When workforce availability does not mean workforce productivity: a capacity-anchored decision-support framework for health system planning in resource-constrained settings — evidence from Mali

When workforce availability does not mean workforce productivity: a capacity-anchored decision-support framework for health system planning in resource-constrained settings — evidence from Mali | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article When workforce availability does not mean workforce productivity: a capacity-anchored decision-support framework for health system planning in resource-constrained settings — evidence from Mali Mamadou Sidibé This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9451846/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Health workforce planning in sub-Saharan Africa has long been dominated by normative approaches that measure the gap between existing resources and WHO standards. While these approaches accurately document insufficiency, they offer limited guidance for decision-makers operating in contexts where internationally recommended staffing norms remain structurally unattainable in the short to medium term. Mali, like many low-income countries, faces a persistent disconnect between national health objectives — including a maternal mortality ratio (MMR) target of ≤146 per 100,000 live births under PRODESS III — and the actual capacity of its health system to absorb the demand induced by these policies. This paper presents HCIS-SIM (Health Capacity Information System — Simulation & Monitoring), a decision-support framework designed to reorient health workforce planning from documenting the impossible toward optimizing the possible. Methods HCIS-SIM integrates three complementary analytical components: (i) Workload Indicators of Staffing Need (WISN), adapted from the WHO methodology, to quantify workforce requirements based on actual workload; (ii) simulated stress testing, adapted from financial and industrial sector approaches, to identify system saturation thresholds before they occur; and (iii) real-time capacity monitoring to measure the gap between theoretical workforce potential and observed productivity, and to generate territory-specific planning trajectories. The framework was applied across three levels of the Malian health pyramid — Kati District Hospital (tertiary level), Sikasso Region (regional level), and Koulikoro Health District (district level) — using official national data sources: the 2020 National Health Information Yearbook (SNISS, Mali Ministry of Health) and the Koulikoro District Health Information System (SLIS). Results Three counter-intuitive findings emerged. First, health workforce resources at Kati District Hospital are adequate in volume according to WISN standards, yet effective productivity reaches less than 45% of theoretical potential — identifying a workforce mobilization failure rather than a workforce shortage. Second, Sikasso Region and Koulikoro Health District present identical MMR levels (500–600 per 100,000 live births) but structurally opposite root causes — volume pressure in Sikasso versus distributional inequity in Koulikoro — demonstrating that uniform national policies cannot simultaneously address both challenges. Third, stress testing reveals that the Malian health system reaches saturation at a mere 20% activity increase, meaning that national health objectives cannot be achieved without prior system capacity reinforcement. Conclusions HCIS-SIM introduces a capacity-anchored approach to health workforce planning that prioritizes optimizing existing resources over documenting normative gaps. By combining workload analysis, stress testing and capacity monitoring, the framework produces actionable, territory-differentiated planning trajectories toward achievable health objectives. These findings have direct implications for health policymakers in resource-constrained settings seeking evidence-based alternatives to normative planning frameworks that systematically generate unachievable targets. HCIS-SIM offers a replicable model for translating available capacity into measurable health system performance improvement. Health workforce planning Capacity-anchored planning WISN Health system stress testing Maternal mortality Mali Sub-Saharan Africa Decision-support framework Background Health systems in sub-Saharan Africa face a persistent and well-documented paradox: despite decades of international investment in health workforce development, the gap between available human resources for health (HRH) and population needs remains structurally wide [1,2]. The World Health Organization projects a global shortage of 10 million health workers by 2030, with sub-Saharan Africa bearing a disproportionate share of this deficit — a region that carries 25% of the global disease burden while hosting less than 2% of the world's trained health personnel [3,4]. In Mali, this crisis is reflected in a maternal mortality ratio (MMR) of 562 per 100,000 live births — among the highest globally — against a national target of ≤146 per 100,000 live births under the third Programme de Développement Sanitaire et Social (PRODESS III) and the Sustainable Development Goals (SDGs) [5,6]. The dominant response to this challenge has been normative planning — measuring the gap between existing workforce levels and WHO-recommended standards, then projecting the resources needed to close that gap [7,8]. While this approach accurately documents insufficiency, it produces a systematic and underappreciated problem: in contexts where internationally recommended staffing norms remain structurally unattainable within foreseeable fiscal horizons, normative planning generates targets that discourage rather than guide action [9]. Decision-makers in resource-constrained settings are left with precise measurements of what they cannot achieve, but limited analytical tools to optimize what they have [10]. This limitation is not merely theoretical. A growing body of evidence from sub-Saharan Africa demonstrates that workforce availability does not automatically translate into workforce productivity [11,12]. Health facilities may meet staffing ratios on paper while operating at a fraction of their productive potential due to organizational factors — dual posting, administrative burden, suboptimal scheduling, and inadequate supervision — that normative frameworks neither capture nor address [13]. The distinction between workforce availability and workforce mobilization efficiency represents a critical analytical blind spot in current health workforce planning methodologies applied to African health systems. Several methodological advances have sought to address the limitations of normative planning. Needs-based approaches, pioneered and systematized by Asamani and colleagues, introduced simulation tools to project workforce requirements based on population health needs rather than population ratios alone [1,2]. These contributions represent a significant advance, yet they remain primarily focused on quantifying the supply-need gap rather than on testing whether existing systems can absorb the demand induced by national health policy objectives [1]. The question of system absorption capacity — whether a health system, as currently organized and resourced, can realistically achieve its stated objectives — has received comparatively little attention in the health workforce planning literature for low-income African countries [14]. This paper addresses that gap. We present HCIS-SIM (Health Capacity Information System — Simulation & Monitoring), a decision-support framework that reorients health workforce planning from documenting normative gaps toward optimizing available capacity. HCIS-SIM does not ask "how far are we from the WHO standard?" — it asks "what can this system realistically produce with what it currently has, and what is the most efficient path toward measurable improvement?" This distinction is not semantic. It is the difference between a planning tool that generates discouragement and one that generates actionable trajectories. The framework integrates three complementary analytical components — WISN-based workforce analysis, simulated stress testing, and real-time capacity monitoring — applied across three levels of the Malian health pyramid using official national data sources. Its application yields three counter-intuitive findings that challenge prevailing assumptions about the nature of health workforce challenges in resource-constrained settings, and generates territory-differentiated planning trajectories toward the national MMR objective of ≤146 per 100,000 live births. The remainder of this paper is structured as follows: the Methods section describes the HCIS-SIM framework and its three analytical components, the data sources, and the application sites; the Results section presents findings from each level of the health pyramid; the Discussion positions these findings within the existing literature and elaborates their implications for health workforce planning policy; and the Conclusions offer recommendations for replication in comparable settings. Methods Framework overview HCIS-SIM is a multi-component decision-support framework designed to assess health system capacity and generate actionable planning trajectories in resource-constrained settings. Rather than measuring deviation from externally defined norms, HCIS-SIM characterizes the productive capacity of a health system as it currently exists — its actual outputs, its mobilization efficiency, and its tolerance to increased demand — and uses this characterization as the foundation for realistic, territory-specific planning. The framework integrates three analytically distinct but operationally complementary components, applied sequentially and iteratively across the health pyramid. Each component addresses a different dimension of the capacity question: what the system needs (WISN), how far it can go before breaking (stress testing), and what it is actually producing (HCIS-SIM monitoring). Together, they produce a systemic reading of health system performance that no single tool can offer in isolation [1,7]. Component 1 — WISN-based workforce analysis The first component applies the Workload Indicators of Staffing Need methodology, developed and validated by the World Health Organization [7,8]. WISN estimates the number of health workers required in a given category by crossing three variables: the volume of health service activities performed over a reference period, a service time standard reflecting the average time a well-trained and motivated health worker requires to perform each activity, and the available working time per health worker per year after accounting for absences, leave, training, and administrative duties [7]. For this study, WISN was applied to three health worker categories central to maternal health service delivery: physicians and medical officers, midwives and obstetric nurses (SF/IO), and paramedical staff. Activity volumes were derived from the 2020 National Health Information Yearbook (SNISS) [5] and the Koulikoro District Health Information System (SLIS) [19]. Service time standards were calibrated against WHO reference values [7,8] and adjusted for the Malian context based on field observations and consultations with district health management teams. The WISN ratio — the ratio of available staff to required staff — was computed for each category and site. A WISN ratio below 1.0 indicates understaffing relative to workload; a ratio above 1.0 indicates theoretical sufficiency. Critically, HCIS-SIM introduces a productivity adjustment coefficient — the ratio of observed productivity to WISN-predicted potential — to capture the mobilization gap between theoretical staffing adequacy and actual service output. This coefficient, absent from standard WISN applications, constitutes one of the original methodological contributions of the framework. Component 2 — Simulated stress testing The second component adapts stress testing methodologies from the financial and industrial sectors to the health system context [15,16]. In financial regulation, stress testing evaluates the resilience of a system under adverse conditions by simulating scenarios of increased pressure and identifying the thresholds at which system integrity is