Research Article. Management of Claims and Reimbursements: The Case of Mediclaim Insurance Policy. Ramesh Bhat and Elan Benjamin Reuben.

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1 Research Article focuses on the analysis and resolution of managerial issues based on analytical and empirical studies. Management of Claims and Reimbursements: The Case of Mediclaim Insurance Policy Ramesh Bhat and Elan Benjamin Reuben The Mediclaim scheme run by the government-owned General Insurance Corporation (GIC) of India is currently the only private voluntary health insurance scheme available in India. This scheme has been in operation since 1986 and from time to time a number of revisions has been made to address the needs of its clients. The analysis of claims and reimbursements under this scheme is scanty. This paper analyses 621 claims and reimbursements pertaining to policy initiation years and of the Ahmedabad branch of GIC's subsidiary. The study estimates that about a third of claims amount increase is due to the problems of adverse selection or provider-induced demand. The analysis of breakup of reimbursements suggests that more than one-third of reimbursements are made towards doctor's fees, followed by diagnostic charges which account for about one-fourth. The findings also suggest that the insurance company took on an average 121 days to settle the claim. Ramesh Bhat is a member of the faculty in the Finance and Accounting Area of the Indian Institute of Management, Ahmedabad. rbhat@iimahd.ernet.in Elan Benjamin Reuben is a Research Assistant at the Schneider Institute of Health Policy, The Heller School for Social Policy and Management, Brandeis University, Massachussets. ereuben@brandeis.edu This paper is part of a research project of Health Policy Development Network (HELPONET), India, coordinated by the Indian Institute of Management, Ahmedabad. Introduction Health insurance can be broadly defined as a financial mechanism that exists to provide protection to individuals and households from expenses incurred as a result of unexpected illness or injury. Under this mechanism, the insurer agrees to compensate or guarantee the insured person against loss by specified contingent event and provide financial coverage for which the insured party pays a premium. The case for health insurance rests on three grounds: first, illness cannot be predicted; second, financial burden of hospitalization is high and cannot be planned; and third, the proportion of people requiring hospitalization due to illness in any large population is small thus enabling risk-pooling (Krishnan, 1996). Pooling of risks, resources, and benefits is the hallmark of any insurance system. In India (and elsewhere), a variety of forms of health insurance, both formal and informal, exists which can be broadly categorized into three groups: staterun schemes (e.g., Employee State Insurance Scheme, CGHS), market-based and voluntary insurance schemes, and schemes offered by member-based organizations (e.g., NGOs and cooperatives). The government-run General Insurance Corporation of India (GIC) and its four subsidiaries offer a number of health insurance schemes including Mediclaim (Box). These schemes are sold on voluntary basis to individuals, groups, and corporate sector. GIC periodically revises the features of medical insurance schemes to make them more effective. Till recently, only GlC-run companies were allowed to offer and sell insurance products in India. The Government of India has now permitted the private sector companies to enter this sector which is likely to have a significant impact on the health insurance initiatives in the country. 'New India Assurance Company Limited, Mumbai; Oriental Insurance Company Limited, New Delhi; United India Insurance Company Limited, Chennai; and National Insurance Company Limited, Calcutta. Vol. 27, No. 4, October-December Vikalpa

2 Box: Mediclaim Scheme The General Insurance Corporation (GIC) and its four subsidiaries and the Life Insurance Corporation (LIC) have designed a number of medical reimbursement schemes. These are sold to individuals and groups on a voluntary basis. These schemes can be broadly classified into three categories: Focus Individual Reimbursement Schemes Group Reimbursement Schemes Specific Medical Reimbursement Policies Schemes Mediclaim Jan Arogya Policy Bhavishya Arogya Policy LIC's Asha Deep Group Mediclaim Policy Group Mediclaim Policy for Cardholders Cancer Insurance Policy Birthright Insurance Scheme Overseas Mediclaim Policy Most of these schemes, at present, are structured as "fixed indemnity" policies with fixed sum insured. The insured person submits bills to the insurance company for the purpose of reimbursement after making payment to providers for services. The most popular health insurance cover is the Mediclaim scheme of GIC offered by GIC companies. Under this scheme, a person between 5 years and 70 years of age can purchase Mediclaim policy (continuing the renewal of the existing policy may be accepted even after 70 years of age). For 3 months to 5 years, children can be covered provided one or more