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The Feasibility of a Shared Data System in the Kenyan Medical Insurance Sector as a Means to Reduce Fraud
November 2014
21
Case
Study
THE FEASIBILITY OF A SHARED DATA
SYSTEM IN THE KENYAN MEDICAL
INSURANCE SECTOR AS A MEANS
TO REDUCE FRAUD
2
This Case Study has been presented
as a final project of the Master in Global
Health ISGlobal – Universitat de Barcelona.
Supervised by Anna Lucas (ISGlobal)
& Joan Tallada (ISGlobal).
Abstract
Fraud in the healthcare in industry is a serious problem with recent
studies estimating that close to a staggering $487 billion per year is
being lost to fraud. Health Insurance Fraud (HIF) leads to increased
policy fees, which in turn leads to a reduction in the number of people
who can afford to insure themselves and are therefore unprotected in
the event of unexpected health crises. Although HIF has become a
widely studied issue in many developed countries, there are currently
very few studies focused specifically on HIF in developing countries,
making it extremely difficult to estimate with any degree of accuracy
the true extent of the problem.
In Kenya, for example, some studies have reported that HIF is reported
to be as high as 40-50% of paid out claims1, 2, and a recent survey
found a radical increase in identified fraudulent claims in the past four
years.3 One fraud reduction method which has been implemented in
numerous programs around the world with a high degree of success is
the sharing of data among the insurance companies in order to better
identify fraudulent claims. Through background research and interviews
with leading anti-fraud experts, two main types of data sharing
programs were identified; all claims data bases and shared fraud listings.
In order to establish the feasibility of implementing either of
these programs in Kenya, further background research and interviews
with key stakeholders was conducted. Along with issues such as mistrust
in the insurance industry and a lack of skilled personnel, competition
in the Kenyan insurance industry was found to be extremely fierce,
a major potential barrier to data sharing. However all respondents
were very receptive to the idea of the implementation of a data sharing
program and based on factors such as cost, complexity and the type
of data submitted, a shared fraud listing was identified as a potentially
beneficial first step in combating HIF in Kenya.
Work published under license from
CreativeCommons.
Attribution-NonCommercial-NoDerivs
Nina Wine
THE FEASIBILITY OF A SHARED DATA
SYSTEM IN THE KENYAN MEDICAL
INSURANCE SECTOR AS A MEANS
TO REDUCE FRAUD
3 Introduction
Insurance Fraud in the Healthcare Industry
In the 2010 World Health Report, the World Health Organization listed
fraud as one of the top ten leading causes of inefficiency in healthcare4
and recent studies have calculated that nearly 6.9% of all healthcare
expenditure is lost to fraud.5 Health Insurance Fraud (HIF), which is
when an individual or organization intentionally defrauds an insurance
company or government run health scheme, generally leads to insurance
companies raising the price of premiums in order to cover HIF
related losses. This in turn puts financial strain on existing policy holders
and pushes out or entirely excludes individuals who are unable to
afford the higher costs. Government and employer sponsored schemes
are also effected, as seen with the recent discovery of the American
Medicare and Medicare fraud schemes which have been estimated to
cost the country tens of billions of dollars annually.6
Due to immensely high health care expenditures in developed countries,
the proportional loss associated with HIF in these countries is
also tremendous; consequently, cases of HIF in developed countries
are highly publicized and frequently studied. However no country is
immune to HIF and although there is currently very little research
which specifically investigates the extent and impact of HIF in developing
countries it is assumed to be a problem of equal, if not greater
magnitude.
Types of Healthcare Insurance Fraud
Perpetrators of HIF can be divided into three groups;
...