Sunday, April 20, 2025

PaJR step by step intiation after web based single click entry into the current PaJR WhatsApp welcome group

For those joining in here and wondering what's the next step to their getting PaJRed:


Initiation to PaJR step by step:

[17/04, 22:20] CBBLE Patient Advocate: Sir would you look after this case please?

[17/04, 22:31] CBBLE Patient Advocate: She is a very intelligent and extraordinary child. The medication she is having for years seems to cause a lot of side effects. 
Her parents had sent me all the de-identified reports, I can send you if you need or if a WhatsApp group is created, her parents can send everything there themselves.

[18/04, 07:17]cm: Will be a pleasure.

Please ask the parents to download this: https://medicinedepartment.blogspot.com/2025/02/informed-patient-consent-and.html?m=1 consent form, read it and sign it in the language of their choice along with their full addresses and mobile phone numbers following which we shall create her PaJR group and start interacting with them as well as our other team members there around her problems to find the best possible evidence based solut

2) Please become your own patient's advocate and share your patient's problems in the following format below taking extreme care not to reveal any identifiers of the patient of his her treating team.

3) Also,

Please describe your patient's sequence of events that led to the current problems. Please begin the sequence of events from the time your patient had absolutely no problems at all. 

Please describe your patient's hourly  routine over 24 hours when he was perfectly alright 

Next please describe what happened to his hourly 24 hour routine once the disease took hold on his life 

Please mention specifically which part of the patient's hourly routine was disrupted 

What are the patient's current requirements from us like if we had to give him a single medicine which problem would they prefer it to address?

Please post the patient's clinical photo of abdomen and arm muscle as demonstrated below in the image here:



Some examples of a PaJR patient's hourly activities:

PaJR involves daily sharing of energy Inputs and outputs from all our patients globally. More here: https://www.researchgate.net/publication/314328348_Patient_Journey_Record_Systems_PaJR_for_Preventing_Ambulatory_Care_Sensitive_Conditions


CUBE home lab Sunday PaJR patient hourly activities to expiate for metabolic syn:

7:00AM-8:00AM after walking the dogs and a little bit of Web 2.0 finger typing

It's time to transfer the dragon fruit cacti to a bigger pot and to set the stage PaJR patient transferred some compost and soil from elsewhere to the new location where it would be repotted. (Pics attached)

Initially tried lifting the soil bags to directly overturn it into the bucket, which was good for the arm muscles but when that didn't work , had to scrape them out with the hands and transfer to the bucket. Good workout for the forearm muscles!

Chopped a bush of Hamelia Patens that was nearly touching the roof and blocking the Sun for the mulberry (pics attached)

Also transferred a large pot from one garden location to another (pic attached).

Finally some more forearm muscle micro exercise trying to de seed these seeded dates (pic attached). 

As a result of the above activity, the picture that apparently resembles a PaJR food plate energy input  is actually a representation of some PaJR micro energy output!



8:00 AM to 9:00 AM PaJR patient activities video: https://youtu.be/V4eZQ5Hi38A?feature=shared

[18/05, 19:37] cm: What about the sequential timelined history in your patient?

[18/05, 19:46] +91: Is there an example format for history we can look at?


[18/05, 20:31]cm: Short history (detailed one below this):

1. The patient has tingling and numbness  of lower limbs since 10yrs extending from tip of toes to shin of tibia. 

2. Burning sensation of feet since 5yrs.

3. Chest pain since 4 days, pricking type on and off. No precipitating factors.


4. Loose stools for one day which was one week back and subsided with medication. 

Has Diabetes  on injection Mixtard 20U BBF, 15U BD since 30 years

Had kidney problem since 13yrs.

Known case of  Heart failure 2° to coronary artery disease since one month.

History of stable angina, one month back. 

Detailed history:

An 80- year- old male ,  born and raised in a middle- class family, had a father who practiced as an Ayurvedic doctor, treating people with herbal medications.

He grew up with two elder sisters, one younger sister, and a younger brother.

In his early years, he experienced the tragic loss of his eldest sister to Hansen's disease.

A few years later, his second elder sister passed away after using herbal medications for suspected cancer.

After his father's death from a brain stroke, financial hardships forced him to leave school.

To support his family, he began working in handlooms while attending night school.

The loss of his sisters motivated him to pursue a career in medicine and follow in his father's footsteps.