compromised [15]. Applied to health systems, this approach simulates progressive increases in service demand — ranging from +10% to +50% above baseline activity levels — and models the resulting impact on workforce utilization rates, facility occupancy, and service delivery capacity. For each application site, three demand scenarios were modeled: a conservative scenario (+20% activity increase), a moderate scenario (+30%), and an ambitious scenario corresponding to the activity volume required to achieve national PRODESS III targets. For each scenario, workforce utilization rates were computed against available staffing levels. Saturation was defined as a workforce utilization rate exceeding 85% — a threshold above which service quality degradation and staff burnout risk become clinically significant [16]. Bottleneck services — those reaching saturation earliest under simulated demand increases — were identified and ranked by criticality. This component provides the prospective dimension absent from both normative planning and standard WISN analysis: it does not ask what the system lacks today, but rather what will break first tomorrow if demand increases as national health policy requires. Component 3 — Real-time capacity monitoring and trajectory modeling The third component constitutes the integrative core of HCIS-SIM. It synthesizes the outputs of the WISN analysis and the stress test with observed service delivery data to produce two outputs: a capacity profile for each application site, and a territory-specific planning trajectory toward defined health objectives. The capacity profile characterizes each site across four dimensions: productive efficiency (observed output as a percentage of WISN-predicted potential), distributional equity (workforce density per 10,000 population by geographic zone), systemic resilience (saturation threshold identified through stress testing), and maternal health performance (MMR, assisted delivery rate, caesarean section rate, CPN4 coverage). These four dimensions are visualized in a decision dashboard designed for use by district health management teams and regional health directorates. The planning trajectory models a progressive, multi-year pathway from the current capacity baseline (N0) toward the national health objective, incorporating annual milestones for workforce reinforcement, infrastructure investment, and organizational improvement. Trajectories are differentiated by territory — recognizing that sites with identical health outcomes may require structurally different interventions — and are constrained by realistic resource mobilization assumptions rather than normative targets [9,14]. Application sites and data sources The framework was applied across three levels of the Malian health pyramid, selected to provide a comprehensive cross-sectional view of health system capacity from the tertiary hospital to the primary care district level. Kati District Hospital serves as the tertiary-level application site. It functions as the second referral level for the Koulikoro region and as a teaching hospital affiliated with the University of Sciences, Techniques and Technologies of Bamako (USTTB). It provides a full range of specialist services including surgery, obstetrics, and emergency care. Sikasso Region serves as the regional-level application site, with a population of approximately 3.9 million inhabitants, seven referral health centers (CSRéf), one regional hospital, and approximately 250–300 community health centers (CSCom). Sikasso presents the highest absolute maternal health burden in Mali due to its demographic weight. Koulikoro Health District serves as the district-level application site, covering a population of 307,187 inhabitants (2021 projection) across one referral health center (CSRéf) and 23 community health centers (CSCom), of which 20 are located in rural areas. The district is representative of the internal territorial disparities that characterize the Malian health system. Primary data sources are the 2020 National Health Information Yearbook (SNISS), published by the Health Planning and Statistics Division (CPS) of the Mali Ministry of Health and Social Development [5], and the District Health Information System (SLIS) of Koulikoro Health District [19]. Supplementary demographic and epidemiological data were drawn from the MTN Strategic Plan 2022–2026 [17] and WHO/UNICEF maternal mortality estimates [3]. Ethical considerations This study is based exclusively on aggregated, de-identified administrative data drawn from official national health information systems. No individual patient data were accessed or analyzed. No ethical approval was required under Malian national research ethics guidelines for studies using publicly available administrative health data. All data sources are explicitly cited and publicly accessible through the Mali Ministry of Health and Social Development. Results Overview Application of the HCIS-SIM framework across three levels of the Malian health pyramid yielded convergent and mutually reinforcing findings. In all three sites, the principal constraint on health system performance was not the absolute volume of available resources but rather the efficiency with which those resources are mobilized, distributed, and organized. Three counter-intuitive results emerged, each challenging a prevailing assumption in health workforce planning for resource-constrained settings. Result 1 — Workforce availability without workforce productivity: evidence from Kati District Hospital WISN analysis of Kati District Hospital indicates that the facility's medical workforce is globally adequate in volume relative to its current activity level. The WISN ratio for physicians and medical officers exceeds 1.0, suggesting that available staffing is theoretically sufficient to absorb current service demand without reinforcement. However, HCIS-SIM capacity monitoring reveals a critical divergence between theoretical workforce potential and observed productive output. Effective medical productivity — measured as the ratio of observed consultations per physician per working day to the WISN-predicted standard of 20–22 consultations per physician per day — reaches less than 45% of theoretical potential. This gap is not attributable to workforce shortage. Analysis of organizational factors identifies three primary determinants of this mobilization failure. First, dual posting — the simultaneous affiliation of physicians with both the public hospital and private practice — systematically reduces the time available for public facility service delivery. Conservative estimates suggest that dual posting reduces effective physician availability by 30 to 45% of contractual working time [11,12]. Second, administrative burden — including patient record management, reporting requirements, and facility administration — absorbs a substantial fraction of clinical time that WISN standards allocate to direct patient care. Third, the absence of structured consultation scheduling generates irregular patient flows that prevent physicians from maintaining consistent daily productivity levels. Stress testing further reveals that this organizational inefficiency generates a paradoxical fragility: despite apparent workforce adequacy, the hospital system reaches saturation at a demand increase of only 20 to 30% above current activity levels. Key bottleneck services — outpatient consultations, emergency care, pharmacy, and laboratory — are the first to saturate, constraining the entire care pathway. Bed occupancy rates, by contrast, remain below 40%, confirming that physical infrastructure is underutilized while human resource mobilization represents the binding constraint. These findings establish a first counter-intuitive result: at Kati District Hospital, the health system's productive constraint is organizational, not volumetric. Recruiting additional staff without addressing mobilization efficiency would not improve system performance. Result 2 — Identical outcomes, opposite root causes: Sikasso Region versus Koulikoro Health District HCIS-SIM capacity monitoring reveals that Sikasso Region and Koulikoro Health District present statistically comparable maternal mortality ratios — estimated at 500–600 and 500–550 per 100,000 live births respectively [3,5] — despite representing structurally opposite health system profiles. This convergence of outcomes with divergence of causes constitutes the most analytically significant finding of this study. Sikasso Region presents a volume pressure profile. With a population of approximately 3.9 million inhabitants, the region faces a quantitative shortage of qualified obstetric personnel — an estimated deficit of 200 to 300 midwives and obstetric nurses relative to WISN-derived requirements. Only 13 Emergency Obstetric and Newborn Care comprehensive facilities (CEmONC) serve the entire region, generating a structural bottleneck that prevents access to emergency obstetric care for a large fraction of the population requiring it. The caesarean section rate of 2–3% — against the WHO minimum threshold of 5% — confirms that access to life-saving obstetric interventions is critically constrained [3,7]. Assisted delivery coverage of 60–70% falls 10 to 20 percentage points below the PRODESS III target of ≥80% [6]. WISN projections indicate that achieving national maternal health objectives in Sikasso would require an additional 36,000 to 54,000 assisted deliveries per year, implying a tripling of current CEmONC capacity and an investment estimated at 1 to 1.5 billion FCFA annually. Koulikoro Health District presents a distributional inequity profile. The district's health workforce — estimated at approximately 127 agents across 24 facilities — is distributed in near-inverse proportion to population need. Urban community health centers (CSCom), serving approximately 20% of the district population, maintain a workforce density of 5.0 per 10,000 inhabitants, while rural CSCom serving 80% of the population operate at 2.6 per 10,000 — a ratio almost twice less favorable [5,19]. An estimated 6 to 7 rural CSCom among the 20 in the district operate without a permanently assigned qualified midwife or obstetric nurse, meaning that deliveries in these facilities are attended by generalist agents or traditional birth attendants. The curative care utilization rate of 0.07 new contacts per inhabitant per year — seven to fourteen times below the WHO reference range of 0.5 to 1.0 — reflects both geographic access barriers and the limited availability of qualified personnel in rural areas [7]. Stress testing confirms that the district's single referral health center (CSRéf) reaches saturation with an additional 250 to 280 obstetric referrals per year, revealing the absence of a functional second referral level within the district as a critical system vulnerability. These findings establish a second counter-intuitive result: Sikasso and Koulikoro present identical maternal mortality ratios produced by structurally opposite mechanisms. A uniform national policy response cannot simultaneously address a volume deficit and a distributional inequity. Territory-differentiated planning is not a refinement — it is a necessity. Result 3 — System saturation precedes objective attainment: stress test findings across sites Stress testing applied consistently across all three application sites reveals a systemic pattern with direct implications for national health planning: in each case, the health system reaches critical saturation thresholds before the activity levels required to achieve PRODESS III objectives are approached. At Kati District Hospital, a 20 to 30% increase in outpatient consultation volume — well below the level implied by universal health coverage expansion targets — generates saturation in four key service areas simultaneously: outpatient consultations, emergency services, pharmacy dispensing, and laboratory processing. Paradoxically, inpatient bed capacity remains substantially underutilized at this saturation point, confirming that the binding constraint is human resource mobilization rather than physical infrastructure. In Sikasso Region, achieving the assisted delivery coverage target of ≥80% under PRODESS III would require absorbing an additional 36,000 to 54,000 deliveries per year — a 40 to 60% increase