The Middle Path ? Here it is

 

 

 

Context :

Health Ministry meet explores middle-path between online pharmacies and retail chemists

( BL  /  01 Sept 2023 )

Extract :

India’s Health Ministry has initiated discussions with e-pharmacies and brick & mortar retailers and chemists on the way forward. 

Discussions held earlier in August saw chemists raise objections to deep discounting practices of e-pharmacies, while the Health Ministry officials were concerned about select practices of these e-pharmacies like allowing sale of medicines without proper prescriptions and so on.

Maintaining privacy of user data across these platforms is another repeated concern that had cropped up during the meeting. 

The e-pharmacies were reportedly asked to present their case too. “ The CDSCO ( Central Drugs Standard Control Organisation) held a stakeholder meeting earlier this month (August). Viewpoints of chemist shops and e-pharmacies and Health Ministry concerns were heard, raised, and discussed.

There is a middle path that is being looked at, a way forward where both offline and online can be accommodated with regulations coming in play,” said an official who was present. 

While there are some who want a complete ban on e-pharmacies, others suggest that US-type regulations come into play, where there are digital prescriptions and these can be universally accessed, but with strict guidelines surrounding sharing of user data. 

 

My  Take :

 

3D - Digital Delivery of Drugs ?  ......  11  Oct  2015  

Extract :

 My Suggestions :

 

*  Govt to upload a Mobile App ( DISPENSER ? ) on Health Ministry web site

*  All doctors must download it , after proper registration / authentication

*  App will get embedded with full details of that doctor who uses it

*  Doctors to enter Patient Data in App  / then enter prescribed Drug Details

*  Doctor will SMS app data entered , to concerned patient's mobile

*  Patient will SMS entered app data to mobile of Chemist of his choice

*  Chemist will deliver the medicine to the patient with Bill Details in App

*  Every action / transaction on App , will get automatically emailed to web

    Servers of

        #  Health Ministry

        #  All India Medical Association

       #   All India Organization of Chemists and Druggists ( AIOCD )

       #   Drug Controller General of India

  

Advantages :

*   Jobs / business of Chemists / Druggists saved

*   Great convenience for patients / no need to visit Drug Store

*   No chance of Chemist " misinterpreting " doctor's handwriting !

*   Full History of

      #  Each Patient ( great benefit to Prescribing doctors )

      #  Each Drug    ( great benefit to Pharma Companies  )

      #  Each Chemist Shop  ( great benefit to Govt / AIOCD / Tax Dept )

      #  Each Doctor  ( great interest to All India Medical Association )

 

With regards,

Hemen Parekh

www.hemenparekh.ai  /  01 Oct 2023

 

 

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27 Years ago : Foundation of NLP ?

 

 

Context :

ARDIS … …………..  09   Dec 1996  

         

I asked , both BARD and ChatGPT :

“My following note was written some 27 years ago in 1996. In this note, I laid out logic for " deciphering " key data about a job-applicant from his totally UNSTRUCTURED TEXT resume. Would you consider my " logic " as a foundation ( albeit somewhat crude ) of NLP ?”

 

BARD replied :

Hi,

Yes, I would consider your logic as a foundation (albeit somewhat crude) of NLP.

Natural language processing (NLP) is a field of computer science that deals with the interaction between computers and human (natural) languages, in particular how to program computers to process and generate human language.

Your logic for deciphering telephone numbers and company names is based on the identification of keywords and phrases that are commonly associated with these entities. This is a common approach in NLP, and it is often used in conjunction with other techniques, such as machine learning, to improve the accuracy of the results.

The specific keywords and phrases that you identified are based on your own knowledge and experience, and they may not be applicable to all cases. However, they provide a good starting point for developing a more robust NLP system.

I would encourage you to continue your research in this area. NLP is a rapidly growing field, and there are many new and exciting developments happening all the time. With continued effort, I am confident that you will be able to develop a system that can accurately decipher telephone numbers and company names from unstructured text.

 

 

ChatGPT replied :

 

It's interesting to see that you had laid out a detailed logic for deciphering key data from unstructured text resumes, such as telephone numbers, company names, and other information about job applicants.

Your approach seems to be an early attempt at using pattern recognition and rule-based methods to extract structured data from free-form text.

While your logic is somewhat specific and tailored to the information you wanted to extract, it does share similarities with the foundation of Natural Language Processing (NLP) techniques.

NLP is a field that involves the interaction between computers and human language. It aims to enable computers to understand, interpret, and generate human language in a way that is both meaningful and useful. Your approach demonstrates some key aspects of NLP, such as:

1.     Pattern Recognition:

Your logic involves recognizing patterns in the way information is presented in the resumes. This is a fundamental concept in NLP, where algorithms are designed to identify and extract patterns from text data.

 

2.     Rule-Based Processing:

Your approach involves using a set of rules to extract information. In NLP, rule-based processing is an early technique where predefined rules guide the system's behavior. Modern NLP approaches often combine rule-based methods with machine learning techniques.

 

3.     Entity Recognition:

The identification of entities like phone numbers, company names, and positions in your approach is akin to entity recognition in NLP, where algorithms identify specific entities like names, dates, locations, etc., in text.

 

4.     Syntax and Context:

Your approach takes into account the syntax and context of the text to identify relevant information. NLP models use syntax and context to understand the relationships between words and phrases in a sentence.

 

5.     Information Extraction:

Your approach aims to extract specific information from unstructured text, which is a core task in NLP.

Information extraction involves locating and classifying information entities within text data

 

While your logic is a step in the right direction, modern NLP has evolved significantly since then.

Advances in machine learning, particularly deep learning, have led to the development of more sophisticated techniques for processing and understanding natural language. Techniques like Named Entity Recognition (NER), Part-of-Speech tagging, dependency parsing, and pre-trained language models have become essential components of NLP pipelines.

Your logic serves as an example of early attempts to tackle NLP challenges, and it's interesting to see how the field has grown and evolved since then.

If you're interested in exploring modern NLP techniques, you'll find a wide range of tools, libraries, and resources available to help you process and analyze text data more effectively.

 

 

With regards,

Hemen Parekh

www.hemenparekh.ai  /  01  Sept  2023