Developing a Bayesian Network Decision Support tool for low back pain: a pilot and protocol (Preprint)

BACKGROUND Low back pain (LBP) is an increasingly burdensome condition for patients and health professionals alike, with increasing persistent pain and disability consistently demonstrated. Previous decision support tools for LBP management have focussed on a subset of factors due to time constraints and ease of use for the clinician. With the explosion of interest in machine learning tools and the commitment from Western governments to introduce this technology, there are opportunities to develop intelligent decision support tools. We will do this for LBP using a Bayesian Network, which will entail constructing a clinical reasoning model elicited from experts. OBJECTIVE This paper proposes a method for conducting a modified RAND Appropriateness procedure to elicit the knowledge required to construct a Bayesian Network from a group of domain experts in LBP, and reports the lessons learned from the internal pilot of the procedure. METHODS We propose to recruit expert clinicians with a special interest in LBP from across a range of medical specialisms e.g. orthopaedics, rheumatology, sports medicine. The procedure will consist of four stages. Stage 1 is an online elicitation of variables to be considered by the model, followed by a face to face workshop. Stage 2 is an online elicitation of the structure of the model, followed by a face to face workshop. Stage 3 consists of an online phase to elicit probabilities to populate the Bayesian Network. Stage 4 is a rudimentary validation of the Bayesian Network. RESULTS Ethical approval has been gained from the Research Ethics Committee at Queen Mary University of London. An internal pilot of the procedure has been run with clinical colleagues from the research team. This showed that an alternating process of 3 remote activities and 2 in-person meetings were required to complete the elicitation without overburdening participants. Lessons learned have included the need for a bespoke, online elicitation tool to run between face-to-face meetings and for careful operational definition of descriptive terms even if widely clinically used. Further, tools are required to remotely deliver training about self-identification of various forms of cognitive bias and explain the underlying principles of a BN. The use of the internal pilot was recognised as being a methodological necessity. CONCLUSIONS We have proposed a method to construct Bayesian Networks that are representative of expert clinical reasoning, in this case for a musculoskeletal condition. We have tested the method with an internal pilot to refine the process prior to deployment, which indicates the process can be successful. The internal pilot has also revealed the software support requirements for the elicitation process, in order that clinical reasoning can be modelled for a range of conditions. CLINICALTRIAL