above current volumes. Stress testing demonstrates that the existing CEmONC network reaches saturation well below this threshold, generating a critical policy implication: without prior reinforcement of emergency obstetric care capacity, increasing assisted delivery coverage through demand-side interventions alone would concentrate complications at already-saturated facilities, potentially worsening maternal outcomes rather than improving them. In Koulikoro Health District, the redistribution of obstetric care from urban to rural facilities — required to achieve equitable coverage — would impose a 2 to 4-fold increase in the obstetric workload of rural CSCom agents currently operating without qualified supervision. Stress testing of the district referral pathway confirms that the CSRéf reaches saturation with modest increases in referral volume, identifying the absence of a functional intermediate referral level as the district's most critical structural vulnerability. These findings establish a third counter-intuitive result: national health objectives in Mali have been defined without prior testing of the system's capacity to achieve them. In all three sites, the health system saturates before objectives are reached — making capacity reinforcement a prerequisite for, rather than a consequence of, policy implementation. Summary of key findings Table 1. Summary of HCIS-SIM findings across three application sites of the Malian health pyramid. Dimension Kati Hospital Sikasso Region Koulikoro District WISN workforce adequacy Adequate (ratio > 1.0) Deficit (−200 to −300 SF/IO) Partial deficit (rural zones) Effective productivity < 45% of potential 60–70% assisted deliveries 0.07 NC/inhab/year Primary constraint Mobilization failure Volume deficit Distributional inequity Saturation threshold +20–30% activity +40–60% deliveries +250–280 referrals MMR / 100,000 LB N/A (referral hospital) 500–600 500–550 PRODESS target feasibility Conditional on reorganization Requires prior CEmONC reinforcement Requires redistribution + referral strengthening Discussion Principal findings in context This study presents three findings that collectively challenge prevailing assumptions in health workforce planning for resource-constrained settings. First, workforce availability does not automatically translate into workforce productivity — a distinction that normative planning frameworks systematically obscure by conflating staffing ratios with service delivery capacity. Second, identical health outcomes can be produced by structurally opposite root causes, rendering uniform national policy responses analytically indefensible. Third, national health objectives in Mali have been defined without prior testing of system absorption capacity, creating a structural misalignment between policy ambition and operational feasibility that HCIS-SIM is specifically designed to reveal and address. These findings are not isolated observations from a single facility or district. They emerge consistently across three application sites representing the full vertical span of the Malian health pyramid — from a tertiary hospital to a regional health system to a primary care district — using official national data sources. Their convergence suggests that the patterns identified are systemic rather than site-specific, and that their implications extend beyond Mali to comparable health systems across sub-Saharan Africa. The mobilization gap — a neglected dimension of workforce analysis The finding that effective medical productivity at Kati District Hospital reaches less than 45% of WISN-predicted potential is, to our knowledge, among the first documented measurements of the gap between theoretical workforce potential and observed mobilization efficiency in a Malian hospital context. It resonates with a growing body of evidence suggesting that workforce waste — defined as the gap between available capacity and actual productive output — is as significant a contributor to health system underperformance in Africa as workforce shortage itself [12,13]. Dovlo's foundational work on workforce wastage in African countries identified absenteeism, dual practice, and task misallocation as primary mechanisms through which available health worker capacity fails to translate into service delivery [12]. Our findings extend this analysis by quantifying the mobilization gap precisely — less than 45% efficiency at Kati — and by identifying its organizational determinants: dual posting, administrative burden, and unstructured consultation scheduling. This level of analytical precision is not achievable through normative approaches that compare staff-to-population ratios without examining how those staff actually deploy their working time. The policy implication is direct and actionable. At Kati District Hospital, recruiting additional physicians without addressing the organizational determinants of mobilization failure would not improve service delivery. The return on investment of organizational reform — structured consultation scheduling, reduced administrative burden, formalized dual posting management — substantially exceeds that of additional recruitment in the short to medium term. This finding inverts the conventional policy response to workforce inadequacy and illustrates the central argument of HCIS-SIM: in resource-constrained settings, optimizing existing capacity frequently offers greater impact than expanding nominal capacity [9,11]. Territory-differentiated planning — from principle to necessity The convergence of maternal mortality ratios in Sikasso and Koulikoro — 500–600 and 500–550 per 100,000 live births respectively — despite structurally opposite health system profiles constitutes the most analytically powerful finding of this study. It demonstrates empirically what planning theory has long argued conceptually: that health outcome indicators are insufficient proxies for the underlying determinants of system performance, and that identical outcomes can mask fundamentally different causal pathways requiring fundamentally different policy responses [1,2]. Sikasso's volume pressure profile — quantitative workforce deficit, insufficient CEmONC network, demand exceeding capacity — calls for a supply expansion response: recruitment of 200 to 300 additional midwives and obstetric nurses, tripling of CEmONC functional capacity, and progressive demand stimulation constrained by supply reinforcement milestones. Koulikoro's distributional inequity profile — adequate aggregate workforce but inverted distribution, rural facilities operating without qualified obstetric staff — calls for a redistribution response: priority redeployment of qualified staff to high-volume rural CSCom, retention mechanisms for rural postings, and strengthening of the referral pathway. These two responses are not merely different in degree — they are different in kind. Applying Sikasso's supply expansion logic to Koulikoro would leave the distributional inequity unaddressed. Applying Koulikoro's redistribution logic to Sikasso would fail to close the absolute workforce gap. A uniform national policy that averages these responses would underperform in both territories. This finding has direct implications for the design of PRODESS IV and for the allocation of international cooperation resources — including those of bilateral partners currently engaged in maternal health support in Mali [6,17]. The methodological contribution here extends beyond the Malian context. Asamani and colleagues have demonstrated the value of needs-based simulation for projecting aggregate workforce requirements [1,2]. HCIS-SIM advances this contribution by introducing the territory-differentiation dimension — showing not only that a national system is undersupplied, but that undersupply manifests differently across territories and requires differentiated responses. This extension is particularly relevant for large, geographically diverse countries where health system performance varies substantially across administrative units. System capacity testing as a prerequisite for policy design The stress test findings across all three application sites reveal a systemic pattern of immediate relevance to national health planning: the Malian health system reaches critical saturation thresholds before the activity levels required by PRODESS III objectives are approached. This finding reframes a fundamental question in health policy design — not "what objectives should we set?" but "can our system reach those objectives without prior capacity reinforcement?" This reframing is the core contribution of HCIS-SIM to the field. The existing literature on health workforce planning provides sophisticated tools for projecting workforce needs [1,2,7,8] and for documenting the gap between available and required resources [10,23]. What it does not provide — for low-income African health systems — is a methodology for testing whether the health system as currently organized can absorb the demand induced by national health policy targets before those targets are adopted. HCIS-SIM fills this gap. The practical implication is consequential. In Sikasso, demand-side interventions to increase facility deliveries — community mobilization, conditional cash transfers, maternity waiting homes — without prior reinforcement of CEmONC capacity would concentrate obstetric complications at already-saturated facilities. The result could be an increase in institutional delivery rates accompanied by a worsening of institutional maternal mortality — an outcome that has been documented in comparable settings where demand-side investments outpaced supply-side capacity [3,16]. HCIS-SIM provides the analytical tool to anticipate and prevent this scenario before it occurs. Positioning HCIS-SIM within the health workforce planning literature HCIS-SIM occupies a distinct position within the health workforce planning literature that merits explicit articulation. It is not a workforce projection model — it does not forecast future supply or demand under baseline assumptions. It is not a normative gap analysis — it does not measure deviation from WHO standards. It is not an implementation science framework — it does not evaluate the fidelity of intervention delivery. HCIS-SIM is a capacity testing framework — a tool designed to answer a specific and previously underserved analytical question: given the health system as it currently exists, what can it realistically produce, and what will constrain its ability to achieve defined objectives? This question sits at the intersection of health workforce analysis, health systems science, and operational research, drawing from each without being fully contained within any. The closest methodological antecedents are the needs-based simulation models developed by Asamani and colleagues [1,2] and the dynamic multi-professional simulation model proposed by MacKenzie and colleagues for high-income settings [18]. HCIS-SIM extends these contributions in three directions: it introduces the productivity adjustment coefficient to capture mobilization efficiency, it adapts stress testing from financial sector methodology to health system analysis, and it generates territory-differentiated trajectories rather than aggregate national projections. These extensions are specifically calibrated to the data environment and decision-making context of low-income African health systems, where planning tools developed for high-income settings frequently fail to produce actionable outputs [9,14]. Limitations Several limitations of this study warrant explicit acknowledgment. First, activity volume data for Koulikoro Health District were partially estimated through proportional extrapolation from regional aggregates published in the SNISS 2020, pending access to disaggregated district-level data available in the DHIS2 system at institutional access level. While this approach introduces estimation uncertainty — acknowledged at ±15% for key indicators — it is consistent with the data environment in which health district managers in Mali must plan, and the framework's conclusions are robust to this range of uncertainty. Second, WISN service time standards were calibrated against WHO reference values with contextual adjustment, rather than derived from primary time-motion studies conducted in Malian facilities. Facility-level