Measuring data collection diligence for community healthcare

Data analytics has tremendous potential to provide targeted benefit in low-resource communities, however the availability of highquality public health data is a significant challenge in developing countries primarily due to non-diligent data collection by community health workers (CHWs). Our use of...

Full description

Saved in:
Bibliographic Details
Main Authors: KARUNASENA, Galawala Ramesha Samurdhi, AMBIYA, M. S., SINHA, Arunesh, NAGAR, R., DALAL, S., ABDULLAH. H., THAKKAR, D., NARAYANAN, D., TAMBE, M.
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2021
Subjects:
Online Access:https://ink.library.smu.edu.sg/sis_research/6569
https://ink.library.smu.edu.sg/context/sis_research/article/7572/viewcontent/EAAMO_21_camera_copy_1___1_.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
id sg-smu-ink.sis_research-7572
record_format dspace
spelling sg-smu-ink.sis_research-75722022-01-10T03:28:53Z Measuring data collection diligence for community healthcare KARUNASENA, Galawala Ramesha Samurdhi AMBIYA, M. S. SINHA, Arunesh NAGAR, R. DALAL, S. ABDULLAH. H., THAKKAR, D. NARAYANAN, D. TAMBE, M. Data analytics has tremendous potential to provide targeted benefit in low-resource communities, however the availability of highquality public health data is a significant challenge in developing countries primarily due to non-diligent data collection by community health workers (CHWs). Our use of the word non-diligence here is to emphasize that poor data collection is often not a deliberate action by CHW but arises due to a myriad of factors, sometime beyond the control of the CHW. In this work, we define and test a data collection diligence score. This challenging unlabeled data problem is handled by building upon domain expert’s guidance to design a useful data representation of the raw data, using which we design a simple and natural score. An important aspect of the score is relative scoring of the CHWs, which implicitly takes into account the context of the local area. The data is also clustered and interpreting these clusters provides a natural explanation of the past behavior of each data collector. We further predict the diligence score for future time steps. Our framework has been validated on the ground using observations by the field monitors of our partner NGO in India. Beyond the successful field test, our work is in the final stages of deployment in the state of Rajasthan, India. This system will be helpful in providing non-punitive intervention and necessary guidance to encourage CHWs 2021-10-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6569 https://ink.library.smu.edu.sg/context/sis_research/article/7572/viewcontent/EAAMO_21_camera_copy_1___1_.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Computing methodologies Machine learning Artificial intelligence Community healthcare Data quality Data collection diligence Clustering Social impact Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Computing methodologies
Machine learning
Artificial intelligence
Community healthcare
Data quality
Data collection diligence
Clustering
Social impact
Databases and Information Systems
spellingShingle Computing methodologies
Machine learning
Artificial intelligence
Community healthcare
Data quality
Data collection diligence
Clustering
Social impact
Databases and Information Systems
KARUNASENA, Galawala Ramesha Samurdhi
AMBIYA, M. S.
SINHA, Arunesh
NAGAR, R.
DALAL, S.
ABDULLAH. H.,
THAKKAR, D.
NARAYANAN, D.
TAMBE, M.
Measuring data collection diligence for community healthcare
description Data analytics has tremendous potential to provide targeted benefit in low-resource communities, however the availability of highquality public health data is a significant challenge in developing countries primarily due to non-diligent data collection by community health workers (CHWs). Our use of the word non-diligence here is to emphasize that poor data collection is often not a deliberate action by CHW but arises due to a myriad of factors, sometime beyond the control of the CHW. In this work, we define and test a data collection diligence score. This challenging unlabeled data problem is handled by building upon domain expert’s guidance to design a useful data representation of the raw data, using which we design a simple and natural score. An important aspect of the score is relative scoring of the CHWs, which implicitly takes into account the context of the local area. The data is also clustered and interpreting these clusters provides a natural explanation of the past behavior of each data collector. We further predict the diligence score for future time steps. Our framework has been validated on the ground using observations by the field monitors of our partner NGO in India. Beyond the successful field test, our work is in the final stages of deployment in the state of Rajasthan, India. This system will be helpful in providing non-punitive intervention and necessary guidance to encourage CHWs
format text
author KARUNASENA, Galawala Ramesha Samurdhi
AMBIYA, M. S.
SINHA, Arunesh
NAGAR, R.
DALAL, S.
ABDULLAH. H.,
THAKKAR, D.
NARAYANAN, D.
TAMBE, M.
author_facet KARUNASENA, Galawala Ramesha Samurdhi
AMBIYA, M. S.
SINHA, Arunesh
NAGAR, R.
DALAL, S.
ABDULLAH. H.,
THAKKAR, D.
NARAYANAN, D.
TAMBE, M.
author_sort KARUNASENA, Galawala Ramesha Samurdhi
title Measuring data collection diligence for community healthcare
title_short Measuring data collection diligence for community healthcare
title_full Measuring data collection diligence for community healthcare
title_fullStr Measuring data collection diligence for community healthcare
title_full_unstemmed Measuring data collection diligence for community healthcare
title_sort measuring data collection diligence for community healthcare
publisher Institutional Knowledge at Singapore Management University
publishDate 2021
url https://ink.library.smu.edu.sg/sis_research/6569
https://ink.library.smu.edu.sg/context/sis_research/article/7572/viewcontent/EAAMO_21_camera_copy_1___1_.pdf
_version_ 1770575992974213120