कुछ सूचनाएं by सुदामा पांडेय धूमिल

सबसे अधिक हत्याएँ
समन्वयवादियों ने की

दार्शनिकों ने
सबसे अधिक ज़ेवर खरीदा

भीड़ ने कल बहुत पीटा
उस आदमी को
जिस का मुख ईसा से मिलता था

वह कोई और महीना था
जब प्रत्येक टहनी पर फूल खिलता था
किंतु इस बार तो
मौसम बिना बरसे ही चला गया
न कहीं घटा घिरी
न बूँद गिरी
फिर भी लोगों में टी.बी. के कीटाणु
कई प्रतिशत बढ़ गए

कई बौखलाए हुए मेंढक
कुएँ की काई लगी दीवाल पर
चढ़ गए
और सूरज को धिक्कारने लगे
— व्यर्थ ही प्रकाश की बड़ाई में बकता है
सूरज कितना मजबूर है
कि हर चीज़ पर एक सा चमकता है

हवा बुदबुदाती है
बात कई पर्तों से आती है —
एक बहुत बारीक पीला कीड़ा
आकाश छू रहा था
और युवक मीठे जुलाब की गोलियाँ खा कर
शौचालयों के सामने
पँक्तिबद्ध खड़े हैं

आँखों में ज्योति के बच्चे मर गए हैं
लोग खोई हुई आवाज़ों में
एक दूसरे की सेहत पूछते हैं
और बेहद डर गए हैं

सब के सब
रोशनी की आँच से
कुछ ऐसे बचते हैं
कि सूरज को पानी से
रचते हैं

बुद्ध की आँख से खून चू रहा था
नगर के मुख्य चौरस्ते पर
शोकप्रस्ताव पारित हुए
हिजड़ो ने भाषण दिए
लिंग-बोध पर
वेश्याओं ने कविताएँ पढ़ीं
आत्म-शोध पर
प्रेम में असफल छात्राएँ
अध्यापिकाएँ बन गई हैं
और रिटायर्ड बूढ़े
सर्वोदयी —
आदमी की सबसे अच्छी नस्ल
युद्धों में नष्ट हो गई
देश का सबसे अच्छा स्वास्थ्य
विद्यालयों में
संक्रामक रोगों से ग्रस्त है

(मैंने राष्ट्र के कर्णधारों को
सड़को पर
किश्तियों की खोज में
भटकते हुए देखा है)

संघर्ष की मुद्रा में घायल पुरुषार्थ
भीतर ही भीतर
एक निःशब्द विस्फोट से त्रस्त है

पिकनिक से लौटी हुई लड़कियाँ
प्रेम-गीतों से गरारे करती हैं
सबसे अच्छे मस्तिष्क
आरामकुर्सी पर
चित्त पड़े हैं ।

Source: Hindi-Kavita.com

A call for radical Unforgiveness

Source: Azaad, Amba. “Fire to the Grass.” The Massachusetts Review, Volume 65, Issue 1, 2024, massreview.org/sites/default/files/10_65.1Azaad.pdf.

IN this essay Amba Azaad makes a strong and comprehensive case for radical unforgiveness.

It’s a gorgeous gorgeous essay and everyone should read it, the following are some quotations that spoke to me.

Resentment and bitterness are treated like bruise marks—evidence of a past crime, but of no further use, meant to be erased as soon as possible.

Victims of abuse have been told so often that true love is forgiving that it feels like a lie to state that their love and unforgiveness can coexist, equally authentic

Just as you cannot truly envision the complex reality of what abuse is without granting that a person can be both loving and abusive, you cannot begin to talk about battered love without talking about unforgiveness.

To love someone who has harmed you, and to fully name and recognize that harm, and to deem it unforgivable, and to continue living in some relationship with each other: that is what the vast majority of people in abusive relationships do. As we come to more open and investigative reckonings of abuse, it behooves us to treat unforgiveness as praxis of survival—not as a dirty byproduct of harm, but as a multifaceted philosophy worth theorizing.

Forgiveness certainly has a place in our social strategizing and mental toolkit; however, deglamorizing its status as a mark of born-again Bodhisattva will help to prevent abusive demands for it. To legitimize unforgiveness, it is necessary to start by toppling the idol of forgiveness: a virtue enshrined in several religious traditions and wielded with particular brutality by modern Christian ideologies against anyone with the temerity to hold the powerful accountable. If we remove divinity from the equation, it is clear that both “to err” and “to forgive” must be analyzed strictly in profane terms of power.

Radical unforgiveness renames your experience from acceptable, and therefore good enough for others, to unacceptable and not to be replicated.

We have been told that unforgiveness is useless so often that it can be hard to redefine what productivity looks like when marginalized and derided forms of labor are taken into account. Holding space, bearing witness: these are seemingly passive forms of productivity. It takes energy to stand still in a crowd that pushes you to move on. The unforgivers are the ones who stay petty, who don’t just get along, and they are the ones who force changes through in organizations where it is easier to let it go.

Here’s a freeing thought: What if one has a responsibility to unforgive, what if one is achieving some measure of restitution by being a stone against the flood that tries to wash away the evidence of wrongdoing? By not being able to forgive, you are not failing at humanity. You are reforming humanity—by being a record keeper, by bearing witness.

Unforgiveness is not the negative space of the absence of a thing; it is a concrete, voluntary action, a choice. Broken relationships are not failures; they are proof of the work of unforgiveness.

I really want to add more quotes but I think this is enough.

Amba ends the essay with acknowledging how unforgiveness has been misappropriated by revenge, and what we can do to prevent that.

Overall, I think this is a call I will be thinking about a lot.

Sultana’s Dream (1905) by Rokeya Sakhawat Hossain

This story was originally published in The Indian Ladies’ Magazine, Madras, 1905. Taken from WikiSource.

One evening I was lounging in an easy chair in my bedroom and thinking lazily of the condition of Indian womanhood. I am not sure whether I dozed off or not. But, as far as I remember, I was wide awake. I saw the moonlit sky sparkling with thousands of diamond-like stars, very distinctly.

All on a sudden a lady stood before me; how she came in, I do not know. I took her for my friend, Sister Sara.

‘Good morning,’ said Sister Sara. I smiled inwardly as I knew it was not morning, but starry night. However, I replied to her, saying, ‘How do you do?’

‘I am all right, thank you. Will you please come out and have a look at our garden?’

I looked again at the moon through the open window, and thought there was no harm in going out at that time. The men-servants outside were fast asleep just then, and I could have a pleasant walk with Sister Sara.

I used to have my walks with Sister Sara, when we were at Darjeeling. Many a time did we walk hand in hand and talk light-heartedly in the botanical gardens there. I fancied, Sister Sara had probably come to take me to some such garden and I readily accepted her offer and went out with her.

