Evidence map›Paper›PMID 36041830›Full record

ArticleGlobal health, science and practice2022

Optimizing the Health Management Information System in Uttar Pradesh, India: Implementation Insights and Key Learnings.

Ankita Meghani, Anand B Tripathi, Huzaifa Bilal, Shivam Gupta, Ravi Prakash, Vasanthakumar Namasivayam, James Blanchard, Shajy Isac, Pankaj Kumar, B M Ramesh

Abstract read
In one paragraph

Article in Global health, science and practice, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Association of affirmative action with health and education outcomes and services in India.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  8. Review
  9. Article
  10. Article
  11. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Ankita MeghaniJohns Hopkins Bloomberg School of Public Health, Department of International Health, Baltimore, MD, USA.
Anand B TripathiIndia Health Action Trust, Lucknow, India.
Huzaifa BilalIndia Health Action Trust, Lucknow, India.
Shivam GuptaJohns Hopkins Bloomberg School of Public Health, Department of International Health, Baltimore, MD, USA.
Ravi PrakashIndia Health Action Trust, Lucknow, India.
Vasanthakumar NamasivayamIndia Health Action Trust, Lucknow, India.
James BlanchardInstitute for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada.
Shajy IsacIndia Health Action Trust, Lucknow, India.
Pankaj KumarNational Health Mission, Government of Uttar Pradesh, Lucknow, India.
B M RameshInstitute for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

An effective health management information system (HMIS) that captures accurate, consistent, and relevant data in a timely fashion can enable better planning and monitoring of health programs and improved service delivery, in turn helping increase the impact of different interventions. In 2009, the Government of Uttar Pradesh (GOUP) implemented HMIS, India's national-level health information platform. However, key challenges, including difficulties in accessing the data through a web-based portal and its limited relevance to decision making and managerial needs, reduced its usability at the district and state levels. In 2015, with the support of the Uttar Pradesh Technical Support Unit, the GOUP created its own data platform, the Uttar Pradesh HMIS (UP-HMIS), to capture data elements missing from HMIS but important to UP decision makers. The UP-HMIS was redesigned to capture these data elements to holistically measure and monitor the performance of health programs and inform decision making at the district and state levels. In addition, the GOUP implemented complementary initiatives to improve data quality and data use processes. To improve HMIS data quality, the GOUP established data validation committee meetings at the block, district, and state levels. To promote the use of these validated data, in 2017, the GOUP developed and implemented the UP Health Dashboard, which ranks each of UP's 75 districts on a set of key HMIS priority health indicators. These policy guidelines have brought greater attention to UP-HMIS data quality and use; however, additional strengthening is required to improve the quality and use of HMIS data. There is a need to increase the overall capacity and understanding of HMIS data, not only for staff with specific data-related responsibilities but also for program managers and senior decision makers.

Indexed as

Health Information SystemsManagement Information SystemsData AccuracyHumansIndia

Identifiers

PMID36041830
PMCPMC9426977

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.