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
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
Proposal template shared by publishers (deidentified):
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