{"60836":{"#nid":"60836","#data":{"type":"event","title":"Reducing Operating Room Labor Costs: Capturing Workload Information \u0026 Dynamic Adjustments of Staffing Level","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETITLE:\u0026nbsp;\u0026nbsp; \u003C\/strong\u003EReducing Operating Room Labor Costs: Capturing\nWorkload Information \u0026amp; Dynamic Adjustments of Staffing Level\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ESPEAKER:\u003C\/strong\u003E\u0026nbsp;\u0026nbsp;\u0026nbsp; Professor Polly Biyu He\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EABSTRACT:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EWe study the problem of\nsetting nurse staffing levels in hospital operating rooms when there is\nuncertainty about the daily workload. We demonstrate in this healthcare service\nsetting how information availability and choices of decision models affect a\nnewsvendor\u0027s performance. We develop empirical models to predict the daily\nworkload distribution and study how its mean and variance change with the\ninformation available. In particular, we consider different information sets\navailable at the time of decision: no information, information on number of\ncases, and information on number and types of elective cases. We use these\nmodels to derive optimal staffing rules based on historical data from a US\nteaching hospital and prospectively test the performance of these rules. Our\nempirical results suggest that hospitals could potentially reduce their\nstaffing costs by an average of 39-49% (depending on the absence or presence of\nemergency cases) by deferring the staffing decision until procedure-type information\nis available. However, in reality, contractual and scheduling constraints often\nrequire operating room managers to reserve staffed hours several months in\nadvance, when little information about the cases is known. This motivates us to\nconsider the problem of adjusting the staffing level given information updates.\nSpecifically, we develop decision models that allow the OR manager to adjust\nthe staffing level with some adjustment costs when he or she has better\ninformation. We study how adjustment costs affect the optimal staffing policy\nand the value of having the flexibility to adjust staffing. We also demonstrate\nhow to implement our adjustment policies by applying the optimal decision rules\nderived from our models to the hospital data. \u003C\/p\u003E\u003Cp\u003EJoint work with Stefano Zenios, Franklin Dexter and Alex Macario. \u003C\/p\u003E\u003Cp align=\u0022center\u0022\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Reducing Operating Room Labor Costs: Capturing Workload Information \u0026 Dynamic Adjustments of Staffing Level"}],"uid":"27187","created_gmt":"2010-09-08 08:34:27","changed_gmt":"2016-10-08 01:52:11","author":"Anita Race","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2010-09-16T12:00:00-04:00","event_time_end":"2010-09-16T13:00:00-04:00","event_time_end_last":"2010-09-16T13:00:00-04:00","gmt_time_start":"2010-09-16 16:00:00","gmt_time_end":"2010-09-16 17:00:00","gmt_time_end_last":"2010-09-16 17:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}