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SCAN-ONC: Scalable Natural Language Processing for Tracking Clinical Data and Improving Healthcare in Oncology.

The SCAN-ONC project explores the use of artificial intelligence (AI) to improve monitoring of symptoms, treatment side effects, advance care planning, and other palliative care quality indicators in advanced lung cancer.

What is the project about?

The main goal is to adapt and test natural language processing (NLP) technology developed and used in the U.S. for use in Norwegian. This technology can read and summarize content from electronic health records (EHR). We will use NLP to extract text-based information from EHR of patients with advanced non-small cell lung cancer (NSCLC) regarding:

  • Tumor tissue samples
  • Symptoms and side effects
  • Goals of Care conversations
  • Prognostic factors
  • Palliative care quality indicators

Why is this important?

Many cancer patients in clinical practice are older and have more comorbidities than those in clinical trials. Assessing treatment risks and discussing therapeutic options can be challenging. Normally, healthcare professionals must manually review the EHR. With NLP, this process becomes faster and more accurate, revealing details that were previously difficult and time-consuming to extract. SCAN-ONC will improve clinical oversight and support decision-making, enabling more personalized treatment.

SCAN-ONC is a collaboration between:

  • Department of Thoracic Medicine, Haukeland University Hospital (HUS)
  • Regional Centre of Excellence for Palliative Care, Western Norway (KLB)
  • Harvard University / Dana-Farber Cancer Institute
  • Helse Fonna
  • Helse Førde
  • Helse Stavanger
  • Helse Vest IKT
  • The Cancer Registry of Norway
  • University of Bergen (UiB)

The project is funded by Helse Vest through strategic research grants for the period 2025–2030.

Cancer patients are experiencing better outcomes with new treatment options, but lung cancer remains the deadliest cancer type. Clinical trial participants are often younger and healthier than patients receiving cancer treatment in clinical practice. Our regional research collaboration has documented challenges faced by patients, families, doctors and nurses, when making treatment decisions under uncertainty about effect and side effects. Individualized risk stratification is difficult and must be discussed with the patient to guide shared decision-making.

To address the need for tracking clinically relevant data, this project aims to establish, validate, and operationalize AI-based NLP technology to identify prognostic factors, symptoms, treatment toxicity, and documentation of treatment discussions in electronic health records (EHR) of NSCLC patients receiving immunotherapy. By using NLP-assisted extraction of clinically significant information which traditionally requires manual chart review, NLP can improve workflow and enable identification of clinically important data previously inaccessible. This study will use AI to address key challenges in cancer care, with results applicable to a wide range of clinical disciplines.

Objectives and aims

Main objective

Adapt NLP technology to the Norwegian language and validate and operationalize text-based data for patients with advanced NSCLC. 

Aims: 

  1. Validate an NLP-based workflow to extract routinely collected data from pathology reports in patients with NSCLC receiving immunotherapy.
  1. Evaluate the effectiveness of NLP in monitoring symptoms and treatment toxicities in patients with advanced NSCLC receiving immunotherapy.
  2. Utilize NLP to evaluate documentation of goals of care conversations in the EHR of patients with advanced NSCLC.
  3. Utilize NLP to assess additional palliative care quality indicators (beyond goals of care) in accordance with clinical guidelines for patients with advanced NSCLC.

Project Leader: Margrethe Aase Schaufel
E-mail: margrethe.aase.schaufel@helse-bergen.no


Research Nurse: Kjersti Solvåg
E-mail: kjersti.solvag@helse-bergen.no

About the project group

Read more about the leader and the other members of the project group.
Project group
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Last updated 12/10/2025