When walking I found to my surprise that it was a fine morning. The town was fully awake and the streets alive with bustling crowds. I was feeling very shy, thinking I was walking in the street in broad daylight, but there was not a single man visible.

Some of the passers-by made jokes at me. Though I could not understand their language, yet I felt sure they were joking. I asked my friend, ‘What do they say?’

‘The women say that you look very mannish.’

‘Mannish?’ said I, ‘What do they mean by that?’

‘They mean that you are shy and timid like men.’

‘Shy and timid like men?’ It was really a joke. I became very nervous, when I found that my companion was not Sister Sara, but a stranger. Oh, what a fool had I been to mistake this lady for my dear old friend, Sister Sara.

She felt my fingers tremble in her hand, as we were walking hand in hand.

‘What is the matter, dear?’ she said affectionately. ‘I feel somewhat awkward,’ I said in a rather apologizing tone, ‘as being a purdahnishin woman I am not accustomed to walking about unveiled.’

‘You need not be afraid of coming across a man here. This is Ladyland, free from sin and harm. Virtue herself reigns here.’

By and by I was enjoying the scenery. Really it was very grand. I mistook a patch of green grass for a velvet cushion. Feeling as if I were walking on a soft carpet, I looked down and found the path covered with moss and flowers.

‘How nice it is,’ said I.

‘Do you like it?’ asked Sister Sara. (I continued calling her ‘Sister Sara,’ and she kept calling me by my name).

‘Yes, very much; but I do not like to tread on the tender and sweet flowers.’

‘Never mind, dear Sultana; your treading will not harm them; they are street flowers.’

‘The whole place looks like a garden,’ said I admiringly. ‘You have arranged every plant so skillfully.’

‘Your Calcutta could become a nicer garden than this if only your countrymen wanted to make it so.’

‘They would think it useless to give so much attention to horticulture, while they have so many other things to do.’

‘They could not find a better excuse,’ said she with smile.

I became very curious to know where the men were. I met more than a hundred women while walking there, but not a single man.

‘Where are the men?’ I asked her.

‘In their proper places, where they ought to be.’

‘Pray let me know what you mean by “their proper places”.’

‘O, I see my mistake, you cannot know our customs, as you were never here before. We shut our men indoors.’

‘Just as we are kept in the zenana?’

‘Exactly so.’

‘How funny,’ I burst into a laugh. Sister Sara laughed too.

‘But dear Sultana, how unfair it is to shut in the harmless women and let loose the men.’

‘Why? It is not safe for us to come out of the zenana, as we are naturally weak.’

‘Yes, it is not safe so long as there are men about the streets, nor is it so when a wild animal enters a marketplace.’

‘Of course not.’

‘Suppose, some lunatics escape from the asylum and begin to do all sorts of mischief to men, horses and other creatures; in that case what will your countrymen do?’

‘They will try to capture them and put them back into their asylum.’

‘Thank you! And you do not think it wise to keep sane people inside an asylum and let loose the insane?’

‘Of course not!’ said I laughing lightly.

‘As a matter of fact, in your country this very thing is done! Men, who do or at least are capable of doing no end of mischief, are let loose and the innocent women, shut up in the zenana! How can you trust those untrained men out of doors?’

‘We have no hand or voice in the management of our social affairs. In India man is lord and master, he has taken to himself all powers and privileges and shut up the women in the zenana.’

‘Why do you allow yourselves to be shut up?’

‘Because it cannot be helped as they are stronger than women.’

‘A lion is stronger than a man, but it does not enable him to dominate the human race. You have neglected the duty you owe to yourselves and you have lost your natural rights by shutting your eyes to your own interests.’

‘But my dear Sister Sara, if we do everything by ourselves, what will the men do then?’

‘They should not do anything, excuse me; they are fit for nothing. Only catch them and put them into the zenana.’

‘But would it be very easy to catch and put them inside the four walls?’ said I. ‘And even if this were done, would all their business – political and commercial – also go with them into the zenana?’

Sister Sara made no reply. She only smiled sweetly. Perhaps she thought it useless to argue with one who was no better than a frog in a well.

By this time we reached Sister Sara’s house. It was situated in a beautiful heart-shaped garden. It was a bungalow with a corrugated iron roof. It was cooler and nicer than any of our rich buildings. I cannot describe how neat and how nicely furnished and how tastefully decorated it was.

We sat side by side. She brought out of the parlour a piece of embroidery work and began putting on a fresh design.

‘Do you know knitting and needle work?’

‘Yes; we have nothing else to do in our zenana.’

‘But we do not trust our zenana members with embroidery!’ she said laughing, ‘as a man has not patience enough to pass thread through a needlehole even!’

‘Have you done all this work yourself?’ I asked her pointing to the various pieces of embroidered teapoy cloths.

‘Yes.’

‘How can you find time to do all these? You have to do the office work as well? Have you not?’

‘Yes. I do not stick to the laboratory all day long. I finish my work in two hours.’

‘In two hours! How do you manage? In our land the officers, – magistrates, for instance – work seven hours daily.’

‘I have seen some of them doing their work. Do you think they work all the seven hours?’

‘Certainly they do!’

‘ No, dear Sultana, they do not. They dawdle away their time in smoking. Some smoke two or three choroots during the office time. They talk much about their work, but do little. Suppose one choroot takes half an hour to burn off, and a man smokes twelve choroots daily; then you see, he wastes six hours every day in sheer smoking.’

We talked on various subjects, and I learned that they were not subject to any kind of epidemic disease, nor did they suffer from mosquito bites as we do. I was very much astonished to hear that in Ladyland no one died in youth except by rare accident.

‘Will you care to see our kitchen?’ she asked me.

‘With pleasure,’ said I, and we went to see it. Of course the men had been asked to clear off when I was going there. The kitchen was situated in a beautiful vegetable garden. Every creeper, every tomato plant was itself an ornament. I found no smoke, nor any chimney either in the kitchen — it was clean and bright; the windows were decorated with flower gardens. There was no sign of coal or fire.

‘How do you cook?’ I asked.

‘With solar heat,’ she said, at the same time showing me the pipe, through which passed the concentrated sunlight and heat. And she cooked something then and there to show me the process.

‘How did you manage to gather and store up the sun-heat?’ I asked her in amazement.

‘Let me tell you a little of our past history then. Thirty years ago, when our present Queen was thirteen years old, she inherited the throne. She was Queen in name only, the Prime Minister really ruling the country.

