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EarlyCheck

Origin language: English
Cover image
Target population
Newborn , +3
Beneficiaries251 - 500
EvidenceNo evidence
Start date2026/01
End dateOngoing
StagePilot project/testing/trials
Country
Kyrgyzstan
Coverage
National
Nb of implementation sites
5
Lead organization
Implementation partners
N/A

Health Focus Area
Mental health
Maternal and child health
Primary Health Care
Services
Decision Support
Learning and training
Predictive health and early diagnosis
Enabling technologies
Artificial Intelligence
Cloud Computing
Phone
Software
Standards
Excel
Tools
Funding sources
Private funds
Business model
No business model
Funders
Summary
EarlyCheck is an online developmental questionnaire designed for parents in areas lacking pediatric neurologists. Developmental delays are often identified late in rural settings, and parents face challenges in assessing their child's normal development without local resources or incurring costs for assessments. The questionnaire provides a user-friendly experience, utilizing plain-language questions aligned with international developmental assessment standards. It evaluates children from birth to 3.5 years across various developmental domains, offering clear guidance: if a child is at risk, they should see a specialist; if they require attention, early support and home activities are recommended. This tool can be used monthly to monitor developmental changes. In an initial study involving 256 children across five sites in two countries, EarlyCheck successfully identified all 44 children needing referral, achieving 74.1% specificity. This immediate feedback enables parents to act promptly rather than waiting anxiously for appointments. It allows children to receive support before formal diagnoses and reduces late presentations within the healthcare system. With funding from the KRSU research laboratory, the initiative plans to screen up to 240 children aged 0-42 months in three additional Kyrgyzstan locations (Baktan, Osh, Manas) over three days per site within three months. Every screened child will be examined, and those flagged at risk will be referred within 30 days. The project aims to combine pilot data and submit findings for peer-reviewed publication within 12 months while establishing referral pathways with regional health authorities in the target areas. Focus areas include maternal and child health, mental health, and primary healthcare, targeting children (5 to 11), families, and health professionals.
Keywords
Self-assessment
Access to care
Artificial intelligence
Child health
Digital health
Publications
Insights - Lessons learnt
Challenges

1. Reference standard. Validated developmental diagnostic assessments and ADOS are available at few urban centres, limiting rural access. The pilot compared screening outcomes with a blinded structured neurological examination. The next stage will include reference-standard assessment in a defined subsample.

2. Data capture. During the pilot, 21% of records (54/256) lacked a codable verdict due to narrative documentation and retrospective classification. The next stage uses a structured electronic case report form with predefined outcome categories and independent verification to ensure complete, reproducible data.

3. Service pathway. Risk identification does not guarantee access to care, particularly in districts without paediatric neurologists. Referral pathways with regional health authorities are being formalised before scaling, with defined response times for at-risk children.

4. Stakeholder engagement. Clinicians may view parent-facing screening as replacing clinical judgement, while families may distrust automated decisions. Implementation includes primary care staff training, clear communication that screening supports rather than replaces clinical assessment, and transparent reporting of limitations.

5. Regulatory and funding context. The regulatory classification of digital screening tools remains undefined, complicating large-scale deployment. Validation awaits funding. The programme is designed to build clinical evidence independently of any single funding source.

Recommendations

Best practices identified

Blinded neurological assessment ensured credible validation and remains the standard for subsequent rounds. Two-tier triage distinguishes children requiring specialist referral from those needing early intervention without referral, reducing unnecessary referrals. Monthly screening enables developmental monitoring and compensates for missed findings. Privacy by design, with no personal identifiers collected, simplified ethical approval and reassured parents. Items are based on ASQ-3, Denver II, M-CHAT and the Guide for Monitoring Child Development, supporting clinical familiarity and specialist acceptance.

Factors that facilitated implementation

Key facilitators included district paediatricians collaborating with the study neurologist, strong parental motivation where specialists were unavailable, and easy access through a messenger application requiring no installation or local IT infrastructure.

Implications for policy, practice, technology and research

Policy priorities include referral protocols agreed with regional health authorities and recognition of digital screening in primary care. Practice requires staff training in two-tier triage. Minimal infrastructure and no data storage support technological scalability. Research priorities include a reference-standard substudy and peer-reviewed publication of the combined dataset. Sustainability may be supported through provider- or state-funded usage fees, keeping screening free for families.

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iDHA (Project ID)
ivj753
Project links
https://t.me/MyEarlycheck_bot
Origin of information
Project stakeholder
Data source link
N/A
Added to the platform on
2026-10-27
Project editors
Last update by
Albina Salbaeva on 2026-10-27
Number of views
0
WHO classifications
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