Public Defence: Meetali Kakad
MD Meetali Kakad at Institute of Clinical Medicine will be defending the thesis “Municipal acute units in Norway. Using operational research methods to model patient flows” for the degree of PhD (Philosophiae Doctor).
An electronic copy of the thesis may be ordered from the faculty up to 2 days prior to the public defence. Inquiries regarding the thesis after the public defence must be addressed to the candidate.
Trial Lecture – time and place
See Trial Lecture.
- First opponent: Associate Professor Vidar Halsteinli, St. Olav Hospital
- Second opponent: Professor Christine Currie, University of Southampton, UK
- Third member and chair of the evaluation committee: Professor Sverre Grepperud, University of Oslo
Chair of the Defence
Associate Professor Stephan Brackmann, University of Oslo
Fredrik A. Dahl, Senior Scientist, Norwegian Computing Center
Admission avoidance initiatives are often used to reduce pressures on hospitals. Municipal acute units (MAUs) are Norwegian admission avoidance units that were made mandatory for all municipalities as of 2016. There are currently few studies looking at the use of MAUs but MAUs have faced criticism in the media for having too many empty beds.
The PhD project informs policy and practice in three ways: by estimating how many MAU beds were required to achieve the original policy goal of transferring 240 000 patient days from hospitals to MAUs, by identifying factors influencing MAU admissions and discharges and by testing scenarios for increasing the use of MAU beds. The secondary aim was to show how simple models and analysis can be applied for these purposes.
We demonstrated how queueing models could have more accurately estimated the number of MAU beds required nationally. We estimated that a 34% increase in the demand for and supply of MAU beds was necessary to meet the policy goal. We used regression modelling to determine whether MAU admission and discharge behaviours varied with occupancy. We found that discharge probability was independent of how full the MAU was. We also found that hospital occupancy was not associated with increased MAU admissions. This might imply that MAUs admit patients not otherwise destined for hospital. Lastly, we developed a simulation model to test scenarios for increasing MAU mean occupancy. We found that MAUs in our sample were rarely full and that by merging them, bed numbers could be reduced by 19 %, whilst still maintaining current service levels.
Our work casts doubt on MAUs ability to relieve pressures on hospitals. It is, however, worth considering that MAUs are popular among patients and their families. and they may address an unmet need that goes beyond the scope of this project. We also demonstrate how the use of relatively simple models and analysis could have informed not only the initial policy but also subsequent planning.
Contact the research support staff.