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Data and measurement of HRH availability, accessibility, acceptability and quality

TEHCNICAL WORKING GROUP (TWG) #3

- DRAFT FOR CONSULTATION - Not to be quoted or referenced without prior permission from the

authors. Not for wider distribution

This paper serves as a background report to inform the Global Strategy for Human Resources for Health. The development of this paper has been coordinated through a thematic working group (TWG), comprising of 2 co-chairs and a group of experts drawn from various Global Health Workforce Alliance (GHWA) constituencies, operating under the oversight of the GHWA Board working group. The views expressed in the paper, do not necessarily reflect the official position of GHWA. All reasonable precautions have been taken by the co-chairs to verify the information presented in the papers

TWG3: HRH data and measurement

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TABLE OF CONTENTS

1. Introduction ................................................................................................................. 3

2. Methods ...................................................................................................................... 3

3. Evidence ...................................................................................................................... 4 A) The necessity to focus on HRH data ................................................................................. 4 B) Emerging issues ................................................................................................................ 4 C) Current challenges and opportunities in health workforce data and measurement ....... 6 D) Inter-operability and alignment ....................................................................................... 7 E) Forward opportunities on HRH data and inter-operability .............................................. 8

4. Forward vision and recommendations ........................................................................ 10

5. Conclusion ................................................................................................................. 13

References ..................................................................................................................... 14

LIST OF BOXES

Box 1: HRH estimate and planning approaches ........................................................................ 6 Box 2: Kenya’s experience with the development of a health workforce information system 7 Box 3: The potential of Health Facility Assessment (HFA) Tools ............................................... 9 Box 4: eHealth, mHealth and inter-operability - Country experiences ................................... 10

ABBREVIATIONS AND ACRONYMS

AAAQ availability, accessibility, acceptability and quality

HFA health facility assessment

HIE health information exchange

HIS health information system

HRIS human resources information system

HRH human resources for health

ISCO International Standard Classification of Occupations

KHWIS Kenya health workforce information system

MDG Millennium Development Goals

MDS minimum data set

TWG thematic working group

UHC universal health coverage

WHA World Health Assembly


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Acknowledgements:

Our thanks to all the individual experts who provided support to the development and review of this paper: Edson Araujo, World Bank; Elsheikh Badr, WHO, EMRO; Michael Bzdak, Johnson & Johnson; Margaret Caffrey, Liverpool School of Tropical Medicine; Mario Dal Poz, University of the State of Rio de Janeiro; Marilyn De Luca, NYC; Gilles Dussault, Instituto de Higiene e Medicina Tropical; Matt Edwards, Centre for Workforce Intelligence, UK ; Alfredo Fort, IntraHealth International; Louise Holly, Save the Children UK; Sofia Lopes, ICS Integrare; Zina Maan Jarrah, Management Sciences for Health; Shiv Mathur, NIHFW; Bjorg Palsdottir, Training for Health Equity Network; Luisa Pettigrew, World Organisation of Family Doctors ; Estelle Quain, USAID; Jody Ranck, SBC Global Advisors; Jessica Rose, USAID; Edward Salsberg, George Washington University; Xenia Scheil-Adlung, ILO; Michael Schoenstein, OECD; Dykki Settle, IntraHealth International (+ colleagues) ; Amani Siyam, WHO; Neil Squires, DFID; Keith Waters, CDC; Alexandra Zuber, CDC.


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1. Introduction

In the next 15 years, countries from all income groups will experience challenges (1,2) that

will arise from and will shape the pathway towards the attainment of universal health

coverage (UHC)(3,4). UHC aims ‘to all people obtain the health services they need without

suffering financial hardship’(5). Financial and social protection are very important

components of this new health paradigm, as highlighted in the ILO 202 Recommendation (6)

and require strong commitment by countries.

Human resources for health (HRH) are recognised as central to achieve UHC, and essential to

respond to countries’ future challenges (7). A responsive and fit for purpose workforce

requires an increasing focus on the availability and accuracy of health workforce data that

enables workforce strategic planning and anticipation of future needs (3,8).

WHO, European Union, OECD, and other international bodies have long-recognised the

policy necessity and urgency of improving health workforce data and measurement.