He got married in 1962.

Later, his younger sister was also diagnosed with Hansen's disease.

After her husband abandoned her, he and his wife took full responsibility for her care until her death.

His younger brother, who was diagnosed with Hansen's disease at the age of six, also passed away eight years ago.

At one point, he purchased a power loom from Bombay and earned a living through it. However, over time, his neighbors also acquired similar looms, leading to increased competition and a decline in customers, which affected his financial stability.

He has four daughters, all of whom are now married.

Fifteen years ago, when his elder sister was diagnosed with diabetes, he decided to get a health check-up. Initially he managed his condition through dietary changes.

A few years later, he began experiencing tingling and numbness in both feet. After consulting at NIMS, he was diagnosed with hypertension, diabetes, and peripheral neuropathy.

Since then, he has been on conservative management.

Full PaJR case report:

https://pajrcasereporter.blogspot.com/2024/10/80m-diabetes-hypertension-30yrs-ckd.html?m=1




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Troubleshooting Humans in the Age of AI: A Playbook for Multidisciplinary, Participatory, Medical Cognition

Abstract:

The future of healthcare will not be built by doctors alone. It will be shaped by a
new generation fluent in medicine, engineering, AI, and human systems thinking -a generation able to troubleshoot complex, living systems with the precision of science and the empathy of care.
 
Troubleshooting Humans in the Age of AI is a playbook for that future.
Blending decades of participatory medical practice with cutting-edge digital tools, it introduces Patient Journey Records (PaJRs), Case-Based Blended Learning Ecosystems (CBBLE), and User-Driven Learning Community Ontologies (UDLCOs) as practical frameworks for uniting clinicians, patients, caregivers,
technologists, and educators in co-creating solutions.

Through real-world case studies and hands-on problem-solving, readers will learn how to harness AI for diagnosis, integrate genomic and device data into care, and embed evidence-based decision-making into every stage of the patient journey —whether in hospitals, clinics, or home-based care settings.

More than a manual, this book is a mission blueprint for those ready to bridge disciplines and reimagine how humans heal together. It is written for medical, engineering, and science students and practitioners who will not only witness, but create, the next chapter of medicine.

In one sentence, describe your book:

A guide to uniting clinicians, patients, caregivers, engineers, and scientists in
troubleshooting human systems through AI, evidence, and participatory medicine.

OR

Uniting patients, doctors, caregivers, engineers and scientists to shape the
multidisciplinary, AI-enabled medicine of tomorrow.

Please write a few paragraphs about your book which can be used for
promotion. Think about – what’s unique, different, engaging. What sort of people
might want to read it, and why.


In the near future, healthcare will no longer be the exclusive domain of doctors.

Engineers, scientists, AI systems, patients, and caregivers will work side-by-side with doctors, co-creating solutions for complex human problems. 

Troubleshooting Humans in the Age of AI is the first playbook to prepare readers for that future — a future where the boundaries between medicine and engineering dissolve into a
single discipline of human systems troubleshooting.

What makes this book unique is its synthesis of three powerful ideas: 

medicine as a multidisciplinary endeavor, medicine as a participatory act, and medicine as an evidence-driven science. Drawing on decades of real-world practice, it introduces frameworks such as Patient Journey Records (PaJRs), Case-Based Blended Learning Ecosystems (CBBLE), and User-Driven Learning Community Ontologies
(UDLCOs) - practical tools for weaving together patient narratives, AI-enabled
diagnostics, genomic data, and clinical expertise into a shared decision-making
process.

This is not a theory-heavy academic text, nor a technology manual. It is a hands-on guide for those who will practice and lead the medicine of the future: medical,
engineering, and science students; young practitioners; educators; and innovators who see healthcare as a collaborative system rather than a hierarchy.

Whether you are a student preparing for a career at the intersection of medicine
and engineering, a doctor adopting participatory care, a biomedical researcher advancing genomic medicine, a patient or caregiver using connected devices at home, a health-tech innovator building AI-enabled solutions, or a hospital leader driving digital transformation, this book offers actionable frameworks and an
inspiring vision for creating the multidisciplinary, patient-centered healthcare systems of the future.

Please list draft Table of Contents:

Table of Contents:

Preface – The Dissolving Boundaries

● A vision for a single discipline of human/animate systems troubleshooting

— where medicine, engineering, and patient intelligence merge.

