The Effect of Digitally Enabled District (DED 2.0) Ecosystem on the Completeness and Timeliness of Maternal and Neonatal Services in Primary Health Care
DED
1 other identifier
interventional
10,800
1 country
1
Brief Summary
DED 2.0 is a stepped-wedge cluster-randomized trial testing whether an integrated digital health ecosystem (KuApps, a service-monitoring dashboard, AI-assisted OCR for handwritten records, two-way WhatsApp-based health communication, and an automated worker-support tool) improves the completeness and timeliness of maternal and neonatal primary health care services, delivered through 45 Puskesmas in Garut Regency, West Java, Indonesia.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for not_applicable
Started Oct 2026
Shorter than P25 for not_applicable
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
September 7, 2026
CompletedFirst Posted
Study publicly available on registry
September 25, 2026
CompletedStudy Start
First participant enrolled
October 1, 2026
CompletedPrimary Completion
Last participant's last visit for primary outcome
May 1, 2027
ExpectedStudy Completion
Last participant's last visit for all outcomes
July 1, 2027
September 25, 2026
September 1, 2026
7 months
September 7, 2026
September 21, 2026
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
Completeness of Antenatal Care (ANC) Services within the Gestational-Age Window
Completeness of ANC services within the gestational-age window, in accordance with the recommended schedule and standard: Trimester 1 (1 visit, 1 ultrasound, 1 physician examination); Trimester 2 (2 visits); Trimester 3 (3 visits, 1 ultrasound, 1 physician examination). Binary, individual-level: 1 = received all scheduled ANC contacts within the nationally recommended gestational-age windows; 0 = otherwise. Denominator = all eligible pregnant women registered at the cluster in that cluster-period. Analysed via generalised linear mixed model with a cluster random effect and categorical calendar period as a fixed effect (Hussey \& Hughes, 2007). This is the study's single registered primary outcome; ANC "12T" completeness, PNC completeness, and PNC standard-assessment completeness are secondary outcomes.
From first ANC contact up to 40 weeks gestation, assessed up to 9 months
Secondary Outcomes (38)
Proportion of Participants with Timely ANC Contacts
Assessed at each gestational-age window, up to 40 weeks gestation
Proportion of Participants with Complete Postnatal Care (PNC)
From delivery through 42 days postpartum
Proportion of Participants with a Complete Standard PNC Assessment
From delivery through 42 days postpartum
Proportion of Neonates with Complete Neonatal Visits
From birth through 28 days of age
Proportion of Neonates Receiving Complete Essential Neonatal Care
From birth through 28 days of age
- +33 more secondary outcomes
Study Arms (2)
DED Intervention Group (Ecosystem of DED)
EXPERIMENTALPrescription for Action (DED Model), a system-level intervention consisting of: KuApps, a digital health application (BidanKu, KaderKu) for frontline health workers, built on OpenSRP/FHIR standards for SATUSEHAT integration; supports electronic patient registration, centralised ID management, and structured care-plan tracking. PWS Dashboard, central monitoring dashboard tracking primary healthcare services and operational data (staffing, drug procurement, vaccine logistics) across District (Dinas Kesehatan), PHC, and Village (Posyandu) levels. AI OCR, converts handwritten text from Maternal and Child Health (KIA) books into structured digital data for automated form-filling and reporting. Health communication: two-way maternal/infant/child health communication via WhatsApp
Control Group (non-DED)
NO INTERVENTIONLegacy Apps: digital health application (SIGIZI, Electronic Medical Records, and ASIK) for frontline health workers. There will be no PWS Dashboard, AI OCR, and Health communication in the control group.
Interventions
The research adopts a stepped-wedge cluster-randomized design. Puskesmas are randomized sequentially to receive the intervention. Following a one-month baseline (retrospective data of Aug-Sept, taken in October), an additional set of 15 clusters begins the intervention at the start of each three-month step: step one covers randomisation and digital-readiness training for that wave, and the remainder of the step is given over to monitoring utilisation of the digital platform. Three successive steps of three months each follow this pattern until the full sample is covered, within a 9-month rollout-and-monitoring window.
Eligibility Criteria
You may qualify if:
- Mothers residing within the catchment area of a participating Puskesmas in Garut Regency during their pregnancy
- Pregnancy registered in the maternal health records of a participating Puskesmas within the study catchment area in Garut Regency
- The pregnant woman provides informed consent to participate in the study.
You may not qualify if:
- \- The participant relocates permanently outside the study area before the outcome-observation window begins.
- \- Located in Garut Regency; at baseline, the DED ecosystem had not been integrated into daily service operations.
