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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