Friday, November 22, 2024

Technology end user driven ecosystem for healthcare

Lecture notes for November 28, shared publicly here in advance as part of a flipped classroom approach as, instead of reading out the below slides together with the offline audience there, we hope to have more questions and discussion in that one hour with them as well as spend more time in a live demonstration of how to capture tech end user, real patient data (after already having obtained patient consent and also discuss the process of obtaining ethical consent) and how to archive it for retrieval and reasoning toward optimal data driven healthcare.



First slide:

STATUTORY WARNING: There's too much information in this online presentation that can make anyone go TLDR (too long didn't read).

The gist of it is:

Understanding the role of technology end users in data capture to develop better predictive analytics toward improving patient outcomes.

This will be exemplified in a 3D offline demo with a real patient where our predictive analytics are likely to fail and the point we would make is that one can't solve the wicked problem of healthcare in one go for even a single patient but it's a journey! Our group does this as a 24x7 regular journey through user driven healthcare UDHC tools such as PaJR and CBBLE (more about them in links below). At the end in the opportunity section of the SWOT analysis we propose a CBBLE program to develop leadership and innovation skills through a medical humanities program the design of which is also linked below.

What literally is technology?


Etymology

The word techne comes from the Greek word for art, skill, craft, and technique. The modern-day English word technology comes from the prefix techne and the suffix ology; both words are of Greek origin.

https://en.m.wikipedia.org/wiki/Techne



Rhetoric: The techne/art/craft of human caring appears to be older than the invention of the wheel.

The trillion celled body itself appears to be a product crafted by a collaborative evolution of cells through ages. 

Rhetoric: Since it's early phases, healthcare technologists/craftsmen have been building a map to navigate individual illness journeys but that map is more like a static textbook and still not as dynamic as a current google map, which helps humans in their car driving journeys!

Healthcare is still largely 99.999% uncharted territory! Although the percentage perspective will vary with your years in healthcare. For example the first year medical student may think it's 99.99% charted territory!

Image with CC licence:https://commons.m.wikimedia.org/wiki/File:Greek_physician_and_patient,_plaster_cast_in_W.H.M.M._Wellcome_M0001578.jpg#mw-jump-to-license


Link to the actual lecture around this slide:

Slide 2: What literally is science?

May have originated from Proto-Indo-European language as *skh1-ie, *skh1-io, meaning "to incise". https://en.m.wikipedia.org/wiki/Science



Image with CC licence: https://commons.m.wikimedia.org/wiki/File:Sickle_and_throwing_knife_at_Manchester_Museum.jpg#mw-jump-to-license

And the image of the sickle and science is contained in an important writing tool for science! The question mark is a very important instrument of scientific scepticism:



Creative commons license: https://en.m.wikipedia.org/wiki/Question_mark#/media/File%3AQuestion_opening-closing.svg

Slide 3

Who are end users of technology?

Artists? Geniuses? Engineers, Developers, Ordinary Humans? Patients? Healthcare professionals?

EVERYONE!





The images show how any tech end user trying to drive a positive illness outcome through the healthcare system is akin to putting together a model art with different stages of uncertainty and finally some diagnostic and therapeutic confirmation in the second image rarely ever going near to the third in terms of certainty!

Image Source: https://youtu.be/sR0G23aC1Vw?feature=shared

More here from another lecture: https://youtu.be/XWUMogh96SM?feature=shared

Rhetoric: All human animals are genius artist end users of technology, carving their own life trajectories and designing their destiny. In recent times digital technology offers them a parallel space to create a digital twin of themselves in virtual universes that can further be embodied into robotic avatars in physical universes toward tech singularity!


Link to the actual lecture around this slide:

Slide 4: 

Science tries to know from events data and subsequent analysis

Technology otoh is artistic utilisation of knowledge to create a product?



Image with CC licence: https://en.m.wikipedia.org/wiki/Wheel#/media/File%3ARoue_primitive.png


Slide 5: Asynchronous communication of knowledge: asynchronous intelligence aka primordial AI and subsequently academic intelligence AI and finally current artificial intelligence AI

More here: https://medicinedepartment.blogspot.com/2021/06/introduction-to-evolution-of-human.html?m=0


Image CC licence: https://commons.m.wikimedia.org/wiki/File:Rock_Shelter_8,_Bhimbetka_02.jpg#mw-jump-to-license

Rhetoric: Human animals invented AI beginning with asynchronous intelligence through their ability to use cave painting tech to convert multidimensional real life data into two dimensional data in an xy axis cave wall that later evolved to paper and electronic media so that they could eventually manage their lives better as artistic modelling was easier in a two dimensional virtual plane than a multi dimensional real plane!

Let's look at where we have come all the way from primordial AI (aka asynchronous intelligence) to modern AI that models primordial AI to produce some currently interesting results particularly if the data capture is asynchronously hyperlocal.


Link to the actual lecture around this slide:


Slide 6:

Role of Hyperlocality in designing care for the Tech end user 

Introduction: https://medicinedepartment.blogspot.com/2024/11/udlco-ai-healthcare-complexity-and.html?m=1


Rhetoric:

Global learning toward hyperlocal caring:

https://userdrivenhealthcare.blogspot.com/2015/06/global-learning-toward-local-caring.html?m=1

Creating persistent clinical encounters to extend the scope of health care beyond its conventional boundaries utilizing social networking technology 

https://www.researchgate.net/publication/344227236_Persistent_Clinical_Encounters_in_User_Driven_E-Health_Care

Slide 7

Evolution of above workflow prototype in different institutions 

https://medicinedepartment.blogspot.com/2021/06/evolution-of-model-forpatient-centered.html?m=1

Formal departmental workflow:


Patient as a teacher:


Rhetoric: 

Blooming real patient OSCE driven CBMEs: 

Most learning is a process of objectively structuring subjective complex multidimensional real life data (blooms level 3-5) into a two dimensional space (blooms level 1 aka knowledge) that can be stored forwarded asynchronously and modeled conceptually to gain understanding (blooms level 2) through further analysis ( level 4) and evaluation (level 5) and then relooped into the learning ecosystem as creative communication/publication (level 6). This learning is cyclic and one can keep moving in and out of these levels at any entry or exit point regardless of level numbers. 

