Health finance providers need to ensure schemes are financially sustainable. Whether the income comes directly from members, from other sources (such as government donors), or a combination of both, sufficient funding needs to be available to cover claims from beneficiaries plus administrative and operational costs.
An implementation of openIMIS will typically enhance a rich database of information, ranging from beneficiary details through to the varying costs of the scheme over time. This data is fundamental to the overall scheme management and helps to ensure effective and efficient services.

Actuarial Analysis

The information held by openIMIS also provides an invaluable dataset for actuarial studies, which use historical trends and patterns to make predictions and evaluate a scheme's financial sustainability. Actuarial studies analyze operational data to offer key insights including the current financial circumstances of a scheme and financial projections based on historical data, population trends, inflation patterns, etc. These analyses look specifically at financial aspects of a scheme and are not designed to consider other important factors, such as the quality of care.
An actuarial analysis is highly dependent on the quality and quantity of the available data, particularly in relation to the utilization of services (claims). Gathering sufficient utilization data can be particularly challenging for new and recently formed schemes, even those with a high number of beneficiaries, however this should not deter schemes from conducting actuarial analyses regularly (every few years) as even small amounts of data can reveal key trends that will inform decision-making.

Historical Data

An actuarial analysis begins by exploring historical data to answer some broad questions including, but not limited to, the following:

• How many people/families are currently enrolled?

• How long have the current members been part of the scheme?

• What are the demographic and socio-economic characteristics (gender, age, geography, etc.) of the population covered by the scheme?

• What amounts have been reimbursed on average (per family/person) within the coverage period?

• What were the demographic and socio-economic characteristics of the beneficiaries for whom claims were made? Can claims be classified according to these characteristics?

• What are the administrative costs to the scheme operator?

Once gathered, the historical data provides the basis for specific analysis into, among others, the following areas:

Exposure analysis

Beneficiary data is analyzed to understand the scheme's 'level of exposure' to providing cover for beneficiaries over the course of one year. The level of exposure is not based solely on the number of beneficiaries enrolled at any time, but considers other factors including the length of time that beneficiaries have been enrolled. For example, if a beneficiary was enrolled half-way through the year, their contribution to the exposure is just 0.5. The exposure analysis also stratifies the data into various demographic aspects such as age and gender.

Claims analysis

In the claims analysis process, data from individual claims is analyzed to understand the incidence of claims, as well as the average costs per claim. This analysis includes various groupings and stratifications based on age, location, and gender of beneficiaries, as well as other health-related factors such as standard diagnoses.
Additional analysis based on the exposure and claims is also conducted to understand the relationship between the contribution collection during enrolment, funding available from other sources, and the costs of claims. This analysis is geared towards answering a central question: does the scheme operator have sufficient financial resources to pay the claims submitted?

Projecting Future Costs

Findings from the analysis of historical data are combined with broader economic and health-related data to form the second part of the actuarial analysis. This stage is focused on generating a model for predicting future income and expenditure that supports decision- and policy-making; helping to ensure the long-term sustainability of the scheme. The projection model generally seeks to answer questions such as:

• What is the mix of beneficiaries likely to be in the future (age, gender, etc)?

• How are claim costs likely to increase?

• How are administrative costs likely to increase?

• What impact would increasing costs have on contributions and/or subsidies?

A projection model generally takes historic data as the 'base year' data and projects the future scenarios as a change in that base data. While designing the projection models, additional data sources are very important, especially in the context of low- and middle-income countries where many underlying factors are changing rapidly. Population data (based on census as well as other sources) that accurately estimates migration rates are important in ascertaining the future exposure of the scheme. Similarly, data derived from the wider healthcare system, indicating the changing burden of disease patterns in the country, are also addressed and included in the prediction model. Following the development of a prediction model, actuarial studies stress-test the models to understand the effect and extent of each factor on the financial sustainability of the health financing scheme.
A full actuarial study that includes an analysis of historic beneficiary, claims, and administrative cost data, together with a stress-tested projection model to predict the costs of the future, will help policy-makers to take decisions that ensures the sustainability of the scheme over time.