Technological advances, connectedness and Internet, as well as the rise of new approaches

for health workforce futures(9), create opportunities for HRH data collection, gathering and

utilization.

This paper reviews the available evidence and outlines opportunities that would inform the

governance and effectiveness of people-centred health and social protection systems. In

particular how to progress towards minimum, inter-operable data sets, develop the

necessary strategic intelligence on HRH and create a connected health workforce.

Developed under the scope of the Global Health Workforce Alliance’s (GHWA) consultation,

this paper on HRH data and measurement is the outcome of one of eight thematic working

groups (TWG), which will inform the development of a global strategy on HRH relevant to

the post-2015 development agenda (2015-2030). The consultation process is described

elsewhere (10).

2. Methods

A mix-method approach was used to collect and analyse information about HRH data and

measurement:

1) Process-tracing of World Health Assembly (WHA) Resolutions

All WHA Resolutions, from 2000 to 2014, were analysed and classified according to: 1)

nature (health-related: health issues, HRH, social security; Non-health related: Financial and

regulatory issues); and 2) content (against the Comprehensive Health Labour Framework for

UHC (11)). A chronology of events linking the resolutions and HRH evidence and advocacy

(e.g. Joint Learning Initiative, Kampala Declaration and the Agenda for Global Action, Recife

Declaration) was established.

2) Non-systematic review of the literature

Literature on the best and most recently available evidence and approaches regarding

‘future scenarios’ and ‘health and social protection systems’; ‘big data’ and ‘connected

health’; ‘workforce strategic intelligence’ were reviewed. This included research papers,

published reports and grey literature, identified by experts of different areas involved in this

TWG.


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3) Review of available data sets containing HRH data

World Bank consultants performed a revision of the available datasets and relevant

literature on HRH data or data initiatives. A total of 10 datasets and 13 papers were

identified and revised.

4) Key informants and country examples

At regional level, key informants reported on HRH data and measurement issues from

regional and countries experiences. Information was collected using a standardized matrix

and where possible, focal points at country level were involved in data gathering. Key

informants validated the information retrieved.

5) Expert panel consultation

Experts were identified with the support of GHWA and were asked to contribute from their

field of expertise. Individual and group participation was established through email, the

GHWA discussion forum and conference calls.

3. Evidence

A) The necessity to focus on HRH data The review of the WHA resolutions generated between 2000 and 2014 shows an increasing frequency of recommendations that call for strengthening the health workforce. During this period, WHO member states generated 374 WHA resolutions. Of the total number of resolutions, 209 are categorized as health-related in nature and are further analyzed. The 165 non-health resolutions, categorized as financial and/or regulatory in nature, were excluded from further analysis. Of the 209 health resolutions, 109 (52%) relate, at least in part, to HRH and associated action(s) in one or more of the nine themes described. The WHA resolutions most frequently associated actions categorized by one or more of the nine themes, in rank order, are: 5) leadership, governance, policy alignment (26.3%), followed by 2) supply/education (24.1%) and 7) productivity/ performance (16.4%). Theme 3) data and measurement ranked (3.7%). While a majority of the 209 health resolutions include reference to HRH, language calling for the development of HRH data, systems and impact measurement is sparse. Furthermore, of the 109 health/HRH nature resolutions, only 7 contain explicit actions related to HRH data, systems and impact measurement reflecting the limited attention to HRH metrics in the global policy architecture of WHO. Over the time period studied, there is a notable trend from 2005 through 2014 when the number of resolutions with HRH actions significantly increased (2005, 2007, 2010, 2011 and 2014). Interestingly, each of these years was followed by one or more major global HRH initiative (World Health Report (2006); creation of the Global Health Workforce Alliance (2006); Kampala Declaration and the Agenda for Global Action (2008); WHO Code of Practice (2010), and the Recife Declaration (2013)). The temporal association between HRH-related resolutions and HRH initiatives and actions by the WHO and other key stakeholders also suggests a bi-directional influence, with stakeholder action pre-dating a WHA resolution. However, despite the period between 2000 through 2014 being marked by HRH ‘crises’ and ‘ver the time period studied, there is a notable trend from 2005 through 2014 when the number of resolutions with HRH actions significantly increased (2005, 2007, rce data and


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measurement are remarkably few in number. And, while effective implementation and evaluation of most health and the HRH-related resolutions implicitly rely on the availability of HRH data, explicit evidence and language in support of building, implementing and/or use of data, systems and impact measurement is lacking. The gap in relation to HRH data and measurement heralds the potential impact that a concerted, global movement for HRH data and measurement could have.

