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Although earlier studies identified a range of patient and provider-related determinants, their relative importance to patient satisfaction remains unclear.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>We used a tree-based machine-learning algorithm, random forests, to estimate relationships between patient and provider-related determinants and satisfaction level in two of the main patient journey stages, registration and consultation, through survey data from 411 patients at a hospital in Abu Dhabi, UAE. Radar charts were also generated to determine which type of questions\u2014demographics, time, behaviour, and procedure\u2014influence patient satisfaction.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Our results showed that the \u2018age\u2019 attribute, a patient-related determinant, is the leading driver of patient satisfaction in both stages. \u2018Total time taken for registration\u2019 and \u2018attentiveness and knowledge of the doctor\/physician while listening to your queries\u2019 are the leading provider-related determinants in each model developed for registration and consultation stages, respectively. The radar charts revealed that \u2018demographics\u2019 are the most influential type in the registration stage, whereas \u2018behaviour\u2019 is the most influential in the consultation stage.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Generating valuable results, the random forest model provides significant insights on the relative importance of different determinants to overall patient satisfaction. Healthcare practitioners, managers and researchers can benefit from applying the model for prediction and feature importance analysis in their particular healthcare settings and areas of their concern.\n<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-021-01519-5","type":"journal-article","created":{"date-parts":[[2021,5,13]],"date-time":"2021-05-13T18:03:03Z","timestamp":1620928983000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Exploring drivers of patient satisfaction using a random forest algorithm"],"prefix":"10.1186","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1555-5012","authenticated-orcid":false,"given":"Mecit Can Emre","family":"Simsekler","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Noura Hamed","family":"Alhashmi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elie","family":"Azar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nelson","family":"King","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rana Adel Mahmoud Ali","family":"Luqman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdalla","family":"Al Mulla","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,5,13]]},"reference":[{"issue":"2","key":"1519_CR1","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1177\/1757913916634136","volume":"137","author":"E Batbaatar","year":"2017","unstructured":"Batbaatar E, Dorjdagva J, Luvsannyam A, Savino MM, Amenta P. 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