AI-supported wound reporting

KickstartAI x LUMC

Through a collaborative effort, the Leiden University Medical Center (LUMC)  and KickstartAI are leveraging AI to deliver two key outcomes: minimized administrative work for nurses and a major uplift in the consistency and quality of electronic wound documentation. This initiative ensures healthcare professionals can focus more energy and time on patient-centered care.

The Problem

Within the LUMC, countless wounds are treated annually. The care provided for those wounds relies heavily on accurate, timely administration. Nurses record the status of wounds in the Electronic Health Record (EHR) to ensure that the information needed to provide appropriate wound care is available across the entire care team.

In practice, this reporting process is manual and time-consuming, and often takes place daily. Nurses first assess the wound, take a photo, upload it to the electronic patient record (EPR), and then complete the TIME form – a structured framework used to document key aspects of wound healing, including tissue condition, infection, moisture balance, and wound edges. At a later stage, a wound care specialist reviews the information and provides an assessment.

With  increasing pressure on healthcare specialists in the Netherlands, this essential administrative task is widely experienced as a significant burden among nurses in the LUMC. Therefore, LUMC and KickstartAI are partnering to develop an AI solution to decrease this administrative workload, standardize documentation, and ultimately allow nurses to dedicate more time to direct patient care.

Goals

The goal of the project is to develop an AI solution using image recognition that can automatically generate and populate structured wound administration reports from a single photograph of a wound.

Approach

The approach consists of three phases 

1. Use Case Identification: Deeply understanding the current process and defining the specific problem to be solved.

2. Model Development: Building and training the core AI models.

3. Implementation and Adoption: Integrating the solution into LUMC’s IT landscape and clinical workflows. 

The KickstartAI x LUMC team is currently finalizing the Use Case Identification phase. This phase involved crucial on-site observation through various shadowing sessions with nurses in surgical wards and the outpatient wound clinic. By combining these practical insights with deep-dive interviews with clinical experts, the team gained a clear understanding of the key pain points. This process ultimately led to a well-defined use case for the next phase: developing a solution for automatic photo-to-wound reporting.

Key features 

The AI system we are developing consists of the following components:

• Segmentation: Detecting the precise wound area within the photograph, including the identification of different tissue types or colors present in the wound bed.

• Object size measurement: Accurately determining the real-life size of the wound and its segmented areas (colors).

• Translation to wound report: Translating the segmented and measured data into a standardized, structured digital wound report in the TIME format suitable for the EHR.

Expected Results

The expected results of the AI solution are twofold:

1. Reduced Workload: The time required for nurses to complete a wound report will decrease dramatically from the current 5–10 minutes to an estimated 1 minute per wound care moment, significantly lowering the administrative burden and freeing up valuable time for direct patient care.

2. Improved Quality: Creating wound reports in a uniform structure and manner during each assessment can improve the consistency and quality of reporting, allowing nurses to document more efficiently while reducing variation in interpretation, level of detail, and photo quality. This standardization will support clearer information exchange between caregivers and contribute to more consistent and effective treatment decisions.