B) Emerging issues

Rapid economic, social, demographic and technological transformations and the new

paradigm of health and social care around UHC and equity – ‘Big picture’ challenges (2,12) -

will impact HRH around the world (13). Innovative strategies and tools are emerging along

with ‘future thinking’ to anticipate these challenges, envisaging better HRH data for better

HRH planning. While the challenges are many, there are many opportunities to significantly

improve HRH data availability, utilization and inter-operability:

1) Horizon scanning exercises - consider the challenges, opportunities and anticipate

how the future may unfold in a set of plausible scenarios, informing governments of

future HRH needs in terms of production, supply and demand. Considering HRH

planning against these scenarios can help decision makers identify risks and

opportunities and formulate and adjust strategies with greater precision. Practical

experiences and good evidence are coming from UK with the work of Centre for

Workforce Intelligence, for example (14,15).

2) ‘Big data’ era - a dramatic growth in the types and quantity of data collected by

both patients and health workers will take place in the coming decade; a new digital

era of ‘big data’ related to health is expected to emerge from the broader use of

technology such as mobile phones and Internet. This means every health worker

interaction with a client will be captured and tracked. The opportunity to apply ‘big

data’ analytical approaches to these tracked care events to better understand health

worker presence, performance, and larger trends will give national and global health

workforce stakeholders an unprecedented picture of the gaps and opportunities for

health workforce strengthening all the way to the facility and community level.

However, understanding and using the increasingly available data requires focus on

tools that enable gathering, storing and utilization of inter-operable data and on

building analytical capacity within the health workforce. This will empower health

providers, managers and decision makers with better information and planning

capacity (16).

3) Health workforce strategic intelligence – is generated from the best evidence

available allowing long-term planning, better policy decisions and delineation,

sensitive and responsive to population health needs. The ‘big data’ and connectivity

era as well as future thinking and horizon scanning exercises are important

contributors to the strengthening the strategic intelligence in HRH.


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Box 1: HRH estimate and planning approaches Health labour market analysis Focuses on supply and demand and provides insight into current dynamics; however lacks the capacity to predict labour market evolution. Forecasting (supply and demand) Useful for estimating future pressures on services but it is not always sensitive to the gap between demand, utilization and need for services. Benchmarks Useful for country comparison but do not consider population needs, distribution, demographics, epidemiological characteristics, the demand and utilization of services, or the dynamics of health labour markets. Geography of health workforce Accounts for population distribution but it do not take into account the different levels of demand and utilization. Modelling: Needs-based approach Considers supply, need and demand of health workers, but there is no consensual definition of need, which is mostly dictated by economic and sociocultural factors.

C) Current challenges and opportunities in health workforce data and measurement

Data, sources and approaches

Data for HRH are available from a variety of sources at national and global levels (17). The most common include population census, labour force surveys, health facility assessments, payroll registries, health facility registries, regulatory bodies registries and others (17). Health professionals’ associations can also be a potential source of information due to the post-registration requirements, including the continuing professional development with annual renewal of licenses, allowing keeping track of health workers entry, career progress and exits. The potential of the data provided by these different sources to produce relevant and valid information for policy decisions is recognised. However there is low use or a lack of capacity to use evidence-based information in HRH planning (17). Furthermore, the use and comparability of data within and across countries is undermined by the diversity of definitions, the lack of norms and standards among HRH measurement tools, and the lack of specificity for HRH policy and planning (17,18). The diversity of data and indicators is also influenced by the approach selected to estimate HRH, which is usually determined by the country context and capacity (19). From basic and widely used indicators (e.g. density of health workers per population) to more complex and modelling strategies (e.g. needs-based approach) a number of approaches use different health workforce data to characterize, estimate and inform a HRH plan. Combining these approaches can offer a more comprehensive planning of HRH. Box 1 gives an overview of these approaches and their limitations.

