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Data Gathering Practices

Why we gather data

Taking Precautions Against Big Data & Hidden Data Gaps

Why it is difficult to source this data

The term “big data” refers to substantial datasets that are analyzed using computer algorithms to extract trends and patterns. This cybernated means of processing data was intended to handle the massive amounts of data that have arisen in our modern, digitized world; however, as time has passed, it has become clear that having a lot of data does not necessarily mean the data is representative or that it is adequately utilized by government or organizational entities. 
Whether intentional or not, big data by its very architecture is designed to reproduce existing biases, allowing systemic prejudice to enter into large datasets. 
Marginalized groups generate less data for a variety of reasons, including unequal access to the resources necessary to engage in data-gathering practices. Therefore, the minimal amount of data on these underrepresented populations is not reliable, yet it continues to be circulated within big data analysis, giving rise to new manifestations of inequality that are difficult to recognize. Over the long term, this can lead to data outputs that are outright discriminatory. In particular, racial minorities and women are at risk due to persistent underrepresentation; this unreliable information originates from historical data that has not been adjusted to account for our modern understanding of data-gathering flaws and awareness of bias. Conclusions based on inequitable or partial data don’t just paint an undependable picture, but also have enduring consequences; this is particularly impactful in healthcare, where misguided data analysis can prompt inaccurate decision-making by clinicians and healthcare policymakers.  This leads to increased medical costs, patient mortality, decreased funding, and health worker shortages, along with other unquantified drawbacks.
“Hidden data gaps” is a term to describe these datasets that are routinely utilized for policymaking, but contain misrepresentation or missing data, without governments being aware.
An algorithm is only as good as the data it works with and inappropriate modeling produces inaccurate predictions. Part of our mission is to conduct and disseminate research on AMTCs in order to advance their role in the global health workforce, and we believe this is only possible when we are collecting and employing valid, current, and inclusive data, as well as staying aware of the pitfalls of big data and avoiding hidden data gaps. We are dedicated to recognizing the individual humans that numbers and figures represent, and using a multitude of sources to ensure our understanding of a situation is true to life and represented in our data.
We embrace traditional statistical techniques and small data curation approaches that have been seen to significantly improve healthcare outcomes; we believe that by applying this methodology, we can bring accurate and effective awareness to the often overlooked or misunderstood AMTC workforce. We value the safety of our members and strive to remain informed on the benefits and drawbacks of current data practices, so that we can maintain responsible data gathering methods.

A multitude of factors contribute to incomplete data on the existing health workforce, globally.  These factors include lack of funding, resource challenges, and technology deficits. It is difficult to gather data reflecting the variety of existing skills-mix along care pathways from rural and marginalized populations. Data tends to focus predominantly on physicians, nurses, midwives, pharmacists, and dentists. 

We rely on data gathering and dissemination in order to fulfill our mission and improve access to current and relevant information on the AMTC role, globally. When policymakers, planners, and other government bodies are well-informed, they can better make decisions that benefit and expand the AMTC profession. This cadre frequently encounters barriers to its promotion for a variety of reasons, including lack of data.  Acting as a resource for research on global AMTCs will allow us to increase understanding and appreciation of this fit-for-purpose workforce. Currently, the broadest visibility of the AMTC cadre exists in literature on task-shifting; this illustrates the power of data in increasing global awareness and advocacy within the health workforce.

Long-term goals

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The World AMTC Network has established our own database that works to track the global AMTC role.  We embrace culturally safe data collection and distribution practices to expand this database, which in turn will be used to educate and promote the AMTC profession.

We strive to source data on the often unseen global healthcare resource of Accelerated Medically Trained Clinicians. We seek to improve information quality by fulfilling data gaps in order to provide health planners with well-rounded data that integrates rural statistics, healthcare outcomes, inequities, and social determinants of health. 

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Data in the Health Information System
Sound and reliable information is the foundation of decision-making across all health system building blocks, and is essential for health system policy development and implementation, governance and regulation, health research, human resources development, health education and training, service delivery and financing.
The health information system provides the underpinnings for decision-making and has four key functions: data generation, compilation, analysis and synthesis, and communication and use. The health information system collects data from the health sector and other relevant sectors, analyses the data and ensures their overall quality, relevance and timeliness, and converts data into information for health-related decision-making.The health information system is sometimes equated with monitoring and evaluation but this is too reductionist a perspective. In addition to being essential for monitoring and evaluation, the information system also serves broader ends, providing an alert and early warning capability, supporting patient and health facility management, enabling planning, supporting and stimulating research, permitting health situation and trends analysis, supporting global reporting, and underpinning communication of health challenges to diverse users. Information is of little value if it is not available in formats that meet the needs of multiple users − policy-makers, planners, managers, health care providers, communities, individuals. Therefore, dissemination and communication are essential attributes of the health information system.


WAN Board Members, May 2021

Big Data's Disparate Impact, Solon Barocas and Andrew D. Selbst, 104 California Law Review 671 (2016)

Big Data, Big Problems: A Healthcare Perspective, Mowafa S Househ et al., Studies in Health Technology and Informatics 238 (2017) 

The Hazards of Data Mining in Healthcare, Mowafa Househ and Bakheet Aldosari, Studies in Health Technology and Informatics 238 (2017) 

WHO Toolkit on Monitoring Health Systems Strengthening, 2008

All reasonable precautions have been taken to verify the information we share. This material is being distributed without warranty of any kind, either expressed or implied.  The reader is responsible for interpretation and use of the material ; in no event shall the WAN be liable for damages arising from its use.
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