parents are covered. The total insurance sum can be up to Rs 5 lakh against accidental and sickness hospitalizations during the policy period. The policy makes it mandatory for the policyholders to declare any pre-existing disease and they are not eligible for reimbursements for the treatment taken for such pre-existing diseases. Other types of illnesses prevalent such as diabetes, hypertension, and high blood pressure or any other disease has to be declared while applying for Mediclaim policy. Generally, policyholders do not declare pre-existing disease. To establish this, the recommendation of panel doctors is considered while processing the claim. The premium of the policy depends on the amount of sum insured and the age of the person. The first significant revision in the Mediclaim policy was made in April In order to promote Mediclaim, the Government of India allowed tax benefit up to Rs 10,000 of premium paid as tax-deductible expense in the year Mediclaim policyholders are entitled for benefit of cumulative bonus of 5 per cent up to a maximum of 50 per cent of sum insured in case there is no claim and there is continuity in policy. The policyholder is eligible for bonus for every claim-free year of insurance subject to a maximum accumulation of ten years. Renewal within a specified period is required for continuation of policies. Under the Mediclaim scheme, the insured is also entitled to reimbursement for the cost of a medical check-up once in every four years provided there are no claims in that period. The Mediclaim policy is only for hospitalization and the provider should be registered having at least 15 inpatient beds. The policy started with the cover to protect the hospitalization costs, but over a period, the definition of hospitalization has been changed keeping in view the technological advancements and procedural protocols for treatment of various illnesses. For example, in case of dialysis, eye surgery, dental surgery, kidney stone removal, etc., where the insured is discharged on the same day, the stay in the hospital for 24 hours is not considered necessary. Mediclaim also covers pre-hospitalization up to 30 days and post-hospitalization expenses up to 60 days. Any disease contracted by. the insured person during the first 30 days from the commencement date of the policy will not be considered for reimbursement in case of fresh proposal, except accidents or injury (called exclusion clause). In some specific cases, the policy has also one-year exclusion clause. Vol. 27, No. 4, October-December Vikalpa

3 The Government of India has passed the Insurance Regulatory and Development Authority (IRDA) bill, which has paved the way for developing appropriate regulations to steer the process and development of this sector. The objectives of the IRDA are to regulate the entry of insurance providers, protect the interests of policyholders, promote efficiency, control and regulate rates, regulate investment of funds, and supervise the insurer, insurance intermediary, and other organizations connected with insurance business. The IRDA is in the process of developing a regulatory framework for new entrants and existing players in the insurance sector. Inter alia, the pricing of products and management of claims is likely to assume considerable importance in these regulations. The past experiences in pricing and managing the claims are useful in providing some understanding of the issues and in developing appropriate policies. However, there is very little documentation of experiences of insurance companies in managing claims and reimbursements in India. Introduced in 1986, the objective of the Mediclaim policy is to provide insurance cover for expenses arising out of illnesses that require hospitalization. The objective of this paper is to present empirical findings about the claims and reimbursements made under the Mediclaim insurance policy offered by one of the GlC-run companies in Ahmedabad city. More specifically, using this case study, the study aims to: Understand the trends and patterns in insurance policies sold by the Ahmedabad branch of GIC subsidiary by examining quarter-wise data dur ing the years , , and Understand the magnitude of reimbursements against premiums collected. Describe the profile (gender, occupation, rela tion with policyholder) of the claimant. Analyse the break-up of expenditure for which claims and reimbursements have been made. Find the delay in the number of days in settling the claims and reasons thereof. Analyse the reimbursement system and its re lationship with costs and claims made. expenditure schedule, the claim form filled by the claimant, all possible bills for the expenditures incurred by the claimant including various diagnostics and other related items. The files also contained the family policy schedule which gave information on the policyholder's name, names of family members insured, the amount of individual insurance, the premium, total family insurance and the total family premium, family discount, and the cumulative bonus details. The proposed form contained additional information such as