More:https://medicinedepartment.blogspot.com/2023/12/ongoing-project-draft-optimizing.html?m=1

Current workflow: 


Theory driven workflow with Gaurd Rails for all technology end users: 

https://userdrivenhealthcare.blogspot.com/2022/09/current-pajr-workflow-and-how-to-make.html?m=1

https://userdrivenhealthcare.blogspot.com/2024/09/pajr-current-roles-and-responsibilities.html?m=1

https://userdrivenhealthcare.blogspot.com/2024/08/template-for-pajr-user-driven-history.html?m=1

https://www.hipaajournal.com/de-identification-protected-health-information/

https://classworkdecjan.blogspot.com/2017/11/de-identifying-patient-data.html?m=1

https://userdrivenhealthcare.blogspot.com/2023/11/glossary-of-user-driven-healthcare.html?m=1

Theory driven workflow with Gaurd Rails for Health professional technology end users:

https://userdrivenhealthcare.blogspot.com/2023/12/pajr-checklist-for-inpatient-interns-in.html?m=1

https://durgakrishna09.blogspot.com/2023/08/graphical-patient-timeline-in-soap.html

http://medicinedepartment.blogspot.com/2020/05/informed-patient-consent-and.html?m=1

https://medicinedepartment.blogspot.com/2023/08/project-illustration-of-how-to-process.html?m=0

Reflective notes: https://medicinedepartment.blogspot.com/2023/06/?m=0

https://userdrivenhealthcare.blogspot.com/2024/01/generalized-feedback-for-internship.html?m=1

https://kandrucherishrollno68.blogspot.com/2023/09/35-year-old-female-presented-with-co.html?m=1

Slide 8: Demo 

Participatory medicine session real time video capture link: 





Previous demos here: https://medicinedepartment.blogspot.com/2023/10/medicine-department-presentations-2023.html?m=1

Our main focus during the one hour session shall be to provide a physical offline demo in the venue as we plan to bring one of our 80 year old patient right there on that day with his and his spouse's signed informed consent.

Demo through real patient case reports made by patient advocate volunteers:

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

https://24fpatient.blogspot.com/2024/11/52m-hypertension-diabetes-5-years-wb.html?m=1

https://narmeenshah.blogspot.com/2024/07/3-year-old-child-type1-diabetes-insulin.html?m=1

https://2patienthealthreport.blogspot.com/2024/06/patienthealthreport.html?m=1

Demo by few health profession students present in the venue sharing their experiences from their online learning portfolios below:

https://96sanjanapalakodeti.blogspot.com/?m=1

https://shivanikommera.blogspot.com

https://sreetejapalakonda29.blogspot.com/?m=1

https://himajav.blogspot.com

More tech end users online learning portfolios:

Informal healthcare learning and awareness volunteers (engineering , humanities, medicine) at our elective student learning hall of fame here: https://medicinedepartment.blogspot.com/2021/03/medicine-department-training-programs.html?m=1

Formal health professional students: 5000 cases in 1000 tech end user online learning portfolios:

https://medicinedepartment.blogspot.com/2022/02/?m=1

Rhetoric: Lectures are largely rhetorical and while they do embellish learning, demonstrations are a practical way to quickly get into the skin of the learning ecosystem!


Last slide: 



Creative commons license: https://commons.m.wikimedia.org/wiki/File:User-FastFission-brain.gif#mw-jump-to-license

SO  WHAT?? 


S

trengths 

Cherry picked and shared above 

Rhetorical past Foundations:




 https://www.amazon.in/User-Driven-Healthcare-Narrative-Medicine-Collaborative/dp/1609600975




https://www.amazon.in/Clinical-Solutions-Progress-User-Driven-Healthcare/dp/1466618760/ref=mp_s_a_1_7?dib=eyJ2IjoiMSJ9.hoaqPiWFhtqEMwMQQwMHh6XA3sAjvmg2CLCeKl5g5H0Ialw2OuleMzx8buI_Uy38jwXVQMd0DmaWlRRrlazufIw_5ZaexQ9p2PXyFOVw5dEa44eog_fkWlg4PrxhAkDUVHYa2f9ZAD_DEirz7irF6A.vddqDkHyAHumWw0iwm8nzCAMqU1uuBI_1AIACU9G4Kg&dib_tag=se&qid=1732295190&refinements=p_27%3ARakesh+Biswas&s=books&sr=1-7


https://www.igi-global.com/journal/international-journal-user-driven-healthcare/41022


W

eaknesses


Hint: A system is only as competent and efficient as it's individual components and participants 

Rhetoric: Practice, practice, practice! The only way to beat weakness toward strength and power is gaining regular knowledge through a life long journey of self directed learning!



O

pportunities

Rhetoric:
User driven alchemy of patient centered learning:
More here in the description box of the video: https://youtu.be/lDd1ikdL94A?feature=shared
Internal motivation chemistry toward generating leaders

Rhetoric: Formal medical education has currently given up trying to encourage real patient centred interaction among medical students during their training phase such that for a few who may engage in it at some point in their careers (even after they become faculty) would realise that the very interaction with real patients who are also humans would be transformative in their understanding of humans (again not everyone may be able to do that on one's own and there is an accepted dearth of faculty who can train people in doing that), which is the kind of training a real politician needs, to understand their polity and work for them rather than their own selves! It has been exemplified in the past by...more here:https://medicinedepartment.blogspot.com/2024/11/udlco-political-training-in-medicine.html?m=1

Our group does this as a 24x7 regular journey through user driven healthcare UDHC tools such as PaJR and CBBLE (more about them in links below). We propose this opportunity for a CBBLE program to develop leadership and innovation skills through a medical humanities program the design of which is also linked below.

