Rural Health 2.0 and User-driven healthcare

Shoubhik Bose Engineer and independent researcher

Health 2.0 - What

Health 2.0 - Why

• Staying informed.

• Medical education.

• Collaboration and practice.

• Managing a particular disease.

• Sharing data for research.

Email v/s Collaboration

Health 2.0 - How

• telemedicine,

• electronic medical records

• mHealth

• Connected Health,

• The use of the internet by patients themselves such as througho messageboards, o blogs.

Rural health 2.0 Technology for low-infrastructure and

remote areas.

Blood test without bleeding ?

• Low-cost.

• No bleeding.

• Trained personnel not needed.

TouchHB

Invented by: Myshkin Ingawale

Anatomy of a social network

Tabula Rasa ( Facebook group )

User-driven healthcare : Our implementation

Mathabhanga district in West Bengal

Findings from a healthcare questionnaire

Absolute survey respondents V/S Commonly known problems

User-driven healthcare - What

User-driven healthcare- Why

• Individual patients should not have to get themselves shunted between multiple health professionals with multiple waiting times.

•The patient’s information travels through a network of dedicated health professionals rather than the patient having to travel physically.

User-driven healthcare- How

1. Collect health information.

1. Team of health experts discuss 'privatey'.

1. Produce a solution.

Dissecting the system

Screen grab: INPUT

Screen grab: OUTPUT

User-driven healthcare - features

• Hassle-free login using Google account.

• Multi-interface input.

• SMS and voice service from low-end phones ( from rural-seekers )

• Anonymised patient names.

• Secure discussion boards.

• Reports-hosting.

• Solution recommendation engine.

• Android Application.

The proposed flow

• Patient sends a voice/text message.

• Message is sent to the server and made available on the web page.

• Medics get notified by email/phone.

InputTIER 1

PATIENT CARE-GIVER

Input - The proposed (alternate) flow

Dial a number and narrate the problem over the phone.

Audio

Speech-to-text engine.

Text ( after translation )

UDHC

Inputs - methods

Scanned narratives

Transliteration in local language

Input - Transliteration

Some of the Patient inputs we have recieved over email/web so far.

The input - How do we anonymise?

• Scientific names used instead of real patient names.

• Mashed up with pincode for geolocation

• Blurring of names in reports manually using image editors before uploading.

ABELMOSCHUS480***ESCULENTUS

The input - Anonymisation( proposed )

1. Image editors native to the web-based application to allow image manipulation after upload.

1. "Identifier detection" algorithms to automatically remove patient identifiers.

1. Image tagging. ( Something similar to Facebook? )

Patient consent form ( English )

Patient Consent form

I give my consent for this information about MYSELF/MY WARD/MY RELATIVE [indicate correct description] relating to my/his/her health to appear in the ‘User Driven Health Care’ UDHC ‘clinical problem solving’ forum and web based electronic health record.

I understand the following:1. UDHC forum is an initiative to promote transparency and ethical practices in health care while qualitatively studying its processes to assess the role of

various (hitherto unidentified) factors that influence healthcare outcomes.

1. UDHC network aims to qualitatively study and in a controlled online-forum, support the phenomenon of sharing an individual's disease related information with other multiple stake holders in healthcare such as similar patients, related health and other professionals to determine if this shared learning activity is beneficial in the individual's healthcare outcome.

1. My information will be published without my real name attached and UDHC ‘clinical problem solving’ forum will make every attempt to ensure my anonymity addressing me solely by my anonymized user-name.

1. I understand, however, that complete anonymity cannot be guaranteed. It is possible that somebody somewhere - perhaps, for example, somebody who looked after me if I was in hospital or a relative - may identify me.

1. UDHC ‘clinical problem solving’ forum will not allow the Information to be used out of context. The text of the information may be edited for style, grammar, consistency, and length.

1. The Information may be published in the UDHC ‘clinical problem solving’ forum and associated journals on paper as well as in the internet, which would be distributed worldwide.

1. Information displayed in the UDHC ‘clinical problem solving’ forum is not supposed to replace advice from the primary care physician of the patient.

Signed Accept (Patient) Witness

Patient consent form ( Hindi )

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हस्�क्षरो / सहमति / असहमति

Inputs - representation [ list ]

Inputs - representation [ graphical ]

Inputs - pitfalls and workarounds

• Rural population has less/no access to the Internet.

• Language barrier – very few might know English.

• Computer illiteracy.

• How to use website forms?

• Narrate as voice using mobile phones.

• Transliteration.

(and even translation)

• Not a problem, mobile phones will do!

• Medical Kiosks administered by computer literate volunteers.

Hidden layerTIER 2

MODERATORS

MEDICAL STUDENTS

CARE-GIVERS

The hidden layer - discussion

OutputTIER 3

CARE-GIVERS CARE-SEEKERSMODERATORS

Evidence-based learning

Recommendation engine

• Patient posts health issue.

• System "predicts" similarity with a previously solved health case.

• Health experts take assistance of previously solved case to provide solution.

• The new solution gets added to the "repository" of previously solved cases.

Data from BMJ

Custom Algorithm:Tag-ifying the health narratives

Heuristic approach for predicting similarity

• Tag- ify

• Determine similarity between 2 narratives.o choose those which have a high number of similar

tags. o Threshold of the percentage of similarity.

• Test with 20% , 40% , 50% , 60% , 80% tags' similarity and analyse whether the algorithmic results match the logically expected results.

Artificial Intelligence Google Prediction algorithm

Quoting the Google Prediction algorithm

• Given a new item, predict a numeric value for that item, based on similar valued examples in its training data.

• Given a new item, choose a category that describes it best, given a set of similar categorized items in its training data.

"Google Prediction" for health narratives

Given a new health narrative ,

choose a health record that describes it best, given a set of similar health records items in its training data.

Given a new health narrative , predict a previously solved case from the archive, based on similar valued examples in its training data.

Scores from our Mathabhanga chapter where the UDHC team is led by Dr.Biswas.

August 2011 November 2012

150 health cases discussed over email

20 to 30 % patients travelled to Bhopal after initial consultation

Crowdsourcing the UDHC platform

Mr. Kar

Who can contribute ?

•Medical students.•Engineers.•Designers.•Doctors.•Moderators.•Translators.•Content editors.•Care-seekers

... and the list goes one.

Anyone can help make build this encyclopedia of health issues.

References

• Swan Health 2.0 model , Emerging Patient-Driven Health Care Models: An Examination of Health Social Networks, Consumer Personalized Medicine and Quantified Self-Tracking, Int. J. Environ. Res. Public Health 2009, 6, 492-525

• BMJ ( 2012 ), BMJ Case Reports - Article archive by date, Retrieved fromhttp://casereports.bmj.com/content/by/year

• Biosense Technologies (2012) , Myshkin Ingawale, Retrieved from http://unreasonableinstitute.org/profile/mingawale/

• UDHC ( 2012 ) Retrieved from http://care.udhc.co.in/STATICS/what.jsp

• Evidence based learning , 5 steps to evidence-based learning , http://www.ebbp.org/steps.html

• Anatomy of a social network, Dave Gray, https://plus.google.com/117373186752666867801/posts/CQRVeKEsUvF

Thank you ,Questions and comments ?You can also get in touch Email: sbose78@gmail.comBlog: code-cooker.blogspot.comTwitter: sbose78Facebook: shoubhik.bose

UDHC website : care.udhc.co.in