address, age, gender, occupation, relationship, hospital admitted, family information, sum insured, etc. In general, the insurance companies compare the claims settled in a particular accounting year with the policy amounts received during that accounting period. The claims settled do not necessarily represent the same policies initiated in that accounting year. Since the number of policyholders and collections is growing, the claim ratio is always underestimated. This case study estimates the correct claim ratio by tracing the claims settled with their policy initiation period. Since the study proposed to have information on all the claims and reimbursement claimants for at least two years, it was required to collect data for at least a four-year period. A policyholder's claim settlements were classified as per the accounting year, i.e., the year in which the policyholder's claim was settled (Figure 1). Since the policy would be valid for one-year period, all policyholders who took the policy during 1st April 1999 to 31st March 2000 could stake their respective claims in two accounting periods, viz., 1st April 1999 to 31st March 2000 and 31st March 2000 to 1st April The settlement of these claims Figure 1: Claim Submission and Settlement Period Claim Settlement Claim Submission Period Data Collection Since the data pertaining to reimbursements and claims were not computerized at the time of starting this study, they were collected manually from the claim files containing documents like the claim form, 1 April Policy Initiation Year 31 March 2000 Vol. 27, No. 4, October-December Vikalpa

4 could take any time after the claim submission date. Since all claim settlements were classified as per the accounting year, they were traced to policy initiation year. The distribution of the cases as per the policy initiation period and claim period is provided in Table 1. Apart from manual data collection, there were other problems like accessing previous years' claim files, problems arising because of lack of standardized way of reporting the information about claims, and non-availability of all the required data from the claim file. In cases where the data were not available from the claim file, they were collected from the policy file as GIC maintained two separate files for claims and policy. Findings The Mediclaim scheme had experienced an impressive growth in the number of persons applying for the scheme. This had happened particularly after the revised policy was introduced in It was estimated that about 2.5 million persons were insured under the scheme. In the following section, we analyse the Mediclaim insurance policy data of the Ahmedabad branch of the GIC's subsidiary. Pattern in Sale of Policies Data on the number of policies issued in the last three years suggested 32 per cent and 50 per cent growth during and respectively. The number of persons insured under these policies had also increased by about 29 per cent and 70 per cent during these two periods respectively. Table 2 provides the quarter-wise data of policies sold and the number of persons insured under these policies. The total premium collected had also registered a growth of 23 per cent and 50 per cent during these two years respectively. The quarter-wise analysis of sale of policies suggested that 40 per cent of the policies were sold in the January-March quarter and more than 60 per cent in the last two quarters of the financial year signifying the effect of fiscal benefit which accrued to the policyholder in terms of tax advantage. The data also suggested that these tax benefits had significant influence on the behaviour of policyholders. Any change in the government policy towards this may have considerable implications for the sale of these policies. Table 2 also provides information on premium collected per insured person for the last three years. It could be observed that the year-end figures of premium collected per insured person were showing a declining trend. The average premium per insured person had declined from Rs 834 to Rs 793 in , registering a decline of about 5 per cent. The average premium per insured person had further gone down to Rs 690 registering a decline of about 13 per cent during Quarter-wise data also showed a decline over the last three years. These factors suggest that although the insurance company attempted to expand its base by issuing more policies and increasing the number of insured persons, the average premium collection did not respond in the same manner. As the number of policies was increasing, the declining average premium also indicated that the new policyholders were either opting for lesser sum insured (people preferring for basic minimum package and sum-insured because of constraints on ability to pay) or were of lower age group than the exiting age group of policyholders. This also suggested that there were limitations in increasing the base of policyholders, as not many persons beyond a threshold income level would buy insurance. This was happening either because of low income level or high tariff structure. Increasing the base of policyholders is the most desirable objective for any insurance company. This diversifies the risks, making insurance a more viable proposition. The experience, however, suggested that there may be constraints in increasing the base of persons insured Table 1: Distribution of Cases as per Policy Initiation Period and Claim Period Policy Initiation Period Claim Period Number of Cases 1st April st March (4.72%) (82.08%) 28 (13.21%) 0 (0.00%) 212 (100.00%) 1st April st March (0.00%) 111 (27.14%) 295 (72.13%) 3 (0.73%) 409 (100.00%) Total Cases Vol. 27, No. 4, October-December Vikalpa

