Every healthcare workforce plan assumes this layer and almost none of them fund it, because everyone assumes it was covered by someone else's training.
For clinical staff it usually was. For the growing population of engineers, analysts and product people now working inside healthcare, it usually was not, and that is where the problem sits.
The people who need this and were never taught it
Healthcare is being rebuilt around data and devices, which has brought a large number of people into the sector who did not train in it.
Software engineers building clinical systems. Data scientists working with patient records. Device engineers designing things that go into or onto a body. Product managers making decisions about clinical workflow. Quality and regulatory staff from other industries.
The World Economic Forum's Future of Jobs Report 2025 found that 92 percent of employers in medical and healthcare services say AI and big data skills are growing in importance for their workforce. Satisfying that demand means recruiting people from outside healthcare, and those people arrive without the foundation the sector silently assumes.
A data scientist who does not know what a normal physiological range looks like cannot tell a modelling artefact from a clinical finding. The model will not tell them either.
What the direction covers
The scope: how the body is built and how its systems work together.
For a workforce program the useful content is not memorising structures. It is four things.
Systems and their normal behaviour. Cardiovascular, respiratory, renal, neurological, endocrine and the rest, including what normal looks like and how wide normal is.
How systems interact. Almost nothing in the body is isolated. Kidney function affects drug clearance, which affects cardiac rhythm. People who think in single systems miss the interactions, and the interactions are where patients get hurt.
Variation. Normal differs by age, sex, body size, pregnancy, fitness and comorbidity. A system designed around a single notion of normal will perform badly for large parts of the population, and this is a recurring and documented problem in medical devices and clinical algorithms.
What measurements mean. What a vital sign actually measures, how it is acquired, what makes it unreliable. Anyone working with clinical data is working with these numbers.
Four failures that trace back to this layer
The implausible output nobody caught. An analysis or a model produces a result that is statistically clean and physiologically impossible. Clinical staff spot it instantly. A team without that background ships it.
The device designed for one body. Devices and algorithms validated on a narrow population and deployed on a wide one. This has happened repeatedly with measurement technologies where skin tone, body size or age affect accuracy, and it is a physiology and variation problem before it is an ethics problem.
The alarm nobody can act on. Monitoring systems designed by people who do not understand which deviations matter generate alerts that clinical staff learn to ignore. Alarm fatigue is a recognised patient safety issue, and it is frequently designed in.
The data field misread. Clinical data is full of values that mean something specific. Treating them as generic numbers produces confident wrong conclusions, and the person producing them has no way of knowing.
Where this sits in the domain
Human anatomy and physiology is the first of ten directions in Astra Trainer's medicine and healthtech domain, which runs through pathophysiology, pharmacology, public health and epidemiology, clinical research, health informatics, medical devices, medical AI and imaging, precision medicine, and healthcare systems and regulation.
It is placed first because everything above it assumes it. For partners bringing engineers and analysts into healthcare, this is usually the first direction scoped, and it is frequently paired with directions from the AI, data and computing domain for people going the other way. Lessons are five minutes, so clinical and technical staff build the layer without leaving the job. You can see the ten directions here.
The roles, named
Allied health and support roles. Healthcare assistants, technicians, therapy assistants. Large populations where this is the core knowledge and the training is often thin.
Medical device engineers. Designing something that interacts with a body requires knowing how the body behaves.
Clinical data analysts and health data scientists. Working with physiological data daily.
Clinical informatics staff. Building and configuring the systems clinicians use.
Medical affairs, technical sales and field support. Talking to clinicians, which requires understanding what they are talking about.
Regulatory and quality staff in medtech. Assessing risk requires understanding physiological consequence.
Structural context: the US Bureau of Labor Statistics projects employment of nurse practitioners, nurse midwives and nurse anesthetists growing about 35 percent between 2024 and 2034, and the World Economic Forum's 2025 report identifies nursing professionals as the only healthcare role among the ten largest-growing jobs worldwide to 2030. The support and technical populations around those roles grow with them.
Who can be trained into it
Engineers and software developers entering healthtech. The largest and least-served group. They need working physiological literacy rather than a clinical education, and the distinction is what makes the training tractable.
Data analysts moving into health data. Same gap, same fix.
Quality and regulatory staff from other industries. Aerospace, automotive, food. The frameworks transfer; the clinical consequence does not, and risk assessment without it is procedural rather than real.
Healthcare support staff. Frequently working in clinical environments with limited formal grounding, and a structured foundation makes them considerably more effective and is a route toward further qualification.
People returning to the sector, where the science has moved since they trained.
This is not clinical training and it does not lead to practice. Medicine, nursing, pharmacy and the allied health professions are licensed, with defined education and registration routes. Structured learning builds understanding that makes people effective in technical, analytical and support roles, and supports registered professionals in maintaining knowledge. It does not qualify anyone to assess, diagnose, treat or advise a patient, and nothing in this direction should be used to inform the care of a specific person.
Where the licensure line sits
Worth being precise about, because this domain attracts confusion that other domains do not.
There are three different things that get conflated.
Understanding physiology is knowledge. It can be taught to anyone and it makes engineers, analysts and support staff better at their jobs.
Clinical competence is the ability to apply that knowledge to a specific patient. It requires supervised clinical practice over years and is assessed within a professional framework.
Licensure is the legal authorisation to practise, granted by a regulator against defined requirements.
A workforce program operates entirely in the first category. It supports people working toward the second and it never touches the third.
Being clear about this is not defensive. It is what makes the program usable, because a healthcare organisation needs to know exactly what a trained person is and is not authorised to do.
What to take from this
This layer is assumed by the sector and missing in most of the people the sector is now hiring.
The failure mode is a technically competent person who cannot tell a clinically implausible output from a real one, and no tool flags that for them.
Variation is the part most often skipped and most often harmful, because systems validated on narrow populations get deployed on wide ones.
Engineers, data analysts and regulatory staff from other industries are the training pools nobody plans for, and they need working literacy rather than a clinical education.
And understanding, competence and licensure are three different things. A program builds the first, supports the second and never claims the third.
Why do engineers and analysts need physiology?
Because they now build systems that act on clinical data and interact with patients. Without the foundation they cannot tell whether an output is physiologically plausible, and nothing else in the pipeline will tell them.
What is the most common failure?
Systems validated on narrow populations and deployed broadly. Normal varies by age, sex, body size, pregnancy, fitness and comorbidity, and designs built around a single notion of normal perform badly for large groups.
Who should be trained in this?
Engineers and developers entering healthtech, data analysts moving into health data, quality and regulatory staff from other industries, and healthcare support staff working without much formal grounding.
Does this qualify anyone clinically?
No. Understanding physiology is knowledge, clinical competence is supervised applied practice over years, and licensure is legal authorisation from a regulator. Training operates in the first only.
Where does this fit in the domain?
First of ten directions in Astra Trainer's medicine and healthtech domain. You can see them here.