Why Dr Maya’s Color-Coded Model Is the Only Practical Way Forward

In late1980s, when digital medicine was in its infancy, and Tech Giants were not born, I created a flow chart (algorithm) based on a pediatric assessment tool (PAT). My intent was noble: to help junior doctors identify serious illness early, admit them and confiedenty send home the ones that were not serious illness or infections. But even then, it became painfully clear that algorithms — no matter how intricate — collapse under the weight of real-world variation.

Every patient’s story is different. Symptoms evolve—context changes. Later in 2007 Dr Jerome Groopman wrote in "How Doctors Think", (https://en.wikipedia.org/wiki/How_Doctors_Think) rigid flowcharts cannot replicate human clinical reasoning; a good clinician must “step out of the box” to think dynamically.

The very notion of a fixed decision tree assumes illness follows a predictable pattern — a dangerous illusion of theoretical idealism. Epidemics, pandemics, and even day-to-day infections defy that assumption. Managing disease during chaos demands adaptability, not blind adherence to an algorithm.

In the 1990s, when digital medicine was in its infancy, I created a flow chart (algorithm) based on a pediatric assessment tool (PAT). The intent was noble: to help junior doctors identify serious illness early, admit them and confidently send home the ones that were not serious illness or infections. But even then, it became painfully clear that algorithms — no matter how intricate — collapse under the weight of real-world variation.

Every patient’s story is different. Symptoms evolve—context changes. As Dr Jerome Groopman wrote in How Doctors Think, rigid flowcharts cannot replicate human clinical reasoning; a good clinician must “step out of the box” to think dynamically.

The very notion of a fixed decision tree assumes illness follows a predictable pattern — a dangerous illusion of theoretical idealism. Epidemics, pandemics, and even day-to-day infections defy that assumption. Managing disease during chaos demands adaptability, not blind adherence to an algorithm.

The Missing Science Behind “Standard” Medicine

When I was asked by a company PDxMD to rank 600 symptoms and signs listed in a spreadsheet and combine the ones in X Axis, with the ones in Y Axis and number them based on my experience and knowledge, I searched the Index Medicus, expecting to find evidence to support clinical judgment — combinations of symptoms, but there were none.

The global medical systems are built without a foundational dataset that differentiates common from serious symptom combinations.

Similarly, antibiotic prescribing remains shockingly unscientific. Duration (five, seven, or fourteen days) and dosage (250 mg vs 500 mg) are based on convention, not proof. Few clinicians consider pharmacokinetics, peak levels, absorption interference from milk or coffee, or the environmental consequences of antibiotic excretion that drive antimicrobial resistance (AMR).

We have institutionalised ritual — not precision.

From Error to Insight: The Birth of Dr Maya

My experience supervising nurses at College Road Surgery crystallised the problem. Diagnostic errors weren’t caused by negligence but by misplaced confidence in procedural correctness — “checking boxes” rather than perceiving patterns.

One nurse diagnosed pneumonia because she thought she heard “crepts.” When I asked her to say what she ment by“Crepts” she did not know, and so I looked at the way she hung her stethoscope on her neck and noticed she had inserted the earpiece backward and what she was hearing that she interpreted was the crinkles that we hear due to the ear piece that rubs on the user's skin in the ears. 

The child was not breathless nor grunting but smiling, happy and healthy. The protocol was followed — yet the outcome was nearly catastrophic, if the registrar in paediatrics trusted the clinical judgment of the nurse. That moment confirmed my conviction: medicine must return to pattern recognition rooted in proper training in medical school, experience gained from doctors with years of diagnosisng, managing critically ill adults and children like me , and not blind obedience to rules written in protocols or guidelines.

For more than 3 years, I meticulously collected the "Presenting Complaints" compiled a list, and observed that most patients panic over 12 core symptoms — fever, sore throat, cough, ear pain, headache, abdominal pain, vomiting, diarrhoea, chest pain, breathlessness, rash, and lumps or eye problems. By mapping overlaps between three-symptom combinations, I created a visual system of circles and colours.

I removed ones that required face-to-face consultation, tests and investigation and created MAYA — the colour-coded symptom intelligence tool that helps patients identify risk levels quickly and accurately. I tested the color code combination, and often changed them based on new infections. When COVID-19 started spreading, I changed the color of High and Very High Fever from Yellow to Green because the WHO or Hospitals did not offer say temperature above 38.4 degree is COVID, and below must be Flu. This was a gray area and so it is important for doctors, and healthcare workers Maya Color Codes need monitering and updating, and so I have allowed the choice.

Dr Maya: The Colour-Coded Revolution

Unlike algorithmic triage tools or commercial symptom checkers, Dr Maya does not diagnose or prescribe. It acts as a traffic-police system that directs patients to the right level of care — family physician, surgeon, paediatrician, pulmonologist, obstetrician, psychiatrist, or other specialists — without inducing fear.

Its safety lies in what it does not do:

• It excludes symptoms requiring diagnostic tests or visual confirmation.
• It avoids emotional and ambiguous descriptors.
• It educates before escalating.

Dr Maya is not an “AI doctor.” It is a disciplined framework for informed self-navigation — a patient-centred safety net. It empowers people to pause, assess, and act rationally rather than react in panic.

Why Demanding Validation Misses the Point

Critics call for randomised control trials to “prove” Dr Maya’s safety — a misunderstanding of its purpose.

You cannot subject a navigation system to the same metrics as a treatment. Maya does not alter physiology or deliver medication; it reorganises information flow.

Removing fear, misinformation, and inappropriate antibiotic use prevents harm that existing systems ignore.

The irony is striking: institutions that have never validated their own algorithmic symptom checkers — or their assumptions about antibiotic dosing — are demanding validation from the one system explicitly designed to correct their flaws.

Evidence in Plain Sight

The pediatric symptom database (PDxMD) I originally developed — the precursor to Maya — was peer-reviewed and later adopted by platforms such as WebMD. Yet, the NHS 111 and other digital triage systems still lack a categorised list of “not serious” symptom combinations, mainly due to fear of litigation.

Dr Maya fills that void with clarity and structure. It is the first system built on combinations, context, and colour-coded cognition — not isolated keywords or probabilistic code.

A Call to the Medical Community

We are standing on the same fault line that produced antibiotic resistance, medical burnout, and digital distrust.

Algorithmic healthcare promises certainty but delivers fragility.

Colour-coded, pattern-based systems like Dr Maya restore resilience — empowering both patients and clinicians to think again.

Our mission is not to replace doctors but to restore medicine’s moral compass: prevent, protect, and allow the body to heal.

If we succeed, our children will inherit a world where people spend less time in fear of illness and more time in the practice of love and care — the accurate measure of health.

Dr Kadiyali Srivatsa

Founder & Creator of Dr Maya GPT

Paediatrician | Innovator | Patient-Centred Care Advocate