‘Our good Queen liked science very much. She circulated an order that all the women in her country should be educated. Accordingly a number of girls’ schools were founded and supported by the government. Education was spread far and wide among women. And early marriage also was stopped. No woman was to be allowed to marry before she was twenty-one. I must tell you that, before this change we had been kept in strict purdah.’

‘How the tables are turned,’ I interposed with a laugh.

‘But the seclusion is the same,’ she said. ‘In a few years we had separate universities, where no men were admitted.’

‘In the capital, where our Queen lives, there are two universities. One of these invented a wonderful balloon, to which they attached a number of pipes. By means of this captive balloon which they managed to keep afloat above the cloud-land, they could draw as much water from the atmosphere as they pleased. As the water was incessantly being drawn by the university people no cloud gathered and the ingenious Lady Principal stopped rain and storms thereby.’

‘Really! Now I understand why there is no mud here!’ said I. But I could not understand how it was possible to accumulate water in the pipes. She explained to me how it was done, but I was unable to understand her, as my scientific knowledge was very limited. However, she went on, ‘When the other university came to know of this, they became exceedingly jealous and tried to do something more extraordinary still. They invented an instrument by which they could collect as much sun-heat as they wanted. And they kept the heat stored up to be distributed among others as required.

‘While the women were engaged in scientific research, the men of this country were busy increasing their military power. When they came to know that the female universities were able to draw water from the atmosphere and collect heat from the sun, they only laughed at the members of the universities and called the whole thing “a sentimental nightmare”!’

‘Your achievements are very wonderful indeed! But tell me, how you managed to put the men of your country into the zenana. Did you entrap them first?’

‘No.’

‘It is not likely that they would surrender their free and open air life of their own accord and confine themselves within the four walls of the zenana! They must have been overpowered.’

‘Yes, they have been!’

‘By whom? By some lady-warriors, I suppose?’

‘No, not by arms.’

‘Yes, it cannot be so. Men’s arms are stronger than women’s. Then?’

‘By brain.’

‘Even their brains are bigger and heavier than women’s. Are they not?’

‘Yes, but what of that? An elephant also has got a bigger and heavier brain than a man has. Yet man can enchain elephants and employ them, according to their own wishes.’

‘Well said, but tell me please, how it all actually happened. I am dying to know it!’

‘Women’s brains are somewhat quicker than men’s. Ten years ago, when the military officers called our scientific discoveries “a sentimental nightmare,” some of the young ladies wanted to say something in reply to those remarks. But both the Lady Principals restrained them and said, they should reply not by word, but by deed, if ever they got the opportunity. And they had not long to wait for that opportunity.’

‘How marvelous!’ I heartily clapped my hands. ‘And now the proud gentlemen are dreaming sentimental dreams themselves.’

‘Soon afterwards certain persons came from a neighbouring country and took shelter in ours. They were in trouble having committed some political offense. The king who cared more for power than for good government asked our kind-hearted Queen to hand them over to his officers. She refused, as it was against her principle to turn out refugees. For this refusal the king declared war against our country.

‘Our military officers sprang to their feet at once and marched out to meet the enemy. The enemy however, was too strong for them. Our soldiers fought bravely, no doubt. But in spite of all their bravery the foreign army advanced step by step to invade our country.

‘Nearly all the men had gone out to fight; even a boy of sixteen was not left home. Most of our warriors were killed, the rest driven back and the enemy came within twenty-five miles of the capital.

‘A meeting of a number of wise ladies was held at the Queen’s palace to advise as to what should be done to save the land. Some proposed to fight like soldiers; others objected and said that women were not trained to fight with swords and guns, nor were they accustomed to fighting with any weapons. A third party regretfully remarked that they were hopelessly weak of body.

‘”If you cannot save your country for lack of physical strength,” said the Queen, “try to do so by brain power.”

‘There was a dead silence for a few minutes. Her Royal Highness said again, “I must commit suicide if the land and my honour are lost.”

‘Then the Lady Principal of the second university (who had collected sun-heat), who had been silently thinking during the consultation, remarked that they were all but lost, and there was little hope left for them. There was, however, one plan which she would like to try, and this would be her first and last efforts; if she failed in this, there would be nothing left but to commit suicide. All present solemnly vowed that they would never allow themselves to be enslaved, no matter what happened.

‘The Queen thanked them heartily, and asked the Lady Principal to try her plan. The Lady Principal rose again and said, “before we go out the men must enter the zenanas. I make this prayer for the sake of purdah.” “Yes, of course,” replied Her Royal Highness.

‘On the following day the Queen called upon all men to retire into zenanas for the sake of honour and liberty. Wounded and tired as they were, they took that order rather for a boon! They bowed low and entered the zenanas without uttering a single word of protest. They were sure that there was no hope for this country at all.

‘Then the Lady Principal with her two thousand students marched to the battle field, and arriving there directed all the rays of the concentrated sunlight and heat towards the enemy.

‘The heat and light were too much for them to bear. They all ran away panic-stricken, not knowing in their bewilderment how to counteract that scorching heat. When they fled away leaving their guns and other ammunitions of war, they were burnt down by means of the same sun-heat. Since then no one has tried to invade our country any more.’

‘And since then your countrymen never tried to come out of the zenana?’

‘Yes, they wanted to be free. Some of the police commissioners and district magistrates sent word to the Queen to the effect that the military officers certainly deserved to be imprisoned for their failure; but they never neglected their duty and therefore they should not be punished and they prayed to be restored to their respective offices.

‘Her Royal Highness sent them a circular letter intimating to them that if their services should ever be needed they would be sent for, and that in the meanwhile they should remain where they were. Now that they are accustomed to the purdah system and have ceased to grumble at their seclusion, we call the system “Mardana” instead of “zenana”.’

‘But how do you manage,’ I asked Sister Sara, ‘to do without the police or magistrates in case of theft or murder?’

‘Since the “Mardana” system has been established, there has been no more crime or sin; therefore we do not require a policeman to find out a culprit, nor do we want a magistrate to try a criminal case.’

‘That is very good, indeed. I suppose if there was any dishonest person, you could very easily chastise her. As you gained a decisive victory without shedding a single drop of blood, you could drive off crime and criminals too without much difficulty!’

‘Now, dear Sultana, will you sit here or come to my parlour?’ she asked me.

‘Your kitchen is not inferior to a queen’s boudoir!’ I replied with a pleasant smile, ‘but we must leave it now; for the gentlemen may be cursing me for keeping them away from their duties in the kitchen so long.’ We both laughed heartily.

‘How my friends at home will be amused and amazed, when I go back and tell them that in the far-off Ladyland, ladies rule over the country and control all social matters, while gentlemen are kept in the Mardanas to mind babies, to cook and to do all sorts of domestic work; and that cooking is so easy a thing that it is simply a pleasure to cook!’