T

hreats  

Thorns of clinical complexity amidst the guardrails



Picture taken from the garden this Sunday after pruning the Indian jujube tree:https://en.m.wikipedia.org/wiki/Ziziphus_mauritiana

Stakeholder trade offs in negotiating virtual transparency accountability vs real threat to privacy and security!

Rhetoric: Healthcare is not easy! It's a journey full of challenges and threats and yet a fantastic way to live life using science and technology to create one's life work of art!


Links to the lecture preceding the demo:

Post lecture demo discussion video:


What's the ask?

Thursday, November 21, 2024

UDLCO: Political training in medicine and the hidden curriculum

 

Summary: The term "hidden curriculum" is loaded with infinite variability of meanings that are largely hidden. Medical education has currently given up trying to encourage real patient centred interaction among medical students during their training phase such that for a few who may engage in it at some point in their careers (even after they become faculty) would realise that the very interaction with real patients who are also humans would be transformative in their understanding of humans (again not everyone may be able to do that on one's own and there is an accepted dearth of faculty who can train people in doing that), which is the kind of training a real politician needs to understand their polity and work for them rather than themselves! It has been exemplified in the past by BC Roy and reading his biography available full text here:https://archive.org/details/bidhanchandraroy00seng/page/n7/mode/1up may provide better insights to the kind of real patient centred medical education that existed during his time and it's current dearth that sometimes gets filled in by a hidden curriculum! The other rhetoric often shared around "medicine is politics" is as old as Virchow and one can read more about it here: https://pubmed.ncbi.nlm.nih.gov/19052033/


Conversational Transcripts:

[21/11, 10:09]vs: Members of the armed forces, scientists, and teachers topped the list of professions Indians trust the most, a new survey has found. According to the Ipsos Global Trustworthiness Index 2022, 64% Indians trusted armed forces members, followed by scientists at 63%, and teachers at 62%. These three professions were closely followed by doctors (55%) and judges (54%). 

The online survey was conducted between May 27 and June 10 among 21,515 adults in 28 countries. The survey also identified the most untrustworthy professions around the world. In India, politicians were to be most untrustworthy with 50% of the respondents holding unfavourable opinion towards them. Ministers in government came in second in the list of most untrustworthy professions in India at 43%, followed by journalists at 35%.

Unquote: https://www.financialexpress.com/life/lifestyle-ipsos-global-survey-indias-most-trustworthy-professions-doctors-armed-forces-2618043/

[21/11, 10:09] rb: Solution:

Let mbbs be the basic qualification for every politician.

MBBS and subsequent residencies are probably the only place where humans get mandatory training in politics as part of a hidden curriculum!

[21/11, 13:44] N: Politics or polytricks   are the hidden curriculum in every field I hope ....it's a part of being in society ..as each one of us have our selfish motives

[21/11, 14:01]Prof Meu: Being selfish and looking after one's own self is no crime, even the old saying " God helps them who help themselves.  But your way of looking after yourself should not damage another person. So being in society, and ensuring your own survival, it is of utmost importance to be diplomatic, yet ethical and humane and that is perhaps the greatest skill one needs to learn and practice.

[21/11, 14:33] A: So very well said !

[21/11, 15:40] N: I am here talking about the bigger picture ......and I am sorry if it's offended you ..nothing personal

[21/11, 15:41] N: I was telling that polytics   is just not only in MBBS  ..but everywhere humans are  ...I hope I made my point clear 😊...

[21/11, 15:43] N: Looking after ones own self is your personal responsibility and utmost priority it's not at all selfish  ..but where do you draw line between self satisfaction and greed ....is.....each one's decision ....

[21/11, 15:44]ss: All Humans are social, political & economic animals! 😷🤐

[21/11, 15:44]N: I am sorry you missed to see my point of view ...

[21/11, 15:44] N: So very true...can add few more adjectives

[21/11, 15:46]Prof Meu: I am not offended

And why should I?

Just as you talked about bigger picture I did the same, but from a different perspective..

😁😁

[21/11, 15:46] N: Oh   great

[21/11, 15:46] N: Thank you for understanding dear😍😍

[21/11, 15:47] N: Vasudhaiva kutukmb kam

[21/11, 15:47]N: All of us are one big family...

[21/11, 15:48]Prof Meu: And in that family some are Pandavas while many are Kauravas....🤣🤣🤣🤣

[21/11, 15:49] N: Exactly you read my mind ...😍🧐

[21/11, 15:49] N: That's exactly what I wanted to say ...😅👍

[22/11, 08:21]rb: Yes the term "hidden curriculum" is loaded with infinite variability of meanings that are largely hidden and I should have been more explicit about what I meant by it!

Medical education has currently given up trying to encourage real patient centred interaction among medical students during their training phase such that for a few who may engage in it at some point in their careers (even after they become faculty) would realise that the very interaction with real patients who are also humans would be transformative in their understanding of humans (again not everyone may be able to do that on one's own and there is an accepted dearth of faculty who can train people in doing that), which is the kind of training a real politician needs to understand their polity and work for them rather than themselves!

It has been exemplified in the past by BC Roy and reading his biography available full text here:https://archive.org/details/bidhanchandraroy00seng/page/n7/mode/1up may provide better insights to the kind of real patient centred medical education that existed during his time and it's current dearth!



