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.
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
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
Slide 2:
Who are end users of technology?
Artists? Genius? Ordinary Human?
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/
More here from another lecture: https://youtu.be/
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!
Slide 3: What literally is science?
May have originated from Proto-Indo-European language as *skh1-ie, *skh1-io
Image with CC licence: https://commons.m.
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.
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.
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://
Image CC licence: https://commons.m.
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!
Slide 6:
Role of Hyperlocality in designing care for the Tech end user
Introduction: https://
Rhetoric:
Global learning toward hyperlocal caring:
https://userdrivenhealthcare.
Creating persistent clinical encounters to extend the scope of health care beyond its conventional boundaries utilizing social networking technology
Slide 7
Evolution of above workflow prototype in different institutions
https://medicinedepartment.
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://
Current workflow:
Theory driven workflow with Gaurd Rails for all technology end users:
https://userdrivenhealthcare.
https://userdrivenhealthcare.
https://userdrivenhealthcare.
https://www.hipaajournal.com/de-identification-protected-health-information/
https://classworkdecjan.blogspot.com/2017/11/de-identifying-patient-data.html?m=1
https://userdrivenhealthcare.
Theory driven workflow with Gaurd Rails for Health professional technology end users:
https://userdrivenhealthcare.
https://durgakrishna09.
http://medicinedepartment.
https://medicinedepartment.
Reflective notes: https://
https://userdrivenhealthcare.
https://kandrucherishrollno68.
Slide 8: Demo
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.
https://24fpatient.blogspot.
https://narmeenshah.blogspot.
https://2patienthealthreport.
Demo by few health profession students present in the venue sharing their experiences from their online learning portfolios below:
https://96sanjanapalakodeti.
https://shivanikommera.
https://sreetejapalakonda29.
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://
Formal health professional students: 5000 cases in 1000 tech end user online learning portfolios:
https://medicinedepartment.
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.
SO WHAT??
S
W
eaknesses
O
pportunities
T
hreats
Thorns of clinical complexity amidst the guardrails
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