Introduction – Your Mission in the Age of AI

● Why the next generation must think beyond “doctor” or “engineer.”
● From expert-driven to collaborative, multi-actor problem-solving.

● How to read and use this playbook.

Part I – Why the Old Maps No Longer Work

1. When Medicine Met Its Limits
● Complexity, chronic disease, and data overload.
● The disappearing family physician and the rise of self-informed patients.
● The low doctor-patient ratio and why participation is no longer optional.
2. The First Bridges Between Worlds
● Engineering labs in hospitals, hospitals in engineering schools.
● Early successes: AI in radiology, low-cost CAR T-cell therapy, portable
devices.
● Patients as early adopters — from home monitoring to AI-driven
self-diagnosis.

Part II – The New Discipline of Troubleshooting Humans


3. Principles of Multidisciplinary, Participatory Medicine

Why every complex problem needs mixed minds.

● The role of clinicians, engineers, scientists, patients, caregivers, and AI.
● The ethics of shared cognition.
4. Frameworks for a Shared Brain
● Patient Journey Records (PaJRs) — narratives as clinical infrastructure.
● Case-Based Blended Learning Ecosystems (CBBLE) — learning in
communities.
● User-Driven Learning Community Ontologies (UDLCOs) — structuring
knowledge for scale.
5. The Tech That Makes It Possible
● AI for pattern detection and decision support.
● Genomic medicine for personalized care.
● Devices, wearables, and the sensor-rich patient home.
● Secure, interoperable systems.

Part III – The Playbook in Action

6. At the Bedside and Beyond

● Using PaJRs to capture patient experience in wards, clinics, and homes.
● Integrating patient-generated data into hospital systems.
● Protecting privacy and consent in participatory care.
7. In the Classroom and the Lab
● Redesigning medical and engineering education for joint problem-solving.
● Teaching evidence-based thinking with real patient data.
● Gamification and peer-led knowledge building.
8. From Stories to Systems
● Converting narratives into structured ontologies.
● Linking to medical literature and existing knowledge bases.
● Creating reusable, scalable decision-support tools.
9. Scaling Across Institutions
● Hospital-to-hospital learning networks.
● National health stacks and public digital infrastructure.
● Global-to-local adaptation of solutions.
10. Risks, Safeguards, and the Precautionary Playbook

Understanding risks: bias, privacy breaches, over-reliance on AI, unequal
access, and legal or institutional challenges.
● Human risks: burnout, trust erosion, and unintended consequences of
shared cognition.
● Practical safeguards: structured SOPs, consent frameworks, ethical
guardrails, and data governance.
● Templates and examples: step-by-step precautions with space for local
adaptation and creative implementation.
● Real-world lessons from cases where risks materialized — and how they
were mitigated.
Part IV – The New Metrics and Leadership Mindset
11. Metrics That Matter & Leadership in Participatory Medicine
● How to measure participation, impact, and learning.
● From adoption to outcomes: tracking what improves care.
● Building cross-disciplinary teams.
● Nurturing a culture of open, iterative learning.
● Case studies of transformation in practice.
Conclusion – The Future of Thinking in Medicine
A call for openness, experimentation, and collaboration between humans and
machines to create care that is intelligent, equitable, and deeply human.

Manuscript information

Estimated word count: 200,000 words
(should be between 35,000 and 200,000 words)

If accepted, when do you expect this title to be complete?

15th November 2025 (might be completed before this)


Second draft July 2025:


Title: Participatory medical cognition 0.0-3.0 and beyond 


Subject Field (select one):

Health Sciences      

Please supply up to 6 descriptive keywords for this title:
medicine, participatory medicine, web 3.o, medical cognition, UDLC, advancements in medicine 

Please write a short descriptive abstract (up to 200 words). Include Purpose/objectives; why this book is needed; methodology; key findings; limitations; impact; contribution to research; contribution to practice; etc. 

Abstract

Participatory Medical Cognition 0.0–3.0. and beyond reimagines how medicine learns, reasons, and decides by centering patient narratives, collaborative intelligence, and Web 3.0 technologies. Traditionally, medical cognition has been hierarchical and expert-driven. But with the rise of participatory medicine and digital collaboration, a new model has emerged—one in which clinicians, patients, students, and machines co-create knowledge and meaning.