Monday, November 18, 2024

UDLCO: AI healthcare complexity and hyperlocality

 Introduction:


Hyperlocal has been described as "information oriented around a well-defined community with its primary focus directed toward the concerns of the population in that community"

Also been described as referring to "the emergent ecology of data (including textual content), aggregators, publication mechanism and user interactions and behaviors which centre on a resident of a location and the business of being a resident"

Hyperlocal content has two major dimensions: geography and time. 

More recently, hyperlocal content has evolved to include GPS enabled internet integrated mobile applications which score highly on both the geographic and the time dimensions. They are capable of delivering content that is relevant not just in a community but relevant right down to the individual within a geographic area that can be measured in meters and blocks not towns and neighborhoods. They are also capable of delivering content relevant at very short timescales such as seconds or minutes not just days or weeks.


  • "Communities can play a key role in addressing health equity issues through hyper local healthcare delivery models that tailor interventions to address specific local needs and culturally sensitive contexts. 
  • Success in delivering on population-specific needs depends on health system’s ability to triangulate data from multiple sources, respond to needs with innovative and timely interventions in collaboration between communities and partners"

UDLCO summary:

The conversations below begin with global discussion around AI healthcare as well as 2.0 healthcare and their limitations that can be tackled through hyperlocal data capture and feedback (blended with AI) content delivered along with an illustration of the same from a user driven healthcare hyperlocal data capture and feedback learning platform called PaJR.


Conversational Transcripts:

[12/11, 17:49] SPAI Health: A poignant reminder on how hype-cycle works (and abysmally falls). Technology is a great tool but use-cases need to be hyperlocal. There is no one size fits all, especially when it comes to healthcare.


[12/11, 18:39] rb: Thanks for this interesting comment!

I was taking some real patients for our workshop with engineers on "tech end user driven healthcare' here and this will be a good opening line!

[15/11, 06:57] AT AI Healthcare: Here is a research publication on *Medical Appropriateness*.


[15/11, 07:54] AT AI Healthcare: Both *Medical Appropriateness* and *Medical Necessity* are necessary for *Value-based Care*.

[15/11, 08:06] S: This is possible, but is a complex issue. Of course using SNOMED it is definitely possible to design such a system. One that is practical and is able to do it in real-time. Raise appropriate alerts and warnings. To be effective, the algorithm should be running in the background, constantly checking for inconsistencies and errors. Needless to say, the design is complicated.


[15/11, 08:20] AT AI Healthcare: You are absolutely right - this is highly complex. Therefore, we used *Neuro-Symbolic AI* to address this complexity real-time. 😊


[15/11, 08:32] S: I did some research of my own. AI vs coded approach. In healthcare, due to legal and ethical issues, it is imperative that errors are avoided or kept at a minimum. We medicos subscribe to the dictum of _primum non nocere_ - firstly, do no harm. AI models have an inherent error - mostly the training & validation data-related. Coded provides precision. It is binary - either true or false. That is why there is no alternative to using something like SNOMED. The large vocabulary, all coded by their concept definition using semantic triple and description logic, provides a wide coverage of the many information that gets collected during any clinical encounter.


[15/11, 09:31] ATAI Healthcare: I use *GREAT* AI. It stands for Generative, Responsible, Explainable, Adaptive, Trustworthy Artificial Intelligence.


[15/11, 10:19] S: You have my very best wishes in convincing the medicos.


[15/11, 11:00] ATAI Healthcare: My last comment on this subject. I don't try or even need to convince a medico. I use medical data to prove my models. Medical being safety critical domain, I know - if my model works on medical, it works in any other domain. Like if you can drive safe without using horn in Bangalore, you can drive anywhere in the world. 😊

[15/11, 11:05] Rkb: I completely agree with the later statement, if u can drive in India u can drive anywhere in the world 😀


[15/11, 11:12] rb: Provided you can pass their licencing exams, which appears to be easier to pass in India!


[15/11, 12:51] S: Having worked with both AI/ML and codes, I am firmly convinced that without codes CDSS is not advisable. Many times diagnosis is only possible on post-mortem. CDSS is used in treatment/management for which high degrees of precision are required.


[15/11, 12:55] ATAI Healthcare: I shall deliver lecturers on *"Architecting Secure Software Systems"* and *"Cybersecurity Cognitive Graph"* at NITK Surathkal at the *Five Days Faculty Development Programme On Software and Systems Security*, 9th to - 13th December 2024" (https://www.nitk.ac.in/upcoming_events). 

There are some vacant seats. If you know any faculty interested in Cybersecurity, please pass on this message.


[15/11, 15:31] SPAI Health: Interesting discussion. Both Dr.A and Dr.S I guess are medicos, but they seem to be so knowledgeable that I suspect they have a PhD in Computer Science too in the side🙂👍! 

One key thing we are realising in the AI community is that LLMs have limitations in specific domains, as they are optimised for freeform reasoning from large, disconnected datasets (case in point is the failure of Babylon to make the cut, which we discussed recently). Rule based use of logic (expert systems) exists from the days of IBM Watson and is perhaps best suited for healthcare but then it lacks the ability to take on board the latest and evolving scientific thoughts. There is also a hybrid track currently upcoming which tries to marry these two ends of the spectrum using domain specific knowledge graphs to upgrade and focus LLMs capabilities. There is also a need to go hyperlocal due to the wide variations in datasets across demographics. To some extent using RAG along with Edge AI is a potential solution.

[15/11, 15:58] S: Dr AT is a _bona fide_ computer scientist. Works in hardcore AI applications. Health is one of his areas of interest. I am a medical professional and health informatics specialist, majorly into data standards, interoperability and analytics. 🙂


[15/11, 21:37] rb: "From the doctor's summing up, Ivan Ilych deduced the conclusion that things looked bad, and that he, the doctor, and most likely everyone else, did not care, but that things looked bad for him. And this conclusion impressed Ivan Ilych morbidly, arousing in him a great deal of pity for himself, of greater anger against this doctor who could be unconcerned about a matter of such importance.”