Structures and mechanisms

Human Resources Information Systems (HRIS) are usually built within countries’ Health

Information Systems (HIS), architected as a ‘business domain’ along with other domains,

such as health services, diagnostics, supply chain, health finance, etc. (20).


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HRIS is becoming an important component of national HRH assessments, however, similarly

to HIS, they are often of a fragmented structure. This is often determined by stakeholder’s

goals, roles and interests rather than by the requirement to function as a comprehensive,

unique and inter-operable structure. This challenge was recently acknowledged in

Resolution 24 of the 66th WHA: a seamless exchange of data within and between health

information systems, hinders care and leads to fragmentation of health information systems

(21).

WHO has been the main promoter of national eHealth and HIS architectures at global and regional level, by developing and dissemination tools such as the WHO/ ITU National eHealth Strategy Toolkit (22). Box 2 briefly describes the experience and outcomes from the implementation of an HRIS Assessment tool in Kenya. This project was conducted as a collaboration between the Centres for Disease Control and Prevention (USA) and Kenyan national authorities with the financial support of PEPFAR.

Finally, efforts were made to establish HRH observatories as congregating structures where

a common vision could be developed, relevant data and valid information could be gathered

and evidence could be translated into policies. However, observatories have evolved

differently in the various regions of the world, differing in terms of objectives and

functioning and lacking standardization of their approaches (23), and therefore, their data

do not provide a comparable picture around the world.

D) Inter-operability and alignment Inter-operability is broadly defined as ‘the ability of two or more systems or their components to exchange information and to use the information that has been exchanged’. (24) Inter-operability systems are also known as health information exchanges (HIEs). The concept applied to health is complex as it goes beyond the elements of syntactic and semantic to technical, structural and service-oriented inter-operability (25).

Box 2: Kenya’s experience with the development of a health workforce information system The Kenyan Health Workforce Information System (KHWIS) was launched at the Nursing Council of Kenya in 2002

1 and is owned and managed by the Government of Kenya and its professional

regulatory bodies of key health professions. It aimed to facilitate deployment of the right health workers (qualifications, skills mix) in the right place (deployment location) at the right time (availability). Progress and improvements were noted in: extension of the retirement age for Kenya’s civil service; the re-allocation of development funding to support the recruitment of 10,500 new health worker positions; and the strengthening of health worker regulation through increased registration and relicensing resulting in unexpected and substantial rises in regulatory bodies’ revenues. In 2011, KHWIS received an Award for Excellence from the WHO for its impact on human resource planning, health management, and the provision of workforce information resulting in positive policy changes. KHWIS can be used as a model for other countries developing HRIS, especially for addressing health workforce shortages and maldistribution. 1with support from the U.S. Centres for Disease Control and Prevention (CDC), and has been the recipient of PEPFAR funding, through CDC, since 2005. With technical support provided by Emory University and CDC, KHWIS

Source: Waters K, et al. Kenya’s Health Workforce Information System: A model of impact on strategic human resources policy, planning and management. International Journal of Medical Informatics. 2013


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Syntactic inter-operability consists in the exchange of information through predefined format and structure and semantic refers to the harmonised information models based on predefined and shared meaning of terms and expressions. While technical inter-operability establishes end-to-end exchange of data among systems, structural inter-operability is based on the exchange of agreed data. The higher level of semantic inter-operability – serv-ice-oriented inter-operability – is realised through standardised interfaces to health care services (26,27). Evidence suggests that standardisation and inter-operability of HRH data is still far from being a reality. A review of the existing data sets and literature about HRH data or data initiatives (including occupational classification, titles, hours worked etc.) showed different definitions and categorization between labour force surveys, central statistical offices and demographic health surveys. Several standards exist or are emerging that can guide country efforts to establish an interoperable HRIS. International Standards for the Classification of Occupations (ISCO) developed by ILO (28) are an important step for standardization and inter-operability of data from different sources and levels, however this is still little used, particularly in low- and middle-income countries. Others include ISO Health Informatics Provider Identification standard (29) and a new data standard to be published by the WHO is the minimum dataset (MDS) for an HRH Registry. Within the scope of eHealth, WHO in collaboration with international partners has developed and piloted tools, which showed great potential for inter-operability. Box 3 briefly describes the potential contributions of the implementation of Health Facility Assessment tools, within the project Optimizing Performance and Quality framework to understand the factors affecting health worker performance and laying foundations for improved quality of health care (30). Also, PEPFAR’s Open Health Information Exchange (OpenHIE) initiative is building on the existing work to establish an open source health worker registry as part of a larger HIE architecture. Countries may now establish health worker registries using these technologies either as standalone applications or a part of a larger national HIE approach (31).

