Project illustration of how to process particular patient data into learning themes and demonstrate it's effect on patients illness outcomes (as well as how it can demonstrate learning competence of the learner)

Let's take an ongoing patient example 

from it's PaJR EHR here: https://chat.whatsapp.com/BkEyIsqmfSI20yynkYhhpI

And case reported here : https://elurivishnuvandana44.blogspot.com/2023/07/46-y-old-male-with-spasm-of-upper-limbs.html?m=1

where we sort out the learning points (themes) according to the afore mentioned points revisited below 👇

1) General knowledge learning (pull existing knowledge) 

2) Discovery driven insightful learning (push and expand knowledge frontiers ) 

Let the ongoing example be 

1) What are the general knowledge learning points that we gain from this patient's data that is easily accessible online? 

a) Diabetes type 2 v type 3 definitions, pathophysiologies and solutions 

b) Alcoholism current problems and solutions 

c) Pancreatitis current problems and solutions 

2) What are the particular discovery driven learning questions around this patient :

a) Internal Medicine : Diagnostic (type 2 v type 3) and therapeutic uncertainty (insulin v oral hypoglycemics for type 3 therapy) in this particular patient with diabetes, pancreatitis and alcoholism 

b) External Medicine (social determinants of health) 

[7/31, 8:25 AM] Keen Student: 

good morning sir,

our diabetic pajr groups are highly active and we are glad that patient’s are giving their time and effort. But what about the cost burden we are putting on them sir? how can we cut it ? As i have seen they are around 3-6 pajr groups in which 7 point profile of grbs is being posted daily.

each strip costs a minimum of 10 rupees which costs them 70 for a day and 

which is 2100 for a month.!

[7/31, 8:40 AM] Rakesh Biswas: Very good question 👏👏

Further themes represented by our students in the same particular patient data :

Clinical complexity 

Biological:

• Chief complaints of spasm of fingers in upper limbs with tremors, generalized weakness, excessive sweating, and decreased pitch of voice.

• History of neck and shoulder pain, leading to surgery in 2007.

• Diagnosis of pancreatitis in 2008, which required surgery.

• Diagnosis of diabetes and initiation of insulin therapy, but poor follow-up with healthcare.

Psychological:

• Experience of depression following the demise of his father in 2004.

• Increased alcohol consumption as a coping mechanism due to peer pressure and curiosity.

• Stress and emotional strain due to spouse health issues and financial burden.

Social:

• Limited educational background, discontinuing studies after 10th standard.

• Agricultural laborer by occupation, engaged in farming and rearing domestic cattle.

• Lives with his wife, daughter, and son in a house he acquired through a home loan.

• Consanguineous marriage and has two children.

• Financial stress due to home loan and family responsibilities.

• Increased alcohol consumption and smoking as social and coping habits.

• Recent stress due to wife’s surgery, leading to binge drinking.

Now coming to the most important and challenging part of our learning 

exercise :

1) Answers to the above raised questions which are again sorted themes in the data 

2) Thematic analysis: Coding is the primary process for developing themes by identifying items of analytic interest in the data and tagging these with a coding label. More here :

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

3) Product from above processing of input data : Learning points from the above thematically analysed patient data 

4) Showing the positive (convenience of oral hypoglycemics v inj insulin since 10 years) and negative (too much initial monitoring monetary investment  potential overdiagnosis and overtreatment) impact of the above learning on the patient's illness outcomes 

How does the above demonstrate learning competence of the learner?

Let's revisit the steps the learner needs to develop the competence within to be able to achieve the impactful learning around the patient :

1) Competence in patient data capture 

2) Competence in asking questions around the captured patient data (also known as sorting the themes for thematic analysis) 

3) Competence in finding the answers to the above questions and generating learning points that 

a) may be already known to other more experienced and advanced learners  

b) hitherto unknown to the world and takes us to the edge of discovery and promise of breaking new ground 

4) Competence to demonstrate and communicate (through publications in local and global logs) as to how the above learning points gleaned from the individual patients can influence their own immediate illness outcomes as well as future similar patient illness outcomes.