Modelling reassurances of clinicians with Hidden Markov models

Valentin Popov, Alesha Ellis-Robinson, Gerald Humphris

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Background: A key element in the interaction between clinicians and patients with cancer is reassurance giving. Learning about the stochastic nature of reassurances as well as making inferential statements about the influence of covariates such as patient response and time spent on previous reassurances are of particular importance.
Methods: We fit Hidden Markov Models (HMMs) to reassurance type from multiple time series of clinicians' reassurances, decoded from audio files of review consultations between patients with breast cancer and their therapeutic radiographer. Assuming a latent state process driving the observations process, HMMs naturally accommodate serial dependence in the data. Extensions to the baseline model such as including covariates as well as allowing for fixed effects for the different clinicians are straightforward to implement.
Results: We found that clinicians undergo different states, in which they are more or less inclined to provide a particular type of reassurance. The states are very persistent, however switches occasionally occur. The lengthier the previous
reassurance, the more likely the clinician is to stay in the current state.
Conclusions: HMMs prove to be a valuable tool and provide important insights for practitioners.
Trial registration: Trial Registration number: ClinicalTrials.gov: NCT02599506. Prospectively registered on 11th March 2015.
Original languageEnglish
Article number11
Number of pages10
JournalBMC Medical Research Methodology
Volume19
DOIs
Publication statusPublished - 9 Jan 2019

Keywords

  • Reassurance
  • Hidden Markov models
  • Fixed effects

Fingerprint

Dive into the research topics of 'Modelling reassurances of clinicians with Hidden Markov models'. Together they form a unique fingerprint.

Cite this