s → {turn.end:.1f}s]")
In practice, we labeled Speaker_00 as “Clinician” and Speaker_01 as “Patient” based on the first 10 seconds of audio (doctors always open the conversation).

Layer 3: Clinical NLP + Named Entity Recognition
This is where generic pipelines fall apart. General-purpose LLMs hallucinate dosages, misattribute symptoms, and miss negations (“no chest pain” becomes “chest pain” — a dangerous error).
We ran scispaCy (clinical NLP library) as a pre-processing filter to extract:
•Symptoms (UMLS entity linking)
•Medications + dosages
•Diagnoses (ICD-10 candidate mapping)
•Negations (critical for clinical accuracy)
import scispacy
import spacy

nlp = spacy.load("en_core_sci_lg")
doc = nlp("Patient denies chest pain. Currently on 500mg metformin twice daily.")

for ent in doc.ents:
print(ent.text, ent.label_)

Output:

chest pain — DISEASE

metformin — CHEMICAL

500mg — DOSAGE

We fed this structured output into the LLM prompt — not the raw transcript. This dramatically reduced hallucination.

Layer 4: LLM Summarization → SOAP Note
With structured NER output + diarized transcript, we prompted Claude (via Anthropic API) to generate the clinical note:
system_prompt = """
You are a clinical documentation assistant.
Generate a structured SOAP note from the encounter transcript.
Use only explicitly stated clinical facts.
Never infer diagnoses not mentioned.
Flag any low-confidence fields with [REVIEW NEEDED].
Output in JSON matching HL7 FHIR DocumentReference schema.
"""

user_prompt = f"""
Clinician: {clinician_turns}
Patient: {patient_turns}
Extracted Entities: {ner_output}

Generate SOAP note.
"""
The [REVIEW NEEDED] flag was a non-negotiable requirement from our clinical stakeholders. Doctors need to trust the output before they sign off — a confidence signal is better than silent errors.

Layer 5: FHIR API → EHR Integration
Generated notes go into Epic via the SMART on FHIR API as a DocumentReference resource:
import requests

fhir_note = {
"resourceType": "DocumentReference",
"status": "current",
"type": {
"coding": [{
"system": "http://loinc.org",
"code": "11506-3",
"display": "Progress note"
}]
},
"content": [{
"attachment": {
"contentType": "text/plain",
"data": base64.b64encode(soap_note.encode()).decode()
}
}]
}

response = requests.post(
f"{FHIR_BASE_URL}/DocumentReference",
json=fhir_note,
headers={"Authorization": f"Bearer {access_token}"}
)

One gotcha: Epic’s sandbox FHIR server is strict about LOINC code correctness. Wrong code = silent 422 error. Always validate against the LOINC database first.

HIPAA: The Layer You Can’t Skip
Every component needs a Business Associate Agreement (BAA):
•AWS Transcribe Medical — Native BAA
•S3 buckets — Must be encrypted at rest (AES-256) and in transit (TLS 1.2+)
•LLM API calls — Use private endpoints or self-hosted models
•Audio retention — Define your policy upfront: 30 days QA or 7+ years like a medical record?
We defaulted to deleting raw audio after 72 hours and retaining only the de-identified structured note.

Real Outcomes
We deployed this pipeline at a mid-size primary care group (12 physicians, ~250 encounters/week).
After 60 days:
•Documentation time per encounter: dropped from ~18 min → ~4 min
•Physician burnout score (validated scale): dropped 13 percentage points (consistent with published research)
•Note completion rate same-day: up from 71% → 96%
•Billing code capture accuracy: improved — the NER layer caught previously missed HCC codes
The physicians weren’t just faster. They told us they felt more present with patients again.

Key Takeaways for Developers
1.Don’t use raw transcripts as LLM input. Pre-process with clinical NLP first or you’ll get hallucinations that could harm patients.
2.Speaker diarization is not optional. Without it, attribution errors corrupt the clinical record.
3.The [REVIEW NEEDED] flag saved the project. Clinicians won’t trust black-box outputs. Build in transparency.
4.FHIR is the integration layer, not just an afterthought. Learn SMART on FHIR early — EHR sandbox environments are painful and slow.
5.HIPAA compliance is architecture, not a checkbox. Design your data flows before you write a single line of code.

