</ AI Dialogue Quality Scoring for Healthcare Network >

An AI-powered scoring and transcription system was implemented for a private clinic network to process thousands of daily dialogues. The solution automated patient communication quality control: critical error detection accuracy increased by 340%, response time improved 7-fold, and costs decreased by 60%.

x4.4

Increased accuracy in problem detection

Reduced problem detection time

40%

Lower quality control costs

Client

A regional network of private clinics and diagnostic centers in several regions. Offering comprehensive diagnostics and support, combining modern technologies and specialists. Approximately 30 centers.

«Medexpert»

Partnering since 2024

Client website

"The neural network literally opened our eyes to how we communicate with patients. Now we don't spend hours listening to recordings but work directly with ready analytics. We analyze the worst calls and use the best ones for training newcomers."

Development Director

Alex

Automating Communication Quality Control

The client is a major regional network of private clinics and diagnostic centers comprising approximately 30 branches. Daily, administrators and doctors handle thousands of patient inquiries via phone and messengers. The project's main goal was complete automation of communication monitoring to enhance service levels. The implementation was driven by the physical impossibility of manually reviewing the entire volume of audio data. Management needed a transparent tool capable of consolidating disparate communication channels into a unified database, converting voice to text, and automatically evaluating staff communication quality. This would enable prompt conflict detection and objective service quality ratings without expanding the quality control team.

Client Challenge: Blind Spots in Auditing Thousands of Dialogues

The primary challenge was the enormous volume of daily communications. Tracking thousands of conversations between patients, doctors, and administrators using traditional methods proved impossible. This created a massive blind spot: critical errors, rudeness, or incompetence went unnoticed, while best practices couldn't be scaled. The human factor needed to be eliminated from the audit process.

Solution: Developing an AI Scoring System MVP in 2 Months

A team of specialists designed a high-load audio processing pipeline architecture. First, we implemented industrial-grade transcription, consolidating all dialogues into a single channel. Then we configured AI scoring across 100 parameters. The system generates daily ratings, highlighting the worst conversations for investigation and the best for training purposes. The basic version cost just $5,000.

Tech Stack: Python, OpenAI Whisper, PostgreSQL, PHP, MySQL

Python and the GPU-optimized (RTX 3090) Whisper model ensured high-speed voice-to-text conversion, processing over 100 hours of audio daily. Additional NLP models corrected raw transcription errors. The PHP, MySQL, and PostgreSQL combination guaranteed reliable data storage, while RESTful API enabled seamless integration of analytics with the client's existing CRM systems.

Impact: 60% Reduction in Quality Control Costs

The software implementation fundamentally transformed the audit process. Problem dialogue and critical error detection accuracy increased by 340% (4.4 times). Incident response speed improved 7-fold thanks to automated daily reports. The client received a high-quality monitoring system that reduced quality control costs by 60%, completely eliminating the need for manual call listening.

Future Plans: Individual Competency-Based Scoring

We'll transition from general courtesy metrics to deep analysis of professional skills. The system will evaluate dialogues based on employee roles: how competently a doctor collected medical history or how well a pharmacist provided recommendations. This will enable precise grading systems and enhance medical expertise across all levels.

Automate Your Quality Control Today

Speech recognition technology has reached a level where manual auditing becomes an unjustifiable resource drain. AI analytics implementation takes minimal time, allowing businesses to scale instantly without losing customers due to hidden errors.

discuss a project

</ Our team >

Julia

Client Relations Specialist. She genuinely loves interacting with people, values trust, and makes sure every client feels cared for, supported, and confident in their choice. She also writes the “TechnoInvestor” column on our corporate blog.

@jamcaj

Vitaly

Lead Developer. Responsible for project architecture and the implementation of innovative solutions. Specializes in AI and draws inspiration from cutting-edge development and automation technologies. Hosts the “TechnoCoder” segment on the company's video blog

@eva3613

Vladimir

UX Designer. Develops intuitive and aesthetically pleasing interfaces, ensuring the logic, usability, and visual consistency of every digital solution the company creates. Writes the “TechnoDesigner” column for the corporate video blog

@XerucuS

Mikhail

AI developer and expert. Develops Telegram and WhatsApp bots, implements CRM and automation, and applies AI technologies to optimize UX/UI and drive project growth.

@Oerby2

Natalia

Lawyer. Ensures legal reliability and transparency in client relationships, reviews contracts, and protects clients’ interests at every stage of their interaction with the company

@assa13nv

Vladimir

Representative of the Laboratory in the U.S. (Florida). Developer.

@vlad_bykanov

Igor

Executive and business consultant. ERP/CRM and process automation developer. Tech evangelist—helps clients grow their businesses using modern technologies and solutions.

@lhtsu

</ Contacts >

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</ AI Dialogue Quality Scoring for Healthcare Network >

Client

Automating Communication Quality Control

Client Challenge: Blind Spots in Auditing Thousands of Dialogues

Solution: Developing an AI Scoring System MVP in 2 Months

Tech Stack: Python, OpenAI Whisper, PostgreSQL, PHP, MySQL

Impact: 60% Reduction in Quality Control Costs

Future Plans: Individual Competency-Based Scoring

Automate Your Quality Control Today

</ Our team >

Julia

Vitaly

Vladimir

Mikhail

Natalia

Vladimir

Igor

</ Contacts >

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