This book traces that transformation from the fragmented, discussion-based systems of Web 2.0 to the structured, intelligent, and decentralized potential of Web 3.0. It presents a practical and visionary framework combining Patient Journey Records (PaJRs), Case-Based Blended Learning Ecosystems (CBBLE), and User-driven learning community  Ontologies (UDLCOs) as building blocks for a future-ready cognition ecosystem.

Using a blended methodology of conceptual grounding, real-world case studies, and implementation guides, the book explores how narrative capture, semantic knowledge graphs, AI integration, and ethical data sharing can empower personalized, context-aware care. It also offers strategies for embedding participatory cognition into medical education, hospital practice, and digital infrastructure.

While acknowledging challenges in interoperability, culture, and scale, this work contributes to research on digital health transformation and provides a playbook for clinicians and educators seeking to build a more intelligent, collaborative, and human-centered healthcare future.

In one sentence, describe your book:

This book reimagines how medicine thinks by exploring the evolution of participatory medical cognition from Web 0.0 to Web 3.0 and beyond, offering a practical playbook for integrating patient narratives, collaborative learning, and intelligent technologies into clinical practice and education.

Please write a few paragraphs about your book which can be used for promotion. Think about – what’s unique, different, engaging. What sort of people might want to read it, and why.


What if medicine could think smarter, together?
"Participatory Medical Cognition 0.0–3.0 and beyond," is a bold, future-facing book that challenges the way healthcare professionals think, learn, and make decisions. In a time where medical knowledge is expanding faster than ever—and where patients are more informed, complex, and involved—this book introduces a transformative framework for collaborative, intelligent, and personalized care.

Drawing on the evolution of healthcare systems from Web 0.0 to Web 3.0, the book explores how technologies like AI, semantic knowledge graphs, and decentralized platforms can empower clinicians, patients, and students to co-create knowledge. It introduces innovative tools such as Patient Journey Records (PaJRs), Case-Based Blended Learning Ecosystems (CBBLE), and User-Defined Logic-Connected Ontologies (UDLCOs), showing how they can reshape everything from bedside decision-making to medical education.
This isn’t just a book for tech enthusiasts. It’s written for frontline doctors, educators, hospital administrators, digital health innovators, and students who are asking: “How can we think better in medicine—not just faster?” It’s a must-read for those who believe that the future of healthcare lies not just in data or algorithms, but in participation, storytelling, and collective intelligence.
Whether you're leading a hospital, redesigning a curriculum, or simply curious about the future of thinking in medicine, this book offers both a vision and a playbook for the intelligent, human-centered systems we need next.


Please list draft Table of Contents

Participatory Medical Cognition 
0.0–3.0 and beyond

A Playbook for Hospitals and Medical Professionals

Preface: Setting the Stage
Introduction: Rethinking How Medicine
Thinks – The Rise of Participatory Medical Cognition

Points that would be covered:

Overview of traditional vs. participatory cognition.
Summary of Web 0.0 → Web 3.0 evolution:

- Web 0.0: Paper-based, physician-as-authority.
- Web 1.0: Static digital repositories (e.g., Medline).
- Web 2.0: Interactive, user-generated platforms (forums, CBBLEs).
- Web 3.0: Intelligent, decentralized, machine-readable systems (UDLCOs, AI, blockchain).

Why this shift matters now: personalization, complexity, overload.
Who this book is for and how to use it.

Part 1: The Old World – Medical Cognition in Web 2.0

Chapter 1: How We Used to Think – The Pre-Digital and Web 2.0 Era
Doctor-centric cognition vs. early participatory models.
Rise of online forums, telehealth, and e-health platforms.
Introduction of Patient Journey Records (PaJRs) and narrative-based learning.

Chapter 2: Toolbox 2.0 – Narrative, Cases, and Critical Thinking
Deep dive into PaJRs and how they’re used.
Case-Based Blended Learning Ecosystems (CBBLE): collaborative learning networks.
Introduction to Critical Realism (CR): Used as a method for students and professionals to uncover deeper causality behind surface symptoms.
“Learning globally to act locally” as a pedagogical anchor.
Part 2: The New Frontier – Medical Cognition in Web 3.0


Chapter 3: Web 3.0 in Healthcare – What Changes?
Web 3.0 features: decentralization, semantic web, AI, patient data ownership.
Limitations of Web 2.0: unstructured, slow, difficult to scale.
Introduction to UDLCOs: structured, reusable, machine-readable knowledge.
Pedagogical use of PaJRs: Postgraduates have used PaJRs within CBBLEs to identify rare patterns, critically appraise clinical decisions, and improve reflective practice.
Tools: blockchain, AI agents, registries, digital public infrastructure.