Unquote



[16/11, 07:10] ATAI Healthcare: Here is Fig 8-1. Diagnostic approach to hemolytic anemia from Benjamin Djulbegovic's *Reasoning and Decision Making in Hematology* published in 2992.


[16/11, 08:31]rb: Excellent illustration of the global limitation in rule based algorithms in tackling clinical complexity (for those not handling real patients hyperlocally) further enhancing the point @⁨SPAI Health⁩ was trying to make. Ben assumes that if coombs negative,  HS would be the most likely diagnosis probabilistically as he's a US citizen but try telling that to an Indian or someone from Dhaka: https://pmc.ncbi.nlm.nih.gov/articles/PMC2975852/


[16/11, 08:34]rb: Many of our patients in our resource limited rural medical college are coombs positive but we don't jump to a diagnosis of AIHA as we know that it could be false positive and largely we still have to weigh everything on the basis of the patient event timeline (aka history) as well as clinical signs, external or internal.


[16/11, 08:55]G: In 1983, my consultant Prof SB was discussing using weighted scores for automated diagnosis. He asked around for symptoms and signs for possible appendicitis and said a more than a 10 score is final. The conversation went like this for the patient we were observing 
Pain abd ——1 
Fever ——1 
High TLC  ——1 
Loss of appetite  ——1/2
Vomiting ——1 
Tender rt Iliac fossa  ——10
Somewhat clinching the diagnosis

[16/11, 08:55] G: Unfortunately AI can’t examine for tenderness

[16/11, 09:10]rb: Yes it can't capture data unless fed. Not long before it grows up to robotic arms

[16/11, 09:11]rb: Couldn't resist sharing this happening in our home health PaJR right now!👇

Learning summary spoiler: AI LLMs are more likely to err about medicine facts on the side of caution lest they damage their customers egos!

Those who don't understand the script can ask meta AI to translate it for them.

Morning PaJR session:

[14/11, 11:36] PaJR moderator: Apnake baki der group a add korle apni unader keo eta ki bhabe kora jete pare sheta dekhiye dite parben?


[14/11, 19:12] Patient Advocate 63M Metabolic Syn: কোনটা ? AI LLM?


[14/11, 19:12] Patient Advocate 63M Metabolic Syn: 

জেলা শহরে চারটা নাগাদ একটা মশলা ধোসা খাওয়া হল।


[14/11, 19:12] Patient Advocate 63M Metabolic Syn: ছটা নাগাদ প্রাতঃভ্রমণে বের হয়ে পৌনে সাতটা নাগাদ গন্তব্যে পৌঁছে কিছুক্ষণ বিশ্রাম নিয়ে সাড়ে সাতটা নাগাদ চায়ের দোকান। ওখানে এক পেয়ালা চিনি ছাড়া দুধ চা ও কিছুক্ষণ পর এক বাটি দেশী ছোলার ঘুগনি খাওয়া হলে আটটা নাগাদ বাজার ঘুরে বাড়ি ফিরতে ফিরতে প্রায় পৌনে নয়টা। তারপর স্নানাদি সেরে সকালের খাওয়া সেরে ও ওষুধ খেয়ে কিছুক্ষণ বিশ্রাম নিয়ে এখন চলেছি জেলা শহরের দিকে। ফিরতে ফিরতে বিকেল হয়ে যাবে।



[14/11, 19:22] Patient Advocate 63M Metabolic Syn: এইমাত্র বাড়ি ফিরে এলাম।



[14/11, 22:11] PaJR moderator: Hain


[14/11, 22:15] Patient Advocate 63M Metabolic Syn: একটু শিখিয়ে পড়িয়ে নিলে অসুবিধা হওয়ার কথা নয়।


[15/11, 04:29] Patient Advocate 63M Metabolic Syn: চারটায় ঘুম ভাঙ্গার পর কিছুক্ষণ যাবত আপনাদের মেসেজগুলো দেখছিলাম। পাঁচটা নাগাদ শয্যাত্যাগ করব।



[15/11, 10:09] PaJR moderator: Apnakei sheta amader baki patient der jonye korte anurodh roilo jar jonye apnake unader group a add kore dicchi



[15/11, 17:25] Patient Advocate 63M Metabolic Syn: চেষ্টা করলাম, একবার দেখে নিয়ে জানান, আপনি যা চাইছেন সেটা হল কি না।


[15/11, 17:33] Patient Advocate 63M Metabolic Syn: স্থানীয় কিছু অনুষ্ঠানের কারণে সকাল সাড়ে দশটা নাগাদ বের হয়ে বাড়ি ফিরতে ফিরতে প্রায় সাড়ে চারটা। মাঝে এক -দেড়টা নাগাদ প্রথমে এক কাপ চিনি ছাড়া দুধ চা ও অর্ধেক স্যাঁকা পাঁপড় ও কিছু পরে এক গ্লাস লাল চা খাই। তিনটা নাগাদ নেমন্তন্নের খাওয়া একটা হোটেলে:
ভাত
কাঁচা শসা
লেবু
ডাল
পাঁপড় ভাজা 
মাছের মাথা দিয়ে বাঁধা কপি 
মিষ্টি কুমড়োর তরকারি 
কাৎলা মাছ ভাপা
পাঁঠার মাংস 
চাটনি ও
একটা বড় রসগোল্লা।


[15/11, 21:44] AI : ঠিক আছে! তাহলে খাবারের তালিকা হলো:
- পুরি/লুচি - ২টা (প্রসাদ) 🍞
- শসা ও গাজর - স্যালাড 🥒🥕
- তিন-চার টুকরো পাঁঠার মাংস 🍖
- আলু লাউ দিয়ে বুটের ডাল 🍲