- Puskesmas had fully implemented the DED ecosystem in daily practice before the study baseline period
- Puskesmas lacks adequate routine service data for outcome measurement throughout the study period
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
Summit Institute for Development
Mataram, West Nusa Tenggara, 83238, Indonesia
Related Publications (10)
Carter BR, Hood K. Balance algorithm for cluster randomized trials. BMC Med Res Methodol. 2008 Oct 9;8:65. doi: 10.1186/1471-2288-8-65.
PMID: 18844993RESULTFarrugia P. Statistics in Brief: The Cluster Randomized Controlled Trial-What Is It and Why Is It Relevant to Research in Surgery? Clin Orthop Relat Res. 2021 Aug 1;479(8):1852-1857. doi: 10.1097/CORR.0000000000001859. No abstract available.
PMID: 34157009RESULTOrgan M, Tandon SD, Diebold A, Johnson JK, Yeh C, Ciolino JD. Evaluating performance of covariate-constrained randomization (CCR) techniques under misspecification of cluster-level variables in cluster-randomized trials. Contemp Clin Trials Commun. 2021 Feb 16;22:100754. doi: 10.1016/j.conctc.2021.100754. eCollection 2021 Jun.
PMID: 33732943RESULTCiolino JD, Diebold A, Jensen JK, Rouleau GW, Koloms KK, Tandon D. Choosing an imbalance metric for covariate-constrained randomization in multiple-arm cluster-randomized trials. Trials. 2019 May 28;20(1):293. doi: 10.1186/s13063-019-3324-5.
PMID: 31138319RESULTHooper R, Quintin O, Kasza J. Efficient designs for three-sequence stepped wedge trials with continuous recruitment. Clin Trials. 2024 Dec;21(6):723-733. doi: 10.1177/17407745241251780. Epub 2024 May 21.
PMID: 38773924RESULTKristunas C, Grayling M, Gray LJ, Hemming K. Mind the gap: covariate constrained randomisation can protect against substantial power loss in parallel cluster randomised trials. BMC Med Res Methodol. 2022 Apr 13;22(1):111. doi: 10.1186/s12874-022-01588-8.
PMID: 35413793RESULTIvers NM, Halperin IJ, Barnsley J, Grimshaw JM, Shah BR, Tu K, Upshur R, Zwarenstein M. Allocation techniques for balance at baseline in cluster randomized trials: a methodological review. Trials. 2012 Aug 1;13:120. doi: 10.1186/1745-6215-13-120.
PMID: 22853820RESULTHemming K, Taljaard M, McKenzie JE, Hooper R, Copas A, Thompson JA, Dixon-Woods M, Aldcroft A, Doussau A, Grayling M, Kristunas C, Goldstein CE, Campbell MK, Girling A, Eldridge S, Campbell MJ, Lilford RJ, Weijer C, Forbes AB, Grimshaw JM. Reporting of stepped wedge cluster randomised trials: extension of the CONSORT 2010 statement with explanation and elaboration. BMJ. 2018 Nov 9;363:k1614. doi: 10.1136/bmj.k1614.
PMID: 30413417RESULTVoldal EC, Hakhu NR, Xia F, Heagerty PJ, Hughes JP. swCRTdesign: An RPackage for Stepped Wedge Trial Design and Analysis. Comput Methods Programs Biomed. 2020 Nov;196:105514. doi: 10.1016/j.cmpb.2020.105514. Epub 2020 May 21.
PMID: 32554025RESULTHussey MA, Hughes JP. Design and analysis of stepped wedge cluster randomized trials. Contemp Clin Trials. 2007 Feb;28(2):182-91. doi: 10.1016/j.cct.2006.05.007. Epub 2006 Jul 7.
PMID: 16829207RESULT
Study Officials
- PRINCIPAL INVESTIGATOR
Yuni Dwi Setiyawati, B.Nutr, MHID, Dietitian
Summit Institute for Development
Central Study Contacts
Yuni Dwi Setiyawati, B.Nutr, MHID, Dietitian, Master's degree
CONTACT
Study Design
- Study Type
- interventional
- Phase
- not applicable
- Allocation
- RANDOMIZED
- Masking
- NONE
- Purpose
- HEALTH SERVICES RESEARCH
- Intervention Model
- SEQUENTIAL
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- B.Nutr, MHID, Dietitian
Study Record Dates
First Submitted
September 7, 2026
First Posted
September 25, 2026
Study Start
October 1, 2026
Primary Completion (Estimated)
May 1, 2027
Study Completion (Estimated)
July 1, 2027
Last Updated
September 25, 2026
Record last verified: 2026-09
Data Sharing
- IPD Sharing
- Will share
- Shared Documents
- STUDY PROTOCOL, SAP
- Time Frame
- Beginning after publication of the primary results
- Access Criteria
- Requests reviewed and approved by Summit Institute for Development (SID), as the study's operational point of contact, notwithstanding Gates Foundation's role as formal Sponsor
De-identified individual participant data, together with the data dictionary, protocol, and Statistical Analysis Plan, will be made available upon request.