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Helse Bergen, exit report: The use of artificial intelligence (AI) in the follow-up of vulnerable patients

About the project

A small minority of patients account for the bulk of the resources expended in the health service. According to Helse Bergen, 10 per cent of its patients account for over half (53 per cent) of all bed days and have a high rate of readmission. 

Similar conditions have also been documented elsewhere in Norway and worldwide. Despite this existing knowledge, little is being done to prevent readmissions. 

Read more about major users of somatic specialist health services in this report from Helse Bergen (pdf) (in Norwegian)


Readmission is defined as being admitted to hospital for emergency treatment within 30 days of discharge from hospital.

A study has shown that, based on routine data, it may be possible to predict and identify which group of patients has the highest risk of frequent readmission. It turns out, however, that such a model works well at the group level but is less accurate at the individual level.

The study, published in the Lancet in 2018, may be found at www.pubmed.gov (external link)

In this project, Helse Bergen wishes to explore whether it might nevertheless be possible to predict, at the individual level, which specific patients will, in all probability, be readmitted to hospital. This could provide a solid foundation for initiating preventive measures and, hopefully, result in fewer stressful days in hospital and better use of the health service's resources.

Helse Bergen wishes to test a two-step model to enable it to predict which patients will most likely be readmitted.

In stage 1, Helse Bergen wishes to establish an automated warning system for patients with a high probability of readmission. The prediction will be made automatically with the help of AI, which will base its result on previous admission records, which are included as standard data variables in Helse Bergen's electronic patient records system. The result is available the moment the patient arrives at the hospital's A&E department.

In stage 2, the objective is for hospital staff and, potentially, municipal healthcare providers, to use the automated prediction and their own informed judgement to score the patient on the basis of their level of functionality/frailty and initiate preventive measures for those patients who need them.

Frailty score

The frailty score is a numerical assessment of the patient's functional level based on the patient's admission history and the doctor's own judgement.

See also "Measuring frailty - when, why and how?" on the Journal of the Norwegian Medical Association. (external link, in Norwegian)

Helse Bergen wishes to develop a simple and user-friendly solution to identify at-risk patients based on the prediction in stage 1 and the frailty score in stage 2. The prediction will be integrated into and collated with the existing patient records/medical follow-up system.

It will be a relatively simple task to share the two-stage model to be tested and reused in other hospitals. This is because all hospitals in Norway have well-structured table data available for each patient, via data that is derived from the hospitals’ electronic patient records and reported onwards to the Norwegian Patients Registry (NPR). This data contains information about previous hospital stays, with data variables linked to gender, age, date of admission, primary and secondary diagnoses, etc.

The clinical scoring means that patients can be identified as early as possible and offered a more suitable treatment pathway. This may involve interventions and follow-up by a Patient-Centred Health Team (PCHT). Such schemes, which involve the patient receiving a coordinated and holistic healthcare package delivered by a cross-functional team from the municipality and the hospital, have been successfully trialled by other hospital trusts. This has, for example, reduced the mortality rate for elderly patients and those with multiple comorbidities, while also reducing the number of emergency admissions and bed days in hospital.

Read the article "Flere pasienter lever bedre med nytt tilbud" at www.forskning.no (in Norwegian) 

The project's utility is linked to two main factors:

  1. Patients may receive better care and avoid readmissions, thereby reducing the total number of days they spend in hospital.
  2. Hospital trusts will be able to reduce the number of bed days for major users and, to a greater extent, divert resources to better personalised follow-up of this patient group in partnership with municipal health services. This will also facilitate follow-up care in the municipality and thereby contribute to more efficient use of society's resources.