Conceptual Framework and Indicator Justification

Current WHO metrics measure biomedical engineer density using a population-based indicator (ρBE × 10,000), calculated as the ratio of certified biomedical engineers (BE) to the population (P) in a specific geographic area and time, multiplied by 10,000 [9]:

(1)

The WHO justifies this indicator by stating that global health systems need biomedical engineering professionals and technicians and they should be considered part of the health workforce [2]. This approach follows methodologies established for other healthcare professionals such as physicians and nurses, who maintain direct contact with individuals requiring medical care.

However, a fundamental distinction exists in the professional intervention object between these professions. While physicians and nurses provide direct diagnosis, care, treatment, and rehabilitation to patients, biomedical engineers intervention focuses on medical devices (MDs) that healthcare professionals use to perform clinical activities. Without these devices, comprehensive healthcare delivery would not be possible

This disparity becomes particularly evident when considering care level distribution. Approximately 90% of essential actions for universal health coverage can be performed in primary healthcare units [5], where the focus is on prevention, treatment, and management of conditions not requiring hospitalization. Consequently, diagnostic, treatment, and rehabilitation medical devices are more prevalent in higher care levels where hospitalization and management of complex conditions, such as chronic metabolic diseases, occur in specialty hospitals.

Given that biomedical engineers’ workload is primarily determined by the volume and complexity of medical equipment rather than population size, a population-based indicator fails to accurately reflect workforce adequacy relative to medical technology infrastructure. Therefore, a novel approach is needed that correctly relates healthcare professionals to their professional intervention object.

Hospital beds as proxy for medical device density

The challenge in directly measuring medical device density lies in the large number and variety of MDs across healthcare systems. To address this, we propose using hospital beds (HB) as a standardized proxy indicator for medical device infrastructure.

A hospital bed is defined as a bed installed in hospitals and rehabilitation centers to accommodate patients during observation, diagnosis, care, or treatment [10]. These beds are used to measure indicators such as hospital discharges, occupancy, and length of stay, covering beds for the treatment of both acute and chronic diseases. Hospital beds are considered the functional units of specialty medical hospitals and represent areas where medical device concentration is highest.

The hospital beds per 1,000 inhabitants (HB × 1,000) indicator is well-established, continuously updated, and widely reported by health systems at national, regional, and city levels through organizations such as the World Bank and WHO, making data readily accessible for calculations [5,10]. The indicator is calculated as:

(2)

where HB represents the number of hospital beds in a health system (HS) at a specific time and location, and P is the population in that period.

Mathematical development of the ρBEhb indicator

The proposed biomedical engineer density per hospital beds (ρBEhb) indicator establishes a direct relationship between biomedical engineering workforce and hospital infrastructure:

(3)

where BE represents the number of biomedical engineers and HB represents hospital beds in a health system for a specified time and geographic location.

To calculate this indicator using available normalized data, a mathematical derivation is required. The WHO reports biomedical engineer density per 10,000 inhabitants (BE × 10,000), expressed as:

(4)

For comparability with the HB × 1,000 indicator, the BE × 10,000 value must be adjusted by dividing by 10 to obtain biomedical engineers per 1,000 inhabitants:

(5)

This adjustment is unnecessary if both indicators originally use the same multiplicative scale. The complete calculation of ρBEhb using normalized data is developed as follows:

(6)

The proposed mathematical formulation demonstrates that when relating the normalized values, the population variable (P) becomes non-relevant for calculating the indicator, focusing solely on biomedical engineers and their professional intervention object (hospital beds as proxy for medical devices).

Methodological reference framework

The development of the ρBEhb indicator follows the methodological guidelines established by the Pan American Health Organization (PAHO) and WHO for health indicator construction, as outlined in the PAHO Strategic Plan 2020–2025 and the Health Indicators conceptual framework [11]. This approach ensures:

• Conceptual validity: the indicator measures what it intends to measure
• Reliability: consistency in measurement across time and contexts
• Comparability: standardized calculation allowing cross-system comparisons
• Data accessibility: utilization of routinely collected health system data
• Policy relevance: actionable information for workforce planning and resource allocation

Study population and inclusion criteria

This study focuses specifically on biomedical engineers (BEs) providing professional services within hospital settings. Following WHO classification [2], we include BEs working under two organizational models:

• Internal staff model: Biomedical engineers employed directly by the hospital as part of the institutional workforce
• Mixed staff model: Biomedical engineers working through combined internal employment and external service contracts

The study excludes biomedical engineers working exclusively in:

• Research and development institutions
• Medical device sales and procurement companies
• Regulatory agencies
• Educational institutions
• Exclusive external service provision without hospital affiliation

This delimitation ensures the indicator reflects workforce capacity directly involved in medical device management, maintenance, and quality assurance within healthcare delivery settings.