[15/11, 21:44] AI : এখন খাবারটা আরও জমজমাট হয়ে গেছে! পুরি/লুচি তো দারুণ, আর পাঁঠার মাংসের সাথে আলু লাউ দিয়ে বুটের ডাল হলে তো স্বাদে কোনো কমতি থাকবে না।

[15/11, 21:44] AI : স্যালাডও স্বাস্থ্যকর এবং তাজা স্বাদ যোগ করবে।


[15/11, 21:44] AI : এটা একটা ভারী এবং পুষ্টিকর খাবার হবে! উপভোগ করো! 😊🍽️


[15/11, 21:45] AI : Let's break it down based on your updated meal:
1. *পুরি/লুচি (২টা)*: Approx 300 kcal (150 kcal each)
2. *শসা ও গাজর (স্যালাড)*: Approx 15 kcal (5 kcal for cucumber and 10 kcal for carrot)
3. *পাঁঠার মাংস (৩-৪ টুকরো)*: Approx 200 kcal (depending on the portion size)
4. *আলু লাউ দিয়ে বুটের ডাল*: Approx 200 kcal (this can vary based on preparation)


[15/11, 21:45] AI : *Total Estimated Nutritional Information:*
- *Total Calories*: Approx 715 kcal
- *Protein*: Approx 30g (from meat and dal)
- *Carbs*: Approx 100g (from puri, vegetables, and dal)
- *Sugars*: Approx 5g (from vegetables)
- *Fat*: Approx 30g (from puri and meat)
- *Fiber*: Approx 10g (from vegetables and dal)

[15/11, 21:45] AI : This meal is quite hearty and provides a good balance of carbs, protein, and healthy fats. The addition of salad adds some freshness and fiber, which is great!



[15/11, 21:47] Patient Advocate 63M Metabolic Syn: এ তো এক মহা বিপদে ফেললেন ! এ তো সবই বলে দারুন/ভালো। !!!


[16/11, 08:42] PaJR moderator: হ্যাঁ দুর্ভাগ্যবশত এআই এলএলএম আমাদের একাডেমিক নার্সিসিজম খাওয়ানোর জন্য ডিজাইন করা হয়েছে!

Yes unfortunately AI LLMs are designed to feed our academic narcissism!



[16/11, 09:29]rb: Our current tryst with AI LLMs in our healthcare education and practice user windows can be summarised as below (albeit experiential anecdotal opinion): 

AI is like a new boisterous team member, kiddishly brilliant in places and yet a complete let down when you start to think you may begin depending on it!

When you are awed by it's brilliant analysis of study data that it has itself looked up you realise later that it's confabulated the paper and the references itself!

When you are beginning to praise it for its good analysis of a real paper URL you have provided, you realise it's changing the numbers every time you keep asking it repeatedly in different groups!

When you are happy it's able to do a marvelous thematic analysis of your thesis data you realise it's again fudged some numbers here and there!





Sunday, November 10, 2024

Medicine Pharmer: Morning newspaper inspired journal club using critical realist heutagogy driven by meta AI

Journal club conversational Transcripts:


Shared in group:


[08/11, 08:15] rb: Thanks for initiating this morning's journal club for all the  staff doctors (minus PGs and UGs).
🙂🙏

The article, "Fatty liver disease epidemic in India: Are we ignoring a silent killer?" (Times of India, Hyderabad), by Amrita Didyala, appears to be an advertorial promoting awareness about non-alcoholic fatty liver disease (NAFLD).

Here's a critical analysis of its potential influence on collective medical cognition:

*Biases and Concerns:*

1. *Sensationalized Language*: The title and content use emotive language, creating fear and urgency, potentially leading to unnecessary anxiety and testing.

2. *Conflict of Interest*: The article may be sponsored by pharmaceutical or medical device companies, which could influence the content.

3. *Lack of Transparency*: No disclosure of financial ties or sponsorship.

4. *Selective Presentation*: Focuses on NAFLD as a "silent killer" while downplaying lifestyle modifications and emphasizing pharmaceutical solutions.

*Influence on Collective Medical Cognition:*

1. *Over-testing and Overtreatment*: The article may contribute to increased fibro scans and genetic testing, potentially leading to:

    - Unnecessary procedures.
    - Overdiagnosis and overtreatment.
    - Increased healthcare costs.


2. *Misinterpretation of Risk*: The article exaggerates NAFLD risks, potentially leading to:
    - Misinformed patient decisions.
    - Unnecessary anxiety.


3. *Non-Evidence-Based Practices*: The article promotes:
    - Fibro scans as a routine diagnostic tool (limited evidence).
    - Genetic testing for NAFLD (unclear benefits).

*Red Flags:*

1. *Unsubstantiated Claims*: Statements like "NAFLD is a silent killer" and "epidemic" are not supported by credible evidence.

2. *Overemphasis on Genetic Predisposition*: Genetic testing is oversold as a predictor of NAFLD risk.

3. *Lack of Discussion on Lifestyle Modifications*: Downplays or omits the importance of lifestyle changes.

*Recommendations for Critical Evaluation:*

1. *Seek Independent Information*: Consult reputable sources (e.g., peer-reviewed journals, professional guidelines).

2. *Evaluate Evidence Quality*: Assess the strength of evidence supporting claims.

3. *Consider Multiple Perspectives*: Seek input from diverse healthcare professionals.

4. *Beware of Emotional Appeals*: Make informed decisions based on evidence.

*Countering Potential Harm:*

1. *Promote Evidence-Based Practices*: Encourage healthcare professionals to adhere to established guidelines.

2. *Foster Critical Thinking*: Educate patients and healthcare professionals to critically evaluate information.

3. *Emphasize Lifestyle Modifications*: Highlight the importance of lifestyle changes in NAFLD management.

By recognizing potential biases and critically evaluating information, healthcare professionals and patients can make informed decisions and mitigate the risks of over-testing and overtreatment.