5 Table 2: Quarter-wise Analysis of Policies Sold Period Policies Issued Persons Insured Premium Collected Premium per Insured April 1997-March 1998 N % N % Rs % Rs April-June July-September October-December January-March Full Year April 1998-March 1999 April-June July-September October-December January-March Full Year April 1999-March 2000 April-June July-September October-December January-March Full Year raising questions on overall viability of the scheme. Also, currently, the insurance companies are selling only one type of product and prospective buyers of insurance do not have adequate choices suitable to their needs. India does not have a comprehensive health insurance programme with the result that only small groups of people belonging to the organized sector enjoy some measure of financial protection against illness (Krishnan, 1996). The tariff structures have generally remained constant. Of course, in some cases, they have increased to reflect inflation factor, but they have rarely declined to reflect the economies of scale due to growing number of policyholders. There is a general impression that the marketing function of GIC companies is less developed and there are issues such as the incentive the agents get by selling Mediclaim policies that need to be resolved. The quality of services is also generally considered to be less satisfactory. Another major weakness of Mediclaim is that it covers only hospitalization and domiciliary hospitalization expenses, leaving out routine outpatient care. In many cases, either the medical claim was disallowed or the policyholders received only partial reimbursement. Premium was high in relation to the claim payments (Ellis, Alam and Gupta, 2000). All these factors, we suspect, affected the sale of policies. Implications of Growth on Claims During the year to , the number of claims increased by 93 per cent. The total amount claimed increased from Rs 3.8 million to Rs 6.21 million showing a growth of 63 per cent. The total amount of reimbursement increased from Rs 3.31 million to Rs 5.55 million an increase of 68 per cent. At the same time, the number of insured persons per claim had gone down. There were about 29 insured persons against one claim in , which declined to 20 persons. One of the objectives of this study was to find out how much amount was paid as reimbursements against the premia collected. It was generally claimed that the Mediclaim scheme was making losses as about 130 per cent of premia were paid out as reimbursements. We segregated and identified the four-year data for reimbursements from 1997 to 2000, the policy initiation years being and Based on this, we calculated the claims ratio for these two years (Table 3). The reimbursements as per cent of premia collected increased from 64 per cent to 88 per cent. For a combined data of two years, this ratio was 77 per cent. However, based on this study, it was not possible to extrapolate this ratio for all-india. Similarly, the number of claims as per cent of number of persons insured was 3.42 during the year This increased to 5.12 per cent during the Vol. 27, No. 4, October-December Vikalpa

6 Table 3: Premia Collected and Reimbursements (1997 to 1999) All Cases Policy Information Number of Policies Number of Persons Insured Average Persons per Policy (Rs) Amount of Premium Collected (Rs) Average Premium per Person Insured (Rs) Claims and Reimbursement Information Number of Claims Total Sum Insured for all Claims (Rs) Sum Insured per Claim (Rs) Number of Persons Insured per Claim Total Amount Claimed (Rs) Average Claim Amount per Claim (Rs) Total Amount Reimbursed (Rs) Average Reimbursement per Claim (Rs) Other Selected Indicators Reimbursements as % of Claims Number of Claims as % of Number of Insured Reimbursements as % of Premia year At the same time, the average amount of claim per claim had reduced from Rs 18,197 to Rs 15,177. This indicated that the insurance company had suffered because of adverse claims ratio but, at the same time the average per capita claims had also reduced. The significant growth in the number of claims could be due to adverse selection problem whereas the reduction in average amount claimed could be because of lower amounts of sum insured by persons applying for claims. Table 3 shows that per claim sum insured declined from Rs 75,000 to Rs 72,000 during and respectively. The studies on insurance are replete with the suggestion that private health insurance suffers from a number of negative factors. There are two major issues in any insurance scheme: problem of moral