Welcome to the 26th WONCA Europe Virtual Conference Programme Scheduling

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Displaying One Session

Hall 2

RESEARCH MASTER CLASS
Session Type
RESEARCH MASTER CLASS
Date
09.07.2021, Friday
Session Time
10:30 AM - 12:00 PM
Room
Hall 2
Session Icon
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PREDICTION OF ALARM SYMPTOMS: WHAT DID THESE BRING US, HOW ARE THESE USED AND WITH WHICH RESULTS

Date
09.07.2021, Friday
Session Time
10:30 AM - 12:00 PM
Room
Hall 2
Lecture Time
10:30 AM - 10:47 AM
Session Icon
Pure Live

Abstract

Abstract Body

Most patients with cancer present to primary care - even when there are strong cancer screening programmes. A large programme of research in the early 2000s identified - and quantified - the symptoms of cancer when presented to primary care. This was crucial, as almost all previous research had been reported from the secondary care, selected population. Thus we now know for all the major cancers what the main symptoms are - and what the risk is of there being a cancer when they report a symptom to primary care.

This knowledge allowed planning of services - in particular how many patients would be expected to need a referral, a chest X-ray, a colonoscopy, etc. It also allowed guidance to be prepared for GPs to help them select who should be investigated rapidly for possible cancer. In the UK, this led to the NICE guidance, NG12, in 2015.

Since the publication of NG12, and up until the COVID-19 pandemic, several markers of improved cancer diagnosis all showed improvements. The number of referrals for urgent investigation rose by about 10% each year, the time to diagnosis of symptoms newly introduced in NG12 fell, the stage of the cancer at diagnosis improved, and there was a fall in the percentage of patients being diagnosed after an emergency presentation. 5-year survival also continues to improve. Much of this improvement ceased with the pandemic.

In recent years further changes have been: a large RCT of electronic tools to identify patients with possible cancer in GP records, an estimate of the resource requirements needed should the UK move from a 3% risk of cancer being the threshold to trigger urgent investigation down to 2%, or even 1%. Finally, the health-economics of symptomatic diagnosis are beginning to be unravelled, as we become better at estimating the benefits of expedited diagnosis.

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STATE OF AFFAIRS IN GENERAL PRACTICE IN THE NETHERLANDS AND LESSONS TO BE LEARNED

Date
09.07.2021, Friday
Session Time
10:30 AM - 12:00 PM
Room
Hall 2
Lecture Time
10:47 AM - 11:04 AM
Session Icon
Pure Live

ARTIFICIAL INTELLIGENCE TECHNIQUES TO FACILITATE THE EARLIER DIAGNOSIS OF CANCER IN GENERAL PRACTICE

Date
09.07.2021, Friday
Session Time
10:30 AM - 12:00 PM
Room
Hall 2
Lecture Time
11:04 AM - 11:21 AM
Session Icon
Pure Live

Abstract

Abstract Body

There is accumulating evidence that artificial intelligence (AI) can assist clinicians to make better clinical decisions, or even replace human judgement, in certain areas of healthcare. This is due to the increasing availability of healthcare data and rapid development of big data analytic methods. There has been increasing interest in the application of AI in medical diagnosis, including machine learning and automated analysis approaches. This talk will present results from two recent reviews. One examines the application of AI techniques to primary care electronic healthcare data. The second examines whether AI/machine learning algorithms which facilitate early detection of skin cancer are accurate and safe enough for use in community and primary care settings.

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WHAT'S IN THE NOTES? EARLY PREDICTION OF CANCER USING FREE TEXT IN ROUTINE PRIMARY CARE DATA

Date
09.07.2021, Friday
Session Time
10:30 AM - 12:00 PM
Room
Hall 2
Lecture Time
11:21 AM - 11:38 AM
Session Icon
Pure Live

Abstract

Abstract Body

What’s in the notes? Early prediction of cancer using free text in routine primary care data

In the Netherlands about 80% of patients, presenting in general practice with symptoms, possibly indicative of cancer are referred to a specialist within two weeks. Previously, symptom analysis studies (Caper Cards) resulted in better understanding of the early detection of cancer by providing a numerical estimation for the prediction of a prevalent cancer. However, the detection of cancer in clinical practice has not sufficiently improved: of all patients with cancer, about 50% are diagnosed only at an advanced stage. General practitioners still diagnose cancer in roughly the same way as was done decades ago.

New roads have to be explored to improve the detection of cancer at an early stage. Promising development s are new bio-markers, emerging genetic panels, screening and innovative imaging techniques. However, these promising perspectives have not materialized yet.

Developments in artificial intelligence (AI), especially Natural Language Processing (NLP), might provide new possibilities. Although new, until now unknown, symptoms are unlikely to be discovered from the Electronic Patient Record (EPR), information residing in the clinical notes along with other structured data in the EPR could provide additional predictive value.

In order to explore this new pathway, we analysed the medical files of general practitioners of about 1.2 million patients originating from four academic general practice networks, spread over the Netherlands. We validated the cancer diagnoses, starting with lung cancer, by the Dutch cancer registration. We then used NLP together with statistical Machine learning prediction models in order to predict the risk of lung cancer 3 months before the referral was made. Today we present the results of this exciting exercise for the first time. We discuss briefly the used methodology and present the predictive performance in terms of discrimination, calibration and accuracy of the predicted probability and the meaning of these results for clinical practice.

On behalf of the AI-DOC team,

Henk van Weert

The AI-DOC team consists of

Ameen Abu-Hanna. Dpt. Medical informatics, Amsterdam UMC

Martijn Schut, Dpt. Medical informatics, Amsterdam UMC

Torec Luik, Dpt. Medical informatics, Amsterdam UMC

Miguel Rios Goana, Dpt. Medical informatics, Amsterdam UMC

Charles Helsper, Julius Centre, University Medical Center Utrecht

Kristel van Asselt, Dpt. General Practice, Amsterdam UMC

Niek de Wit, Julius Centre, University Medical Center Utrecht

Henk van Weert, Dpt. General Practice, Amsterdam UMC

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LIVE Q&A

Date
09.07.2021, Friday
Session Time
10:30 AM - 12:00 PM
Room
Hall 2
Lecture Time
11:38 AM - 11:58 AM
Session Icon
Pure Live