time-motion studies would strengthen the precision of productivity gap estimates and are recommended for future applications of HCIS-SIM. Third, the stress test scenarios model demand increases as uniform across service categories, whereas real demand growth in response to policy interventions is typically uneven — concentrated in specific services such as antenatal care, facility delivery, and emergency obstetrics. Disaggregated stress testing by service category would provide finer-grained saturation analysis and is a planned extension of the framework. Fourth, as a framework paper applied across three case study sites, this study does not provide statistical power to generalize findings beyond the application sites. The findings are presented as illustrative demonstrations of framework capability rather than as definitive estimates of system-wide parameters. Larger-scale application across all ten health districts of Koulikoro region and all seven districts of Sikasso region would provide the empirical breadth necessary for policy-grade generalization. These limitations notwithstanding, the framework's findings are grounded in official national data sources, internally consistent across three application sites, and directionally robust under reasonable sensitivity assumptions. They provide a sufficient empirical foundation for the methodological and policy conclusions presented. Conclusions Health systems in resource-constrained settings do not fail solely because they lack resources. They fail because available resources are insufficiently mobilized, inequitably distributed, and deployed without prior testing of the system's capacity to absorb the demand that national health policies generate. HCIS-SIM was developed in direct response to this observation — not as a critique of existing planning frameworks, but as a complement that addresses the analytical dimension they systematically leave unresolved. Three conclusions emerge from this study with direct relevance for health policymakers, planners, and international cooperation partners operating in comparable settings. First, measuring workforce availability is not equivalent to measuring workforce productivity. The 55% mobilization gap documented at Kati District Hospital demonstrates that normative staffing adequacy can coexist with significant service delivery underperformance. Health workforce planning frameworks that do not incorporate a productivity adjustment component will systematically overestimate system capacity and underestimate the organizational determinants of performance gaps. HCIS-SIM introduces this component as a standard analytical element applicable across facility types and health system levels. Second, identical health outcomes require territory-differentiated policy responses. The convergence of maternal mortality ratios in Sikasso and Koulikoro — produced by structurally opposite mechanisms — demonstrates that national health indicators are insufficient guides for policy design. Planning frameworks that aggregate performance data to the national level without disaggregating causal mechanisms will generate policy responses that are simultaneously over-prescribed for some territories and under-prescribed for others. HCIS-SIM provides the analytical infrastructure for territory-differentiated planning at scale. Third, system capacity testing must precede, not follow, objective-setting. The consistent finding that the Malian health system reaches saturation before national health objectives are approached establishes capacity testing as a prerequisite for credible health policy design. Objectives defined without prior capacity testing are not ambitious — they are unfeasible. HCIS-SIM offers a replicable, data-efficient methodology for conducting this test using routinely collected administrative health data. Taken together, these conclusions support a reorientation of health workforce planning in resource-constrained settings — from documenting the gap between what exists and what international norms prescribe, toward optimizing what exists and building realistic trajectories toward what is achievable. This reorientation does not lower ambition. It grounds ambition in evidence, and translates evidence into action. HCIS-SIM is currently applied in Mali across three levels of the national health pyramid. Its extension to additional districts, regions, and comparable health systems across West Africa represents the natural next step — one that the authors invite collaborators, policymakers, and research partners to join. References Asamani JA, Christmals CD, Reitsma GM. Advancing the Population Needs-Based Health Workforce Planning Methodology: A Simulation Tool for Country Application. Int J Environ Res Public Health. 2021;18(4):2113. doi:10.3390/ijerph18042113 Asamani JA, Christmals CD, Reitsma GM. The needs-based health workforce planning method: a systematic scoping review of analytical applications. Health Policy Plan. 2021;36(8):1325-1343. doi:10.1093/heapol/czab022 World Health Organization, UNICEF, UNFPA, World Bank Group, UNDESA/Population Division. Trends in maternal mortality 2000 to 2020. Geneva: WHO; 2023. IntraHealth International. Four Steps toward a Stronger African Health Workforce in 2030 and Beyond. Washington DC: IntraHealth; 2023. Ministère de la Santé et du Développement Social du Mali. Annuaire du Système National d'Information Sanitaire et Sociale (SNISS) 2020. Bamako: CPS/MSDS; 2021. République du Mali. Programme de Développement Sanitaire et Social (PRODESS III) 2014–2023. Bamako: MSDS; 2014. World Health Organization. Workload Indicators of Staffing Need (WISN): User's Manual. Geneva: WHO; 2010. World Health Organization. Workload Indicators of Staffing Need (WISN): Selected Country Implementation Experiences. Hum Resour Health Obs Ser. 2016;15. Nyoni J, Christmals CD, Asamani JA, et al. The process of developing health workforce strategic plans in Africa: a document analysis. BMJ Glob Health. 2022;7(Suppl 1):e008418. doi:10.1136/bmjgh-2021-008418 Asamani JA, Ismaila H, Plange A, et al. The cost of health workforce gaps and inequitable distribution in the Ghana Health Service. Hum Resour Health. 2021;19(1):43. doi:10.1186/s12960-021-00590-3 McKinsey Global Institute. Overcoming sub-Saharan Africa's health workforce paradox. McKinsey & Company; November 2024. Dovlo D. Wastage in the health workforce: some perspectives from African countries. Hum Resour Health. 2005;3:6. doi:10.1186/1478-4491-3-6 Okoroafor SC, Ongom M, Mohammed B, et al. The health workforce status in the WHO African Region. BMJ Glob Health. 2022;7(Suppl 1):e008317. doi:10.1136/bmjgh-2021-008317 Approaches and Components of Health Workforce Planning Models: A Systematic Review. Int J Environ Res Public Health. 2023. doi:10.3390/ijerph20136209 Basel Committee on Banking Supervision. Principles for sound stress testing practices and supervision. Basel: Bank for International Settlements; 2009. Dussault G, Franceschini MC. Not enough there, too many here: understanding geographical imbalances in the distribution of the health workforce. Hum Resour Health. 2006;4:12. doi:10.1186/1478-4491-4-12 République du Mali. Plan Stratégique de lutte contre les Maladies Tropicales Négligées (MTN) 2022–2026. Bamako: MSDS; 2022. MacKenzie A, Tomblin Murphy G, Audas R. A dynamic, multi-professional, needs-based simulation model to inform human resources for health planning. Hum Resour Health. 2019;17:42. doi:10.1186/s12960-019-0376-5 Système Local d'Information Sanitaire (SLIS). District Sanitaire de Koulikoro. Données consolidées 2020–2021. Bamako: MSDS; 2021. WHO. Global Strategy on Human Resources for Health: Workforce 2030. Geneva: WHO; 2016. Goma FM, Murphy GT, Libetwa M, et al. Pilot-testing service-based planning for health care in rural Zambia. BMC Health Serv Res. 2014;14(Suppl 1):S7. doi:10.1186/1472-6963-14-S1-S7 Tomblin Murphy G, Birch S, MacKenzie A. Simulating future supply of and requirements for human resources for health in high-income OECD countries. Hum Resour Health. 2016;14:1–18. Asamani JA, Amertil NP, Ismaila H, et al. The imperative of evidence-based health workforce planning and implementation: lessons from nurses and midwives unemployment crisis in Ghana. Hum Resour Health. 2020;18:16. doi:10.1186/s12960-020-0462-5 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9451846","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":630878009,"identity":"9c4b8e48-078d-4148-8266-4e134523e4b4","order_by":0,"name":"Mamadou Sidibé","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYFCCAwwHeAwOMPBLgHkSMsRrkZzBwNgA1MJDnEU8QH0GN8BaGAhr0W08/vDAm4I7csa3m48/ulFjwcPAfvjoBnxazA6cMTg4x+CZsdmdY4nNOceADuNJS7tBQAvDYR6Dw4nbbuQYNuewAbVI8JgR0HL8AUhL/eYZIC3/iNJywACkJcFAAqglt40oLRC/GM64kZY4O7dPgoeNoF9uHH/84c2fO/L8M5IPfM75VifHz374GF4tDBIH0ATY8CoHAf4GgkpGwSgYBaNgpAMAuk1TZolRBoIAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Mamadou","middleName":"","lastName":"Sidibé","suffix":""}],"badges":[],"createdAt":"2026-04-17 17:54:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9451846/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9451846/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108072084,"identity":"d98e1fc8-8d6e-475e-a230-7cf2c8f15473","added_by":"auto","created_at":"2026-04-29 06:11:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":220259,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9451846/v1/6cedf179-ff27-409b-9322-57d7c3a12f07.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"When workforce availability does not mean workforce productivity: a capacity-anchored decision-support framework for health system planning in resource-constrained settings — evidence from Mali","fulltext":[{"header":"Background","content":"\u003cp\u003eHealth systems in sub-Saharan Africa face a persistent and well-documented paradox: despite decades of international investment in health workforce development, the gap between available human resources for health (HRH) and population needs remains structurally wide [1,2]. The World Health Organization projects a global shortage of 10 million health workers by 2030, with sub-Saharan Africa bearing a disproportionate share of this deficit — a region that carries 25% of the global disease burden while hosting less than 2% of the world's trained health personnel [3,4]. In Mali, this crisis is reflected in a maternal mortality ratio (MMR) of 562 per 100,000 live births — among the highest globally — against a national target of ≤146 per 100,000 live births under the third Programme de Développement Sanitaire et Social (PRODESS III) and the Sustainable Development Goals (SDGs) [5,6].\u003c/p\u003e\n\u003cp\u003eThe dominant response to this challenge has been normative planning — measuring the gap between existing workforce levels and WHO-recommended standards, then projecting the resources needed to close that gap [7,8]. While this approach accurately documents insufficiency, it produces a systematic and underappreciated problem: in contexts where internationally recommended staffing norms remain structurally unattainable within foreseeable fiscal horizons, normative planning generates targets that discourage rather than guide action [9]. Decision-makers in resource-constrained settings are left with precise measurements of what they cannot achieve, but limited analytical tools to optimize what they have [10].\u003c/p\u003e\n\u003cp\u003eThis limitation is not merely theoretical. A growing body of evidence from sub-Saharan Africa demonstrates that workforce availability does not automatically translate into workforce productivity [11,12]. Health facilities may meet staffing ratios on paper while operating at a fraction of their productive potential due to organizational factors — dual posting, administrative burden, suboptimal scheduling, and inadequate supervision — that normative frameworks neither capture nor address [13]. The distinction between workforce availability and workforce mobilization efficiency represents a critical analytical blind spot in current health workforce planning methodologies applied to African health systems.