Nowadays,　relevant　data　collected　from　hospital　buildings　remain　insufficient　because　hospital　buildings　often　have　stricter　environmental　requirements　resulting　in　more　limited　data　access　than　other　building　types.　Additionally,　existing　window-opening　behavior　models　were　mostly　developed　and　validated　using　data　measured　from　the　experimental　building　itself.　Hence,　their　accuracy　is　only　assessed　by　the　algorithm＇s　evaluation　index,　which　limits　the　model＇s　applicability,　given　that　it　is　not　tested　by　the　actual　cases　nor　cross-verified　with　other　buildings.　Based　on　the　aforementioned　issues,　this　study　analyzes　the　window-opening　behavior　of　doctors　and　patients　in　spring　in　a　maternity　hospital　in　Beijing　and　develops　behavioral　models　using　logistic　regression.　The　results　show　that　the　room　often　has　opened　windows　in　spring　when　the　outdoor　temperature　exceeds　20　?.　Moreover,　the　ward　windows＇　use　frequency　is　more　than　10　times　higher　than　those　of　doctors＇　office.　The　window-opening　behavior　in　wards　is　more　susceptible　to　the　influence　of　outdoor　temperature,　while　in　the　doctors＇　office,　more　attention　is　paid　to　indoor　air　quality.　Finally,　by　embedding　the　logistic　regression　model　of　each　room　into　the　EnergyPlus　software　to　simulate　the　CO2　concentration　of　the　room,　it　was　found　that　the　model　has　better　applicability　than　the　fixed　schedule　model.　However,　by　performing　cross-validation　with　different　building　types,　it　was　found　that,　due　to　the　particularity　of　doctors＇　offices,　the　models　developed　for　other　building　types　cannot　accurately　reproduce　the　window-opening　behavior　of　doctors.　Therefore,　more　data　are　still　needed　to　better　understand　window　usage　in　hospital　buildings　and　support　the　future　building　performance　simulations　of　hospital　buildings.