E) Future opportunities on HRH data and inter-operability Public policy demand presents an opportunity to focus on the information that policy makers need on demographics, epidemiology, health workforce stocks, inflows and outflows, with a move beyond counting only the number of health workers to understanding the determinants and solutions to a labour market imbalance. Scaling up WHO’s MDS would increase the use of standardized definitions. As well, expanding the use of the ILO ISCO classification will raise data comparability and guide country efforts towards inter-operability. Inter-operability has been developed and catalysed under the scope of eHealth and been driven by discussions around HIS. High, middle- and low-income countries have made steps towards inter-operability of health information. Recently, a number of countries had the chance to report their experiences and developments at the 2nd WHO Forum on eHealth Standardization and Interoperability (February 2014) (32). A range of national strategies are used in countries like Estonia and Finland with relatively long track records (since 1996), to Switzerland where oversight and budgets are devolved to 26 cantons, to problems posed by the varying readiness of parts of the Indian health system, or Bangladesh, where the


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adoption of health data standards and use of information communication technology in a health care system with many constraints are important issues; or China which invested over the last four years in national eHealth projects. This shows the different stages and process used by countries of different income groups however it also shows an increasing engagement of countries towards improving data and information. This highlights the opportunity to increase attention to the importance of HRH as a ‘business domain’ in the architecture of a HIS and reinforce the development of HRIS as an inter-operable part of the system. As HIE’s focus primarily on the exchange of electronic health records, the diversity of recording mechanisms need to be considered (paper registries, etc.). However, the technology available, the raise of ‘big data’ and of connected health information can attract countries to an electronic dataset system, and help establish a full HIS architecture. On the other hand, while many countries will struggle to fully digitize health records, others may find the need to make sense of the data in an even bigger challenge. This will raise the need to train people at all levels on the theory and techniques of analytics to build capacity in the health workforce data. HFA and other tools (box 3) can potentially increase inter-operability of data at the same time as feeding HIS with relevant data. These tools are used as monitoring tools and are complementary to facility registers, which quality tends to improve overtime (17).

As countries develop their national HIS, the standardized MDS and the inter-operability approaches can be incorporated. As HIS and HRIS architectures are being defined - how their different data sets inter-operate and the standards they use to do so - they will need to consider how health worker information is shared within the larger national HIS architecture, and in turn how eHealth and mHealth feed into the system, beyond routine health information. eHealth and mHealth strategies already in use show great potential to support the integration of a connected and inter-operable system (see box 4).

Box 3: The potential of Health Facility Assessment (HFA) Tools

An important complement to routine information systems.

Provides reliable quantitative and qualitative data from observations at specific points in

time from different levels of care.

Offers a range of possibilities to collect information about health workforce at ground level:

providing knowledge about who the health workers are, what their capacities and their

needs are; allows the correlation of findings between workplace conditions and health

worker characteristics and perceptions; can also collect patient perceptions on health care.

Allows exchange of electronic health record information between points of care across

points of service, which, when supported by registries, health worker registries, and client

registries enhances the potential for inter-operability and health information exchange.

This information is available and largely underutilized.


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4. Forward vision and recommendations