\u003c/p\u003e\n\u003cp\u003eSeveral methodological advances have sought to address the limitations of normative planning. Needs-based approaches, pioneered and systematized by Asamani and colleagues, introduced simulation tools to project workforce requirements based on population health needs rather than population ratios alone [1,2]. These contributions represent a significant advance, yet they remain primarily focused on quantifying the supply-need gap rather than on testing whether existing systems can absorb the demand induced by national health policy objectives [1]. The question of system absorption capacity — whether a health system, as currently organized and resourced, can realistically achieve its stated objectives — has received comparatively little attention in the health workforce planning literature for low-income African countries [14].\u003c/p\u003e\n\u003cp\u003eThis paper addresses that gap. We present HCIS-SIM (Health Capacity Information System — Simulation \u0026amp; Monitoring), a decision-support framework that reorients health workforce planning from documenting normative gaps toward optimizing available capacity. HCIS-SIM does not ask \"how far are we from the WHO standard?\" — it asks \"what can this system realistically produce with what it currently has, and what is the most efficient path toward measurable improvement?\" This distinction is not semantic. It is the difference between a planning tool that generates discouragement and one that generates actionable trajectories.\u003c/p\u003e\n\u003cp\u003eThe framework integrates three complementary analytical components — WISN-based workforce analysis, simulated stress testing, and real-time capacity monitoring — applied across three levels of the Malian health pyramid using official national data sources. Its application yields three counter-intuitive findings that challenge prevailing assumptions about the nature of health workforce challenges in resource-constrained settings, and generates territory-differentiated planning trajectories toward the national MMR objective of ≤146 per 100,000 live births.\u003c/p\u003e\n\u003cp\u003eThe remainder of this paper is structured as follows: the Methods section describes the HCIS-SIM framework and its three analytical components, the data sources, and the application sites; the Results section presents findings from each level of the health pyramid; the Discussion positions these findings within the existing literature and elaborates their implications for health workforce planning policy; and the Conclusions offer recommendations for replication in comparable settings.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003e\u003cstrong\u003eFramework overview\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eHCIS-SIM is a multi-component decision-support framework designed to assess health system capacity and generate actionable planning trajectories in resource-constrained settings. Rather than measuring deviation from externally defined norms, HCIS-SIM characterizes the productive capacity of a health system as it currently exists — its actual outputs, its mobilization efficiency, and its tolerance to increased demand — and uses this characterization as the foundation for realistic, territory-specific planning.\u003c/p\u003e\n\u003cp\u003eThe framework integrates three analytically distinct but operationally complementary components, applied sequentially and iteratively across the health pyramid. Each component addresses a different dimension of the capacity question: what the system needs (WISN), how far it can go before breaking (stress testing), and what it is actually producing (HCIS-SIM monitoring). Together, they produce a systemic reading of health system performance that no single tool can offer in isolation [1,7].\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eComponent 1 — WISN-based workforce analysis\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe first component applies the Workload Indicators of Staffing Need methodology, developed and validated by the World Health Organization [7,8]. WISN estimates the number of health workers required in a given category by crossing three variables: the volume of health service activities performed over a reference period, a service time standard reflecting the average time a well-trained and motivated health worker requires to perform each activity, and the available working time per health worker per year after accounting for absences, leave, training, and administrative duties [7].\u003c/p\u003e\n\u003cp\u003eFor this study, WISN was applied to three health worker categories central to maternal health service delivery: physicians and medical officers, midwives and obstetric nurses (SF/IO), and paramedical staff. Activity volumes were derived from the 2020 National Health Information Yearbook (SNISS) [5] and the Koulikoro District Health Information System (SLIS) [19]. Service time standards were calibrated against WHO reference values [7,8] and adjusted for the Malian context based on field observations and consultations with district health management teams.\u003c/p\u003e\n\u003cp\u003eThe WISN ratio — the ratio of available staff to required staff — was computed for each category and site. A WISN ratio below 1.0 indicates understaffing relative to workload; a ratio above 1.0 indicates theoretical sufficiency. Critically, HCIS-SIM introduces a productivity adjustment coefficient — the ratio of observed productivity to WISN-predicted potential — to capture the mobilization gap between theoretical staffing adequacy and actual service output. This coefficient, absent from standard WISN applications, constitutes one of the original methodological contributions of the framework.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eComponent 2 — Simulated stress testing\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe second component adapts stress testing methodologies from the financial and industrial sectors to the health system context [15,16]. In financial regulation, stress testing evaluates the resilience of a system under adverse conditions by simulating scenarios of increased pressure and identifying the thresholds at which system integrity is compromised [15]. Applied to health systems, this approach simulates progressive increases in service demand — ranging from +10% to +50% above baseline activity levels — and models the resulting impact on workforce utilization rates, facility occupancy, and service delivery capacity.\u003c/p\u003e\n\u003cp\u003eFor each application site, three demand scenarios were modeled: a conservative scenario (+20% activity increase), a moderate scenario (+30%), and an ambitious scenario corresponding to the activity volume required to achieve national PRODESS III targets. For each scenario, workforce utilization rates were computed against available staffing levels. Saturation was defined as a workforce utilization rate exceeding 85% — a threshold above which service quality degradation and staff burnout risk become clinically significant [16]. Bottleneck services — those reaching saturation earliest under simulated demand increases — were identified and ranked by criticality.\u003c/p\u003e\n\u003cp\u003eThis component provides the prospective dimension absent from both normative planning and standard WISN analysis: it does not ask what the system lacks today, but rather what will break first tomorrow if demand increases as national health policy requires.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eComponent 3 — Real-time capacity monitoring and trajectory modeling\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe third component constitutes the integrative core of HCIS-SIM. It synthesizes the outputs of the WISN analysis and the stress test with observed service delivery data to produce two outputs: a capacity profile for each application site, and a territory-specific planning trajectory toward defined health objectives.\u003c/p\u003e\n\u003cp\u003eThe capacity profile characterizes each site across four dimensions: productive efficiency (observed output as a percentage of WISN-predicted potential), distributional equity (workforce density per 10,000 population by geographic zone), systemic resilience (saturation threshold identified through stress testing), and maternal health performance (MMR, assisted delivery rate, caesarean section rate, CPN4 coverage). These four dimensions are visualized in a decision dashboard designed for use by district health management teams and regional health directorates.\u003c/p\u003e\n\u003cp\u003eThe planning trajectory models a progressive, multi-year pathway from the current capacity baseline (N0) toward the national health objective, incorporating annual milestones for workforce reinforcement, infrastructure investment, and organizational improvement. Trajectories are differentiated by territory — recognizing that sites with identical health outcomes may require structurally different interventions — and are constrained by realistic resource mobilization assumptions rather than normative targets [9,14].\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eApplication sites and data sources\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe framework was applied across three levels of the Malian health pyramid, selected to provide a comprehensive cross-sectional view of health system capacity from the tertiary hospital to the primary care district level.\u003c/p\u003e\n\u003cp\u003eKati District Hospital serves as the tertiary-level application site. It functions as the second referral level for the Koulikoro region and as a teaching hospital affiliated with the University of Sciences, Techniques and Technologies of Bamako (USTTB). It provides a full range of specialist services including surgery, obstetrics, and emergency care.\u003c/p\u003e\n\u003cp\u003eSikasso Region serves as the regional-level application site, with a population of approximately 3.9 million inhabitants, seven referral health centers (CSRéf), one regional hospital, and approximately 250–300 community health centers (CSCom). Sikasso presents the highest absolute maternal health burden in Mali due to its demographic weight.\u003c/p\u003e\n\u003cp\u003eKoulikoro Health District serves as the district-level application site, covering a population of 307,187 inhabitants (2021 projection) across one referral health center (CSRéf) and 23 community health centers (CSCom), of which 20 are located in rural areas. The district is representative of the internal territorial disparities that characterize the Malian health system.\u003c/p\u003e\n\u003cp\u003ePrimary data sources are the 2020 National Health Information Yearbook (SNISS), published by the Health Planning and Statistics Division (CPS) of the Mali Ministry of Health and Social Development [5], and the District Health Information System (SLIS) of Koulikoro Health District [19]. Supplementary demographic and epidemiological data were drawn from the MTN Strategic Plan 2022–2026 [17] and WHO/UNICEF maternal mortality estimates [3].\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eEthical considerations\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThis study is based exclusively on aggregated, de-identified administrative data drawn from official national health information systems. No individual patient data were accessed or analyzed. No ethical approval was required under Malian national research ethics guidelines for studies using publicly available administrative health data. All data sources are explicitly cited and publicly accessible through the Mali Ministry of Health and Social Development.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003e\u003cstrong\u003eOverview\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eApplication of the HCIS-SIM framework across three levels of the Malian health pyramid yielded convergent and mutually reinforcing findings. In all three sites, the principal constraint on health system performance was not the absolute volume of available resources but rather the efficiency with which those resources are mobilized, distributed, and organized. Three counter-intuitive results emerged, each challenging a prevailing assumption in health workforce planning for resource-constrained settings.