Chapter 4: From 1X to 10X – What Web 3.0 Unlocks
Shift from manual to intelligent analysis: e.g., Early signals of rare diseases detected via AI analysis of UDLCOs.
Personalized, hyperlocal decision-making.
AI-assisted learning vs. slow CBBLE threads.
Web 3.0 systems will integrate with EMRs, hospital dashboards, national registries, and decision-support tools to enhance usability and continuity of care.

Part 3: Building It – A Practical Playbook


Chapter 5: Starting the Shift – Narrative as Infrastructure
Building a culture of participatory medicine.
How to implement secure, consented PaJR capture via EMRs and apps.
Training clinicians in narrative medicine.
Anonymity & Data Privacy: Structured de-identification protocols will be applied to all PaJRs, stripping personally identifiable data and ensuring consented use. No narrative element traceable to a patient will be stored in UDLCOs.
Ethical challenges and solutions.


Chapter 6: Transforming Medical Education – Teaching Participatory Cognition
Why traditional medical education doesn’t match modern cognitive needs.
Integrating PaJRs into bedside teaching, case discussions, digital electives.
Real-world use by students: Postgraduates and undergraduates in pilot CBBLEs have contributed to early diagnostic insights, shared reflective narratives, and even co-authored publications derived from PaJRs.
Gamification and peer-led knowledge structuring.
Bridging digital, linguistic, and cognitive gaps in learning.

Chapter 7: From Story to Structure – Creating UDLCOs

Creating logic-connected ontologies from PaJRs and CBBLE insights.
NLP, machine learning, and semantic tools for knowledge extraction.
Governance, version control, and ontology curation.

Cross-referencing with literature: UDLCO insights can be linked to existing ontologies (e.g., SNOMED, MeSH) and validated against current medical literature to enhance both novelty and reliability.


Chapter 8: Tech Stack 3.0 – Integration and Interoperability

Linking AI tools with EMRs, dashboards, and CDSS platforms.

Blockchain for secure versioning and traceable provenance.

Designing intuitive user interfaces.

Bridging the language divide: Natural language processing can support multilingual documentation and translation, enabling students and doctors to learn from patient stories even when language or culture differs.


Part 4: Measuring What Matters – Evaluating Transformation


Chapter 9: Metrics That Matter – How to Know It’s Working

PaJR Metrics: volume, quality, diversity.
CBBLE Metrics: participation, insight generation, global-to-local application.
UDLCO Metrics: creation frequency, reuse, impact on diagnosis/treatment.
Integration Metrics: adoption of tools, interoperability success, efficiency gains.
Clarification of “systems”: Metrics will assess integration with EMRs, decision-support systems, dashboards, knowledge repositories, and institutional platforms.


Chapter 10: Building a Culture of Continuous Learning

Creating feedback loops across users, cases, and tools.
Embedding continuous evaluation into hospital workflows.
Scaling across institutions and contexts.
Case studies and qualitative stories: Surveys and interviews with frontline users, plus impact stories where UDLCOs led to faster diagnosis or safer treatment, will be documented as proof of value.

Conclusion: The Future of Thinking in Medicine

Participatory medical cognition as a human–machine evolution.
Building ethical, equitable, intelligent ecosystems of care and learning.
A call for openness, experimentation, and collaboration.

Manuscript information
Estimated word count (should be between 35,000 and 200,000 words)

200,000 words

If accepted, when do you expect this title to be complete?

15th November 2025 (might be completed before this)

Please note any special features : Yes would be included.

(photographs, tables, diagrams illustrations etc.)

Please indicate if any parts of the proposed title will include sections in a language other than English: No

                             Permissions 

Has any part of this work been published previously? 
(for example, in a different language; as a journal article; 
as a book chapter; in a previous edition).
 