Data sources and collection

1. a). Biomedical Engineer Data

Data on the number of biomedical engineers was obtained from two primary sources:

• WHO Global Health Workforce Statistics and the Medical Devices unit reports [2,9] National health system employment records and workforce databases
• For the Mexican case study, data were extracted from:
• Ministry of Health (Secretaría de Salud) official employment statistics
• National Health System annual reports (2017–2022) [12]
• National Institute of Statistics and Geography [13]
• Mexican Social Security Institute (IMSS) workforce registries [14]
• Institute for Social Security and Services for State Workers (ISSSTE) personnel records [15]

1. b). Hospital Bed Data

Hospital bed data were obtained from:

• World Bank Health Nutrition and Population Statistics database [10]
• OECD Health Statistics [5,6]
• WHO Global Health Observatory data repository [2]
• National health system infrastructure reports [16–18]
• For Mexico, specific sources included:
• Ministry of Health Statistical Yearbooks (Anuarios Estadísticos)
• National Health Information System
• Hospital infrastructure registries by state and institution

1. c). Temporal and Geographic Scope

• Study period: 2017–2022 (six-year longitudinal analysis)
• Geographic scope: Mexican healthcare system (national level)
• Data frequency: Annual measurements
• Reference date: December 31st of each year for workforce counts; annual averages for hospital bed availability

1. d). Indicator Calculation and Analysis
   1. i). Annual ρBEhb Calculation

For each year from 2017 to 2022, the ρBEhb indicator was calculated following equation (6):

• Extraction of BE × 10,000 data from national workforce records
• Conversion to BE×1,000 by dividing by 10 (equation 5)
• Extraction of HB × 1,000 data from infrastructure databases
• Division of BE × 1,000 by HB × 1,000 to obtain ρBEhb

This analysis identified:

• Overall trend direction (positive or negative)
• Peak variations and inflection points
• Relationship with external events (e.g., SARS-CoV-2 pandemic)
• International Benchmarking

Mexican ρBEhb values were compared with available international reference data from:

• High-income OECD countries
• Latin American and Caribbean countries with comparable economic development
• WHO regional benchmarks

Comparisons considered both absolute ρBEhb values and population-adjusted metrics to contextualize Mexico's position relative to international standards.

Data quality and indicator robustness considerations

The reliability and validity of the ρBEhb indicator are contingent upon the quality of underlying health system data. Several factors influence indicator robustness: Comprehensive national legislation recognizing medical devices as essential healthcare infrastructure. Systematic and standardized data collection procedures for workforce and infrastructure metrics. Regular auditing and validation of reported data. Clear definitions and consistent application of inclusion criteria for biomedical engineers and hospital beds and centralized health information systems with interoperability across institutions.

Limiting factors affecting indicator reliability: Incomplete workforce registries, particularly for mixed staff models. Inconsistent reporting across different healthcare subsystems (public, social security, private). Variations in hospital bed definitions and counting methodologies. Geographic disparities in data collection rigor and Temporal delays in data reporting and validation.

For the Mexican case, data quality varies across institutions and regions. The federal Ministry of Health maintains the most comprehensive databases, while state-level and private sector data may have gaps. These limitations are acknowledged and discussed in the context of results interpretation.

In summary, Table 1 presents the technical specifications for the 𝝆BEhb developed for this indicator, based on information provided by PAHO/WHO in 2018.

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Table 1. Technical Specification for the Density of Biomedical Engineers per Hospital Beds (ρBEhb).

https://doi.org/10.1371/journal.pone.0350988.t001

This study utilized publicly available, aggregated health system data without individual identifiers. No human subjects were directly involved, and no ethical approval was required. All data sources are cited appropriately, and institutional data sharing agreements were respected.

Indicator scope and comparability across heterogeneous healthcare systems

The ρBEhb analysis reveals a critical paradox in the Mexican healthcare system. While Mexico's absolute number of biomedical engineers appears comparable to the United States, this masks a fundamental dual deficit: insufficient hospital beds and inadequate biomedical engineering workforce when adjusted for population size and international standards.

Mexico has approximately 1.0 hospital bed per 1,000 inhabitants, significantly below the OECD average of 4.3 beds per 1,000. To reach this benchmark, Mexico would need approximately 430,000 additional hospital beds. Each bed requires multiple medical devices and proportional biomedical engineering support for maintenance, calibration, and safety assurance. Current workforce levels are insufficient for both existing infrastructure and needed expansion.

The ρBEhb is a workforce density indicator relative to available infrastructure, analogous to established metrics such as physicians per bed or nurses per bed, none of which distinguish by unit type in their base formulation. Its value does not reside in precisely capturing the technological workload of each institution, but in offering a systemic metric that enables aggregate comparisons across health systems.

