{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,6,7]],"date-time":"2022-06-07T10:15:50Z","timestamp":1654596950060},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T00:00:00Z","timestamp":1654473600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,6,6]]},"abstract":"<jats:p>In primary care allocating appointments to sequential requests can result in sub-optimal scheduling. Optimal scheduling requires hiring of consultants to analyze historical patterns. Many practices focus their resources on larger problems instead of optimizing appointment schedules. We simulate simple heuristics to compare their performance with optimal schedules uncovered using offline optimization models. We use uncapacitated appointment calendars for a nationally representative heterogeneous primary care panel to meet all patients\u2019 requests. The stochastic nature of appointment requests gives a distribution for daily appointments and for the uncovered optimal capacity. The First Minimum heuristic gives near-optimal schedules and can be easily implemented in small practices using pen-and-paper, without any investment in computer-systems.<\/jats:p>","DOI":"10.3233\/shti220222","type":"book-chapter","created":{"date-parts":[[2022,6,7]],"date-time":"2022-06-07T09:34:24Z","timestamp":1654594464000},"source":"Crossref","is-referenced-by-count":0,"title":["Simple Heuristics for Near-Optimal Appointment Scheduling in Primary Care"],"prefix":"10.3233","author":[{"given":"Prashant","family":"Meckoni","sequence":"first","affiliation":[{"name":"Department of Mechanical & Industrial Engineering, University of Massachusetts, Amherst, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hari","family":"Balasubramanian","sequence":"additional","affiliation":[{"name":"Department of Mechanical & Industrial Engineering, University of Massachusetts, Amherst, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2021: One World, One Health \u2013 Global Partnership for Digital Innovation"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI220222","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,7]],"date-time":"2022-06-07T09:34:24Z","timestamp":1654594464000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI220222"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,6]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti220222","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,6]]}}}