Risk Based Verification: Simple implementation using Hesabu and DHIS2
Performance Based Financing (PBF) is an innovative, results-oriented approach that incentivizes providers based on their achievement of agreed-upon, measurable performance targets. Incentives include financial payments, bonuses, and public recognition. In Performance Based Health Financing, monitoring and evaluation is key. Program implementing partners should constantly monitor field activities through verification of field work for them to be able to rationally provide adequate resources.
To minimise efforts, avoid wasting resources, reduce cost and time spent performing monitoring and evaluation activities, the Performance Based Financing program in Zimbabwe has introduced a risk based approach on data verification. On the risk based approach, Health Entities are categorised based on how accurately they report services they delivered to clients. The implementing partner will perform a verification of the reported figures. There will also possibly be a counter verification through an independent partner.
When there are discrepancies in claimed and verified figures, for the Zimbabwe PBF program entities are classified into three categories, RED, AMBER and GREEN based on the discrepancy. Through Risk based verification, entities that have higher discrepancy receive more verification priority than those with lower discrepancies performing well. The table below shows the categories and the verification schedules. Bluesquare is building a software system to support the PBF program operations including the Risk Based Verification
RBV Categories and verification schedules
|Category||Criteria||Quantityverification(Facility Supervisors)||Quantityverification(Field officers)|
|Green||Reported within 5% marginof error in the last 2quarters||One visit per quarterto assess 18indicators across 3months||One visit per 6months per HF toassess 18indicators across 6months|
|Amber||Reported between 5% and 10%margin of error in the last 2quarters||Twovisitsperquarter and verify 36indicators across 3months. Mentoringand focus on rootcause noted.||One visit everyquarter per HF toassess 36 indicatorsacross 3 months|
|Red||Reported beyond 10%margin of error in the last 2quarters||Every month visit toassess all 18 indicators.Mentoring and focuson root cause noted.||One visit everyquarter to assess all18 indicators for each month|
Formula for computing the error margin:
(Claimed Value – Verified Value)/Claimed Value
Each indicator’s monthly error margin is awarded a score and those scores are aggregated and used to categorize the entities
RBV Implementation using Hesabu and DHIS2
At Bluesquare we developed a tool called Hesabu that allows PBF programs to integrate with HMIS systems, not replacing them but instead it enhances them, offering additional capabilities with more advanced formulas, the capacity to use data from sibling or parent org units and sliding periods. Think of it as an Excel sheet running on top of your DHIS2 – you can define formulas, apply formulas to the output of other formulas in order to create a whole chain of transformation and, more importantly, send any result (final or intermediary) back to DHIS2 as a normal data element value.
With this tool, we were able to implement the risk based verification algorithm which included computing discrepancies between claimed and verified values on selected service delivery indicators, awarding risk based verification scores for each entity and finally classifying the entities into the respective RBV categories biannually. Hesabu is of great help on computing and associating a score with each indicator for the six month period, aggregate them to compute the final score for each facility which could not be achieved just with DHIS2 indicators. After performing the RBV score computations and categorization, all the computed results are sent back to the HMIS, in this case DHIS2 for visualisation and reporting.
Visualization in DHIS2
After Hesabu provides the data back into DHIS2, charts and graphs are created to visualise the data on RBV that managers can now use to enhance their operations.