Evidence map›Paper›PMID 33827459›Full record

ArticleBMC pregnancy and childbirth2021

The role of digital clinical decision support tool in improving quality of intrapartum and postpartum care: experiences from two states of India.

Gulnoza Usmanova, Kamlesh Lalchandani, Ashish Srivastava, Chandra Shekhar Joshi, Deepak Chandra Bhatt, Anand Kumar Bairagi, Yashpal Jain, Mohammed Afzal, Rashmi Dhoundiyal, Jyoti Benawri and 7 more

Abstract readMulticenter Study
In one paragraph

Article in BMC pregnancy and childbirth, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

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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

17 authors.

Gulnoza UsmanovaJhpiego-An Affiliate of Johns Hopkins University, New Delhi, 110020, India.
Kamlesh LalchandaniJhpiego-An Affiliate of Johns Hopkins University, New Delhi, 110020, India.
Ashish SrivastavaJhpiego-An Affiliate of Johns Hopkins University, New Delhi, 110020, India.
Chandra Shekhar JoshiJhpiego-An Affiliate of Johns Hopkins University, New Delhi, 110020, India.
Deepak Chandra BhattJhpiego-An Affiliate of Johns Hopkins University, New Delhi, 110020, India. deepakbhatt001@gmail.com.
Anand Kumar BairagiJhpiego-An Affiliate of Johns Hopkins University, New Delhi, 110020, India.
Yashpal JainJhpiego-An Affiliate of Johns Hopkins University, New Delhi, 110020, India.
Mohammed AfzalJhpiego-An Affiliate of Johns Hopkins University, New Delhi, 110020, India.
Rashmi DhoundiyalJhpiego-An Affiliate of Johns Hopkins University, New Delhi, 110020, India.
Jyoti BenawriJhpiego-An Affiliate of Johns Hopkins University, New Delhi, 110020, India.
Tarun ChaudharyDepartment of Health and Family Welfare, NHM, Jaipur, Rajasthan, 302001, India.
Archana MishraMaternal Health, NHM, Bhopal, Madhya Pradesh, 462011, India.
Rajni WadhwaProject Management Unit, ASMAN: Alliance for Saving Mothers and Newborns, Mumbai, 400021, India.
Pompy SridharMSD for Mothers, Mumbai, 4000098, India.
Nupur BahlReliance Foundation, Mumbai, 400021, India.
Pratibha GaikwadTata Trusts, Mumbai, 400005, India.
Bulbul SoodJhpiego-An Affiliate of Johns Hopkins University, New Delhi, 110020, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundComputerized clinical decision support (CDSS) -digital information systems designed to improve clinical decision making by providers - is a promising tool for improving quality of care. This study aims to understand the uptake of ASMAN application (defined as completeness of electronic case sheets), the role of CDSS in improving adherence to key clinical practices and delivery outcomes.

methodsWe have conducted secondary analysis of program data (government data) collected from 81 public facilities across four districts each in two sates of Madhya Pradesh and Rajasthan. The data collected between August -October 2017 (baseline) and the data collected between December 2019 - March 2020 (latest) was analysed. The data sources included: digitized labour room registers, case sheets, referral and discharge summary forms, observation checklist and complication format. Descriptive, univariate and multivariate and interrupted time series regression analyses were conducted.

resultsThe completeness of electronic case sheets was low at postpartum period (40.5%), and in facilities with more than 300 deliveries a month (20.9%). In multivariate logistic regression analysis, the introduction of technology yielded significant improvement in adherence to key clinical practices. We have observed reduction in fresh still births rates and asphyxia, but these results were not statistically significant in interrupted time series analysis. However, our analysis showed that identification of maternal complications has increased over the period of program implementation and at the same time referral outs decreased.

conclusionsOur study indicates CDSS has a potential to improve quality of intrapartum care and delivery outcome. Future studies with rigorous study design is required to understand the impact of technology in improving quality of maternity care.

Indexed as

Quality ImprovementAsphyxia NeonatorumDecision Support Systems, ClinicalElectronic Health RecordsFemaleGuideline AdherenceHealth Plan ImplementationHumansIndiaInfant, NewbornObstetric Labor ComplicationsPerinatal CarePractice Guidelines as TopicPractice Patterns, Physicians'PregnancyProgram EvaluationCDSSHealth information technologyIntrapartum careMaternal healthmHealthNew-born healthPostpartum careQuality improvement

Identifiers

PMID33827459
PMCPMC8028806

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

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 Socratic graph.