Modelling reassurances of clinicians with hidden Markov models
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...
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| Format: | Article |
| Language: | English |
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BMC
2019-01-01
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| Series: | BMC Medical Research Methodology |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12874-018-0629-0 |
| _version_ | 1818290938563264512 |
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| author | Valentin Popov Alesha Ellis-Robinson Gerald Humphris |
| author_facet | Valentin Popov Alesha Ellis-Robinson Gerald Humphris |
| author_sort | Valentin Popov |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T02:36:07Z |
| format | Article |
| id | doaj.art-eadd8757b16046ec9cb1a9b7e48d9e81 |
| institution | Directory Open Access Journal |
| issn | 1471-2288 |
| language | English |
| last_indexed | 2024-12-13T02:36:07Z |
| publishDate | 2019-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Research Methodology |
| spelling | doaj.art-eadd8757b16046ec9cb1a9b7e48d9e812022-12-22T00:02:24ZengBMCBMC Medical Research Methodology1471-22882019-01-0119111010.1186/s12874-018-0629-0Modelling reassurances of clinicians with hidden Markov modelsValentin Popov0Alesha Ellis-Robinson1Gerald Humphris2School of Mathematics and Statistics, University of St AndrewsSchool of Medicine, University of St AndrewsSchool of Medicine, University of St AndrewsAbstract 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.http://link.springer.com/article/10.1186/s12874-018-0629-0ReassuranceHidden Markov modelsFixed effects |
| spellingShingle | Valentin Popov Alesha Ellis-Robinson Gerald Humphris Modelling reassurances of clinicians with hidden Markov models BMC Medical Research Methodology Reassurance Hidden Markov models Fixed effects |
| title | Modelling reassurances of clinicians with hidden Markov models |
| title_full | Modelling reassurances of clinicians with hidden Markov models |
| title_fullStr | Modelling reassurances of clinicians with hidden Markov models |
| title_full_unstemmed | Modelling reassurances of clinicians with hidden Markov models |
| title_short | Modelling reassurances of clinicians with hidden Markov models |
| title_sort | modelling reassurances of clinicians with hidden markov models |
| topic | Reassurance Hidden Markov models Fixed effects |
| url | http://link.springer.com/article/10.1186/s12874-018-0629-0 |
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