The post-2015 agenda requires a new discourse on HRH, aligned with a public policy agenda on governance, accountability and equity and emerging approaches to develop strategic intelligence on the global labour market. This discourse should use the ‘opportunity’ provided by new information and evidence to influence the global agenda and improve decision-making processes for the future of the health workforce. Improvements in HRH information architecture and inter-operability can generate core indicators based on the four critical dimensions - availability, accessibility, acceptability and quality (AAAQ). This will allow to plan a fit for purpose, fit for practice health workforce and help monitor the pathway towards effective coverage and social protection (3). This approach will facilitate comparability of the health workforce landscape (within and across countries), to increase responsiveness to demand (fuelled by accountability/ transparency/equity) and will allow capturing and report better information for evidence-informed policies. Data should include a comprehensive overview of the workforce characteristics (public and private practice, etc.) and capture remuneration patterns (multiple sources, not only public sector payroll), workers‘ competences (including the role of alternative cadres, disaggregated across cadres and between different levels of care); performance (systematic data collection on productivity and quality of care); monitor absence and absenteeism and their root causes (important to differentiate their causes and consequences for service delivery); describe labour dynamics of mobility (rural vs urban, public vs private, migration); monitor HRH trends and movements. HRIS structures should be able to integrate this information and facilitate the process of inter-operability and the overall strengthening of HRH data and measurement (3). One of the keys to success in digital health will be the need to cooperate with a growing range of stakeholders. Innovation will likely arise from innovative frameworks that will

Box 4: eHealth, mHealth and inter-operability - Country experiences Bangladesh Mobile phone health services: All public health facilities have mobile phones. People living in the catchment areas of each facility may call and a doctor on duty answers and gives advice. This service is free of cost. A mHealth service called MAMA (Mobile Alliance for Maternal Action) has been established as part of mHealth for safe pregnancy and childcare. Telemedicine: A total of 42 telemedicine centres in district and Upazila based hospitals have been established. Rural citizens are now covered by telemedicine. All functioning community clinics and union health centres have been given Internet connectivity through provision of one laptop and one broadband wireless Internet modem in each. Thailand Mobile phone health services/ telemedicine: All health facilities, particularly rural health centres, have access to mobile phones, tablets, computers to communicate from a distance, with patients between health care providers from different levels of care or settings. These instruments also assist in the development of HMIS and HRH information systems in rural areas. Computer system: HMIS is being developed using a computer-based system and all health facilities have and access computers. The HRH information system is developed based on the computer program and plans to link HRH information with HMIS are in process.


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reduce the transaction costs of collaborating and hybrid public-private cooperation that enables technological transformation in an economically sustainable manner, as well as being transparent and serving the public good. It is also necessary to accept that new technologies and issues of people, processes, and cultures will compound the complexity. Standards drive for simplicity and regularity while reality is more entropic in developing new data. This complexity and uncertainty should be recognized and supported/minimized by top-down architectural approach with iterative review and evolutionary approaches. It is important that countries invest in strengthening their analytical capacity of HRH and health system data to inform evidence-based decisions on workforce and health system planning. HRIS need not be complex or costly but should be seen as a facilitator of inter-operability and the overall strengthening of HRH data and measurement. A set of conditions need to be met at national and global levels to increase the chance of successfully implementing minimum, inter-operable data sets for HRH:

- Policies and guidelines need to be available to guide countries in the process of

standardization and inter-operability.

- WHO’s Minimum Data Set and ILO’s ISCO need to be disseminated and incorporated

into the main data collection processes.

- Countries experiences, capacities and stages of implementation of inter-operability

need to be considered when implementing international guidelines – allowing for

country-specific adjustment of guidance and policies.

- Government and stakeholder involvement and long-term commitment are essential.

Those stakeholders are critical to establish, deliver and sustain a mandate of HRH

information sharing and exchange.

- HRH needs to be considered within the context of other sectors and considered as a

‘transnational business’ factor for inter-operability of HRH data, for example the

pharmaceutical industry. Public-private partnerships (32) are considered good

promoters in favour of inter-operability motivated by the market and benefiting

perhaps of more energy than a global policy body.

- Synergies between sectors and between stakeholders (e.g. health and technology; education; labour and employment; health professionals’ associations; civil society) can boost standardization, health information exchange and inter-operability. For example, data that allows linking health sector employment trends to trends in the broader labor market and economic cycle.

- Data security and ethical issues need to be addressed.

