← Back to Ensemble Hospitalier de la Côte pilots generative AI to assist doctors drafting discharge letters with Smile

Source proof for Ensemble Hospitalier de la Côte pilots generative AI to assist doctors drafting discharge letters with Smile

Source-bound proof

Verified source excerpts for every supported field

Each colour maps a published value to the exact source passage used to support it. Only bounded excerpts are public; administrators can inspect the complete captured source.

11 fields supported

Title

Ensemble Hospitalier de la Côte pilots generative AI to assist doctors drafting discharge letters with Smile

derived · high

…FR Breadcrumb Home Smile IT Case Studies: Concrete Results in Digital Projects Assisting doctors with Generative AI Ensemble Hospitalier de la Côte Assisting doctors with Generative AI Healthcare AI The context Assistance with…

…on to the needs are paramount. The result AI for a concrete impact in real life The presentation to physicians at the end of the Proof of Concept (PoC) in Live Demo mode was a success. All the physicians present felt that the proposed solution could save them time…

Description

Ensemble Hospitalier de la Côte worked with Smile to reduce the time doctors spend drafting hospital discharge letters in internal medicine. Two priority features were developed: contextual prediction of the investigation text based on the patient's diagnoses and laboratory results, and automated retrieval and formatting of laboratory results. Nearly 3,000 discharge letters were used for training. After a proof-of-concept live demo to 15 doctors, physicians self-estimated time savings of 10 minutes for a simple letter to 30 minutes for a complex one, and it was decided to proceed with phased industrialization.

quote · high

…rding to the client's choice Training the model and confronting it with reality Nearly 3,000 discharge letters were used for training. The reality of the historical data differed from certain best practices — this…

…that the proposed solution could save them time when writing discharge letters. Their self-estimates ranged from 10 minutes for a simple letter to 30 minutes for a complex one. Regardless of the accuracy of this self-assessment, the crucial element for us…

…t with writing by suggesting missing values in the text was very well received. Following this demonstration, it was decided to proceed with the industrialization of the solution. This will be done in phases, each validated jointly by the IT department and th…

…alier de la Côte Assisting doctors with Generative AI Healthcare AI The context Assistance with drafting discharge letters for internal medicine Following an invitation to an event organised by Synotis in March 2024 in Genev…

Company

Ensemble Hospitalier de la Côte

quote · high

…FR Breadcrumb Home Smile IT Case Studies: Concrete Results in Digital Projects Assisting doctors with Generative AI Ensemble Hospitalier de la Côte Assisting doctors with Generative AI Healthcare AI The context Assistance with…

Industry

Healthcare

classification · high

…erative AI Ensemble Hospitalier de la Côte Assisting doctors with Generative AI Healthcare AI The context Assistance with drafting discharge letters for internal medicine Following an i…

Problem

The hospital wanted to optimize medical time, particularly in drafting hospital discharge letters, which are mandatory after each stay and require considerable time for drafting and proofreading, and in the manual transcription of laboratory results, a time-consuming and error-prone task.

quote · high

…h 2024 in Geneva, the Centre Hospitalier de la Côte (EHC) requested support, as it was looking to optimize medical time, particularly on the drafting of hospital discharge letters, which are mandatory after each stay, and the manual transcription of laboratory results, a time-consuming and error-prone task. The objective Reduce writing time, improve the quality of discharge letters Th…

Solution

After analysis, two priority features were identified: a contextual prediction of the investigation text based on the patient's diagnoses and laboratory results, and the automation of the retrieval and formatting of laboratory results. The model was trained on nearly 3,000 discharge letters.

quote · high

…r administrative burden. After analysis, two priority features were identified: a contextual prediction of the investigation text based on the patient's diagnoses and laboratory results, offering superior personalized content compared to generic templates; the auto…

…results, offering superior personalized content compared to generic templates; the automation of the retrieval and formatting of laboratory results, eliminating the risk of copying errors while improving the document structure…

…rding to the client's choice Training the model and confronting it with reality Nearly 3,000 discharge letters were used for training. The reality of the historical data differed from certain best practices — this…

Business value

At the end of the proof of concept, all physicians present felt the solution could save them time when writing discharge letters; their self-estimates ranged from 10 minutes for a simple letter to 30 minutes for a complex one. These are self-estimates rather than measured results.

quote · high

…cians at the end of the Proof of Concept (PoC) in Live Demo mode was a success. All the physicians present felt that the proposed solution could save them time when writing discharge letters. Their self-estimates ranged from 10 minutes for a simple letter to 30 minutes f…

…that the proposed solution could save them time when writing discharge letters. Their self-estimates ranged from 10 minutes for a simple letter to 30 minutes for a complex one. Regardless of the accuracy of this self-assessment, the crucial element for us…

AI capabilities

Generative AI

classification · high

Assisting doctors with Generative AI | Smile Aller au contenu principal Smile.Ai Smile Our references Careers Contac…

Implementation approach

Smile involved doctors and compared models: a framework prioritized features, with 10 proposed and costed features of which 2 were developed as a priority according to the client's choice. The solution was presented to 15 doctors in live demo mode at the end of the proof of concept, after which it was decided to proceed with industrialization in phases, each validated jointly by the IT department and the physicians.

quote · high

…oving the document structure and reducing the workload of writers. Our approach Involve doctors and compare models Framework for prioritizing features to be developed Exchange workshop 10 proposed and costed features 2 features developed as a priority, according to the client's choice Training the model and confronting it with re…

…stration, it was decided to proceed with the industrialization of the solution. This will be done in phases, each validated jointly by the IT department and the physicians, always with the aim of providing a solution that effectively addresses the obs…

Headline outcome

derived · high

…that the proposed solution could save them time when writing discharge letters. Their self-estimates ranged from 10 minutes for a simple letter to 30 minutes for a complex one. Regardless of the accuracy of this self-assessment, the crucial element for us…

Use case type

Content generation

classification · medium

…r administrative burden. After analysis, two priority features were identified: a contextual prediction of the investigation text based on the patient's diagnoses and laboratory results, offering superior personalized content compared to generic templates; the auto…

Capture details

Captured: 27 Oct 2026, 06:02 UTC
Extractor: fetch-strip@1
Snapshot hash: dbe7a721760f2550674210298710bca71a12c3c692fc6928513f2023cb449a6b

Live source Back to use case