*Specific Critique of the Article:*

1. *Provide balanced information*: Include lifestyle modifications and nuanced discussions of NAFLD risks.

2. *Disclose financial ties*: Clearly acknowledge sponsorship or financial relationships.

3. *Support claims with evidence*: Use credible sources to substantiate statements.


[08/11, 08:19]rb: Journal club continuation:


A 2.2-fold increased risk associated with the PNPLA3 gene variant does not necessarily imply causality for fatty liver. Correlation does not equal causation.

_Limitations and Considerations:_

1. *Multifactorial disease:* NAFLD is influenced by various factors, including diet, exercise, obesity, insulin resistance, and genetic predisposition.

2. *Confounding variables:* The study may not have fully accounted for lifestyle factors, socioeconomic status, or other genetic variants that contribute to NAFLD.

3. *Gene-environment interaction:* The expression of PNPLA3 may be influenced by environmental factors, making it challenging to isolate the genetic effect.

4. *Penetrance and expressivity:* The PNPLA3 variant may have incomplete penetrance (not all carriers develop NAFLD) and variable expressivity (severity differs among carriers).


5. *Population-specific findings:* Results may not generalize to other populations with different genetic backgrounds or environmental exposures.

_Alternative Explanations:_

1. *Association, not causation:* The PNPLA3 variant might be a marker for other underlying factors contributing to NAFLD.

2. *Linkage disequilibrium:* The PNPLA3 variant may be linked to other genetic variants that contribute to NAFLD.

_Contextualizing the Findings:_

1. *43/191 non-carriers with NASH:* This highlights the significance of non-genetic factors in NAFLD development.


2. *Prevalence of NASH:* 20.5% in non-carriers vs. 43.8% in carriers suggests that environmental factors contribute substantially to NASH risk.

To establish causality, further research should investigate:

1. *Mendelian randomization studies:* Examining the effect of PNPLA3 variants on NAFLD risk using genetic instruments.

2. *Functional studies:* Investigating the biological mechanisms by which PNPLA3 influences liver fat accumulation and NAFLD progression.

3. *Gene-environment interactions:* Examining how lifestyle factors modify the expression and effects of PNPLA3.

These are from further critical appraisal of a journal article that we were driven to search after getting inspired to appraise the advertorial shared earlier


[08/11, 08:21]rb: Evidence from journal article surrounding the PNPLA3 gene and fatty liver in PICO format (article referenced at the bottom):

*P (Population):* Individuals with non-alcoholic fatty liver disease (NAFLD)

*I (Intervention/Exposure):* Presence of PNPLA3 rs738409 G allele (I148M variant)

*C (Comparator):* Individuals without PNPLA3 rs738409 G allele

*O (Outcome):* Risk of developing non-alcoholic steatohepatitis (NASH) and advanced liver fibrosis

*Representative Reference:*

- Romeo et al. (2010) - "Genetic variation in PNPLA3 confers susceptibility to nonalcoholic fatty liver disease" [1]

*Data:*

- *PNPLA3 rs738409 G allele carriers:* 49.2% of NAFLD patients (95/194)

- *Non-carriers:* 22.5% of controls (43/191)

- *NASH risk:* 2.22-fold increased risk (95% CI: 1.51-3.27)


- *Advanced liver fibrosis risk:* 3.26-fold increased risk (95% CI: 1.84-5.77)

- *Liver fat content:* 15.3% higher in G allele carriers (p=0.001)

*Absolute Values:*

- *NASH prevalence:* 43.8% in G allele carriers vs. 20.5% in non-carriers

- *Advanced liver fibrosis prevalence:* 24.7% in G allele carriers vs. 7.5% in non-carriers

This study demonstrates a strong association between the PNPLA3 rs738409 G allele and increased risk of NASH and advanced liver fibrosis in NAFLD patients.


Reference:

[1] Romeo et al. (2010). Genetic variation in PNPLA3 confers susceptibility to nonalcoholic fatty liver disease. Nature Genetics, 42(2), 146-149. doi: 10.1038/ng.511

[08/11, 08:23] op: Good information Sir

[08/11, 08:24]rb: Thanks for inspiring us to look for it 🙂🙏



Creative commons license: https://commons.m.wikimedia.org/wiki/File:NAFLD_liver_progression.svg#


UDLC Feedback learning loops:

[10/11, 20:55]st: We will stop buying toasters and grills now.. AI can pick up any thing and totally roast it. We just have to set the temperature , depth and flavour😀


[10/11, 20:58]st: Grok 2 did a pretty good job in roasting .. this is perplexity -. Let's analyze the content for potential logical flaws and scientific deviations:

## Potential Issues in the Article

**Prevalence Claims**

The article states that fatty liver disease (FLD) has an "estimated 30% prevalence in the general population" of India[2]. However, it doesn't provide a source for this statistic or specify whether this refers to non-alcoholic fatty liver disease (NAFLD) or includes alcoholic fatty liver disease as well.

**Genetic Claims**

1. The article mentions a "particular gene, PNPLA3" that makes people more vulnerable to fatty liver without alcohol consumption[2]. While PNPLA3 variants have been associated with NAFLD, presenting it as a single causative factor is an oversimplification.

2. The claim that Indians have a "special gene making them susceptible to fatty liver" is not accurately presented[2]. PNPLA3 variants are found in various populations worldwide, not exclusively in Indians.

**Epidemiological Assertions**

1. The article suggests that India might be the "biggest capital of fatty liver" based on the prevalence among patients with diabetes and obesity[2]. This extrapolation from a specific subgroup to the general population is problematic.

2. The statement that 30% of hospital beds are occupied by liver patients, with fatty liver being the primary underlying reason, is presented without supporting data or context[2].

