Ghali, William A.Yergens, Dean W2015-09-022015-11-202015-09-022015Yergens, D. W. (2015). Deriving Evidence for Healthcare Decision Makers: The Case of Patient Flow (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/25561http://hdl.handle.net/11023/2422In an ideal world, decision makers in health systems would make optimal decisions in a timely manner, fully informed by relevant information and evidence delivered in a timely way. This paradigm of Evidence-informed Decision Making (EIDM) is one that health systems can realistically strive for, so that decision makers can be enabled with the best available evidence for informed operational, tactical and strategic decisions. Methods to generate and convey evidence-based information include various types of literature reviews; data collection and statistical analysis through existing data sources or primary data collection; and the timely presentation of data and information in various presentation formats, sometimes referred to as dashboards and/or scorecards. One particularly challenging area in the healthcare sector, where EIDM could be of benefit, is in the management of patient flow in acute care hospitals. This dissertation showcases a mixed methodology program of work, applied to several sub-studies examining several aspects of patient flow in acute care hospitals: 1) A scoping review on the topic of patient flow in hospitals demonstrated a wide body of literature, but an overall lack of hospital-wide approaches; 2) An analysis of existing administrative data examined sepsis patients admitted to the hospital via the emergency department (ED) and demonstrated that patient outcomes may be impacted by Intensive Care Unit (ICU) occupancy; 3) A narrative review on information dashboards; 4) An accompanying survey study of ward-level patient flow decision-makers demonstrated a variety of views (and some fundamental disagreement) on what dashboards and scorecards actually are, but also some general enthusiasm for their potential in an EIDM paradigm; 5) A scoping review exploring novel machine learning methods demonstrated their potential application around various patient flow concepts in hospital settings; 6) A systematic review and meta-analysis of Medical Assessment Units (MAUs), solicited by health system decision-makers, summarized existing published evidence on MAUs and their benefits; and finally 7) An accompanying national environmental scan survey study of existing MAU-like initiatives in Canada. This dissertation illustrates how a variety of methods can be used to support a paradigm of EIDM in the area of patient flow.engUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.EpidemiologyHealth Care ManagementHealth Services ResearchHealth Care ManagementEvidence Informed Decision Makingdoctoral thesishttp://dx.doi.org/10.11575/PRISM/25561