What determines if and where the patient should be operated on in the back, and what does the choice mean for the patient?

About the Project

Lower back pain is the leading cause of non-fatal health loss (years lived with disability, YLD). Back disorders account for nearly 10% of medically certified sick leave and disability diagnoses in nearly 11% of those on disability benefits. Spondylosis refers to chronic degenerative changes in the vertebrae, facet joints, and intervertebral discs. It can be considered the underlying condition that predisposes individuals to conditions that may be relevant for surgical treatment, such as lumbar disc herniation in younger patients or spinal stenosis in older patients. Both of these conditions will be included and examined in this project. The patient population is characterized by reduced quality of life and increased frailty, especially among the elderly.

There has been a significant increase in spinal surgery over the past 20 years. In Norway, the treatment rate for publicly funded spinal surgery increased from 78 to 120 operations per 100,000 inhabitants per year from 1999 to 2013, and over 7,400 operations for degenerative spinal disorders were performed in Norway in 2021. From 2014 to 2018, the rate of surgical treatment for degenerative spinal disorders was lower for residents in Health North (91%) compared to the national average. The proportion of patients who were operated on in their own area was also significantly lower in Health North (77% in 2014-2018) than in the rest of the country.

Project Purpose

Long waiting times for treatment are one of the main challenges the specialist healthcare service faces today. Waiting times may be increased due to incorrect indication for surgery. If there is too liberal an indication for spinal surgery, this can have several negative consequences. Patients operated on with insufficient indication risk poorer treatment outcomes, and more patients on waiting lists will increase waiting times. If a decision-making tool based on artificial intelligence can contribute to better selection, this could alleviate suffering for both patients who are not operated on with insufficient indication and for patients who are operated on with the correct indication with shorter waiting times. This streamlining of waiting lists can provide health benefits and significant direct and indirect cost savings, for example, through shorter sick leave before surgery and less need for municipal services with better outcomes, thereby improving the conditions for living longer in one's own home, which is a defined goal of the Government.

Another challenge in the healthcare sector is recruitment. With the aging population, there will be a need for intelligent solutions that facilitate treating more patients with fewer healthcare personnel. AI-based decision-making tools can contribute to achieving a sustainable healthcare service. Along with shorter waiting times, fewer patient travels related to the choice of treatment location are expected. A desired side effect would be a reduction in greenhouse gas emissions and less strain on the climate and environment. We see our study in line with the current focus on climate, environment, and sustainability.
Knowledge about what influences patients' choices of treatment location in general and in subgroups is central to planning treatment offerings. It is assumed that waiting time is a significant factor, and understanding the consequences this has for patients is important for assessing how much equalization of waiting times should be prioritized. We aim to bring forth a strong patient perspective in our study and will use PROMs and sick leave as measures of treatment outcomes.

Our study will, among other things, shed light on the availability of surgical treatment for degenerative spinal disorders in the elderly and thus contribute to a health policy debate on elderly care. The research participants have strong clinical ties, which provides professional proximity to the research results. This can, in turn, increase local initiatives and support for improvement processes in the follow-up of national health policy. The research group also includes participants who are professional and administrative leaders with extensive networks in regional and national health administration. This will be actively used to communicate findings to leaders in the healthcare service to help reduce inequalities.

Research Questions

Subproject 1

Use of decision support based on an artificial intelligence model with data from quality registers. A pilot study with quantitative and qualitative methods in the spinal surgical environments in Health North.

1. Is the decision support and associated workflow acceptable for participating surgeons and patients?
2. Is there a difference in patient-reported outcomes 12 months after surgery, comparing participants and non-participants?

Subproject 2

National register-based observational study:

1. Is there age-dependent variation in waiting times for surgery for degenerative spinal disorders in Norway, and what does it mean for the patient?
2. What predicts failed treatment in older patients undergoing surgery for degenerative spinal disorders in Norwegian hospitals?