Where Clinics Meet Insurers: Healthcare Software Development Case Studies

Four builds from our developers' project history, each at a different point where patient, clinic and insurer data have to move as one system.

Until December 7, anyone with Medicare can switch plans for next year. On January 1, 2027, insurers covered by a federal rule must open FHIR APIs for claims data and prior authorization. Medicare's home telehealth flexibilities run through the end of that year. All three deadlines depend on data crossing from patient to clinic to insurer without breaking.

Healthcare software development in the US mostly means keeping that data moving. The four case studies below come from Cortance developers' project history. They cover a clinical and claims data platform, voice AI for prior authorization calls, a telemedicine video layer and a Medicare enrollment site. As of October 2026, our developers' records list 51 healthcare-tagged projects from 40 engineers, and industry tags cover 67% of their projects. The broader picture sits in our breakdown of where our engineers work across industries.

Why US healthcare software development has to serve the patient, the clinic and the insurer at once

US healthcare software development serves three parties at once because one visit produces three kinds of records. The clinic writes a clinical note, the insurer receives a claim, and many services need a prior authorization before they happen. Each record travels in its own format, through its own system, often owned by a different company.

None of the three is going away soon. In a 2023 national survey of health information exchanges, 96% reported receiving HL7 v2 admission messages, while FHIR adoption was described as slower, according to ASTP/ONC. X12 transactions remain the HIPAA standards for claims and authorizations. The new CMS rule adds FHIR on top of them rather than replacing them. A developer on this bridge therefore spends much of the job translating between generations of standards.

Two 2025 benchmarks show what the translation gaps cost. Physicians in the AMA's Prior Authorization Physician Survey complete about 40 prior authorization requests a week, and they and their staff spend 13 hours a week on them. The CAQH Index found that only 40% of medical prior authorizations ran fully electronically; the rest went through portals, phone, mail or fax.

Buyers now ask about this bridge before anything else.

Yevhen Vavrykiv, CEO of Cortance, says US healthcare requests usually open with one question: has the team optimized processes inside healthcare facilities and connected them to insurance companies? In the US, he explains, providers and insurers work as one whole, so the exchange between them has to run without failures. Clients also ask about Medicare and Medicaid experience and about Pega architects.

Each case below sits at a different crossing on that bridge.

Healthcare data integration: clinical and claims data in one HIPAA-ready model

Oleksandr D. built a .NET and Azure Data Factory platform that brings a healthcare provider's clinical, claims and operational data into one normalized model. Sources include EHR systems, SQL Server databases, SFTP feeds, and REST APIs, including HL7 and FHIR messages. Protected health information stays encrypted and masked from ingestion to storage.

Challenge: two message generations and a claims feed

HL7 v2 and FHIR describe the same patient in very different ways. A v2 message is pipe-delimited text split into segments such as PID for patient identity and OBX for an observation, and the standard allows site-specific Z-segments. FHIR packs the same facts into JSON resources such as Patient, Observation and Encounter. Claims data arrives in yet another shape, usually as database extracts or files. The provider needed all of it in one model that analysts could query without knowing where a record came from.

Daily volumes were large, and the platform had to accept new sources without a rebuild. Every feed also carried protected health information, so the security model had to cover each hop, not only the final database.

Approach: configuration-driven pipelines and security at every hop

Oleksandr built metadata-driven, parameterized pipelines in Azure Data Factory. A new source became a set of configuration values, not a new pipeline. In our view, that choice decides the long-term cost of a healthcare data platform, because hand-written pipelines multiply as fast as the feeds do.

Incremental loading and change data capture limited each run to records that had changed since the last one. Validation, cleansing and normalization then mapped HL7 and FHIR content into a unified model in Azure Data Lake and Azure SQL. Order matters here: records were validated before they joined the shared model, so a malformed feed could not spread errors downstream unnoticed.

The controls map to technical safeguards the HIPAA Security Rule names, including access control, audit controls, integrity and transmission security. Secrets lived in Azure Key Vault, and services reached each other through managed identities, which keeps credentials out of code. Data was encrypted at rest and in transit, and sensitive fields were masked.

Result: a monitored platform promoted through three environments

Azure Monitor and Log Analytics tracked pipeline health continuously, with automated alerts on failure. Changes moved through Dev, Test and Prod via CI/CD in Azure DevOps. The project record contains no before-and-after figures, so we describe the system rather than attach a percentage nobody measured.

