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Risky business

Risky business

Insight: finance

Simon Moody
Consulting Actuary, Milliman London Healthcare Practice

Joanne Buckle
Principal & Consulting Actuary, Milliman London Healthcare Practice


In the government's white paper Liberating the NHS: Commissioning for Patients, the Department of Health (DH) set out the intended arrangements for GP commissioning.

Simon Moody
Consulting Actuary, Milliman London Healthcare Practice

Joanne Buckle
Principal & Consulting Actuary, Milliman London Healthcare Practice


In the government's white paper Liberating the NHS: Commissioning for Patients, the Department of Health (DH) set out the intended arrangements for GP commissioning.

One of the key responsibilities for clinical commissioning groups (CCGs) is to break even on their commissioning budget. To do so, CCGs will need to manage and control financial risk.
The DH splits risk into:

  • Insurance risk: uncontrollable and random fluctuations in the healthcare needs of a population.
  • Service risk: typically arising from the mismanagement of controllable activities. This includes poor prescribing or referral practices.

The finer details of how under/overspend against allocated budget are rewarded/penalised are still unclear, but two things are reasonably certain. First, each CCG will be required to minimise exposure to uncontrollable insurance risk. And second,there will be incentives to encourage and reward CCGs who successfully manage financial risk, with the likelihood of penalties for those who fail to do so, particularly those who also fail to control service risk.

Service risk is managed directly by the actions of GPs within CCGs. Insurance risk can be managed through the use of risk pooling or insurance solutions.

With a long-established history in insurance, actuaries are familiar with the concepts of risk mitigation, management and pooling. We understand how appropriate data can be collected, modelled and analysed in order to understand and quantify financial risk and uncertainty. We also understand the value of commercial (re)insurance markets, where they may be appropriate, and by working in the industry we have ready access to potential (re)insurance providers.

Using such solutions is about exploring access to external capital, or the pooling of risk, to smooth financial results in the event of unexpected circumstances. It is therefore about developing potential risk sharing or risk transfer solutions to ensure the ongoing sustainability of clinical commissioning groups (see Figure 1).

Inline figure

In this basic model, the financial risk to the CCG is the risk that the Actual Spend in the financial year (the sum of E, A, B and C) is greater than the Expected Spend (E).

With stop-loss insurance, the CCG may wish to insure all uncontrollable spend in excess of E (ie, in this case, the sum of B plus C). The mismanaged service spend (A) is uninsurable and would be retained by the CCG.

The derivation of E is important. From the stop-loss insurer's perspective, the value of E drives the probability of claim. For the CCG, the value of E therefore drives the premium they would pay for the insurance protection.

E should ideally represent the 'best estimate' of the CCG's expected spend during the financial year. But this can be a complex calculation and must be actuarially risk-adjusted to allow for the health and demographic status of the population registered to the CCG over the course of the year.

Financial risk is highly correlated to utilisation management, which is directly related to efficiency. Actuaries can identify where utilisation management can help improve results and where the greatest opportunity to do so lies.

For example, using actuarial risk-adjusted data, we have assisted primary care trusts (PCTs) in pinpointing inefficiency in clinical areas by population segment. In the US, actuarial methodologies are used to analyse hospital inpatient admissions, length of stay, avoidable visits to the emergency department, use of expensive drugs, and use of advanced imaging tests.

When compared to most efficient practices, based on the underlying burden of illness of the population, it estimates potentially avoidable utilisation and costs. Combining actuarial cost models with clinical guidelines, population health management can be improved, together with better control of risk and a reduction in excessive utilisation of health services.

Actuarial techniques can also be used to identify those populations where disease management programmes would be most effective. Often there is an absence of a rigorous evaluation before such programmes, interventions and technologies are implemented.

This leads to inefficient use of increasingly scarce resources, and decision-making based on data, analysis or observations that may be flawed or lacking in rigour. Actuaries build complex financial return on investment models that enable commissioners to evaluate their clinical options and estimate the financial implications of these. These models directly project the costs and savings expected from implementation of the intervention by comparing the pre- and post-implementation scenarios and allowing for the cost and clinical impact of the strategy itself.

The most valuable component of this type of modelling is often the scenario testing that quantifies the uncertainty. The identification of the key uncertainties, and how best to manage or mitigate them, may be more useful than the best estimate scenario itself. With uncertainty and risk there often is no definitive 'answer' and it is the confidence intervals around the best estimate that often provide the most valuable information.

It is unlikely that CCGs will all need their own full-time actuary to analyse data and produce the necessary models to manage financial risk. Some models can be produced at a regional or national level and adapted locally, while other CCGs will require bespoke models depending on the type of risk pooling and reinsurance solutions they investigate. However, some actuarial input will be critical to understand the range of potential risk management solutions and the financial implications of these.

Under the government's proposals, GPs (or more likely CCGs) will need to take the baton of data management and analysis from PCTs to support their commissioning responsibilities. GPs will not only need to use their own data effectively, but also patient data from sources outside of primary care. The current lack of clarity around the ownership of data and the fragmented nature of data being collected are obstacles to more effective and efficient care, as they prevent analysis and improvement and the ability to monitor outcomes.

For a complex organisation such as the NHS, the context very rarely relates to one type of service. Yet data are routinely collected and analysed in silos, resulting in poor data in tertiary care, a general inertia to release data in primary care, as well as systems that do not connect at the patient level.

To manage financial risk you first need to understand it. To understand it, you need to model it. And to model it, 
you need good quality, credible data that can be built up to form a complete picture of patient needs and healthcare use across all services.


Actuarial risk-adjusted data: the process of adjusting base or benchmark data to allow for differences in expected risk (eg, age, gender, geography, etc), in order to ensure a like-for-like comparison across data sets.

Return-on-investment models: models that combine actuarial science with health economics techniques to impart a broad understanding of the financial and clinical costs and benefits of new health interventions.

(Re)insurance markets: a collection of insurers and reinsurers who insure pre-defined financial losses in exchange for an insurance premium.

Stop-loss insurance: a specialised type of insurance that indemnifies losses from unexpected extreme events beyond
a pre-specified threshold.

Utilisation management: the process of managing the demand
for and use of health services using a variety of methods including evidence-based best-practice clinical protocols, benchmarking of key metrics such as admission rates and length of stay against actuarially risk-adjusted best-practice standards, etc.


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