No 


Are there any permissions required from other copyright holders? Please note that, should you require permissions for your project, your proposal will not go to review until you supply evidence that the owners of this permission are willing to release the rights. 
Please specify

None required 


Please list related titles in this field

1) Symptom to Diagnosis An Evidence Based Guide

2) User-driven Healthcare and Narrative Medicine: Utilizing Collaborative Social Networks and Technologies


3) Clinical Solutions and Medical Progress through User-Driven Healthcare


4)Participatory Research for Health and Social Well-Being

5) Digital Health Approach for Predictive, Preventive, Personalised and Participatory Medicine (Advances in Predictive, Preventive and Personalised Medicine

6) Conscious Notebook: A Narrative Human Ontology

How will your proposed book differ from these? 

It will ensure that audience actually participate in collective medical cognition to live the book's learning points and utilise it toward expanding the horizons of human trouble shooting

What new perspective will it offer?

It will demonstrate hands on how humans are able to effectively trouble shoot their problems utilising principles of clinical engineering and collective cognition to tailor precision medicine requirements for every human need.

Who is the primary market for this title?

Engineering and medical students as well as practitioners globally 

Please list up to three secondary markets for this title

Healthcare administration

Healthcare entrepreneurs

AI Healthcare 

What societies, professional bodies etc. are interested in this field?

Society for Participatory Medicine

Society for Medical Decision Making (SMDM)


What conferences, events, symposia etc. take place in this field?



What social media groups (on Facebook, Twitter, LinkedIn, etc.) could we promote this book to?






What publications, sites, blogs etc. might be interested in this book/in reviewing this book?


Please list up to four qualified people who might be interested in reviewing this title? Please supply email/contact addresses.

Amy Price, Editor, Journal of Participatory Medicine

Carmel Martin, Editor, Clinical Complexity, Journal of evaluation in clinical practice

JP Sturmberg, Editor, Systems and Clinical complexity 

Teaching, learning and research

What are the main research centres in this field?




What are the main courses in this field?




What level of study would this title appeal to?

Undergraduate, post graduate, doctoral levels 

Would this title appeal to professionals, managers or other practitioners? Which ones?

Professional clinical engineering entrepreneurs, doctors and legal experts 

Could your book be a set text/recommended reading for a course or courses? If so, please name the course, institution and course leader if known 



https://www.sctimst.ac.in/academic%20and%20research/academic/Programmes/Joint%20Programmes%20of%20SCTIMST,%20IIT%20Madras%20and%20CMC,%20Vellore/

https://medicine.careers360.com/articles/top-10-medical-courses-in-india


First draft April 2025:

Thanks for your valuable feedback around topics that we associate with what we label as medical cognition and it would be a pleasure to invite you to join our book writing team on a book titled, "Participatory Medical cognition Web 2.0-3.0 where we are currently drafting a proposal for Cambridge publishers.

Web 0.0 is pre internet era of late 80s when we were medical students when everything was offline and on paper and most people hadn't seen computers or internet or mobile phones. I recall even as a PG in the 90s we used to read a regular column in the Times of India newspaper about what was going on in the internet because we didn't have any computer to actually see it ourselves!

Then slowly came web 1.0 which was more like a book on a website with no chance of human interaction with the people who had created the web content. 

Web 2.0 evolved from the year 2000 from email groups to social media and was characterized by online virtual world global human interactions generating learning outcomes and impacting real world outcomes. 

Our previous book largely showcased that and one can read it here👇


Currently with the advent of LLMs we have entered the Web 3.0 era

One of the first steps all of us can take here is to go through our students online portfolio archives here containing 5000 cases and try to classify the various web n.o numbers they can identify here: 


In contrast check out the web n.o numbers in the single online learning portfolio active since last one year belonging to a student's mother who is doing it voluntarily once our students stopped last year 👇


One may be able to recognise more and more Web 3.0 features since last one year

Below are a few links to text and podcasts to Medical Cognition and its evolution from Web 0.0 to Web 3.0 including one around our previous book on Medical Cognition Web 2.0

Podcasts:

Medical Cognition 1.0


Medical Cognition 2.0:


Medical Cognition 3.0:




Our previous book linked above was on integrating medical education and practice in the times of Web 2.0

This next book we are proposing is all about integrating medical education and practice in the current Web 3.0 era that's growing exponentially by leaps and bounds and may not remain bound by paper volumes!

Can this result in a playbook that anyone can implement in their hospital?