Prior authorization automation: voice AI for the calls insurers still require

Anton Z. built frontend features for a voice-AI platform that takes phone work off medical practices. The AI places insurance authorization calls and pharmacy and provider credentialing calls, navigates phone menus, waits on hold and follows templates set by the practice. Anton built the user-facing side: the screens where staff set that work up and follow it.

Challenge: two routes to the same authorization

A FHIR-based prior authorization runs in three steps. The CMS Prior Authorization API workflow recommends a Da Vinci implementation guide for each one. Coverage Requirements Discovery tells the provider whether authorization is needed. Documentation Templates and Rules collects the required paperwork, and Prior Authorization Support submits the request and returns the decision. That route is mandatory only for payers covered by the CMS rule: Medicare Advantage, Medicaid, CHIP and plans on the federal exchanges.

For every other payer, the route is often still a phone line, and that gap shapes healthcare software development for practices. A staff member dials, works through an interactive voice menu, waits, reads out patient and procedure details and writes down a reference number. That is the work this platform hands to an AI, and it is the part of prior authorization that no API deadline removes for now.

Approach: a control surface for an AI acting on the practice's behalf

Anton built features in React and TypeScript from Figma designs and, with the backend team, connected the APIs that drive the voice assistant. The stack included WebSockets for real-time communication. He also removed unnecessary re-renders to keep data-heavy pages fast and wrote test cases for component logic.

We believe the interface carries more responsibility here than in most products. An AI that speaks to an insurer on a clinic's behalf creates commitments, reference numbers and follow-ups that staff must verify. A call stuck in a phone menu needs a different reaction than a call waiting on hold, so live status is a safety feature, not decoration. The decision itself stays with people: insurers have pledged that medical necessity denials will be reviewed by a licensed and qualified clinician, according to the AMA.

Result: routine insurer calls moved off practice staff

Anton's features shipped as part of a platform that takes routine authorization and credentialing calls off practice staff. The record has no measured time savings, so this case reports none.

Telemedicine video with WebRTC: keeping the visit alive on a bad connection

Bohdan B. built the core video consultation interface for a telemedicine platform that connects patients with licensed doctors and psychologists around the clock, on web and mobile. His work covered the WebRTC connection itself, video quality under poor networks and an automatic fallback to audio when video could no longer hold.

Challenge: clinical calls on home networks

Telehealth is the part of healthcare software development where network quality becomes clinical quality. Patients join from home Wi-Fi, mobile data, and corporate networks, and some sit behind firewalls or address translation that blocks a direct connection. In a consultation, a frozen picture or a dropped call is more than a support ticket, because a clinician loses the thread of an assessment. Audio also matters on its own terms. Medicare beneficiaries can receive audio-only telehealth services in their homes through December 31, 2027, according to the CMS telehealth FAQ.

Approach: negotiation, adaptation and graceful degradation

Bohdan managed the full RTCPeerConnection lifecycle, including the SDP offer and answer that agree on codecs, and ICE with STUN and TURN servers to find a working network path. When a direct path fails, TURN relays the media, keeping the call up at the cost of extra latency. He configured H.264 and VP8 codecs and adaptive bitrate logic, so video quality dropped step by step on weak connections instead of freezing.

Live quality indicators showed bitrate, packet loss, frame rate and resolution to both the patient and the clinician during the session. When conditions fell below a threshold, the platform switched from video to audio-only automatically and kept the session open. Both sides could see why the picture changed, which spared the clinician from guessing whether the patient had left.

Result: a consultation that degrades instead of dropping

The delivered interface kept consultations running across changing network conditions, with visible quality signals and a defined fallback path. As with the other cases, no uptime or completion figures are in the record. For the architecture behind combined video and sensor streams, see our guide to WebRTC for telehealth and remote patient monitoring.

Medicare enrollment website built for Annual Enrollment Period traffic

Dmytro S. built backend services and frontend modules for a Medicare enrollment website whose traffic peaks during the Annual Enrollment Period. The platform used Web Components micro-frontends over a .NET backend and a SQL data layer, under strict limits on bundle size, accessibility and performance. Infrastructure ran on Azure and was defined in Terraform.

Challenge: seven and a half weeks with no ramp-up

Medicare open enrollment runs from October 15 to December 7 every year, and few deadlines in healthcare software development are this fixed. Enrollment sites see their heaviest traffic during that window, so the system hits peak load on its most important days. Users include older adults and people with disabilities, which makes accessibility a core requirement rather than a polish item.