hazard and problem of adverse selection. Moral hazard problem refers to the mediclaim policyholder demanding all kinds of medical services regardless of cost including those which offer the slightest possible health benefit and the providers having financial incentive to recommend the costliest of treatments, even if they are of little value to the patients or charging more because the patient is insured. Adverse selection problem refers to people at high risk buying the Mediclaim insurance policy. Insurance lowers the price of care at the point of treatment and increases demand (consumer moral hazard). The economic effects of health insurance are on the demand for health care and supply of health care (Mills, 2000). In order to minimize the negative consequences arising out of these problems, the GIC has framed several rules and regulations (Box). Using the claims data of four years, we made an attempt to understand the magnitude of the problem because of adverse selection and other negative consequences. We assumed the experience of in terms of average claim and number of claims against number of insured as standard. The average claim was Rs 18,197 and the number of claims was 3.42 per cent of the total number of persons insured. Using these two parameters as standards, we estimated what would have been the position if there was a similar experience during and compared this with the actual of (Table 4). At efficiency levels, the total amount of claims should have been Rs 4.98 million whereas the actual amount of claim is Rs 6.21 million an adverse variance of Rs 1.23 million. This was the combined effect of two main components: One, the adverse selection problem as reflected by claims as percentage of number of persons insured and, two, less consumption of medical services as reflected in Vol. 27, No. 4, October-December Vikalpa

7 Table 4: Analysis of Claims As per Last Actual Figures Year's Efficiency Average Amount per Claim Claims as % of 3.42 Number of Persons Insured 5.12 Number of Insured Persons 7995 (Actual for ) 7995 Total Amount (Rs) reduction in the average amount of claim per claim from Rs 18,197 to Rs 15,177. The former was negative while the latter was positive. This implied that the actual variance because of adverse selection was much higher than the variance as calculated above. The average claims, which declined from Rs 18,197 to Rs 15,177, saved Rs 3,020 per claim. At the rate of 3.42 per cent of 7,995 insured persons, this worked out to be Rs 0.83 million. This was the amount the insurance company had saved because of less consumption of health care services, which would partly offset the losses. Considering this saving, the total variance because of adverse selection or provider-induced demand would be much higher and it worked out to be Rs 2.06 million. This was equal to excess claims, which was 1.70 per cent (5.42 per cent minus 3.42 per cent) of 7,995 insured persons multiplied by Rs 15,177 per claim. This analysis suggested that about one-third of claims during arose because of adverse selection problem or provider-induced demand problem. " The numbers and percentages shown here have been rounded up to two decimal points. The actual computations have been done without rounding off. Profile of Claimants Table 5 gives the profile of claimants on three counts: gender, occupation, and relationship with the principal policyholder. Using claims and reimbursement information, the profiles of diseased and broad disease groups were analysed. Table 6 presents the difference in means tests (both parametric t-statistics and non-parametric Mann-Whitney U-test statistics) for hospital days, sum insured, premium paid, amount claimed, amount reimbursed, and age based on gender, occupation, relation, and disease group classification. The means of these six variables were not statistically different between males and females. When the claims were segregated based on earning and non-earning member (occupation), the averages of sum-insured, premium paid, and age were statistically significantly different between these two groups. If the diseased happened to be the earning member (having business or in service), he was likely to have a higher sum-insured, paid higher premium, and was in the higher age group than if the diseased was a non-earning member (student, housewife or retired). However, there was no significant difference in the amounts these two groups claimed or were reimbursed. There was also no difference in the hospitalization days of these groups. All claims were segregated into two groups: self and relation. Except hospitalization days, the means of all other claim and reimbursement parameters were significantly different. If the diseased happened to be the policyholder (self), he was likely to have a higher sum-insured, made higher claim, and was in'the higher age group. Table 5: Profile of Claimants Gender Cases % Age Claim Reimbursement Female Male Total Occupation Cases % Relation Cases % Housewife Self Business Spouse (Wife: 150) Service Son Student Daughter Retired Blood Relation Professional Not Specified Not Specified Total Total Vol. 27, No. 4, October-December Vikalpa