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eResult 1 \u0026mdash; Workforce availability without workforce productivity: evidence from Kati District Hospital\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eWISN analysis of Kati District Hospital indicates that the facility\u0026apos;s medical workforce is globally adequate in volume relative to its current activity level. The WISN ratio for physicians and medical officers exceeds 1.0, suggesting that available staffing is theoretically sufficient to absorb current service demand without reinforcement.\u003c/p\u003e\n\u003cp\u003eHowever, HCIS-SIM capacity monitoring reveals a critical divergence between theoretical workforce potential and observed productive output. Effective medical productivity \u0026mdash; measured as the ratio of observed consultations per physician per working day to the WISN-predicted standard of 20\u0026ndash;22 consultations per physician per day \u0026mdash; reaches less than 45% of theoretical potential. This gap is not attributable to workforce shortage. Analysis of organizational factors identifies three primary determinants of this mobilization failure.\u003c/p\u003e\n\u003cp\u003eFirst, dual posting \u0026mdash; the simultaneous affiliation of physicians with both the public hospital and private practice \u0026mdash; systematically reduces the time available for public facility service delivery. Conservative estimates suggest that dual posting reduces effective physician availability by 30 to 45% of contractual working time [11,12]. Second, administrative burden \u0026mdash; including patient record management, reporting requirements, and facility administration \u0026mdash; absorbs a substantial fraction of clinical time that WISN standards allocate to direct patient care. Third, the absence of structured consultation scheduling generates irregular patient flows that prevent physicians from maintaining consistent daily productivity levels.\u003c/p\u003e\n\u003cp\u003eStress testing further reveals that this organizational inefficiency generates a paradoxical fragility: despite apparent workforce adequacy, the hospital system reaches saturation at a demand increase of only 20 to 30% above current activity levels. Key bottleneck services \u0026mdash; outpatient consultations, emergency care, pharmacy, and laboratory \u0026mdash; are the first to saturate, constraining the entire care pathway. Bed occupancy rates, by contrast, remain below 40%, confirming that physical infrastructure is underutilized while human resource mobilization represents the binding constraint.\u003c/p\u003e\n\u003cp\u003eThese findings establish a first counter-intuitive result: \u003cstrong\u003eat Kati District Hospital, the health system\u0026apos;s productive constraint is organizational, not volumetric. Recruiting additional staff without addressing mobilization efficiency would not improve system performance.\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eResult 2 \u0026mdash; Identical outcomes, opposite root causes: Sikasso Region versus Koulikoro Health District\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eHCIS-SIM capacity monitoring reveals that Sikasso Region and Koulikoro Health District present statistically comparable maternal mortality ratios \u0026mdash; estimated at 500\u0026ndash;600 and 500\u0026ndash;550 per 100,000 live births respectively [3,5] \u0026mdash; despite representing structurally opposite health system profiles. This convergence of outcomes with divergence of causes constitutes the most analytically significant finding of this study.\u003c/p\u003e\n\u003cp\u003eSikasso Region presents a volume pressure profile. With a population of approximately 3.9 million inhabitants, the region faces a quantitative shortage of qualified obstetric personnel \u0026mdash; an estimated deficit of 200 to 300 midwives and obstetric nurses relative to WISN-derived requirements. Only 13 Emergency Obstetric and Newborn Care comprehensive facilities (CEmONC) serve the entire region, generating a structural bottleneck that prevents access to emergency obstetric care for a large fraction of the population requiring it. The caesarean section rate of 2\u0026ndash;3% \u0026mdash; against the WHO minimum threshold of 5% \u0026mdash; confirms that access to life-saving obstetric interventions is critically constrained [3,7]. Assisted delivery coverage of 60\u0026ndash;70% falls 10 to 20 percentage points below the PRODESS III target of \u0026ge;80% [6]. WISN projections indicate that achieving national maternal health objectives in Sikasso would require an additional 36,000 to 54,000 assisted deliveries per year, implying a tripling of current CEmONC capacity and an investment estimated at 1 to 1.5 billion FCFA annually.\u003c/p\u003e\n\u003cp\u003eKoulikoro Health District presents a distributional inequity profile. The district\u0026apos;s health workforce \u0026mdash; estimated at approximately 127 agents across 24 facilities \u0026mdash; is distributed in near-inverse proportion to population need. Urban community health centers (CSCom), serving approximately 20% of the district population, maintain a workforce density of 5.0 per 10,000 inhabitants, while rural CSCom serving 80% of the population operate at 2.6 per 10,000 \u0026mdash; a ratio almost twice less favorable [5,19]. An estimated 6 to 7 rural CSCom among the 20 in the district operate without a permanently assigned qualified midwife or obstetric nurse, meaning that deliveries in these facilities are attended by generalist agents or traditional birth attendants. The curative care utilization rate of 0.07 new contacts per inhabitant per year \u0026mdash; seven to fourteen times below the WHO reference range of 0.5 to 1.0 \u0026mdash; reflects both geographic access barriers and the limited availability of qualified personnel in rural areas [7]. Stress testing confirms that the district\u0026apos;s single referral health center (CSR\u0026eacute;f) reaches saturation with an additional 250 to 280 obstetric referrals per year, revealing the absence of a functional second referral level within the district as a critical system vulnerability.\u003c/p\u003e\n\u003cp\u003eThese findings establish a second counter-intuitive result: \u003cstrong\u003eSikasso and Koulikoro present identical maternal mortality ratios produced by structurally opposite mechanisms. A uniform national policy response cannot simultaneously address a volume deficit and a distributional inequity. Territory-differentiated planning is not a refinement \u0026mdash; it is a necessity.\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eResult 3 \u0026mdash; System saturation precedes objective attainment: stress test findings across sites\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eStress testing applied consistently across all three application sites reveals a systemic pattern with direct implications for national health planning: in each case, the health system reaches critical saturation thresholds before the activity levels required to achieve PRODESS III objectives are approached.\u003c/p\u003e\n\u003cp\u003eAt Kati District Hospital, a 20 to 30% increase in outpatient consultation volume \u0026mdash; well below the level implied by universal health coverage expansion targets \u0026mdash; generates saturation in four key service areas simultaneously: outpatient consultations, emergency services, pharmacy dispensing, and laboratory processing. Paradoxically, inpatient bed capacity remains substantially underutilized at this saturation point, confirming that the binding constraint is human resource mobilization rather than physical infrastructure.\u003c/p\u003e\n\u003cp\u003eIn Sikasso Region, achieving the assisted delivery coverage target of \u0026ge;80% under PRODESS III would require absorbing an additional 36,000 to 54,000 deliveries per year \u0026mdash; a 40 to 60% increase above current volumes. Stress testing demonstrates that the existing CEmONC network reaches saturation well below this threshold, generating a critical policy implication: without prior reinforcement of emergency obstetric care capacity, increasing assisted delivery coverage through demand-side interventions alone would concentrate complications at already-saturated facilities, potentially worsening maternal outcomes rather than improving them.\u003c/p\u003e\n\u003cp\u003eIn Koulikoro Health District, the redistribution of obstetric care from urban to rural facilities \u0026mdash; required to achieve equitable coverage \u0026mdash; would impose a 2 to 4-fold increase in the obstetric workload of rural CSCom agents currently operating without qualified supervision. Stress testing of the district referral pathway confirms that the CSR\u0026eacute;f reaches saturation with modest increases in referral volume, identifying the absence of a functional intermediate referral level as the district\u0026apos;s most critical structural vulnerability.\u003c/p\u003e\n\u003cp\u003eThese findings establish a third counter-intuitive result: \u003cstrong\u003enational health objectives in Mali have been defined without prior testing of the system\u0026apos;s capacity to achieve them. In all three sites, the health system saturates before objectives are reached \u0026mdash; making capacity reinforcement a prerequisite for, rather than a consequence of, policy implementation.\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eSummary of key findings\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Summary of HCIS-SIM findings across three application sites of the Malian health pyramid.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDimension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eKati Hospital\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSikasso Region\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eKoulikoro District\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eWISN workforce adequacy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAdequate (ratio \u0026gt; 1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDeficit (\u0026minus;200 to \u0026minus;300 SF/IO)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePartial deficit (rural zones)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEffective productivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 45% of potential\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60\u0026ndash;70% assisted deliveries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.07 NC/inhab/year\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePrimary constraint\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMobilization failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eVolume deficit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDistributional inequity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSaturation threshold\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e+20\u0026ndash;30% activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e+40\u0026ndash;60% deliveries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e+250\u0026ndash;280 referrals\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMMR / 100,000 LB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eN/A (referral hospital)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e500\u0026ndash;600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e500\u0026ndash;550\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePRODESS target feasibility\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eConditional on reorganization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRequires prior CEmONC reinforcement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRequires redistribution + referral strengthening\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003ch2\u003e\u003cstrong\u003ePrincipal findings in context\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThis study presents three findings that collectively challenge prevailing assumptions in health workforce planning for resource-constrained settings. First, workforce availability does not automatically translate into workforce productivity — a distinction that normative planning frameworks systematically obscure by conflating staffing ratios with service delivery capacity. Second, identical health outcomes can be produced by structurally opposite root causes, rendering uniform national policy responses analytically indefensible. Third, national health objectives in Mali have been defined without prior testing of system absorption capacity, creating a structural misalignment between policy ambition and operational feasibility that HCIS-SIM is specifically designed to reveal and address.