‘Yes, tell them about all that you see here.’

‘Please let me know, how you carry on land cultivation and how you plough the land and do other hard manual work.’

‘Our fields are tilled by means of electricity, which supplies motive power for other hard work as well, and we employ it for our aerial conveyances too. We have no rail road nor any paved streets here.’

‘Therefore neither street nor railway accidents occur here,’ said I. ‘Do not you ever suffer from want of rainwater?’ I asked.

‘Never since the “water balloon” has been set up. You see the big balloon and pipes attached thereto. By their aid we can draw as much rainwater as we require. Nor do we ever suffer from flood or thunderstorms. We are all very busy making nature yield as much as she can. We do not find time to quarrel with one another as we never sit idle. Our noble Queen is exceedingly fond of botany; it is her ambition to convert the whole country into one grand garden.’

‘The idea is excellent. What is your chief food?’

‘Fruits.’

‘How do you keep your country cool in hot weather? We regard the rainfall in summer as a blessing from heaven.’

‘When the heat becomes unbearable, we sprinkle the ground with plentiful showers drawn from the artificial fountains. And in cold weather we keep our room warm with sun-heat.’

She showed me her bathroom, the roof of which was removable. She could enjoy a shower bath whenever she liked, by simply removing the roof (which was like the lid of a box) and turning on the tap of the shower pipe.

‘You are a lucky people!’ ejaculated I. ‘You know no want. What is your religion, may I ask?’

‘Our religion is based on Love and Truth. It is our religious duty to love one another and to be absolutely truthful. If any person lies, she or he is….’

‘Punished with death?’

‘No, not with death. We do not take pleasure in killing a creature of God, especially a human being. The liar is asked to leave this land for good and never to come to it again.’

‘Is an offender never forgiven?’

‘Yes, if that person repents sincerely.’

‘Are you not allowed to see any man, except your own relations?’

‘No one except sacred relations.’

‘Our circle of sacred relations is very limited; even first cousins are not sacred.’

‘But ours is very large; a distant cousin is as sacred as a brother.’

‘That is very good. I see purity itself reigns over your land. I should like to see the good Queen, who is so sagacious and far-sighted and who has made all these rules.’

‘All right,’ said Sister Sara.

Then she screwed a couple of seats onto a square piece of plank. To this plank she attached two smooth and well-polished balls. When I asked her what the balls were for, she said they were hydrogen balls and they were used to overcome the force of gravity. The balls were of different capacities to be used according to the different weights desired to be overcome. She then fastened to the air-car two wing-like blades, which, she said, were worked by electricity. After we were comfortably seated she touched a knob and the blades began to whirl, moving faster and faster every moment. At first we were raised to the height of about six or seven feet and then off we flew. And before I could realize that we had commenced moving, we reached the garden of the Queen.

My friend lowered the air-car by reversing the action of the machine, and when the car touched the ground the machine was stopped and we got out.

I had seen from the air-car the Queen walking on a garden path with her little daughter (who was four years old) and her maids of honour.

‘Halloo! You here!’ cried the Queen addressing Sister Sara. I was introduced to Her Royal Highness and was received by her cordially without any ceremony.

I was very much delighted to make her acquaintance. In the course of the conversation I had with her, the Queen told me that she had no objection to permitting her subjects to trade with other countries. ‘But,’ she continued, ‘no trade was possible with countries where the women were kept in the zenanas and so unable to come and trade with us. Men, we find, are rather of lower morals and so we do not like dealing with them. We do not covet other people’s land, we do not fight for a piece of diamond though it may be a thousand-fold brighter than the Koh-i-Noor, nor do we grudge a ruler his Peacock Throne. We dive deep into the ocean of knowledge and try to find out the precious gems, which nature has kept in store for us. We enjoy nature’s gifts as much as we can.’

After taking leave of the Queen, I visited the famous universities, and was shown some of their manufactories, laboratories and observatories.

After visiting the above places of interest we got again into the air-car, but as soon as it began moving, I somehow slipped down and the fall startled me out of my dream. And on opening my eyes, I found myself in my own bedroom still lounging in the easy-chair!

Indian language NLP/NLU

I have been learning things about semantic search and somehow found myself back to wondering about how to train a Hindi or Malayalam language model. I have done this in the LSTM days, and while I’ve kept up with some research, I haven’t with the engineering. Anyway, so if you want to do some language modelling in indic languages, your best bet would be to begin with something that AI4Bharat team has come up with. This is a language modelling and NLP team at IIT Madras who have released what seem to be the latest and the largest models for Indic languages. Because of the sad  sad paucity of monolingual corpora in Indian languages, they have generally done multi lingual training.

 

To get an idea of just how bad things are,  these are how many tokens per language the largest models for indic languages have

Lets compare that with English, the C4 dataset based on commoncrawl is trained on 1.4Trillion tokens.

Several order of magnitudes larger

Even in terms of the variety of data, most English LLMs are trained on a mix of scientific articles, blogs, news, legal documents, books, novels etc. whereas AI4Bharat which collated the largest dataset is largely just news and Wikipedia.

Not only are the datasets small, the benchmarking tasks are also several order of magnitudes smaller. This means we can never really compare the performance of a indic model with an enlglish one.

Another problem : We don’t have good tokenizers for different Indian languages. The indicBert team uses the same bert tokenizer, designed for English,with some modification. Malayalam is an agglutinative language,it seems silly to represent it using a tokenizer made for enlish or hindi. There are several tokenizers who have tried to do this, but none seem to speak to larger models or with the transformers ecosystem and not too many people seem to be using them.

What all this means is that unless we really rally and get the datasets of our languages much larger, we are destined to have our languages represented exclusively with relation to English, which really is the approach google and others suggest for dealing with “low resource” languages. This is appalling.

I deeply appreciate the work the IITM team is doing, but someone needs to fund them and a dozen other labs like them several order of magnitudes more than they are.

 

 

citation for indicBert

@inproceedings{kakwani2020indicnlpsuite,
    title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},
    author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},
    year={2020},
    booktitle={Findings of EMNLP},
}

 

 

 

Surviving Violence: A study from Tamil Nadu

@swarraj’s tweet lead me to this report

 

 Source: Swarna Rajagopalan with ACR Sudaroli, Sandhya Srinivasan and Anu Aroon, Stuck in a Circle: Surviving Domestic Violence and Everyday Resilience in Tamil Nadu, Queen Mary University of London, 2023 [PDF Link]

This is my summary and some takeaways

Technical Details: This is a multi-method study which collected primary data from survivors of domestic violence and other key stakeholders,  centering the experience of victim-survivors. It was conducted in 3 districts in Tamil Nadu; Ariyalur, Chennai and Vellore. The sampling method was Snowballing. 60 woman and 1 transman survivors were interviewed. Besides them, witnesses, members of the society and family were interviewed, as well as 62 support-providers like lawyers, medical workers,  NGOs, and the Police.