What’s Next
We’re currently experimenting with:
•Real-time ambient mode — streaming note generation during the encounter, not after
•Specialty-specific models — oncology and cardiology have very different note structures
•Agentic prior auth — using the structured note output to auto-draft insurance prior authorization requests (currently the #1 admin time sink)
At Prolifics, we’ve been building enterprise healthcare integrations for decades. The combination of LLMs + FHIR APIs is genuinely the most exciting shift we’ve seen in clinical workflow tooling — and we’re just getting started.

Let’s Talk
Have you built anything similar? What was the hardest part of your EHR integration — the FHIR API, the HIPAA compliance layer, or getting clinician buy-in?

Drop your experience in the comments. I’d especially love to hear from anyone who’s tackled real-time ambient scribing — the streaming latency challenges are brutal and I suspect others are hitting the same walls we did.

6 SAP S/4HANA Migration Moves That Actually Work (From the Trenches)

Kalyani Bansod — Thu, 09 Apr 2026 07:41:18 +0000

The problem nobody talks about
Most SAP migrations don’t fail because of tech.
They fail because teams underestimate complexity.

I’ve seen projects where:
• Data looked “clean” but broke during migration
• Custom code silently failed post-go-live
• Downtime windows got blown out by hours
And the worst part? Everything looked “green” in planning.

If you're working on an SAP S/4HANA migration, you already know—this isn’t just an upgrade. It’s a full system rethink.

Let’s break down what works in real-world migrations.

The challenge (in simple terms)
Moving to S/4HANA means:
• Old ERP → New data model
• Custom code → Compatibility issues
• Legacy DB → In-memory architecture
• Business processes → Need redesign
So, you're not just migrating data.
You're aligning data + code + business logic + infrastructure all at once.
That’s where most teams struggle.

What we did (step-by-step, real workflow)
Here’s a practical flow we used in a mid-size enterprise migration.

1. System assessment before touching anything
We started with a deep system scan.

Tools used:
• SAP Readiness Check 2.0
• Custom Code Analyzer
• Simplification Item Catalog

What we checked:
• Add-ons compatibility
• Data volume
• Custom code usage
• Business processes impacted

Key insight:
~30% of custom code was unused.
We removed it before migration → saved time + reduced risk.

2. Clean the data (seriously, don’t skip this)
Data migration is where things break.

We focused on:
• Removing duplicates
• Archiving old transactional data
• Fixing inconsistencies

Tools:
• SAP Data Services
• Migration Object Modeler (MOM)

Workflow:
Extract → Clean → Validate → Load (test system)
We ran this cycle multiple times.

Why?
Because your first migration run will fail. That’s normal.

3. Choose the right migration approach
There are 3 main approaches:
• Greenfield → Fresh implementation
• Brownfield → System conversion
• Landscape Transformation → Hybrid
We chose Brownfield.

Why?
• Existing system was stable
• Business didn’t want process changes
• Faster timeline
But we still redesigned some processes later.

4. Fix custom code compatibility
This is where devs come in.
S/4HANA removes/changes many tables and functions.

Example:
• Old table: MKPF, MSEG
• New: MATDOC

What we did:
• Ran custom code checks
• Identified incompatible queries
• Rewrote SQL logic

Key change:
Moved from:
SELECT * FROM MSEG

To:
Use CDS Views / New Data Model

Tools:
• ABAP Test Cockpit (ATC)
• Code Inspector

5. Set up the right architecture
We deployed:
• SAP S/4HANA on HANA DB
• App server + DB server split
• Fiori frontend for UI
Architecture flow:
User → Fiori UI → App Server → HANA DB
Key improvements:
• Real-time reporting
• Faster query execution
• Simplified data model

6. Test like production depends on it (because it does)
Testing wasn’t just QA.
We did:
• Unit testing
• Integration testing
• UAT (User Acceptance Testing)
• Performance testing
Critical step:
Simulate real workloads.

We used:
• Batch job simulation
• High-volume transaction testing

Lesson learned:
Most issues show up only under load.

Real results (what improved)
After go-live:
• Reporting speed improved by ~60%
• Batch jobs ran 40% faster
• Data footprint reduced by 35%
• Custom code reduced by ~30%

But the biggest win?
System became easier to maintain.
Less legacy = fewer surprises.

