Stethoscope AI application cuts hours of healthcare admin workload

Admin tasks and paperwork eat into doctor time, leading to longer waits, slower care, and declining patient engagement. In collaboration with a leading medical university, Hiflylabs developed an AI application to address this issue, providing doctors with rapid access to an in-depth view into a patient’s medical history. It combines AI and data engineering strategies to integrate and structure multi-format patient data from various sources, generating insights in clicks with validation and guardrails in place.

MINS

From query to patient insight

HRS

Of daily doctor admin work reduced

Challenge

Doctors are burdened with admin tasks that take more time than patient care: nearly two hours for every one hour spent with patients. Much of this effort goes to retrieving medical information scattered across fragmented sources such as lab results, treatment data, and EHR.

AI can drastically speed up the process: find, integrate, and summarise patient data in seconds. But challenges of reliability, compatibility, and trust in AI tools remain. Any application in healthcare must not only solve a real problem but also meet stringent standards and integrate into existing healthcare infrastructure.

Solution

Hiflylabs developed an AI application that reduces admin overhead and accelerates time to clinical insights. It integrates patient data from various resources, structures it, and delivers information and summaries to doctors on an easy-to-use web interface.

Drawing on rich cross-industry experience with unstructured data, the team designed a modular solution, tested multiple LLMs through PoCs, and, in collaboration with clinicians, defined use cases where advanced analytics can reliably support clinician practices with proper guardrails and validation mechanisms. The current solution includes:

Secure integration with a major patient data platform and medical systems to aggregate diverse patient data.
Agentic workflows and NLP methods to process unstructured patient data (PDFs, images, clinical notes) and generate structured summaries in a matter of clicks.
Combination of domain-specific and general LLMs with prompt strategies and training to handle multilingual healthcare-specific content in patient data.
Architecture approach and LLM stack that can be implemented within on-premises infrastructure with tight security requirements and cost control.
RAG chatbot to query patient data with natural language, delivering instant insights within seconds while linking to sources for validation.
Interactive dashboard and custom visualizations to show patient data dynamics and group insights for faster, more effective diagnostics.

The project started with risk-free time-saving applications—clinical admin work, diagnostic support, data summarization, and patient outcome documentation. As trust in AI and adoption grow, AI can extend into more advanced use cases, such as triage, treatment recommendations, and anomaly detection to flag patients needing urgent intervention.

Ultimately, the initiative aims to improve patient engagement, optimize doctors’ time, and deliver a more efficient and reliable healthcare experience.