\u003c/p\u003e\n\u003cp\u003eThese findings are not isolated observations from a single facility or district. They emerge consistently across three application sites representing the full vertical span of the Malian health pyramid — from a tertiary hospital to a regional health system to a primary care district — using official national data sources. Their convergence suggests that the patterns identified are systemic rather than site-specific, and that their implications extend beyond Mali to comparable health systems across sub-Saharan Africa.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eThe mobilization gap — a neglected dimension of workforce analysis\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe finding that effective medical productivity at Kati District Hospital reaches less than 45% of WISN-predicted potential is, to our knowledge, among the first documented measurements of the gap between theoretical workforce potential and observed mobilization efficiency in a Malian hospital context. It resonates with a growing body of evidence suggesting that workforce waste — defined as the gap between available capacity and actual productive output — is as significant a contributor to health system underperformance in Africa as workforce shortage itself [12,13].\u003c/p\u003e\n\u003cp\u003eDovlo's foundational work on workforce wastage in African countries identified absenteeism, dual practice, and task misallocation as primary mechanisms through which available health worker capacity fails to translate into service delivery [12]. Our findings extend this analysis by quantifying the mobilization gap precisely — less than 45% efficiency at Kati — and by identifying its organizational determinants: dual posting, administrative burden, and unstructured consultation scheduling. This level of analytical precision is not achievable through normative approaches that compare staff-to-population ratios without examining how those staff actually deploy their working time.\u003c/p\u003e\n\u003cp\u003eThe policy implication is direct and actionable. At Kati District Hospital, recruiting additional physicians without addressing the organizational determinants of mobilization failure would not improve service delivery. The return on investment of organizational reform — structured consultation scheduling, reduced administrative burden, formalized dual posting management — substantially exceeds that of additional recruitment in the short to medium term. This finding inverts the conventional policy response to workforce inadequacy and illustrates the central argument of HCIS-SIM: in resource-constrained settings, optimizing existing capacity frequently offers greater impact than expanding nominal capacity [9,11].\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eTerritory-differentiated planning — from principle to necessity\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe convergence of maternal mortality ratios in Sikasso and Koulikoro — 500–600 and 500–550 per 100,000 live births respectively — despite structurally opposite health system profiles constitutes the most analytically powerful finding of this study. It demonstrates empirically what planning theory has long argued conceptually: that health outcome indicators are insufficient proxies for the underlying determinants of system performance, and that identical outcomes can mask fundamentally different causal pathways requiring fundamentally different policy responses [1,2].\u003c/p\u003e\n\u003cp\u003eSikasso's volume pressure profile — quantitative workforce deficit, insufficient CEmONC network, demand exceeding capacity — calls for a supply expansion response: recruitment of 200 to 300 additional midwives and obstetric nurses, tripling of CEmONC functional capacity, and progressive demand stimulation constrained by supply reinforcement milestones. Koulikoro's distributional inequity profile — adequate aggregate workforce but inverted distribution, rural facilities operating without qualified obstetric staff — calls for a redistribution response: priority redeployment of qualified staff to high-volume rural CSCom, retention mechanisms for rural postings, and strengthening of the referral pathway.\u003c/p\u003e\n\u003cp\u003eThese two responses are not merely different in degree — they are different in kind. Applying Sikasso's supply expansion logic to Koulikoro would leave the distributional inequity unaddressed. Applying Koulikoro's redistribution logic to Sikasso would fail to close the absolute workforce gap. A uniform national policy that averages these responses would underperform in both territories. This finding has direct implications for the design of PRODESS IV and for the allocation of international cooperation resources — including those of bilateral partners currently engaged in maternal health support in Mali [6,17].\u003c/p\u003e\n\u003cp\u003eThe methodological contribution here extends beyond the Malian context. Asamani and colleagues have demonstrated the value of needs-based simulation for projecting aggregate workforce requirements [1,2]. HCIS-SIM advances this contribution by introducing the territory-differentiation dimension — showing not only that a national system is undersupplied, but that undersupply manifests differently across territories and requires differentiated responses. This extension is particularly relevant for large, geographically diverse countries where health system performance varies substantially across administrative units.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eSystem capacity testing as a prerequisite for policy design\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe stress test findings across all three application sites reveal a systemic pattern of immediate relevance to national health planning: the Malian health system reaches critical saturation thresholds before the activity levels required by PRODESS III objectives are approached. This finding reframes a fundamental question in health policy design — not \"what objectives should we set?\" but \"can our system reach those objectives without prior capacity reinforcement?\"\u003c/p\u003e\n\u003cp\u003eThis reframing is the core contribution of HCIS-SIM to the field. The existing literature on health workforce planning provides sophisticated tools for projecting workforce needs [1,2,7,8] and for documenting the gap between available and required resources [10,23]. What it does not provide — for low-income African health systems — is a methodology for testing whether the health system as currently organized can absorb the demand induced by national health policy targets before those targets are adopted. HCIS-SIM fills this gap.\u003c/p\u003e\n\u003cp\u003eThe practical implication is consequential. In Sikasso, demand-side interventions to increase facility deliveries — community mobilization, conditional cash transfers, maternity waiting homes — without prior reinforcement of CEmONC capacity would concentrate obstetric complications at already-saturated facilities. The result could be an increase in institutional delivery rates accompanied by a worsening of institutional maternal mortality — an outcome that has been documented in comparable settings where demand-side investments outpaced supply-side capacity [3,16]. HCIS-SIM provides the analytical tool to anticipate and prevent this scenario before it occurs.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003ePositioning HCIS-SIM within the health workforce planning literature\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eHCIS-SIM occupies a distinct position within the health workforce planning literature that merits explicit articulation. It is not a workforce projection model — it does not forecast future supply or demand under baseline assumptions. It is not a normative gap analysis — it does not measure deviation from WHO standards. It is not an implementation science framework — it does not evaluate the fidelity of intervention delivery.\u003c/p\u003e\n\u003cp\u003eHCIS-SIM is a capacity testing framework — a tool designed to answer a specific and previously underserved analytical question: given the health system as it currently exists, what can it realistically produce, and what will constrain its ability to achieve defined objectives? This question sits at the intersection of health workforce analysis, health systems science, and operational research, drawing from each without being fully contained within any.\u003c/p\u003e\n\u003cp\u003eThe closest methodological antecedents are the needs-based simulation models developed by Asamani and colleagues [1,2] and the dynamic multi-professional simulation model proposed by MacKenzie and colleagues for high-income settings [18]. HCIS-SIM extends these contributions in three directions: it introduces the productivity adjustment coefficient to capture mobilization efficiency, it adapts stress testing from financial sector methodology to health system analysis, and it generates territory-differentiated trajectories rather than aggregate national projections. These extensions are specifically calibrated to the data environment and decision-making context of low-income African health systems, where planning tools developed for high-income settings frequently fail to produce actionable outputs [9,14].\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eSeveral limitations of this study warrant explicit acknowledgment. First, activity volume data for Koulikoro Health District were partially estimated through proportional extrapolation from regional aggregates published in the SNISS 2020, pending access to disaggregated district-level data available in the DHIS2 system at institutional access level. While this approach introduces estimation uncertainty — acknowledged at ±15% for key indicators — it is consistent with the data environment in which health district managers in Mali must plan, and the framework's conclusions are robust to this range of uncertainty.\u003c/p\u003e\n\u003cp\u003eSecond, WISN service time standards were calibrated against WHO reference values with contextual adjustment, rather than derived from primary time-motion studies conducted in Malian facilities. Facility-level time-motion studies would strengthen the precision of productivity gap estimates and are recommended for future applications of HCIS-SIM.\u003c/p\u003e\n\u003cp\u003eThird, the stress test scenarios model demand increases as uniform across service categories, whereas real demand growth in response to policy interventions is typically uneven — concentrated in specific services such as antenatal care, facility delivery, and emergency obstetrics. Disaggregated stress testing by service category would provide finer-grained saturation analysis and is a planned extension of the framework.\u003c/p\u003e\n\u003cp\u003eFourth, as a framework paper applied across three case study sites, this study does not provide statistical power to generalize findings beyond the application sites. The findings are presented as illustrative demonstrations of framework capability rather than as definitive estimates of system-wide parameters. Larger-scale application across all ten health districts of Koulikoro region and all seven districts of Sikasso region would provide the empirical breadth necessary for policy-grade generalization.\u003c/p\u003e\n\u003cp\u003eThese limitations notwithstanding, the framework's findings are grounded in official national data sources, internally consistent across three application sites, and directionally robust under reasonable sensitivity assumptions. They provide a sufficient empirical foundation for the methodological and policy conclusions presented.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eHealth systems in resource-constrained settings do not fail solely because they lack resources. They fail because available resources are insufficiently mobilized, inequitably distributed, and deployed without prior testing of the system's capacity to absorb the demand that national health policies generate. HCIS-SIM was developed in direct response to this observation — not as a critique of existing planning frameworks, but as a complement that addresses the analytical dimension they systematically leave unresolved.