There are seven sections in the report.

Section 1 SETTING THE SCENE

The report opens with background information about the state of Tamil Nadu from key studies based on  NFHS, SDG, Census etc. highlighting the data on women’s health and experience of violence, and goes on to describe the questions in the interviews and the responses and then describes the methodology in detail.

Section 2  UNDERSTANDING DOMESTIC VIOLENCE

It then goes on to describe attitudes and beliefs of the interviewees about gender violence, laws about it, what constitutes it, and stances on punishment and resolution etc.  are elicited.

The interviews were done with great sensitivity and did not shy away from asking very uncomfortable questions. There were both structured as well as non-structured bits in each interview. Some of the questions were

  • Why do you think women experience Domestic Violence?
  • If a friend or family member told you they were experiencing domestic violence, what advice would you give them?
  • Is there ever a situation/circumstance when a woman should stay in an abusive/violent relationship? If so, why?

Some of the findings from attitudes towards violence are

  • Survivors understood that beyond physical violence, sexual, emotional, verbal and economic abuse constituted domestic violence.
  • They agreed that women who experience violence should seek help.
  • Community members agreed with these views, and  that domestic violence is committed because of cultural factors, interpersonal misunderstandings and alcohol abuse.
  • Community members also believed that women should and would put up with the violence up to a certain threshold of cruelty or affecting children, before leaving.
  • Support service providers largely agreed with an inclusive definition of domestic violence, but were less sure about whether it was a criminal office and the perpetrator should be punished

Summarized in the report as:

While society’s understanding of domestic violence has broadened, the a significant percentage believe that it is to be expected in relationships and are ambivalent about how best to respond. A survivor therefore steps out of an abusive situation into a support ecosystem that may or may not validate her experience.

Section 3 THE EXPERIENCE OF VIOLENCE  looks into the details of the experience of violence (who, how, what etc.), why survivors think they are subjected to violence, ways of coping, seeking help etc. The community is also asked the same questions but keeping the experience of the survivor as the center.

Section 4  looks into the support  available to survivors. This includes friends, family, the police, NGOs, lawyers etc. and their experiences in dealing with domestic violence and their understanding of the experience of it, the causes of it and the ways to solve it.

Section 5  looks into how well the Protection of Women from Domestic Violence Act, 2005 is implemented  (I didn’t read this too closely)

Section 6 asks what patriarchy looks like, with stories from survivors and witnesses and others. eg

Section 7 deals with the people’s views on developing resilience and what it means. This section spoke to me deeply.

This project arose out of a broader quest to understand resilience in both intellectual and practical terms. Where do humans find their resilience? What are the external supports and impediments for it? How does any society (or state) build the resilience of its people in the face of adversity? We took this question to a section of people vulnerable because of their gender in a patriarchal society and because of their everyday experience of violence in the very contexts that are meant to keep us safe—intimate relationships, family and home

The report ends with suggestions to what both the civil society organizations and the government apparatus could do to improve things, informed by study.

Each of these suggestions is a gem and carefully argued. Everyone should read the report [PDF Link].

Studies  from other  states in India and other policy briefs can be found in the resources section of the project’s website

Takeaways:

  1. At a cultural level, the understanding of domestic violence has changed a lot, and while everyone seems to agree that it is a bad thing and ought to stop, the solutions are not all that clear or trustworthy (to the society).
  2.  We need to  support survivors of domestic violence in the communities we are part of. The support can be in many ways, but it should probably begin with speaking up  about this in our families and providing support to family members who are survivors. I think this is key. We need to be clear and loud as individuals about what our stance is in the family-social units we are part of.
  3. Individuals should attempt to provide structural support in some forms more widely.
  4. Is there a model of heterosexual marriage that is not based on privileging the male and asking for submission from the female? Without widespread demonstration of the alternate model, for the family unit and the greater culture that we are part of, there really is no alternative that can be adopted.
  5. Women who leave abusive spouses and/or seek help and  justice need to be celebrated.
  6. There is really no benefit to women “adjusting”. Adjusting is based towards benefits the norms that created this problem. I recognizing that that is the only recourse available in many situations at present.
  7. Corruption masquerades as pragmatism.

I could view this problem as legacy code issues. While the number of bugs in the system make you want to burn it all down or lose hope, most of what we have came via iterative development. I mean the methods, techniques, heuristics, design-patterns and more that make up culture. that is reductive, i agree but useful to me.

The report makes me grateful to the feminists who have fought to get the laws and legal structures in place to deal with this issues.

It makes me hopeful to read the vast majority of the interviewees want education, financial stability, and social support and see these as being part of the cure.

This is reinforces my  belief that individual financial empowerment is severely understated and underutilized as a source for reducing harms in the nation at large.

What level is your EMR/EHR?

Level 1 EMRs

Warning: The following text is needlessly cruel

Level 1 the PITA : EMRs that double the work — systems where the EMR is used for documentation or compliance, but all the real work happens in written form, so this just increases the net amount of work done. [Admittedly, this is a problem of the administrative system, not so much the EMR itself, at times] (a large  majority of  EMR deployments in india are this kind)

Level 2 the copycat : EMRs that successfully replicate all the physical record keeping and so work isn’t double, but  worsens things because replicating physical workflows in-toto  invariably will make everything slower (This is what most new EMR implementations are)

Level 3 The trying-very-hard-to-be-cool: EMRs that have some automation and short cuts built in, like medication templates, discharge summary templates and suchlike which in some areas, significantly reduce the time taken to do a task and are overall nearly as fast as paper and pen ( I’ve seen a few of these)

Level 4 The we-do-design-thinking-and-listen-to-the-money : EMRs that have thought through the clinical processes  and removed all parts that do not need to be there, and have largely click and pick workflows these are faster to use than other EMR/EHR and might even be faster than pen and paper. (I’ve seen maybe one of these of these)

Level 5 : EMRs that are faster than pen and paper, assist and improve decision making and make things easier for patients. (Haven’t met one of these so far)

Book review: The Truth Pill by Thakur & Thikkavarapu

An Important book written in ALL CAPS

Topics covered in this book:

  • How approval for new drugs, new fixed drug combinations and new generics are supposed to work and how they actually work
  • What are the quality standards and monitoring mechanisms in place for scientific medicine, as well as AYUSH.
  • Laws governing AYUSH, scientific medicine and the production of drugs in India.

