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

BACKGROUND: Staffing 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. METHODS: The 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. RESULTS: Participants 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. CONCLUSIONS: The study provides a good reference for easing WISN implementation for new users and for WHO to continue promoting and improving upon it.

Using modeling and scenario analysis to support evidence-based health workforce strategic planning in Malawi.

BACKGROUND: A well-trained and equitably distributed workforce is critical to a functioning health system. As workforce interventions are costly and time-intensive, investing appropriately in strengthening the health workforce requires an evidence-based approach to target efforts to increase the number of health workers, deploy health workers where they are most needed, and optimize the use of existing health workers. This paper describes the Malawi Ministry of Health (MoH) and collaborators' data-driven approach to designing strategies in the Human Resources for Health Strategic Plan (HRH SP) 2018-2022. METHODS: Three modelling exercises were completed using available data in Malawi. Staff data from districts, central hospitals, and headquarters, and enrollment data from all health training institutions were collected between October 2017 and February 2018. A vacancy analysis was conducted to compare current staffing levels against established posts (the targeted number of positions to be filled, by cadre and work location). A training pipeline model was developed to project the future available workforce, and a demand-based Workforce Optimization Model was used to estimate optimal staffing to meet current levels of service utilization. RESULTS: As of 2017, 55% of established posts were filled, with an average of 1.49 health professional staff per 1000 population, and with substantial variation in the number of staff per population by district. With current levels of health worker training, Malawi is projected to meet its establishment targets in 2030 but will not meet the WHO standard of 4.45 health workers per 1000 population by 2040. A combined intervention reducing attrition, increasing absorption, and doubling training enrollments would allow the establishment to be met by 2023 and the WHO target to be met by 2036. The Workforce Optimization Model shows a gap of 7374 health workers to optimally deliver services at current utilization rates, with the largest gaps among nursing and midwifery officers and pharmacists. CONCLUSIONS: Given the time and significant financial investment required to train and deploy health workers, evidence needs to be carefully considered in designing a national HRH SP. The results of these analyses directly informed Malawi's HRH SP 2018-2022 and have subsequently been used in numerous planning processes and investment cases in Malawi. This paper provides a practical methodology for evidence-based HRH strategic planning and highlights the importance of strengthening HRH data systems for improved workforce decision-making.

Making the transition to workload-based staffing: using the Workload Indicators of Staffing Need method in Uganda.

BACKGROUND: Uganda's health workforce is characterized by shortages and inequitable distribution of qualified health workers. To ascertain staffing levels, Uganda uses fixed government-approved norms determined by facility type. This approach cannot distinguish between facilities of the same type that have different staffing needs. The Workload Indicators of Staffing Need (WISN) method uses workload to determine number and type of staff required in a given facility. The national WISN assessment sought to demonstrate the limitations of the existing norms and generate evidence to influence health unit staffing and staff deployment for efficient utilization of available scarce human resources. METHODS: A national WISN assessment (September 2012) used purposive sampling to select 136 public health facilities in 33/112 districts. The study examined staffing requirements for five cadres (nursing assistants, nurses, midwives, clinical officers, doctors) at health centres II (n = 59), III (n = 53) and IV (n = 13) and hospitals (n = 11). Using health management information system workload data (1 July 2010-30 June 2011), the study compared current and required staff, assessed workload pressure and evaluated the adequacy of the existing staffing norms. RESULTS: By the WISN method, all three types of health centres had fewer nurses (42-70%) and midwives (53-67%) than required and consequently exhibited high workload pressure (30-58%) for those cadres. Health centres IV and hospitals lacked doctors (39-42%) but were adequately staffed with clinical officers. All facilities displayed overstaffing of nursing assistants. For all cadres at health centres III and IV other than nursing assistants, the fixed norms or existing staffing or both fell short of the WISN staffing requirements, with, for example, only half as many nurses and midwives as required. CONCLUSIONS: The WISN results demonstrate the inadequacies of existing staffing norms, particularly for health centres III and IV. The results provide an evidence base to reshape policy, adopt workload-based norms, review scopes of practice and target human resource investments. In the near term, the government could redistribute existing health workers to improve staffing equity in line with the WISN results. Longer term revision of staffing norms and investments to effectively reflect actual workloads and ensure provision of quality services at all levels is needed.