Areas that can be covered in the book (in no particular order, each could be a chapter) - 

1. Single case trials / pragmatic trials (how multiple disconnected single case journeys lead to insights about a new case)

2. Unraveling of complex medical cases (imaginary pillow et al)

3. Pedagogical tool (for PG students)

4. Participatory medicine (where patient, advocate, doctor, experts, enthusiasts are all participating in the patient's recovery; everyone at KIMS contributes to a fund for patients)

5. Transparent, evidence based medicine (papers shared, discussions in front of patient)

6. Technology used (Meta AI, food shot auto recognition, AI evaluation, handwriting reading, summarisation, chronological restructuring, etc)

7. Crowdsourced (patient sent) data (readings, food plates, tests, other doctor opinions, internet findings, self experiments)

8. Getting over the urban-rural, language, socio-economico divide (multi-lingual whatsapp chats, patients abruptly leaving groups not understanding the english, keeping socio economic conditions in mind when prescribing more tests)

9. The open source, open access, responsible way (ensuring everything is anon

Below is the current outline part of the book content in our proposal but feel free to add and mold it.

Medical Cognition: From Web 2.0 to Web 3.0 - A Playbook for Hospitals and Medical Professionals


Part 1: Understanding the Foundations - Medical Cognition in the Web 2.0 Era


Chapter 1: The Evolution of Medical Cognition:
Traditional doctor-centric models of medical thinking.
The emergence of participatory medicine and user-driven healthcare in the Web 2.0 era.
The role of online patient portals, telehealth, and early social networks in healthcare (e-healthcare, Health 2.0).
The shift towards patient empowerment and collaborative care models.
Introduction to narrative medicine and the importance of individual patient stories.
Limitations of Web 2.0 in fully leveraging patient-generated knowledge.

Chapter 2: Key Concepts of Web 2.0 Participatory Medicine:
In-depth explanation of Patient Journey Records (PaJRs): de-identified case reports including clinical history and patient narratives as a foundation for learning.
Understanding Case-Based Blended Learning Ecosystems (CBBLE): digital platforms for global collaborative learning and discussion around PaJRs.
Introduction to Critical Realist (CR): a framework for critical analysis and deeper understanding of complex healthcare situations by combining self-directed learning with critical realism to uncover underlying mechanisms.
The concept of "learning globally to act locally" within CBBLE.


Part 2: The Paradigm Shift - Embracing Medical Cognition 3.0


Chapter 3: The Dawn of Web 3.0 in Healthcare:
Defining Web 3.0 and its core characteristics (decentralization, open access, user ownership, semantic web, AI integration) [Not directly from sources but inferred].

How Web 3.0 technologies (e.g., blockchain, agentic AI, registries, Digital Public Infrastructure, semantic knowledge graphs) can address the limitations of Web 2.0 in medical cognition
The potential for a more decentralized, participatory, recursive ecosystem for learning and care.
The role of community ontologies (UDLCO) as organized, reusable knowledge structures readable by both humans and machines.

Chapter 4: The 10X Value Proposition: How Web 3.0 Enhances Medical Cognition:
Enhanced Knowledge Discovery:
Web 2.0: Manual analysis of PaJRs and discussions within CBBLE.
Web 3.0: AI-driven analysis of structured UDLCOs to identify complex patterns and causal pathways across a vast number of cases with greater speed and accuracy.

Example: Identifying subtle early indicators of rare diseases by cross-referencing nuanced patient narratives within UDLCOs that would be missed in manual analysis of Web 2.0 data.

Hyperlocal-Personalized Medicine:

Web 2.0: Contextual understanding through individual case reviews.

Web 3.0: AI leveraging UDLCOs to understand the interconnectedness of individual patient data (multi-modal data) with a broader knowledge base, leading to highly tailored diagnostic and treatment approaches.

Example: AI suggesting personalized treatment modifications based on UDLCO-derived insights into how patients with similar complex co-morbidities responded to various interventions, going beyond standard clinical guidelines.

Accelerated Learning and Knowledge Sharing:

Web 2.0: Collaborative learning through CBBLE discussions, which can be time-consuming.

Web 3.0: Instant access to a dynamic, machine-readable knowledge base (UDLCOs) that synthesizes insights from countless cases and expert analyses, facilitating rapid knowledge dissemination and application.

Example: A junior doctor in a rural setting instantly accessing UDLCOs related to a rare presentation, providing synthesized insights and potential diagnostic pathways based on the collective experience captured and structured within the system.