What I liked about the book:

  • Thorough research into how things are supposed to work in the area of drug regulations in India and an even more detailed research and experience into the million ways in which it is failing to work. Seriously, these two know their drug regulation problems.
  • Thorough understanding of the main players, organizations, lobbies etc. involved in this messy situation
  • Stupendous amounts of cred as far as I am concerned due to being a whistleblower as well as a RTI crusader/activist and all the litigation they have tried.
  • Focus on transparency, access to information, rights to quality
  • Documentation of various types of issues and the corruption in different bodies
  • Clear-sighted tracking of the bureaucratic games and political shenanigans around drug laws in the country.

What I learned from the book

  • We have a lot of outdated laws regulating drugs and other allied health products. These are exploited and used by both the governance people and the drug companies to push a lot of outright shitty medicines to the public
  • There is a LOT of corruption, incompetence and lack of transparency in the organizations that are supposed to be watchdogs and regulators
  • Successive governments and bureaucracies have generally found ways to pass the buck, do ass-saving paper pushing and pandering to sentiments rather than make any substantial and scientific changes.
  • We have a lot of under-equipped and underfunded drug regulators and inspectors
  • The quality of drugs produced in India is for all practical purposes at the mercy of the individual pharma company that does the production and there’s very little if any, real oversight or really punishment for even serious lapses.
  • Every month somewhere in this country, at least one pharma company is shown to have produced sub standard drugs and even then the judiciary seems to think harsh punishments are not needed. I mean, people have died and the dudes who made those meds never saw jail time.

Problems with the book

The whole book is narrated by someone who is shouting at YOU AND ALL THE IDIOTS WHO RUN THIS COUNTRY AND ALL THE CORRUPT DRUG MANUFACTURERS, THE INCOMPETENT JUDICIARY, THE CLUELESS LAW MAKERS OF NEWLY-INDEPENDENT INDIA WHO DID NOT HAVE 21ST CENTURY VALUE SYSTEMS OR KNOWLEDGE AND DRUG RIGHTS ACTIVISTS WHO ARE ALL CORRUPT TOO.

My blood pressure was measurably higher when I was reading this book. Seriously. There’s like four people the authors have anything nice to say about from 1923 -2023 (and two of them are dead). Oh, and the USFDA. They LOVE the FDA and the way things are done in the United States. 

<sarcasm> I mean, I don’t blame them, they’ve been fighting the good fight all their lives when literally everyone else in India is asleep, incompetent and corrupt. </sarcasm>

I get it, though, one of the authors has been constantly targeted and harassed by various government agencies and big pharma. So I get where the tone is coming from, I know righteous indignation, I used to have a lot of it. Now I just have reflux and indigestion.

But the roid-rage-hulk-smash tone of the book made me want to put it down and stop reading about fifteen times. I stayed the course only because they did thorough research, and what is an activist without some righteous rage, really. 

The other issues are that the policy and legal recommendations made in the book without fail made me wince, flinch and facepalm. The authors forget just how poor this country is, and just how many people there are.  The authors also don’t know a damn thing about the practice of medicine in the real India. They are operating from an america-centric and america-learned model of how health should be dealt with.

The most important implied recommendation of this book seems to be BURN IT ALL DOWN. 

Throughout the book they mention the dozens of parliamentary and other committees that have looked into these problems, and how so few of these reports see the light of day and fewer still get anything changed. Because the suggestions are not useful, or due to the unwillingness of the governance system to make any real changes.

This book has the same problem.

For eg.

  • They repeatedly suggest stronger laws, after describing just how poorly existing laws are implemented.
  • They suggest that online pharmacies will solve the supply chain problem (omg i cannot even)
  • They suggest that small pharmacists who are found to have bad drugs should be punished heavily.
  • They repeatedly recommend increasing the legal pressure on smaller players in the supply chain.

You cant improve state capacity by making tougher laws.

You can’t fix corruption in high places by punishing the small guy.

Some medication is most definitely in many many cases better than no medication, talk to a doctor, any doctor.

You cannot copy-paste quality standards designed in USA and Europe to solve for India.

That’s not how anything works. 

Would I prefer my patients got 40mg of  Telmisartan every time they buy a drug, YES.

Do we need to improve the capacity of drug inspectors and dis-entangle the regulatory nightmare, HELL YES.

But do we do it by shutting down everyone who only has 20 mg of telmisartan in their 40mg tablets, heavens no. The 20 mg the patient gets at 0.05 Paise a tablet is worlds better than the 0 mg he will get at 2 rupees a tablet.

In the words of the y00ths I interact with, the authors desperately need to touch grass.

What are Artificial Neural Networks and how do they work? A non technical explanation

Note: This is a guide for people without a math and code background, written by a medico for other people from a medical/biology background. If you are the mathy type, this is not for you, try this instead

Introduction: What is this AI business?

Artificial Intelligence (AI) is an umbrella term for scientific fields whose aim is to mimic or replicate human-like skills with computers. A large part of this field is driven by computer science and mathematics.

One of AI computing’s main goals is to create self-learning or self-training systems or algorithms. The field of AI computing that tries to create, test and use such algorithms is called Machine Learning (ML). Artificial Neural Networks (ANNs) are one such algorithm which are very popular and have lead to a lot of breakthroughs in what computers can do.

Artificial Neural Networks (ANNs) are a type of software algorithm that is composed of bits of code that can do math and store information (neurons), that pass information (inputs) back and forth between each other, making slight changes till a particular result (output) is achieved.

This article describes a largely non-mathematical and no-code explanation for how it does this.

What are ANNs

Layers of the retina

Consider the Retina, it has layers and when light hits these layers, it triggers (gets converted into) various types of signals (chemical and electrical) and all these signals gets passed on to the Optic nerve and then to the Occipital lobe, which working in concert with the rest of the brain interprets what the signal means and produces an output- vision.[1].

This is not how an ANN works.

Like with the retina though, ANNs are code (neurons) arranged in layers sandwiched between an Input layer, which receives the signal, and an output layer which produces the results, and finally a verifier which determines if the right output was received or not.

A whisper (neural) network diagram

Why are they called neurons and artificial neural nets (ANNs)?

There’s a theory that neurons learn by passing on information that meets a certain criteria (activation threshold) to other neurons (spreading of activation) and getting repeated feedback about how to correct these activations from those neurons till it activates for the right signal or passes on the right information. [2].

For example, when you learn to do a physical task, like opening the fridge, the very first time you do it, your muscles and the nerves that control the muscles don’t really know how much force to use, or how much signal should pass between which neurons controlling which muscles to open the door smoothly. But, the system picks a starting amount of power, and tries, and the sense organs give feedback that says is this working or not, and based on that, it will adjust the amount of power needed. Over time the flow of information and signals between all the nerves and the muscles and the various organs involved in opening this door is so good and so optimized that it becomes effortless.

