{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T10:19:32Z","timestamp":1768817972545,"version":"3.49.0"},"reference-count":38,"publisher":"Emerald","issue":"6","license":[{"start":{"date-parts":[[2021,6,29]],"date-time":"2021-06-29T00:00:00Z","timestamp":1624924800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["INTR"],"published-print":{"date-parts":[[2021,11,12]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>Digital healthcare has become one of the most important Internet applications in the recent years, and digital platforms have been acting as interfaces between the patients and physicians. Although these technologies enhance patient convenience, they create new challenges in platform management. For instance, on physician rating websites, information overload negatively influences patients' decision-making in relation to selecting a physician. This scenario calls for an automated mechanism to provide real-time rankings of physicians. Motivated by an online healthcare platform, this study develops a method to deliver physician ranking on platforms by considering patients' browse behaviors and the capacities of service resources.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>The authors use a probabilistic model for explicitly capturing the browse behaviors of patients. Since the large volume of information in digital systems makes it intractable to solve the dynamic ranking problem, we design a ranking with value approximation algorithm that combines a greedy ranking policy and the value function approximation methods.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The authors found that the approximation methods are quite effective in dealing with the ranking optimization on the digital healthcare system, and it is mainly because the authors incorporate the patient behaviors and patient availability in the model.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>To the best of the authors\u2019 knowledge, this is one of the first studies to present solutions to the dynamic physician ranking problem. The ranking algorithms can also help platforms improve system and operational performance.<\/jats:p><\/jats:sec>","DOI":"10.1108\/intr-10-2020-0609","type":"journal-article","created":{"date-parts":[[2021,6,26]],"date-time":"2021-06-26T10:16:01Z","timestamp":1624702561000},"page":"2076-2095","source":"Crossref","is-referenced-by-count":5,"title":["Physician ranking optimization based on patients' browse behaviors and resource capacities"],"prefix":"10.1108","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9492-6960","authenticated-orcid":false,"given":"Xin","family":"Pan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3035-0581","authenticated-orcid":false,"given":"Hanqi","family":"Wen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0981-1072","authenticated-orcid":false,"given":"Ziwei","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2592-8238","authenticated-orcid":false,"given":"Jie","family":"Song","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3588-1859","authenticated-orcid":false,"given":"Xing Lin","family":"Feng","sequence":"additional","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2021,6,29]]},"reference":[{"issue":"6","key":"key2021122215521083400_ref001","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.1509\/jmr.08.0468","article-title":"Location, location, location: an analysis of profitability of position in online advertising markets","volume":"48","year":"2011","journal-title":"Journal of Marketing Research"},{"key":"key2021122215521083400_ref002","first-page":"5","article-title":"Diversifying search results","year":"2009"},{"key":"key2021122215521083400_ref003","first-page":"1742","article-title":"Optimal greedy diversity for recommendation","year":"2015"},{"issue":"4","key":"key2021122215521083400_ref004","first-page":"935","article-title":"Clicks, discontinuities, and firm demand online","volume":"18","year":"2009","journal-title":"Journal of Economics and Management Strategy"},{"key":"key2021122215521083400_ref005","first-page":"317","article-title":"Consideration set generation in commerce search","year":"2011"},{"issue":"4","key":"key2021122215521083400_ref006","doi-asserted-by":"crossref","first-page":"886","DOI":"10.1287\/opre.2016.1505","article-title":"A Markov chain approximation to choice modeling","volume":"64","year":"2016","journal-title":"Operations Research"},{"issue":"3","key":"key2021122215521083400_ref007","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1287\/opre.1080.0567","article-title":"A column generation algorithm for choice-based network revenue management","volume":"57","year":"2009","journal-title":"Operations Research"},{"issue":"8","key":"key2021122215521083400_ref008","doi-asserted-by":"crossref","first-page":"3617","DOI":"10.1287\/mnsc.2019.3372","article-title":"Position ranking and auctions for online marketplaces","volume":"66","year":"2020","journal-title":"Management Science"},{"issue":"2","key":"key2021122215521083400_ref009","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1287\/opre.2014.1256","article-title":"Assortment optimization under variants of the nested logit model","volume":"62","year":"2014","journal-title":"Operations Research"},{"key":"key2021122215521083400_ref010","article-title":"Product ranking on online platforms","year":"2018"},{"issue":"5","key":"key2021122215521083400_ref011","doi-asserted-by":"crossref","first-page":"1322","DOI":"10.1287\/opre.2017.1628","article-title":"Revenue management under the Markov chain choice model","volume":"65","year":"2017","journal-title":"Operations Research"},{"issue":"4","key":"key2021122215521083400_ref012","doi-asserted-by":"crossref","first-page":"794","DOI":"10.1287\/opre.2014.1286","article-title":"Appointment scheduling under patient preference and no-show behavior","volume":"62","year":"2014","journal-title":"Operations Research"},{"key":"key2021122215521083400_ref013","article-title":"Attention, consideration then selection choice model","year":"2017"},{"key":"key2021122215521083400_ref014","article-title":"Managing flexible products on a network","year":"2004"},{"key":"key2021122215521083400_ref015","unstructured":"Gallego, G., Li, A., Truong, V.-A. and Wang, X. 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