Development and Implementation of a Birth Forecasting Tool to Optimize Resources in Obstetrical Care During the COVID-19 Pandemic: Mixed-Methods Study

Abstract

BACKGROUND: Medical resource allocation is important to ensure availability of care, especially in challenging circumstances like a pandemic. In fields of unpredictable care demand such as obstetrics, forecasting may help manage scarce resources. OBJECTIVE: The development, validation, and implementation of a regional birth forecasting tool to support obstetrical staff planning in the Utrecht region during the COVID-19 pandemic. METHODS: We combined predicted birth dates retrieved from Saltro, a large regional primary care laboratory, with data from the Dutch national perinatal registry (Perined) and Statistics Netherlands for model development. We created and implemented an HTML tool visualizing these forecasts, which were discussed during the regional acute obstetric health care network meetings. Six months after implementation, we assessed the impact of the tool using an evaluative stakeholder meeting. RESULTS: We achieved a performance accuracy (R) of 0.45, 0.61, and 0.67 (all actual number of births within 95% CI) forecasting the number of births in the region, pooled in 1-, 2-, and 3-weekly bins, respectively. After presenting these findings to stakeholders, we implemented a forecasting tool using the 2-week bin model. The evaluative stakeholder meeting proved that the tool improved communication, awareness of health care need, and collaborations among health care providers in the Utrecht region. Additionally, stakeholders identified additional applications, such as communication with patients and training of obstetric health care providers. CONCLUSIONS: Implementation of a forecasting tool for the number of births based on available data across the health care system added value to obstetrical care by providing insight into care demand, and increasing communication, awareness, and collaboration between health care providers. Further research should aim at improving regional obstetric acute care by fostering data sharing in order to improve health care demand forecasts.