Lessons learned from implementation of the Workload Indicator of Staffing Need (WISN) methodology: an international Delphi study of expert users

Abstract

BackgroundStaffing of health services ought to consider the workload experienced to maximize efficiency. However, this is rarely the case, due to lack of an appropriate approach. The World Health Organization (WHO) developed and has promoted the Workload Indicators of Staffing Need (WISN) methodology globally. Due to its relative simplicity compared to previous methods, the WISN has been used extensively, particularly after its computerization in 2010. Many lessons have been learnt from the introduction and promotion of the methodology across the globe but have, hitherto, not been synthesized for technical and policy consideration. This study gathered, synthesized, and now shares the key adaptations, innovations, and lessons learned. These could facilitate lesson-learning and motivate the WHO’s WISN Thematic Working Group to review and further ease its application.MethodsThe study aimed to answer four questions: (1) how easy is it for the users to implement each step of the WISN methodology? (2) What innovations have been used to overcome implementation challenges? (3) What lessons have been learned that could inform future WISN implementation? and (4) what recommendations can be made to improve the WISN methodology? We used a three-round traditional Delphi method to conduct a case study of user-experiences during the adoption of the WISN methodology. We sent three email iterations to 23 purposively selected WISN expert users across 21 countries in five continents. Thematic analysis of each round was done simultaneously with data collection.ResultsParticipants rated seven of the eight technical steps of the WISN as either “very easy” or “easy” to implement. The step considered most difficult was obtaining the Category Allowance Factors (CAF). Key lessons learned were that: the benefits gained from applying the WISN outweigh the challenges faced in understanding the technical steps; benchmarking during WISN implementation saves time; data quality is critical for successful implementation; and starting with small-scale projects sets the ground better for more effective scale-up than attempting massive national application of the methodology the first time round.ConclusionsThe study provides a good reference for easing WISN implementation for new users and for WHO to continue promoting and improving upon it.