\u003c/p\u003e\n\u003cp\u003eThree conclusions emerge from this study with direct relevance for health policymakers, planners, and international cooperation partners operating in comparable settings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFirst, measuring workforce availability is not equivalent to measuring workforce productivity.\u0026nbsp;\u003c/strong\u003eThe 55% mobilization gap documented at Kati District Hospital demonstrates that normative staffing adequacy can coexist with significant service delivery underperformance. Health workforce planning frameworks that do not incorporate a productivity adjustment component will systematically overestimate system capacity and underestimate the organizational determinants of performance gaps. HCIS-SIM introduces this component as a standard analytical element applicable across facility types and health system levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSecond, identical health outcomes require territory-differentiated policy responses.\u0026nbsp;\u003c/strong\u003eThe convergence of maternal mortality ratios in Sikasso and Koulikoro — produced by structurally opposite mechanisms — demonstrates that national health indicators are insufficient guides for policy design. Planning frameworks that aggregate performance data to the national level without disaggregating causal mechanisms will generate policy responses that are simultaneously over-prescribed for some territories and under-prescribed for others. HCIS-SIM provides the analytical infrastructure for territory-differentiated planning at scale.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThird, system capacity testing must precede, not follow, objective-setting.\u0026nbsp;\u003c/strong\u003eThe consistent finding that the Malian health system reaches saturation before national health objectives are approached establishes capacity testing as a prerequisite for credible health policy design. Objectives defined without prior capacity testing are not ambitious — they are unfeasible. HCIS-SIM offers a replicable, data-efficient methodology for conducting this test using routinely collected administrative health data.\u003c/p\u003e\n\u003cp\u003eTaken together, these conclusions support a reorientation of health workforce planning in resource-constrained settings — from documenting the gap between what exists and what international norms prescribe, toward optimizing what exists and building realistic trajectories toward what is achievable. This reorientation does not lower ambition. It grounds ambition in evidence, and translates evidence into action.\u003c/p\u003e\n\u003cp\u003eHCIS-SIM is currently applied in Mali across three levels of the national health pyramid. Its extension to additional districts, regions, and comparable health systems across West Africa represents the natural next step — one that the authors invite collaborators, policymakers, and research partners to join.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAsamani JA, Christmals CD, Reitsma GM. Advancing the Population Needs-Based Health Workforce Planning Methodology: A Simulation Tool for Country Application. Int J Environ Res Public Health. 2021;18(4):2113. doi:10.3390/ijerph18042113\u003c/li\u003e\n\u003cli\u003eAsamani JA, Christmals CD, Reitsma GM. The needs-based health workforce planning method: a systematic scoping review of analytical applications. Health Policy Plan. 2021;36(8):1325-1343. doi:10.1093/heapol/czab022\u003c/li\u003e\n\u003cli\u003eWorld Health Organization, UNICEF, UNFPA, World Bank Group, UNDESA/Population Division. Trends in maternal mortality 2000 to 2020. Geneva: WHO; 2023.\u003c/li\u003e\n\u003cli\u003eIntraHealth International. Four Steps toward a Stronger African Health Workforce in 2030 and Beyond. Washington DC: IntraHealth; 2023.\u003c/li\u003e\n\u003cli\u003eMinist\u0026egrave;re de la Sant\u0026eacute; et du D\u0026eacute;veloppement Social du Mali. Annuaire du Syst\u0026egrave;me National d'Information Sanitaire et Sociale (SNISS) 2020. Bamako: CPS/MSDS; 2021.\u003c/li\u003e\n\u003cli\u003eR\u0026eacute;publique du Mali. Programme de D\u0026eacute;veloppement Sanitaire et Social (PRODESS III) 2014\u0026ndash;2023. Bamako: MSDS; 2014.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Workload Indicators of Staffing Need (WISN): User's Manual. Geneva: WHO; 2010.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Workload Indicators of Staffing Need (WISN): Selected Country Implementation Experiences. Hum Resour Health Obs Ser. 2016;15.\u003c/li\u003e\n\u003cli\u003eNyoni J, Christmals CD, Asamani JA, et al. The process of developing health workforce strategic plans in Africa: a document analysis. BMJ Glob Health. 2022;7(Suppl 1):e008418. doi:10.1136/bmjgh-2021-008418\u003c/li\u003e\n\u003cli\u003eAsamani JA, Ismaila H, Plange A, et al. The cost of health workforce gaps and inequitable distribution in the Ghana Health Service. Hum Resour Health. 2021;19(1):43. doi:10.1186/s12960-021-00590-3\u003c/li\u003e\n\u003cli\u003eMcKinsey Global Institute. Overcoming sub-Saharan Africa's health workforce paradox. McKinsey \u0026amp; Company; November 2024.\u003c/li\u003e\n\u003cli\u003eDovlo D. Wastage in the health workforce: some perspectives from African countries. Hum Resour Health. 2005;3:6. doi:10.1186/1478-4491-3-6\u003c/li\u003e\n\u003cli\u003eOkoroafor SC, Ongom M, Mohammed B, et al. The health workforce status in the WHO African Region. BMJ Glob Health. 2022;7(Suppl 1):e008317. doi:10.1136/bmjgh-2021-008317\u003c/li\u003e\n\u003cli\u003eApproaches and Components of Health Workforce Planning Models: A Systematic Review. Int J Environ Res Public Health. 2023. doi:10.3390/ijerph20136209\u003c/li\u003e\n\u003cli\u003eBasel Committee on Banking Supervision. Principles for sound stress testing practices and supervision. Basel: Bank for International Settlements; 2009.\u003c/li\u003e\n\u003cli\u003eDussault G, Franceschini MC. Not enough there, too many here: understanding geographical imbalances in the distribution of the health workforce. Hum Resour Health. 2006;4:12. doi:10.1186/1478-4491-4-12\u003c/li\u003e\n\u003cli\u003eR\u0026eacute;publique du Mali. Plan Strat\u0026eacute;gique de lutte contre les Maladies Tropicales N\u0026eacute;glig\u0026eacute;es (MTN) 2022\u0026ndash;2026. Bamako: MSDS; 2022.\u003c/li\u003e\n\u003cli\u003eMacKenzie A, Tomblin Murphy G, Audas R. A dynamic, multi-professional, needs-based simulation model to inform human resources for health planning. Hum Resour Health. 2019;17:42. doi:10.1186/s12960-019-0376-5\u003c/li\u003e\n\u003cli\u003eSyst\u0026egrave;me Local d'Information Sanitaire (SLIS). District Sanitaire de Koulikoro. Donn\u0026eacute;es consolid\u0026eacute;es 2020\u0026ndash;2021. Bamako: MSDS; 2021.\u003c/li\u003e\n\u003cli\u003eWHO. Global Strategy on Human Resources for Health: Workforce 2030. Geneva: WHO; 2016.\u003c/li\u003e\n\u003cli\u003eGoma FM, Murphy GT, Libetwa M, et al. Pilot-testing service-based planning for health care in rural Zambia. BMC Health Serv Res. 2014;14(Suppl 1):S7. doi:10.1186/1472-6963-14-S1-S7\u003c/li\u003e\n\u003cli\u003eTomblin Murphy G, Birch S, MacKenzie A. Simulating future supply of and requirements for human resources for health in high-income OECD countries. Hum Resour Health. 2016;14:1\u0026ndash;18.\u003c/li\u003e\n\u003cli\u003eAsamani JA, Amertil NP, Ismaila H, et al. The imperative of evidence-based health workforce planning and implementation: lessons from nurses and midwives unemployment crisis in Ghana. Hum Resour Health. 2020;18:16. doi:10.1186/s12960-020-0462-5\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Health workforce planning, Capacity-anchored planning, WISN, Health system stress testing, Maternal mortality, Mali, Sub-Saharan Africa, Decision-support framework","lastPublishedDoi":"10.21203/rs.3.rs-9451846/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9451846/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHealth workforce planning in sub-Saharan Africa has long been dominated by normative approaches that measure the gap between existing resources and WHO standards. While these approaches accurately document insufficiency, they offer limited guidance for decision-makers operating in contexts where internationally recommended staffing norms remain structurally unattainable in the short to medium term. Mali, like many low-income countries, faces a persistent disconnect between national health objectives — including a maternal mortality ratio (MMR) target of ≤146 per 100,000 live births under PRODESS III — and the actual capacity of its health system to absorb the demand induced by these policies. This paper presents HCIS-SIM (Health Capacity Information System — Simulation \u0026amp; Monitoring), a decision-support framework designed to reorient health workforce planning from documenting the impossible toward optimizing the possible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHCIS-SIM integrates three complementary analytical components: (i) Workload Indicators of Staffing Need (WISN), adapted from the WHO methodology, to quantify workforce requirements based on actual workload; (ii) simulated stress testing, adapted from financial and industrial sector approaches, to identify system saturation thresholds before they occur; and (iii) real-time capacity monitoring to measure the gap between theoretical workforce potential and observed productivity, and to generate territory-specific planning trajectories. The framework was applied across three levels of the Malian health pyramid — Kati District Hospital (tertiary level), Sikasso Region (regional level), and Koulikoro Health District (district level) — using official national data sources: the 2020 National Health Information Yearbook (SNISS, Mali Ministry of Health) and the Koulikoro District Health Information System (SLIS).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThree counter-intuitive findings emerged. First, health workforce resources at Kati District Hospital are adequate in volume according to WISN standards, yet effective productivity reaches less than 45% of theoretical potential — identifying a workforce mobilization failure rather than a workforce shortage. Second, Sikasso Region and Koulikoro Health District present identical MMR levels (500–600 per 100,000 live births) but structurally opposite root causes — volume pressure in Sikasso versus distributional inequity in Koulikoro — demonstrating that uniform national policies cannot simultaneously address both challenges. Third, stress testing reveals that the Malian health system reaches saturation at a mere 20% activity increase, meaning that national health objectives cannot be achieved without prior system capacity reinforcement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHCIS-SIM introduces a capacity-anchored approach to health workforce planning that prioritizes optimizing existing resources over documenting normative gaps. By combining workload analysis, stress testing and capacity monitoring, the framework produces actionable, territory-differentiated planning trajectories toward achievable health objectives. These findings have direct implications for health policymakers in resource-constrained settings seeking evidence-based alternatives to normative planning frameworks that systematically generate unachievable targets. HCIS-SIM offers a replicable model for translating available capacity into measurable health system performance improvement.\u003c/p\u003e","manuscriptTitle":"When workforce availability does not mean workforce productivity: a capacity-anchored decision-support framework for health system planning in resource-constrained settings — evidence from Mali","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-29 06:10:19","doi":"10.21203/rs.3.rs-9451846/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bb044133-9844-4bab-b5f4-a3ccfab7bf7b","owner":[],"postedDate":"April 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-29T06:10:19+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-29 06:10:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9451846","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9451846","identity":"rs-9451846","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}