This is thought to be because of the back and forth “this is working, this is not working, too much pressure, too little pressure, wrong angle, right angle” messages that are rapidly passing between the hand and the brain. And it is this self correcting back and forth messaging system (back propagation of error) which eventually figures out the right way to solve the problem by finding the right amount and type of information (signal) passed between the neurons involved in this action.

This is called the connectionist theory of cognition, and this system of learning is called a connectionist system because the way it works is thought to be due to the strength (weight) of its connections and finding the right connections.

Most neuroscientists believe this theory to be completely wrong about how the brain actually learns [3]

But, it’s either got a kernel of truth in it, or due to some mysterious reasons, this system of learning works when turned into computer code and so is called an artificial neural network.

So what does each neuron in these ANNs do?

Two things

  1. When it receives a signal it transforms it and sends it forward to other neurons it is connected to. (Forward pass)
  2. When it receives some information back from neurons about what was wrong with the earlier output, it adjusts the output and then passes the information along to the neurons behind it. (Backward pass)

Think of it like a complicated game of Chinese whispers.

You’ve got people standing in a row, and the game host says “life is meaningless” to the first person. Person one hears “wife is meaningless” and blurts it out to the next person, who hears “wife is weaning less”, the next person hears “life is winning less”, and so it goes. Now, in this version of the game, instead of revealing the answer, the verifier who is standing right at the end says, you were wrong by x percentage. So the last person passes this to the person before them, and this process is repeated a very large number of times till the message being received back is “life is meaningless”. Note that the last person isn’t told that the right answer is “life is meaningless”, only that it was wrong by a small amount.

Now consider that this is being done in parallel with multiple rows and each rows hears a different parts of the message, and what the verifier wants to hear is ” life is meaningless, cried the nihilist, while munching on stale peanuts he stole from the existentialist’s larder”

Sounds like a nightmare, and a mathematical nightmare it is for people like me who start getting hypoglycemic when letters become numbers and numbers get letters added to them, sometimes before, sometimes after, sometimes on top etc. Fortunately, the programs do all the math themselves and we just need to know two things. What matrix multiplication is what a linear equation is.

Matrix multiplication is a method of multiplying two sets of numbers arranged in a particular way. A matrix looks kinda like this

$\begin{bmatrix} 1 & 2\\ 2 & 4 \\ 5 & 6\ \end{bmatrix}$

So it’s like a table of numbers but they encode some other information like which axis a number is on and stuff. There are specific rules about how two or more matrices can be multiplied. We won’t go into that because it is boring.

The reason we want to know about this is that in machine learning one thing we do a lot is turn all our data into matrices and then multiply them.

We represent numerical data as vectors and represent a table of such data as a matrix. The study of vectors and matrices is called linear algebra Source: Math for ML

Irrespective of what kind of data you need to provide as input, all of it gets converted into numbers. But you already know that that’s how computers work. We just use a specific method for representing these when doing ML, which is turning everything into vectors or matrices.

Linear equations (which is what linear algebra uses) all kinda look like $ax+b =c$

This is a mathematical formula which will always produce a straight line if the values are changed and plotted.

This means that, if you know the $a$ and the $b$ and the $c$, you can guess what the $x$ is and solve the equation [no duh].

But even if you don’t know what $c$ is, but can find out if the number you came up with (your $c$) is bigger than or smaller than $c$, you can adjust the $x$ to get the right answer.

The reason why we need this kind of a mental contortion is because remember that in our Chinese whispers example, the person at the verification end doesn’t say what the answer is, only that the given answer is right or wrong and some directional information. This behaviors is not just to create confusion, it’s designed to facilitate learning, because what we want is not a network that has memorized the answers (which it would if you give it the answer), but a network that’s learned the method to arrive at the answer (which you torture it into learning) .

So if you have a formula like $50x + 70 = 520$ and you don’t know the answer is $520$, but have this oracle who can tell you if the number you come up with is bigger or smaller than the real answer, how would you solve this?

You could begin by guessing $x=1$, and get the answer $c_1= 120$, ask the oracle is $c_1 >= c$ and you will hear $FALSE$.

Which means your $x$ needs to be bigger. OK, how about $x=10?$ This will give us $570$.

Oracle, is $c_{10} >= c ? -> TRUE$

Now we know we (probably) need to make the $x$ smaller to get the right answer.

As you can imagine, depending on how you change the $x$, you will within a few “steps” up and down, get the right answer. [4]

This painful iterative way is exactly how neural networks self correct.

Let’s say we don’t really know the right $a$ or $b$ either. Can we still solve this problem?

How can we come up with the $a$ and the $b$?

Not surprisingly, we can use the earlier approach of just picking some random numbers, putting them into the equation and then adjusting them up and down till we get the formula we’re looking for.

To keep the moving parts minimal, let’s make it so that $b$ is a constant, so we just pick a random number and leave it as a constant throughout all the iterations. $x$, we pick randomly as before but we will adjust it as we get more information. To begin with we can pick a random number for $a$ and after that we can use a different strategy, described below to update it.

OK, let’s use this to solve a pressing real world problem. Lets say we need to make a neural network that correctly identifies the various parts of a goat from a photograph.

So first we convert a photo into a matrix of numbers. [5]

Then we show the input neuron a part of the photo near the goat’s head. And we’re gonna ask it to guess what it is, but in linear-equation-math.

So to begin with, let $a =1 , b=1 \space and \space x=0$

(keep your imaginations alive)

$Step \space 1: a_1x_1 + b = c_1$ or $1*1+0 =1$

(In non math, this humble output $1$ means “this is a goat’s horn”)

Now, based on the information that it gets back, it will know if the x should “step” up or down. Let’s say the feedback says, “too high bro”.

So then, let’s step down the $x$ and let $x_2 =0.5$

Given that we already have a piece of data sitting in our neuron, which is the last output ($c_1$), instead of starting with a brand new $a$ (or answer), we can just modify this answer using the new $x$ and the same constant $b$, that way it’s nudged in the right direction. So we use $a_2 = c_1$ . This is made possible because $c_1$ isn’t a random number, it’s the result of the assumptions you made earlier. Or, it is “This is a goat’s horn”, which you can step down or modify using the new information.

$Step \space 2: a_2*0.5 + b = c_2$

What happened mathematically is that we produce a result something like “this is a goat’s neck”.

You can imagine, that if you repeat this enough times, based on how large your “step” for $x$ is, and based on what the other neurons in the system are saying, you will at some point get the right value of $c$, which could be “this is a goat’s right earlobe”, and this is now what it has “learned”.