**Mechanism of PNPLA3**

The explanation of how PNPLA3 works is oversimplified and potentially misleading. The article states that the gene "increases the breakdown of fats and starts killing the cells in the liver"[2]. In reality, certain PNPLA3 variants are associated with increased hepatic fat accumulation, not increased fat breakdown.

**Lack of Nuance**

The article doesn't adequately distinguish between simple fatty liver, which is often benign, and more severe forms like non-alcoholic steatohepatitis (NASH). This lack of distinction could lead to unnecessary alarm.

**Limited Perspective**

While lifestyle factors like lack of exercise are mentioned, the article doesn't discuss other important risk factors for NAFLD, such as diet, metabolic syndrome, or other genetic factors beyond PNPLA3.

## Conclusion

While the article raises awareness about an important health issue, it contains several scientific inaccuracies and oversimplifications. As a medical professional, you're right to be critical of such reporting. It's crucial to rely on peer-reviewed scientific literature and official health organization reports for accurate information on epidemiological trends and genetic factors in diseases like NAFLD.

Sources
[1] fatty-liver-disease-epidemic-in-india-are-we-ignoring-a-silent-ki https://timesofindia.indiatimes.com/city/hyderabad/fatty-liver-disease-epidemic-in-india-are-we-ignoring-a-silent-ki
[2] Fatty Liver Disease Epidemic in India: Are We Ignoring a Silent Killer? | Hyderabad News - Times of India https://timesofindia.indiatimes.com/city/hyderabad/fatty-liver-disease-epidemic-in-india-are-we-ignoring-a-silent-killer/articleshow/115063211.cms


[10/11, 21:03]st: But when I asked it to rewrite in unbiased and include most recent statistics , it did an amazing job -

## Prevalence of Fatty Liver Disease in India

Recent studies indicate that non-alcoholic fatty liver disease (NAFLD), now referred to as metabolic dysfunction-associated steatotic liver disease (MASLD), is becoming increasingly prevalent in India. A systematic review and meta-analysis published in 2022 reported the following findings:

- The pooled prevalence of NAFLD in India is 38.6% among adults and 35.4% among children[2][5].
- The prevalence appears to be similar in males and females[5].

However, it's important to note that these figures may vary depending on the population studied and the diagnostic methods used.

## Risk Factors and Associated Conditions

NAFLD/MASLD is closely associated with metabolic syndrome components:

- Among individuals with diabetes, the prevalence ranges from 55.5% to 59.7%[5].
- In overweight or obese populations, the prevalence is between 64.6% and 95%[5].
- For those with metabolic syndrome, the prevalence is around 73%[5].

## Regional Variations

The prevalence of NAFLD in India shows significant regional variations:

- In rural West Bengal, the prevalence was reported to be as low as 8.7%[2].
- In urban areas like Chandigarh, it was reported to be as high as 53.5%[2].

## Progression and Severity

While many cases of NAFLD remain asymptomatic, a significant proportion can progress to more severe forms:

- A retrospective histological study of 1000 NAFLD patients found that over 60% had a NAFLD activity score ≥5, indicating definite non-alcoholic steatohepatitis (NASH)[2].
- Significant fibrosis (≥F2) was observed in 36.3% of NAFLD patients, and cirrhosis in 11%, according to an interim analysis of the Indian Consortium on NAFLD (ICON-D)[2].

## Diagnosis and Screening

Early diagnosis remains a challenge:

- In a cohort of 50,000 people screened at Apollo Hospitals, 33% had fatty liver as observed on ultrasound, but only one in 3 of these individuals had elevated liver enzymes in their blood tests[8].

## Conclusion

While the prevalence of NAFLD/MASLD in India is significant, it's crucial to interpret these statistics cautiously. Many studies have been conducted in hospital settings or urban areas, which may not be representative of the entire population. Additionally, diagnostic criteria and methods can vary between studies, affecting reported prevalence rates.

Further large-scale, population-based studies across diverse regions of India are needed to provide a more comprehensive and accurate picture of the NAFLD/MASLD burden in the country. Early screening, lifestyle modifications, and management of associated metabolic conditions remain key strategies in addressing this growing health concern.

Sources
[1] Rising concern: 35 per cent of Indian children affected by fatty liver ... https://www.newindianexpress.com/xplore/2024/Jun/04/rising-concern-35-per-cent-of-indian-children-affected-by-fatty-liver-disease
[2] Indian Literature Review on Nonalcoholic Fatty Liver Disease https://journals.lww.com/ghep/fulltext/2024/04010/indian_literature_review_on_nonalcoholic_fatty.2.aspx
[3] Health Ministry revises non-alcoholic fatty liver disease (NAFLD ... https://indianexpress.com/article/health-wellness/non-alcoholic-fatty-liver-disease-nafld-guidelines-health-ministry-revises-prevention-9595734/
[4] 40% in India suffer from non-alcoholic fatty liver: Doctors https://economictimes.indiatimes.com/industry/healthcare/biotech/healthcare/40-in-india-suffer-from-non-alcoholic-fatty-liver-doctors/articleshow/92109732.cms
[5] Prevalence of Non-alcoholic Fatty Liver Disease in India https://pmc.ncbi.nlm.nih.gov/articles/PMC9168741/
[6] Prevalence of Non-alcoholic Fatty Liver Disease in India https://www.sciencedirect.com/science/article/abs/pii/S0973688321005740
[7] Every 3rd Indian affected by fatty liver disease: Minister Jitendra Singh https://www.business-standard.com/health/every-3rd-indian-affected-by-fatty-liver-disease-minister-jitendra-singh-124070500748_1.html
[8] Tackling the fatty liver disease epidemic - The Hindu https://www.thehindu.com/opinion/op-ed/tackling-the-fatty-liver-disease-epidemic/article68281573.ece


[10/11, 21:15]rb: Amazing 👏👏

Only issue is are those references real?

Again having to check those through Google searches itself is a painful detractor!