Objective: This study was designed to develop a model for predicting nurse scheduling needs in a hospital unit based on historical patient census and nurse staffing requirements.
Background: Many hospitals use outdated non-data driven methods for nurse scheduling.
Methods: Historical nurse scheduling and staffing datasets for 2015, 2016, and 2017 from a 33-bed surgical unit in an inner-city urban hospital in Portland, Oregon, were used to build a predictive model for nurse scheduling needs.
Results: The patient census for 2017 was three patients higher than the two previous years and showed a variation in the day of the week, with a consistent weekly trend of more nurses needed at the beginning of the week and fewer needed during the weekend.
Conclusion: Based on model predictions, nurse scheduling in this unit should vary by day of the week, which has not historically been done.
","abstract_html":"<p>Objective: This study was designed to develop a model for predicting nurse scheduling needs in a hospital unit based on historical patient census and nurse staffing requirements.</p> <p>Background: Many hospitals use outdated non-data driven methods for nurse scheduling.</p> <p>Methods: Historical nurse scheduling and staffing datasets for 2015, 2016, and 2017 from a 33-bed surgical unit in an inner-city urban hospital in Portland, Oregon, were used to build a predictive model for nurse scheduling needs.</p> <p>Results: The patient census for 2017 was three patients higher than the two previous years and showed a variation in the day of the week, with a consistent weekly trend of more nurses needed at the beginning of the week and fewer needed during the weekend.</p> <p>Conclusion: Based on model predictions, nurse scheduling in this unit should vary by day of the week, which has not historically been done.</p>","abstract_has_math":false,"creators":["Bowie, Danielle"],"institution":null,"degree_name":"Doctor of Nursing Practice (DNP)","degree_level":"Open Access Thesis","degree_discipline":"Yale University School of Nursing","degree_department":null,"school":null,"contributors":["Margaret L. Holland"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-01-01T08:00:00Z","date_published":"2018-01-01T08:00:00Z","updated_at":"2026-07-24T06:16:00Z","subjects":[],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://elischolar.library.yale.edu/ysndt/1052","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Margaret L. Holland"]},{"key":"dc:creator","label":"Author","values":["Bowie, Danielle"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Yale University School of Nursing"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Open Access Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Nursing Practice (DNP)"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://elischolar.library.yale.edu/ysndt/1052"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Objective: This study was designed to develop a model for predicting nurse scheduling needs in a hospital unit based on historical patient census and nurse staffing requirements.
Background: Many hospitals use outdated non-data driven methods for nurse scheduling.
Methods: Historical nurse scheduling and staffing datasets for 2015, 2016, and 2017 from a 33-bed surgical unit in an inner-city urban hospital in Portland, Oregon, were used to build a predictive model for nurse scheduling needs.
Results: The patient census for 2017 was three patients higher than the two previous years and showed a variation in the day of the week, with a consistent weekly trend of more nurses needed at the beginning of the week and fewer needed during the weekend.
Conclusion: Based on model predictions, nurse scheduling in this unit should vary by day of the week, which has not historically been done.
"]},{"key":"dc:title","label":"Title","values":["The Development And Implementation Of A Forecasting Model For Inpatient Nurse Scheduling"]}]}],"canonical_facts":{"dc:contributor":["Margaret L. Holland"],"dc:creator":["Bowie, Danielle"],"dc:description.abstract":["Objective: This study was designed to develop a model for predicting nurse scheduling needs in a hospital unit based on historical patient census and nurse staffing requirements.
Background: Many hospitals use outdated non-data driven methods for nurse scheduling.
Methods: Historical nurse scheduling and staffing datasets for 2015, 2016, and 2017 from a 33-bed surgical unit in an inner-city urban hospital in Portland, Oregon, were used to build a predictive model for nurse scheduling needs.
Results: The patient census for 2017 was three patients higher than the two previous years and showed a variation in the day of the week, with a consistent weekly trend of more nurses needed at the beginning of the week and fewer needed during the weekend.
Conclusion: Based on model predictions, nurse scheduling in this unit should vary by day of the week, which has not historically been done.
"],"dc:identifier":["https://elischolar.library.yale.edu/ysndt/1052"],"dc:title":["The Development And Implementation Of A Forecasting Model For Inpatient Nurse Scheduling"],"thesis:degree_discipline":["Yale University School of Nursing"],"thesis:degree_level":["Open Access Thesis"],"thesis:degree_name":["Doctor of Nursing Practice (DNP)"]},"updated_at":"2026-07-24T06:16:00Z"}