Building a Sickle Cell Disease Tracking Program
A step-by-step guide to establishing an SCD patient tracking program — from needs assessment and stakeholder mapping through technology selection, rollout, and evaluation.
Chapter 1.Needs Assessment
Every successful SCD tracking program begins with a thorough needs assessment. This should document the estimated number of SCD patients in the coverage area by genotype, map existing health facilities with SCD diagnostic and treatment capacity, evaluate current data collection practices (paper vs. electronic, completeness, quality), and identify the current care cascade — what proportion of expected patients are diagnosed, enrolled, and receiving recommended therapies.
Key informant interviews with healthcare providers, program managers, patients, and caregivers provide qualitative insights. Common themes include late diagnosis due to limited screening, loss to follow-up driven by distance and cost, medication stockouts, limited specialist support at primary care level, and absence of tracking systems for patient outcomes.
Chapter 2.Stakeholder Mapping
SCD tracking programs operate within complex stakeholder ecosystems. Key categories include government health authorities (policy, regulation, potentially funding), healthcare facility administrators and clinical staff (frontline implementers), patient advocacy organizations and sickle cell foundations (community connections), international organizations and development agencies (technical assistance, funding), academic institutions (evidence generation, training), and technology partners (platform development and support).
Each stakeholder should be engaged with a tailored value proposition demonstrating how the tracking program serves their specific objectives. Government officials care about population health metrics and cost-effectiveness. Clinicians care about tools that improve patient care without adding burden. Patients care about access to better care and continuity. Mapping these interests ensures effective engagement and sustained commitment.
Chapter 3.Technology Selection
Key criteria include offline capability (essential for limited connectivity areas), mobile optimization (field agents use smartphones), data security and privacy compliance, scalability, interoperability with existing systems, and total cost of ownership. Common approaches include custom-built applications, configurable platforms like Tracka with SCD-specific functionality, general-purpose tools like ODK adapted for clinical use, and extensions of existing systems like DHIS2 or OpenMRS.
Programs should request demonstrations with realistic scenarios, check references from similar deployments, assess vendor commitment to the African market, evaluate data ownership and portability terms, and consider long-term sustainability. A purpose-built SCD platform like Tracka offers advantages in clinical workflow alignment, data model completeness, and integrated analytics.
Chapter 4.Data Collection Standards
Programs should define a minimum dataset for every patient: demographics, clinical identifiers (genotype, diagnosis date, method), and baseline parameters (hemoglobin, weight, height). Visit-specific datasets should align with clinical guidelines — routine follow-ups capture medications, adherence, symptoms, vitals, and lab results; acute visits add presenting complaint, severity, treatment, and disposition.
Data quality targets should cover completeness (percentage of required fields populated), accuracy (verified through spot checks), and timeliness (data entered within a defined period). Tracka enforces quality through required field validation, range checks, logical consistency rules, and real-time completeness scoring visible to agents and supervisors.
Chapter 5.Rollout Plan
A phased approach reduces risk. Phase 1 pilots at 3 to 5 facilities selected for geographic diversity, patient volume, facility leadership support, and reasonable connectivity. This 3 to 6 month phase validates the platform, refines workflows, identifies training gaps, and generates early results. Phase 2 expands to a full state or region, testing scalability, supervisory structures, and supply chain coordination. Phase 3 scales to additional regions with accelerated timelines.
Each phase should have clearly defined success criteria including patient enrollment targets, data completeness rates, visit adherence, and user satisfaction. Regular stakeholder review meetings assess progress and make data-driven decisions about proceeding to the next phase.
Chapter 6.Training
Training is the single most important success factor. Programs should address multiple roles: field agents (data collection), clinicians (patient management), supervisors (quality monitoring), and program managers (analytics). Training should combine classroom instruction with hands-on practice, integrating clinical SCD management with platform training.
Ongoing support is equally important: quarterly refresher sessions, peer mentoring, WhatsApp support groups, in-app help documentation, and dedicated support teams. Training of trainers (ToT) models build local capacity for sustainable ongoing training as staff turn over.
Chapter 7.Monitoring & Evaluation
Process indicators measure implementation: facilities activated, patients enrolled, visits recorded, data completeness, system uptime. Outcome indicators measure health impact: treatment coverage, visit adherence, complication rates, mortality. Regular data review meetings create accountability — weekly at facility level, monthly at district level, quarterly at program level.
Impact evaluation should be planned from the outset, using quasi-experimental or mixed-methods designs with baseline data as comparator. Published evidence of program effectiveness is essential for securing sustained funding and influencing policy. Dashboard visualizations make key indicators accessible to all stakeholders.