Approach: small bundles and tests at four levels

Dmytro developed ASP.NET Core services and SQL data layers sized for enrollment-period load from the first day. On the frontend, he built React modules packaged as Web Components, so each piece could be embedded in a page as a standard HTML custom element. Small bundles were a hard requirement, because every extra kilobyte is downloaded by every visitor during the busiest weeks.

Test suites covered unit, integration, end-to-end and visual layers, with Cypress among the tools. TeamCity and Octopus Deploy handled continuous delivery, and Terraform defined the Azure environment. Infrastructure written as code means an environment can be rebuilt instead of repaired by hand, which matters most when there is no quiet week to debug in.

Result: an enrollment platform designed for its worst week

The result was a platform built to carry its peak from launch, with accessibility and performance treated as acceptance criteria. The record lists no traffic or conversion figures, and we report none.

Four healthcare builds compared across the patient-clinic-insurer bridge

Each case solves a different part of the same problem. Read across a row to see where a project sat, what moved through it and what limited the design.

Read the table by column and a second pattern appears. The data platform and the enrollment site share a .NET and Azure stack. Their hardest problems were opposite, though: too many inputs in one, too many users at once in the other. A shared constraint tells a hiring manager more than a shared framework does.

The stacks behind healthcare software development requests

Healthcare software development requests reach us with a stack attached, and the project records above show why it varies so much. Data and claims platforms tend to run on .NET and C# with Azure, as in the first and fourth cases. Telemedicine depends on WebRTC engineers who understand ICE, TURN and codec negotiation. Our developers' healthcare records also include Python work in computer vision and biomedical text processing, and Java on mobile and backend projects. For these requests, we match developers with healthcare project experience in these stacks, and we look for the same kind of cross-stack experience, not just the same framework.

Four questions test that history quickly on a first call:

1. Did the data you handled include claims or authorizations, or only clinical records?
2. Which formats did you parse or produce: HL7 v2, FHIR, X12 or vendor-specific files?
3. Where did protected health information live, and how was access to it controlled?
4. What did your system do when a partner's API, phone line or network failed?

Strong answers name a mechanism, such as change data capture, masked fields, a TURN relay or an audio fallback. Weak answers list technologies without saying what they protected.

Payer-provider healthcare software development FAQ

1. What does the CMS Interoperability and Prior Authorization rule require, and when? The CMS Interoperability and Prior Authorization rule requires impacted payers to run four FHIR APIs by January 1, 2027. Those interfaces handle patient access, provider access, payer-to-payer exchange and prior authorization. Impacted payers include Medicare Advantage, Medicaid, CHIP and federal exchange plans. Operational changes, such as faster decision timeframes and specific denial reasons, already apply.
2. Can a Medicare telehealth visit switch to audio-only? Medicare beneficiaries can receive audio-only telehealth services in their homes through December 31, 2027, according to the CMS telehealth FAQ. Platforms serving Medicare patients therefore treat audio as a real care format, not only a backup. A well-built WebRTC client switches to audio when video fails and keeps the session open, so the visit continues.
3. Voice AI or FHIR APIs: which should automate prior authorization? FHIR APIs suit payers covered by the CMS rule, while voice AI fits the requests that still run by phone. The CAQH Index found only 40% of medical prior authorizations fully electronic. Healthcare software development teams serving practices usually need both routes until commercial insurers catch up.
4. Is a developer with HIPAA experience the same as a HIPAA-compliant vendor? No: HIPAA compliance is an obligation of the covered entity and its business associates, not a trait of one developer. Developer experience means knowing how to build the controls, such as encryption, key management, audit logging and field masking. Contracts, risk analysis and business associate agreements remain the organization's job.
5. What makes a Medicare enrollment website harder to build than a standard web app? A Medicare enrollment website must absorb its peak load in one fixed window, from October 15 to December 7. An outage in those weeks leaves no time to recover. Accessibility carries more weight because users include older adults and people with disabilities, which pushes teams toward small bundles, layered testing and infrastructure defined as code.

Building across the bridge, not on one side of it

The four projects above sit at different crossings of one system: provider data, insurer calls, patient video and Medicare enrollment. US healthcare software development rarely stays on one side of that system. The January 1 API deadline should move more prior authorization onto FHIR, while phone work, enrollment peaks and audio-only visits stay for now.

Before hiring for a healthcare build, write down which crossing your product touches and which standard carries its data. Then ask every candidate to show a project that sat at that same point. The same test works for full vendors, such as those in our review of US healthcare software development companies.