6 success factors that made the difference

Clean before you move
Don’t migrate garbage.
Fix data + remove unused code early.
Start with system insights
Use readiness tools.
Guessing = risk.
Don’t blindly keep old processes
Even in Brownfield, rethink workflows.
Some legacy processes don’t belong in S/4.
Treat custom code as a liability
Every line of old code = potential failure point.
Refactor aggressively.
Test under real conditions
Not just “does it run?”

Test:
• Load
• Scale
• Edge cases

Involve dev + business together This isn’t just IT. Business logic changes = system changes.

Developer-focused takeaways
If you’re a developer working on S/4HANA:
• Learn CDS Views (they replace heavy SQL usage)
• Understand the new data model (MATDOC, etc.)
• Use ATC early, not late
• Avoid SELECT * — performance matters more now
• Think in terms of data flow, not just tables

Final thought
SAP S/4HANA migration isn’t “hard” because of technology.
It’s hard because everything is connected.
Data → Code → Business → Infrastructure
Miss one, and the whole thing shakes.
Let’s discuss

What’s been the hardest part of your SAP migration so far?
• Data issues?
• Custom code headaches?
• Performance surprises?
Curious to hear real stories from others building in this space.

How Leading Digital Transformation Providers Are Revolutionizing Business with AI in 2025

Kalyani Bansod — Wed, 22 Oct 2025 08:56:07 +0000

In 2025, digital transformation providers are fundamentally reshaping the way businesses operate, compete, and create value by harnessing the power of artificial intelligence (AI). As organizations face increasing pressure to innovate rapidly, leading digital transformation companies have embraced AI as a core component of their service offerings, enabling clients to modernize legacy systems, automate complex workflows, and deliver highly personalized customer experiences.

One of the key factors driving this revolution is the integration of advanced AI technologies such as generative AI, machine learning, and predictive analytics into digital initiatives. These technologies allow companies to analyze vast amounts of data in real time, uncovering insights that enable smarter decision-making and improved operational efficiency. For example, AI-powered automation streamlines repetitive tasks such as data entry and customer service, freeing employees to focus on high-value work and accelerating overall productivity.

Furthermore, AI consulting services play an essential role in helping businesses strategically deploy AI solutions. Consulting firms guide organizations in identifying the right AI applications aligned with business goals while addressing challenges around data quality, ethics, and governance. As a result, companies can reduce risks and maximize return on investment, leading to measurable improvements like cost reductions, faster product development cycles, and enhanced customer satisfaction.

The impact of AI-driven digital transformation extends beyond efficiency gains. It enables entirely new business models and revenue streams by unlocking innovative ways to engage customers and deliver value. For instance, AI capabilities like personalized marketing, real-time customer support, and intelligent recommendation systems build deeper customer loyalty and increase lifetime value. Industries such as manufacturing, healthcare, finance, and telecommunications are witnessing transformative growth by leveraging AI-enabled digital platforms tailored to their specific needs.

Leading digital transformation providers also facilitate seamless integration of AI with other emerging technologies, including cloud computing, Internet of Things (IoT), and blockchain. This convergence creates a robust ecosystem that supports scalable and agile digital strategies, allowing businesses to respond rapidly to market changes and emerging opportunities.

However, success in this AI revolution requires more than simply adopting new technologies. It demands a holistic approach involving cultural change, upskilling of talent, and reimagining organizational processes. The top digital transformation companies excel by offering end-to-end services—from initial strategy and technology selection to implementation and ongoing optimization—ensuring their clients sustain competitive advantage in an increasingly digital world.

In summary, in 2025, leading digital transformation providers empowered by AI are revolutionizing business by driving efficiency, innovation, and customer-centricity. They enable enterprises to unlock new growth opportunities, streamline operations, and build future-proof capabilities. Organizations that strategically partner with these providers and embrace AI as a fundamental enabler will be best positioned to thrive in the digital era.

This transformation is not just about technology; it’s about reshaping business for a smarter, faster, and more connected future. The companies leading the way are those who understand AI’s potential and harness it thoughtfully to create lasting impact.

Unlocking Business Value in 2026: The Power of Data Engineering & Analytics

Kalyani Bansod — Tue, 07 Oct 2025 06:53:24 +0000

Have you ever wondered what actually powers smart business decisions these days? Spoiler alert: It isn’t just about having tons of data. The real game-changer is what happens after the data lands in your lap.

Why Data Isn’t Valuable… Until You Use It
Every online action—from a website click to a phone tap—creates new data points. If you work with tech or business teams, you’ve seen firsthand how quickly data piles up. But raw data, by itself, is basically a messy attic. It’s the cleaning, sorting, and interpreting that makes it valuable.

