Quantify PPH burden and drivers across the insured population, implement a reproducible prospective data pipeline for routine monitoring, and track PHC performance under the new capitation model through validated KPIs and dashboards that support payment reconciliation, provider feedback, and policy decisions.
Scope of Work
- Design and implement a monitoring framework to track PHC performance under the new capitation payment model, integrated with the PPH/ACSC study and prospective claims pipeline.
- Monitor financial flows (capitation payments, adjustments, bonuses/penalties), service utilization, quality, and outcomes (including PPHs) at facility and provider levels.
- Retrospective analysis of 2 years of hospital and primary care claims
- Development of clear ACSC definitions and algorithms (ICD codes, admission types, episode definitions)
- Data linkage across facility types (primary care, outpatient, inpatient), deduplication, and creation of patient-level episodes
- Risk adjustment and stratified analyses (age, sex, comorbidity, geography, socioeconomic proxy)
- Prospective pipeline: ETL, validation checks, automated reporting/dashboarding
- Stakeholder validation (clinicians, PHC managers, actuaries/insurance managers)
- Provide routine and ad hoc analytics to support payment reconciliation, provider feedback, and policy adjustments.
Key Activities and Contract Deliverables
Inception and planning
- Review existing documentation, data dictionaries, and prior analyses.
- Produce a detailed analysis plan and data management plan (DMP) including proposed ACSC definitions and statistical approaches.
- Confirm legal/ethical and data governance requirements; coordinate approvals.
Data acquisition and preparation
- Work with IT and providers to obtain retrospective claims (hospital inpatient, outpatient, PHC visits) and prospective access arrangements.
- Map and harmonize data elements (patient ID, demographics, encounter dates, ICD/diagnosis codes, procedure codes, facility codes, payer flags, cost/charges).
- Perform data cleaning, missingness assessment, and standardization.
- Define capitation cohorts and enrollee attribution rules (who is assigned to each PHC and how they are kept current).
- Build ETL processes to ingest roster/enrolment files, monthly capitation payment records, and utilization/encounter/claims data; link these with the PPH dataset.
Data Analytics
- Compute PMPM (per-member-per-month) spending, service mix, utilization rates, referral rates, and cost of referrals/hospitalizations attributable to each PHC.
- Perform payment reconciliation checks: compare expected capitation disbursements against enrollee lists and recorded encounters; detect discrepancies and potential upcoding or underreporting.
- Evaluate changes in potentially preventable hospitalizations and other outcome measures pre- and post-capitation (difference-in-differences, interrupted time series, matched controls where feasible).
Outputs and Deliverables
- Implement dashboards and automated reports with facility-level drilldowns, trend charts, and alert logic (e.g., sudden rise in referrals, drop in service volume).
- Provide feedback packages and monthly/quarterly performance summaries for PHCs and supervisors; support targeted audits and capacity-building where performance is poor.
- Support design of pay-for-performance or quality adjustment mechanisms tied to capitation (define thresholds, measurement windows, and adjustment formulas).
- Document methods, code, and SOPs for attribution, KPI computation, reconciliation, and reporting.
- Support RSSB data and monitoring team to develop and operationalize a set of KPIs to evaluate capitation performance.
Contract Management
The Independent Contractor will submit all deliverables to the Project Director, Rwanda Primary Health Care, who will manage this contract and monitor progress towards deliverables. The Independent Contractor will work with the office of the Chief Benefits Officer, Rwanda Social Security Board, for day-to-day activities.
Qualifications
Experience
- 7+ years of progressive experience in econometric analyses in the context of health financing, health insurance data, or primary health care performance.
- Strong background in developing data-driven policy recommendations, including translating analytical results into actionable insights for decision-makers.
- Prior experience working with or supporting government health financing agencies or national health systems preferably in LMICs.
Education
- Advanced degree (Masters or higher) in Health Economics, Biostatistics, Public Health, Health Informatics, Data Science, Epidemiology, or a related quantitative discipline.
Technical Skills
- Proficiency in econometric modelling and statistical programming using Stata, R, or Python.
- Strong SQL and database management skills for large datasets.
- Proven track record in designing and implementing data pipelines, including ETL processes, data cleaning, linkage, and validation across multiple data sources
- Strong quantitative and analytical skills, including an understanding of risk adjustment, utilization metrics, and cost-effectiveness measures.
- Experience with data visualization and reporting tools (e.g., Power BI, Tableau, or R Shiny)
Skills and Abilities
- Strong analytical and problem-solving skills
- Ability to synthesize complex data into clear recommendations
- Excellent communication and presentation skills, including translating technical findings into actionable policy insights.
- Able to work collaboratively with cross-functional teams and diverse stakeholders
- Strong organizational and project management skills to meet deadlines.