Improved Decision Support:

Web 2.0: Clinician-driven interpretation of data and guidelines.

Web 3.0: AI-powered decision support systems that leverage UDLCOs to provide context-aware recommendations, considering the nuances of individual patient journeys and emergent patterns.

Example: An AI system suggesting alternative diagnostic possibilities for a patient with atypical symptoms based on patterns identified in UDLCOs from seemingly unrelated cases that share underlying contextual factors (e.g., environmental exposures, behavioral predispositions).

Democratization of Medical Knowledge:

Web 2.0: Increased patient access to information, but often unstructured and potentially overwhelming.
Web 3.0: Structured, contextually rich UDLCOs making complex medical knowledge more accessible and understandable for both patients and professionals, fostering shared decision-making.
Example: Patients with chronic conditions accessing UDLCOs related to their disease to gain a deeper understanding of different treatment pathways, potential long-term effects, and patient-reported outcomes, enabling more informed discussions with their healthcare providers.



Part 3: Implementing Medical Cognition 3.0 - A Practical Playbook


Chapter 5: Setting the Stage - Building Your Foundation:
Establishing a culture of participatory medicine and narrative capture.
Implementing systems for secure and consented collection of PaJRs (leveraging existing EMR systems and patient-facing platforms).
Creating and managing CBBLE groups focused on specific medical specialties or complex cases.
Training medical professionals on the principles of narrative medicine and active listening.
Ensuring data de-identification and ethical considerations.

Chapter 6: Building the Knowledge Ecosystem - Creating UDLCOs:
Developing standardized templates and protocols for structuring insights derived from PaJRs and CBBLE discussions.
Utilizing semantic web technologies and AI tools for automated extraction and structuring of knowledge into UDLCOs.
Establishing governance and curation processes for UDLCOs to ensure accuracy and relevance.



Strategies for linking UDLCOs to existing medical ontologies and knowledge bases.
Tools and platforms for UDLCO creation and management 

Chapter 7: Integrating Web 3.0 Tools and Technologies:
Leveraging AI for automated analysis of PaJRs and UDLCOs (natural language processing, machine learning, knowledge graph construction).
Exploring the potential of blockchain for secure and transparent data sharing and management of UDLCOs [Inferred].
Integrating UDLCOs with clinical decision support systems and EMR platforms.
Developing user-friendly interfaces for accessing and querying UDLCOs for clinicians and potentially patients.
Addressing technical challenges and interoperability issues.



Part 4: Measuring Success and Continuous Improvement


Chapter 8: Rubrics for Evaluating Implementation Success:
PaJR Contribution Metrics:
Number and quality of PaJRs submitted by clinicians and potentially patients.
Diversity of cases captured (specialties, complexity, patient demographics).
Completeness and richness of narratives within PaJRs.
CBBLE Engagement Metrics:
Level of participation in CBBLE discussions (number of posts, threads, expert engagement).
Quality of insights and critical analysis within discussions.
Evidence of "learning globally to act locally" – application of shared knowledge in local clinical practice.
UDLCO Creation and Utilization Metrics:
Number of structured UDLCOs created and curated.
Frequency of UDLCO access and utilization by clinicians and AI systems.
Impact of UDLCO-derived insights on diagnostic accuracy, treatment decisions, and patient outcomes (measured through audits and outcome studies).
Web 3.0 Tool Integration Metrics:
Successful integration of AI and other Web 3.0 technologies with existing systems.
User adoption rates of new tools and platforms.
Efficiency gains in knowledge discovery and decision support.


Qualitative Feedback:
Surveys and interviews with clinicians and patients to assess the perceived value and impact of the implemented system on medical cognition and patient care.
Analysis of case studies showcasing breakthroughs or improved outcomes attributed to the Web 3.0 approach.

Chapter 9: Fostering a Culture of Continuous Improvement:
Establishing feedback mechanisms for refining PaJR collection, CBBLE engagement, and UDLCO development.
Regularly evaluating the impact of the implemented system on key performance indicators (e.g., diagnostic accuracy, time to diagnosis, treatment effectiveness, patient satisfaction).
Encouraging ongoing research and innovation in leveraging Web 3.0 for medical cognition.

Adapting the playbook based on learnings and emerging best practices.

Conclusion: The Future of Medical Cognition - A Collaborative and Intelligent Ecosystem

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