Enter: Data Engineering (The Unsung Hero)
Imagine trying to find your favorite book in a dark, cluttered storeroom. That’s what dealing with unstructured, scattered data feels like. Data engineering is the process that builds the “plumbing”—pipes, shelves, and lights—so your information is organized and accessible.
Modern data engineers rely on cloud platforms, smart automation, and plenty of best practices to make sure data remains clean, integrated, and scalable as businesses grow.

Data Analytics: The Brain After the Brawn
Once you’ve got clean, reliable data, analytics takes the spotlight. It’s more than just charts—it’s about discovering real patterns, forecasting demand, and answering questions like “What do users really want next?”
The coolest part? Tools today can analyze data in real time, help spot market trends, predict product needs, or personalize customer journeys way before competitors catch on.

Why AI & ML Are Taking Over Analytics
Algorithms learn fast. Artificial intelligence (AI) and machine learning (ML) have begun to automate repetitive analytics tasks, uncover hidden insights, and even spot anomalies before they become big problems. Think about automatic fraud detection or instant customer churn prediction—AI’s fingerprint is everywhere.

Trust, Security, and Governance
Sharing data across teams and systems is great… until you hit questions around privacy or security. Having robust data lineage and governance frameworks ensures you can trace every record, keep sensitive information protected, and prove compliance when the auditors come knocking.

Future-Proofing: What’s Next?
In 2025, smart data strategies mean moving to the cloud, automating data pipelines, and adopting DataOps. The future belongs to organizations that teach every employee to read data confidently—this is not just IT’s job anymore!

Final Thoughts
If you’re just starting in data engineering, analytics, or thinking about how your company uses data, remember: It’s not about collecting more data. It’s about building solid systems to unlock game-changing insights. Your next breakthrough might just be hiding in your pipeline.

Snowflake Agent: Empowering Developers with AI-Powered, Multi-Cloud Data Integration

Kalyani Bansod — Thu, 14 Aug 2025 08:39:50 +0000

In today’s data-driven landscape, developers need more than just storage—they need real-time, scalable, and intelligent data platforms that empower building next-gen applications and delivering business insights fast. Snowflake Agent is built exactly for this purpose.

Powered by the robust Snowflake Data Cloud and enhanced by Prolifics’ expertise, Snowflake Agent provides a multi-cloud data platform that unifies scattered data sources across AWS, Azure, and GCP. Its flexible architecture supports diverse workloads, including data engineering, analytics, AI, and machine learning, all in one seamless environment.

Why Developers Should Choose Snowflake Agent

• Effortless Multi-Cloud Integration: Connect and manage data natively across major cloud providers without vendor lock-in.
• Zero Infrastructure Management: Forget patching, tuning, or maintenance—Snowflake handles backend operations so you focus on creating value.
• Scalable, Usage-Based Pricing: Scale instantly to match workload demands, paying only for what you use.
• Built-In AI and Machine Learning: Deploy machine learning models directly within Snowflake, enabling AI-powered business insights without moving data out of the platform.
• Unified Data Engineering Workflows: Integrate ETL pipelines, data transformation, and governance all in one platform to streamline your development lifecycle.
• Secure and Compliant: Built-in support for security standards like HIPAA, GDPR, and PCI makes compliance effortless—ideal for regulated industries.
• Automated Governance and Data Lineage: Track data sources and transformations with ease, supporting better auditability and trust in your data pipelines.
Key Developer Use Cases
• Real-time analytics dashboards fed by unified cross-cloud data.
• AI and predictive analytics models within your data ecosystem.
• Automated compliance reporting driven by data governance workflows.
• Building personalized customer experiences via integrated retail data.
• Monitoring and predictive maintenance in manufacturing applications.

Getting Started with Snowflake Agent

Developers can kickstart projects quickly with Prolifics Accelerators like SmartStart for Snowflake (a rapid 30-day proof-of-concept) and AI and ML FastTrack for rapid deployment of machine learning models inside Snowflake. Migration from legacy systems is streamlined via Migration Factory tools, minimizing downtime.

Ready to supercharge your data projects with a future-proof, AI-optimized, multi-cloud platform? Explore how Snowflake Agent can accelerate your development and data integration strategies today.
To know more read this blog : Snowflake Agent