The Effect of Digitally Enabled District (DED 2.0) Ecosystem on the Completeness and Timeliness of Maternal and Neonatal Services in Primary Health Care: A Stepped-Wedge Cluster Randomised Trial in Garut Regency, Indonesia
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 10,800
- 试验地点
- 1
- 主要终点
- Completeness of Antenatal Care (ANC) Services within the Gestational-Age Window
研究概览
简要总结
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.
详细描述
DED 2.0 (Digitally Enabled Districts) is designed to examine whether integrated digital health tools can strengthen the performance of Primary Health Care (PHC) in Indonesia. The package targets several interconnected areas: (1) strengthening district-level human resources in core components of knowledge and skills for digitalisation; (2) establishing adaptable and modifiable digital infrastructure (e.g., data pipelines, dashboards, and KuApps) that local personnel can configure to their own needs; (3) building sustainable implementation capacity in both workforce skills and financing; and (4) enabling high-fidelity scale-up of digitalisation across Puskesmas, resulting in improvements in primary healthcare delivery.
While local health information systems face fragmentation, with multiple disconnected applications causing duplicate data entry, inconsistent indicators, and delayed decision-making, DED 2.0 addresses these issues by building an integrated digital system that enables seamless data integration across services, real-time monitoring of population health, improved coordination among health workers, and stronger data-driven planning and decision-making. This is expected to enhance the completeness, timeliness, visibility, and use of routine health data, allowing earlier identification of service gaps and high-risk cases, strengthening care coordination and follow-up, increasing timely referrals, and improving monitoring of maternal and neonatal services.
Beyond technology adoption, DED 2.0 places equal emphasis on strengthening human resources: training health workers to use the digital systems, building local technical capacity, fostering coaching and peer learning through selected health-worker "Champions," and accelerating digital adoption across facilities through a tiered "train the trainer" model. DED is hypothesised to increase the completeness and timeliness of antenatal, postnatal, and neonatal services; improve facility-based delivery and skilled birth attendance; and ultimately contribute to improved maternal and neonatal outcomes.
The trial uses a cluster-randomised stepped-wedge design, with Puskesmas as the unit of intervention, in a single district, Garut Regency, West Java, Indonesia. A one-month baseline (retrospective data of Aug-Sept, taken in October) is followed by three sequential steps of three months each, at each of which a new sequence of 15 clusters (one-third of the 45 eligible PHCs) crosses over from control to intervention status. All 45 sites have received the intervention by the start of the third step, reaching full coverage in April 2027; outcome monitoring for the final wave continues through the end of that step in May 2027, a 9-month rollout-and-monitoring window in total. Each cluster undergoes an initial preparatory stage, a phased pre-intervention digital-readiness phase, followed by a data-driven action phase, sequencing digital readiness first to improve managerial and behavioural accuracy and consistency.
DED is expected to serve as a replicable framework for district-level digital health transformation across Indonesia, generating practical evidence on implementing and sustaining digital health systems in primary health care and informing national scale-up strategies.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Sequential
- 主要目的
- Health Services Research
- 盲法
- None
入排标准
- 性别
- Female
- 接受健康志愿者
- 是
入选标准
- •(Pregnant Women):
- •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.
排除标准
- •(Pregnant Women):
- •- The participant relocates permanently outside the study area before the outcome-observation window begins.
- •Inclusion Criteria (Puskesmas Level):
- •- Located in Garut Regency; at baseline, the DED ecosystem had not been integrated into daily service operations.
- •Exclusion Criteria (Puskesmas Level):
- •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
研究组 & 干预措施
DED Intervention Group (Ecosystem of DED)
Prescription 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
干预措施: Digitally Enabled District (DED) (Device)
Control Group (non-DED)
Legacy 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.
结局指标
主要结局
Completeness of Antenatal Care (ANC) Services within the Gestational-Age Window
时间窗: From first ANC contact up to 40 weeks gestation, assessed up to 9 months
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.
次要结局
- Proportion of Participants with Timely ANC Contacts(Assessed at each gestational-age window, up to 40 weeks gestation)
- Proportion of Neonates Receiving Complete Standard Neonatal Examination(From birth through 28 days of age)
- 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)
- Maternal Body Height(Assessed at each ANC contact, up to 40 weeks gestation)
- Proportion of neonates with timely neonatal visits(From birth through 28 days of age)
- Proportion of Pregnancy Outcomes(At delivery, (up to 9 months from Puskesmas enrolment))
- Proportion of Neonates with Birth Weight ≥ 2,500 g(At delivery (up to 9 months from Puskesmas enrolment))
- Proportion of Facility-Based Deliveries(At delivery (up to 9 months from Puskesmas enrolment))
- Proportion of Deliveries Attended by a Skilled Birth Attendant(During pregnancy (from the first ANC contact, up to 40 weeks gestation), at delivery, and up to 42 days postpartum)
- Proportion of Documented Complications with Documented Management(During pregnancy (from the first ANC contact, up to 40 weeks gestation), at delivery, and up to 42 days postpartum)
- Mean Arterial Pressure (MAP)(Assessed at each ANC contact, up to 40 weeks gestation)
- Blood Pressure (Systolic)(Assessed at each ANC contact, up to 40 weeks gestation)
- Blood Pressure (Diastolic)(Assessed at each ANC contact, up to 40 weeks gestation)
- Maternal Body Weight(Assessed at each ANC contact, up to 40 weeks gestation)
- Body Mass Index (BMI)(Assessed at each ANC contact, up to 40 weeks gestation)
- Maternal Mid-Upper Arm Circumference (MUAC)(At each ANC contact, up to 40 weeks gestation)
- Proportion of participants with Chronic Energy Deficiency (CED)(At each ANC contact, from first ANC contact up to 40 weeks gestation)
- Occurrence of Stock Out of Essential ANC Medications and Supplements per Month(From study initiation through study completion, assessed up to 9 months)
- Puskesmas Performance Score (PKP)(Every 3 months, from study initiation through study completion, assessed up to 9 months)
- Workload Distribution per Health Worker(from study initiation through study completion, assessed up to 9 months)
- Proportion of ANC contacts recorded by cadres (Task Shifting)(From study initiation through study completion, assessed up to 9 months)
- Proportion of Health Workers Actively Using the KuApps per Observation Month(From study initiation through study completion, assessed up to 9 months)
- Availability of digital infrastructure(From study initiation through study completion, assessed up to 9 months)
- Electronic medical record integration(From study initiation through study completion, assessed up to 9 months)
- Proportion of routine reports submitted on time(From study initiation through study completion, assessed up to 9 months)
- Training Costs(From study initiation through study completion, assessed up to 9 months)
- Infrastructure cost(From procurement through study completion, assessed up to 9 months)
- Dashboard and Software Costs(From application deployment through study completion, assessed up to 9 months)
- Digital interoperability/integration costs(From integration initiation deployment through study completion, assessed up to 9 months)
- Technical support & maintenance costs(From study initiation through study completion, assessed up to 9 months)
- Healthcare service costs per unit(From study initiation through study completion, assessed up to 9 months)
- Cost time horizon and period(From initial expenditure through study completion, assessed up to 9 months)
- Number of additional services(From study initiation through study completion, assessed up to 9 months)
- Number of Participants completing the complete continuum of maternal care(From ANC1 through 42 days postpartum)
- Incremental Cost per Disability-Adjusted Life Year (DALY) Averted(At trial end, through study completion (up to 9 months of rollout and follow-up))
研究者
Yuni Dwi Setiyawati
B.Nutr, MHID, Dietitian
Summit Institute for Development, Indonesia