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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; Providers, Policy

holders and Payers (Insurers), with the Providers generally committing

the highest amount of fraud.7, 3

Provider

- Billing for services never rendered 8, 9, 3, 7

- Unbundling (billing each step of

a procedure as a separate procedure) 8, 9, 7

- Accepting bribes or kickbacks for referrals3, 7

- Performing unnecessary procedures

or tests8, 3

- Prescribing unnecessary drugs3

- Upcoding 8, 7, 9

Policy Holder

- Obtaining duplicate prescriptions through

various doctors9, 3

- ID card misuse (use of card by someone

other than the cardholder)8; 3

- Falsifying Records8, 3

- Invented or embellished claims8, 3

- Multiple policies3, 7

Table 1

Most Common

Types of Fraud, by

Perpetrator Group

THE FEASIBILITY OF A SHARED DATA

SYSTEM IN THE KENYAN MEDICAL

INSURANCE SECTOR AS A MEANS

TO REDUCE FRAUD

4 Methods of HIF Reduction, Data Sharing Specific

In order to combat the financial losses incurred from fraud, health

insurance companies and healthcare schemes must adopt strategies to

reduce the toll on their organizations. Fraud reduction methods used

by insurance companies can generally be classified into three different

groups:7

1. Fraud prevention aims to reduce the occurrence of fraudulent acts.

2. Fraud detection entails identifying and investigating suspicious

claims before they are paid out.

3. Fraud enforcement (aka “pay and chase”) means that the claim is

paid out first and if subsequent research then discovers that the claim

was falsified, the company then attempts to recoup the funds.

Data Sharing as a Means of Reducing Health Insurance Fraud

Numerous methods are currently being used by the health insurance

industry in its effort to prevent and reduce fraud, varying widely in

complexity, cost and effectiveness. One relatively inexpensive method

which is becoming increasingly widespread is the sharing of relevant

industry data amongst insurance companies. By combining the claims

data of individual insurers within a shared database, the ability of analysts

to detect fraudulent claims is drastically expanded.

Health Insurance Fraud in Kenya

This paper will focus specifically on the effect of HIF in Kenya, where

some estimates put the rate of fraudulent claims as high as 40-50%

of paid claims.1, 2 HIF is acknowledged to be an issue in Kenya and

there is increasing press coverage regarding the problem, however the

true extent of HIF and the impact it has on the health care system is

poorly understood and fraud reduction methods among the insurance

companies vary widely.

Aim

This paper will first give a general background of the status of health

care in Kenya with a specific focus on the state of the health insurance

industry and HIF, and then will look at the different types of data

sharing systems currently in use globally by the insurance industry to

reduce HIF. Following will be the results of interviews conducted with

representatives of key interest groups in the Kenyan medical insurance

industry to ascertain their views on data sharing and HIF and finally,

the paper will examine some of the specific barriers that might be faced

when implementing a data sharing system among the Kenyan insurance

companies and will give recommendations on the feasibility of

undertaking a data sharing project.

THE FEASIBILITY OF A SHARED DATA

SYSTEM IN THE KENYAN MEDICAL

INSURANCE SECTOR AS A MEANS

TO REDUCE FRAUD

5 Results

Overview of the Kenyan Health Care Sector

Although the health care system in Kenya is improving there are still

considerable problems such as a significant lack of resources, the acute

shortage of healthcare workers, long wait times and a shortage of crucial

drug supplies. The health care system is primarily funded by out of

pocket payments (payments made by the individual or household to cover

medical expenses) which constitute nearly 38% of health care funding,

with the government financing just under 29% of the health system.10

Poor quality of the public health system coupled with acess fees for all

but the most basic servies make the private health sector an attracitve

option for those who can afford it.

Due to high rates and a general mistrust of the health insurance industry,

the Kenyan insurance sector has a very low rate of penetration

with nearly 35 million of a population of 44 million currently without

insurance coverage.11 Additionally, it has been found that the rate of

households unwilling

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