Nevertheless, the authors acknowledge that hospital beds are not a homogeneous unit: a bed in an intensive care unit represents a substantially different biomedical engineering workload than a bed in a general ward. Taking this into account, the authors propose as future work the development of an extended version of the indicator incorporating a hospital complexity weighting factor — designated ρBEhb-c — operationalized through existing classification frameworks such as the IMSS Hospital Complexity Index, the PAHO three-level care classification, or the Case Mix Index used in U.S. hospital systems as a proxy for technological intensity.

Public policy strategies for synchronized infrastructure and workforce growth

The ρBEhb indicator serves three essential functions: Track workforce adequacy trends within healthcare systems over time. Compare Mexico's capacity against countries with recognized healthcare quality and guide decisions on biomedical engineering education, hiring, and resource allocation

The analysis from 2017 to 2022 showed generally positive trends, with a notable 14.41% increase from 2020 to 2021 during the COVID-19 pandemic. However, this reactive response highlights the need for proactive rather than crisis-driven workforce planning.

The gap identified relative to the OECD average requires a public policy approach operating along two simultaneous and coordinated axes. First, at the normative level, the authors propose that Mexican health authorities adopt the ρBEhb as an official planning indicator within the National Health System, linking it directly to hospital infrastructure investment plans. This would ensure that every hospital construction or expansion project carries an explicit biomedical engineering staffing projection, preventing the historical decoupling between available beds and specialized technical personnel.

Second, at the regional level, the authors recommend designing biomedical engineering academic programs and placement positions directed toward states with the greatest ρBEhb deficits, articulating universities with local health systems to ensure graduates are absorbed where need is most critical.

Furthermore, given that Mexico's health system encompasses multiple coexisting subsystems — IMSS, ISSSTE, SEDENA, PEMEX, and IMSS-Bienestar — the ρBEhb can be disaggregated by institution within each state, identifying not only where deficits exist but in which subsystem they are most critical. This is particularly relevant because hiring strategies and budgetary mechanisms differ substantially across institutions, requiring tailored rather than uniform intervention approaches.

State-level ρBEhb analysis should be conducted to identify geographic disparities and guide targeted federal investment in both infrastructure and workforce deployment, ensuring regional equity in healthcare capacity. Implementation of comprehensive tracking systems for medical devices, biomedical engineering workforce distribution, and equipment performance across all healthcare subsystems will provide the data infrastructure necessary for evidence-based decision-making.

These investments represent economic efficiency, as adequate biomedical engineering capacity reduces medical device downtime, prevents premature equipment failure, and improves patient safety—estimated at just 0.5–1% of total health expenditure while yielding substantial returns in service quality and system performance.

Impact and significance

For Mexico, adopting the ρBEhb indicator represents a paradigm shift from abstract population-based metrics to functional capacity assessment. This approach reveals that Mexico's healthcare challenge is not merely workforce shortage but systemic infrastructure underdevelopment requiring simultaneous investment in hospital beds and biomedical engineering professionals.

The indicator's value extends beyond Mexico to other Latin American and middle-income countries facing similar structural challenges. Traditional metrics may suggest adequate workforce levels while actual functional deficits compromise medical technology safety and healthcare quality.

Study limitations

Data quality varies across Mexican healthcare institutions and subsystems. The fragmentation of Mexico's health system — with subsystems including IMSS, ISSSTE, SEDENA, PEMEX, and IMSS-Bienestar operating independent information platforms — generates inevitable asymmetries in data quality and granularity. This is not exclusive to Mexico: it is a common pattern in health systems of middle-income countries, reinforcing the need to propose generalizable solutions.

To improve biomedical engineering and hospital bed data collection and standardization in Mexico and similar contexts, the authors propose two concrete mechanisms. First, the creation of a National Registry of Biomedical Engineers in Clinical Functions, updated annually and linked to the hospital and medical unit registry, would make the relationship between biomedical professionals and institutions traceable and auditable.

Second, the Mexican Secretariat of Health should incorporate differential budget classifications enabling identification of biomedical engineers by contractual modality — permanent staff, fee-for-service, service contracts, mixed schemes, or research positions. Currently, the absence of this distinction requires indirect estimations that introduce uncertainty into the indicator's numerator.National-level aggregates obscure regional disparities requiring more granular analysis. Future research should establish optimal ρBEhb thresholds for different hospital complexity levels and correlate workforce ratios with patient outcomes and medical device safety metrics. The time series analyzed in this study (2017–2022) will be subject to update in subsequent work incorporating post-pandemic data (2023–2026), enabling evaluation of the sustainability of the positive trends identified.