So the next time it sees something like a earlobe, it will be able to identify it instantly and not confuse it with other dangling objects that goats possess.

And isn’t it cool that we could come up with a way to just make a wild guess, then with feedback adjust the wild guess into a coherent answer? (Wait, is that how humaa learning works?)

I think now you might be able to see that $x$ is in a sense the the importance or weight you give the input $a$ to produce an output $c$ and the $b$ acts as a a constant nudge in a particular direction or a bias.

So, in summary

A neuron gets “inputs $(a)$”, multiplies it by a “weight $(x)$” adds it to a “bias $(b)$” to produce an “output $(Y)$” (I know we called it a $c$ earlier, but confusion is our friend). How do we get to a final output? By summing these iterations. In math:

$\displaystyle\sum (weights*inputs) + bias = Y$

That fancy squiggle means SUM of the things to its right.

And the updating of the weights happens by passing back information about if our answer was right or wrong and in which direction. In computer science this is called back-propagation of error with gradient descent, that’s not important, but it is useful for showing off.

We’re nearly done, there’s just one more thing I mentioned earlier, a threshold of activation, which will wrap up the whole thing neatly.

While a neuron produces all this data, we don’t want it to fire all the time right? Suppose what it “heard” was too soft for a meaningful answer, or if it was so “loud” so it PASSES ALONG A SHOUT LIKE THIS!!!!!!. These kinda signals can increase the error, or confusion.

So for hygiene, it’s better that we pass on only information that is of uniform volume all over the network and only if it passes some kind of a test of importance (quality check). To do this, we could pass the output $(Y \space or \space c)$ through a mathematical transformation that achieves this. This transformation is called the activation function.

Depending on the type of task, the activation function could be something like, take the mean, or convert into a range between 0 and 1 or something else

Those of you who remember your physiology lectures might notice how much this is like an activation potential for a neuron. That is not a coincidence. What ANNs do is inspired by real neurons.

An activation function is often represented with the Greek letter phi ($\phi$)

To update our earlier equation

$\phi(\Sigma(weights*inputs) + bias) = Y$

So now we have a way of making guesses that slowly move in the right direction, and we have an activation function that makes sure only the good stuff gets passed along in a standardized way.

Wait, now we know how a linear equation can be solved with guessing, but this technique isn’t limited to linear equations. Almost any kind of mathematical equation could be solved by initially random guessing and then passing the information about how wrong you are back, and updating the guess. In math this is called the universal approximation theorem and it is kind of a big deal.

That’s it. That’s how a neural network do.

Congratulations you now have a working understanding of how math equations can be fiddled with to get to the right answer. And that is the basis of all artificial neural networks, and a whole lot of AI and ML. [6]

Further Reading

  1. Han, Su-Hyun, Ko Woon Kim, SangYun Kim, and Young Chul Youn. “Artificial Neural Network: Understanding the Basic Concepts without Mathematics.” Dementia and Neurocognitive Disorders 17, no. 3 (2018): 83. https://doi.org/10.12779/dnd.2018.17.3.83.
  2. If you prefer a more mathy explanation :
    Michael A. Nielsen, “Neural Networks and Deep Learning”, Determination Press, 2015

Footnotes

[1]: Seeing: Introduction to Psychology ↩︎

[2]:Connectionism on Stanford encyclopedia of philosophy. It’s a fun read. ↩︎

[3]: Papadatou-Pastou, M. (2011). Are connectionist models neurally plausible? A critical appraisal. Encephalos, 48(1), 5-12. ↩︎

[4]:Some of you smarty pants would like to point out that a $>=$ wont ever give us the right answer but please sit down this is a loosely true mathematical explanation and other people get it, this isn’t for you anyway. ↩︎

[5]: At this point you just have to trust me that this can be done, but also check out this post about pixels and greyscale images and how they are formed ↩︎

[6]: Some of you might want to ask, say, what is artificial intelligence then? Well, AI, is just artificial neural networks making a whole lot of really really complex guesses and then self correcting them till they are kinda accurate. ↩︎

What All EHRs/EMRs get wrong

This is what a handwritten history sheet (which is where we write down the most important details that we extract from a patient or responder about the case). Source

 

And this is what the most popular EHR/EMR User interfaces that are meant to enter the same details as the history sheet look like

See a difference?

No, I don’t mean the ridiculous amount of clutter, the confusing information architecture or just the general pathetic state of these UIs.

I mean, the history sheet is not a system of transcribing what the patient said or transcribing what data was collected. It is a notation system, a language of its own, with data arranged spatially in a particularly way, symbols used to convey information, abbreviations, and a system to indicate what is important and a method for tracking the temporal profile of the case.

The EHRs are incoherent because they are talk-transcription interfaces. Not notational interfaces.

Imagine asking an artist who is composing music, to write down, in English, the notes in each chords, the tempo and what not. Would that looks coherent? Why would you do that when you have a musical notation system?

The clinical case file, handwritten is a few hundred year old technology that’s had iterative improvements in its quality and information architecture and it works! A case note written by an doctor in Bengaluru makes perfect sense when read by a doctor in Mumbai. It makes sense when read by a different specialist. It makes sense to nurses, to pharmacists to therapists of various kinds.

It doesn’t, however make sense to the administration. EHRs serve administrative needs. Not communicative.

 

Video of the talk Telemedicine Policies and Standards in India

Dr. Gowri Kulkarni addresses doctor attitudes and components of practicing telemedicine, Jasmine George of Hidden Pockets speaks of patient experiences, experiences with seeking sexual and reproductive health online and access to safe abortions using telemedicine.

Everything I said is reproduced and augmented in an earlier post on telemedicine policies in India. Here I will point to the discussion by Dr. Gowri (GK) and Jasmine George (JG)

1:50 — History of telemedicine in India ( GK)

5:40 — How telemedicine became so prominent due to Covid and how reproductive health issues were handled. Provider and patient perspective — (JG)

12:00 — How have doctors responded to telemedicine? Fears, expectations etc. (GK)

38:00 — How does one deal with the nuances of consent via telemedicine? (GK, JG, AP) This part is very important, and the discussion bring out a lot of the complexities involved in informed consent and technology, and the national health stack.

56:24 — Negotiating privacy and safety in crisis helplines and abortion and reproductive health (JG)

58:00 — Is this the future? What can Telemedicine do and not do? –(GK, JG)

1:07:00 — Unethical practices in clinical medicine now, why they exist and the future — (GK)

1:12:00 — The changing nature of the social contract with doctors, and the need for change in practices — (JG) A vital point here being made by Jasmine, she speaks of how instead of the tort approach, we need positive laws keeping stakeholders in consultation.

1:15:20 — Challenges in scaling telemedicine — (GK)