Based on the evidence and documentation collected, we recommend the following lines of

action for the post-2015 Global Strategy on HRH:

1. Stimulate demand for and proactive use of health workforce data in

international public policy, e.g.

a. Encourage a global discourse on “what are the health workforce

implications?” to trigger demand for and analysis of workforce data,

particularly on global health initiatives and programming linked to the


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future health target(s) in the Sustainable Development Goals and

Universal Health Coverage.

b. Member States of the WHO establish a governance mechanism so that

all future resolutions presented at WHA and regional committees

include an impact assessment of the health workforce implications

resulting from the technical or policy recommendations and how the

resolution will contribute to strengthening HRH.

c. Similarly, the World Bank, GFATM, GAVI, USAID, DFID and other

development partners establish equivalent governance mechanisms so

that all grants/loans include an impact assessment of the health

workforce implications and how specific programming will contribute

to strengthening HRH.

2. Stimulate demand for, and proactive use and reporting of health workforce

data in national/sub-national health policy, e.g.

a. Establish a culture of data management at national /sub-national level,

with support for the implementation of WHO’s Minimum Data Set on

health workforce by all Member States (and/or at sub-national level

where applicable)

b. Stimulate national/sub-national reporting of health workforce data by

means of standardized, annual reporting against a Minimum Data Set

to the WHO Global Health Observatory.

3. WHO to collaborate with relevant partners (e.g. GHWA, WB, OECD, ILO etc) to

develop a National Health Workforce Account that extends the Minimum Data

Set to a comprehensive account of key performance indicators on the health

workforce labour market (e.g. with modules on education, employment,

retention, salaries/remuneration, productivity etc).

a. Develop and publish the National Health Workforce Account by end

2015.

b. Member States support the adoption and implementation of a

National Health Workforce Account through a WHA resolution in May

2016.

c. Establish milestones for progressive implementation by Member

States, with progress reports in 2020, 2025 and 2030.

4. Request the UN Secretary-General’s office to ensure the SDG accountability

framework and the health targets include specific language on the health

workforce implications; including recommendations and metrics on the health

workforce data needs (e.g similar to the Commission on Information and

Accountability) in order to be able to track progress on HRH and report on it as

part of the future SDG reporting.

5. WHO and partners to stimulate creation of interoperable datasets and an

‘open access’ culture. All workforce data (respecting personal confidentiality)

are to be viewed as a global public good and shared in the public domain by

governments, health care professional associations and development partners.


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a. Norms and standards should be developed by WHO to guide this

process.

b. Public access for different levels of decision-making and research should be fit for purpose (e.g. for facility managers, regional health authorities, national decision- and policy-makers and with the international community).

c. Create incentives for the collection, reporting and analysis of

workforce data to inform transparency and accountability. The

incentives must account for the roles of the public, private and faith-

based sectors and could consider social impact bonds and multilateral

funding instruments to stimulate a data revolution.

d. Walk the talk” – take a focused group of low-income countries to scale

with mutlisectoral partnerships and rewards

6. Professionalise the development, management and analysis of health

workforce data and evidence-informed policy analysis.

a. WHO and appropriate partners (e.g the World Bank, OECD and others)

to develop a professional education programme for government and

development partners on workforce science/surveillance. This

professional programme, accredited as a post-graduate qualification,

should be available with a vocational fellowship programme.

b. The workforce science should inform national and sub-national policy

and planning, with linkages to Global Health Security; the return on

investment from domestic resources; and the Geography of HRH).

7. Monitor and regularly assess the impact of the interventions (e.g. technology

development; implementation of the MDS; HRIS development;) in terms of

process, outcomes, and investments. Include a feedback loop where

information can constantly inform decision making and allow regular

adjustments (e.g. use platforms that allow adaptation as HRH scenarios and

decision making process changes)

5. Conclusion

Heading towards 2030, universal health and social protection systems will bring challenges

to HRH around the world, while at the same time, technology and the post-2015 momentum

will create opportunities for the development of a ‘strong and responsive’ health workforce,

based on information and evidence.

The availability, accessibility and comparability of data will enable timely use of information

and capacitate workforce strategic intelligence to inform governance in decision-making

processes. A number of essential conditions are already in place, setting the basis for the

scale up of a standardized approach to HRH. What is necessary is the global and national

commitment that can be realized in a strong Global Strategy on HRH, closely linked to the

SDG accountability framework that is being developed.

The pathway to 2030 is based on small but steady steps towards standardization, connectivity and inter-operability of HRH data, followed by an increased capacity within countries to translate knowledge into policy actions.


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