Glossary of terms :
Medical cognition :
"The reality of the internet at the time of Web 1.0.
Like a giant library, it provided access to information, but the opportunity to become an author was limited to a privileged few.
A decade later, this library was transformed into a vibrant forum. The shelves are still there, but you can now add your own books and journals, leave comments and interact with other readers. This is the
dynamic and interactive era of Web 2.0,
where users have moved from being passive readers to active participants, enriching the web with their own content and ideas.
With Web 3.0, you enter a new world –
an intelligent, networked library where books are not only read, but understood and recommended by machines to potential readers with the right profile. This world of Web 3.0 uses artificial intelligence and blockchain technology to make the internet not only more interactive, but also more secure and efficient"
Unquote:
https://blog.seeburger.com/the-evolution-of-the-internet-web-1-0-web-2-0-web-3-0-web-4-0/
Link to podcasts
Medical Cognition 0.0 to 1.0
Medical Cognition 1.0 to 2.0:
The above is about our past book on Medical Cognition 1.0-2.0
Medical Cognition 3.0:
The above is on our current work through which we plan to pivot our next book on Medical Cognition 2.0-3.0
Other than routine medical cognition tools of system 1 eyeballing pattern recognition, we use routine tools of system 2 asynchronous intelligence AI, aka primordial AI aka academic learning to solve real patient problems.
Developing the Medical metacognition problem statement at the beginning of the introduction to all our ongoing projects is because, it's at the core of all our projects using both system 1 and 2 cognitive processing:
System 2 thinking began as an asynchronous academic tool to make communication and thinking slower to suit our individual workflows.
However this essence of academics also makes our three dimensional existential reality two dimensional as that helps to somehow better analyze our three dimensional existence manifest in daily random events and even manipulate the randomness toward apparently improved outcome events.
Of all the routine system 2 tools, we have been largely enamoured by a few that we have written about in the past and continue to use them daily in our community patient follow up and family adoption through online PaJR groups which are the online components of our case based blended learning ecosystem CBBLE and the two have evolved from what has been often described in the past as "user driven healthcare" which has it's own big fat text book here : https://www.amazon.in/User-
as well as had a journal with the same name since 2011 here: https://www.igi-global.
Medical cognition can be formally defined as a "broad area consisting of various system 1 and system 2 human cognitive tools to resolve clinical complexity (diagnostic and therapeutic uncertainty). These tools are often used through various medical cognitive platforms such as synchronous face to face interactions (often system 1) and asynchronous communication and learning between multiple stakeholders in connected web space (user driven healthcare UDHC, patient journey records PaJR) and blended to form "case based blended learning ecosystems CBBLE (often a blend of system 1 and 2).
User driven healthcare UDHC : Subset of "Medical Cognition' globally where multiple users, all healthcare stakeholders including patients, interact online to understand and take decisions on meeting patient requirements.
Here's about how it transformed into the current CBBLE since 2017 at Narketpally : https://www.ncbi.nlm.nih.gov/
Medical cognition has been put on steroids with the advent of LLM!
While every year has brought new tech advances in the form of a variety of LLMs and has been a boon for some of us who have enjoyed riding the crest of this wave for a lot many perhaps (our primary beneficiaries of medical education, patients and secondary beneficiaries, students, all in our PaJR groups), it may have been a rough weather challenge progressing with the day partly because of a voluminous increase in TLDR, which also necessitates LLM summaries toward sense making attempts as well as having to look up terminologies using glossaries such as this one.
TLDR: https://en.m.wikipedia.org/wiki/TL%3BDR
WR: Will read
Flipped classroom: https://en.m.wikipedia.org/wiki/Flipped_classroom
CBBLE (pronounced cable) : Case based blended learning ecosystem that is available locally in many institutions and some are connected globally to each other. CBBLE is different from UDHC in that it is not purely online but blended offline and online.
PaJR : The key concept lies in the use of regular patient reported outcomes to locate the phase of illness in a
patient journey.
More here : https://userdrivenhealthcare.
Ontology : "theory of objects and their ties. It provides criteria for distinguishing different types of objects (concrete and abstract, existent and nonexistent, real and ideal, independent and dependent) and their ties (relations, dependencies and predication)."
Every medical student may remember how important it was to know the relations of every organ in their first introduction to medicine through human anatomy.
UDLCO :
User driven learning community ontologies
3) UDL :
User driven learning
4) Patient centered UDLCO (particular patient knowledge aka age old precision medicine ) :
Sample :
Here's an example of a pico rubric in the UDLCO conversational learning format that sets the context.
Once you scroll down by 60% of the page you begin to see the pico and further dissection of the clinical significance around the efficacy of the drug under discussion 👇
Context with PICO not yet consolidated:
5) General medical knowledge centered UDLCO sample :
Contribution of Anatomy dissection and autopsies to growth of Medical knowledge and Organ transplantation--
Benhur effect and the horcrux EHR:
CRH: While PaJR and CBBLE are tools that facilitate data capture for participatory Medical cognition to integrate medical education practice, CRH is a processing tool once primary data is captured that leads to collective critical realist thinking either with individual PaJR patient raw conversational data or population based already processed and structured data getting further processed through user driven team based learning to create user driven learning community ontologies UDLCOs.
More about UDLCOs in the above links but essentially these are real patient contextual data woven together to create a case based reasoning tapestry accessible to AI driven processing and Web 3.0 medical cognition. More about case based reasoning here: https://pmc.ncbi.nlm.nih.gov/articles/PMC544898/ and here: https://www.mdpi.com/2076-3417/14/16/7130
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