8 Table 6: Means Difference Tests: Profile of Diseased and Various Mediclaim Policy Claim-related Information Gender Female Male t-statistic Mann- Whitney U-test Statistic Probability X 2 Value Hospitalization Days Sum Insured Premium Paid Amount Claimed Amount Reimbursed Age of Claimant Earning Member Non-earning Member Occupation t-statistic Mann-Whitney U-test Statistic Probability X 2 Value Hospitalization Days Sum Insured * * Premium Paid * * Amount Claimed Amount Reimbursed Age of Claimant * * Relation Self Other t-statistic Relation Mann-Whitney U-test Statistic Probability X 2 Value Hospitalization Days Sum Insured * * Premium Paid * * Amount Claimed * * Amount Reimbursed * * Age of Claimant * * Broad Disease Groups Communicable Non- Communicable Accidents Kruskal-Wallis and Injury Test Statistic fy 2 ) Hospitalization Days * Sum Insured * Premium Paid * Amount Claimed * Amount Reimbursed * Age of Claimant * * Significant differences at 5 per cent level of significance. communicable diseases still account for about 50 per Disease Groups and Claims cent of mortality in India. These diseases are preventable. The claims arising out of these cases can All cases of claims were also analysed in terms of disease of the claimant. A broad classification of diseases be reduced provided the insurance company develops some appropriate interventions such as emphasis suggested that in about 22 per cent of cases, the claimant suffered from some form of communicable disease. This on preventive and primary care. In 64 per cent of indicated that the insurance companies faced significant cases, the diseased suffered from non-communicable operating risks. The diseases. Accidents and injuries have become one Vol. 27, No. 4, October-December Vikalpa

9 important cause of health problem in recent times. In this case study, these accounted for about 14 per cent of cases (Table 7). The means of hospitalization days, sum-insured, amount claimed, amount reimbursed, and age were statistically significantly different across three groups of diseases. The hospitalization days in case of communicable diseases were highest at 4.63 days as compared to 4.12 and 3.89 in other two cases respectively. The amount claimed and reimbursed was the highest in case of noncommunicable diseases. Age of claimants in noncommunicable diseases was also higher than the other two groups. The claimants under non-communicable diseases had higher sum-insured. This could be due to adverse selection problem as the claimants under this group had predicted their risk group and gone for higher sum-insured. The disease mix of claimants was significantly different from the national level average. This was also due to high urban bias of the scheme. It is not clear what disease mix the insurance tariff advisory committee take into account while deciding the tariffs for the Mediclaim scheme. The disease mix has implications for the claims and reimbursements and is an important factor in determining the tariff structure of the scheme. It is clear that the scheme attracted significantly more clients from the noncommunicable disease groups. Break-up of Claims and Reimbursements The data on claims and reimbursements were further analysed in terms of hospitalization days, number of days taken to settle the claim, age profile of the diseased, break-up of claims and reimbursements, and medical reference fee incurred by the insurance company (Table 8). The break-up of claims and reimbursements was done in four broad categories: room charges, fees charged by doctor, diagnostic charges, and charges for medicines. Some of the major findings were as follows: The average length of stay of the diseased in the hospital was about four days. Table 7: Broad Classification of Diseases Broad Classification Cases % National Level Statistics (%) Communicable Non-communicable Injury and Accidents Total The diseased took about 12 days to get hospi talized from the day of illness. The claimant fell sick after 177 days of the start of the policy. The average age of the diseased was 39 years. The average sum-insured was Rs 78,000 and the premium paid Rs 1,049. The family insurance covered, on an average, three members for which the average sum insured was Rs 2,00,000 and the average premium was Rs 2,500. The break-up of claims and reimbursements is also provided in Table 8. The average claim and reimbursement had been Rs 16,208 and Rs 14,278 respectively. The break-up is provided in Figure 2. What is the relationship between the types of expenditures claims and reimbursements? The correlation matrix in Table 9 shows this relationship. The highest correlation is between doctor's fees claim and the diagnostics charges claim and the second highest correlation is between doctor's fees and cost of medicines. On examining the break-up of reimbursements, the association between doctor's fees and diagnostic charges is not as strong as it is in the claims correlation matrix. Characteristics of Payment System In practice, payment systems were not always 100 per cent reimbursement-based. The actual reimbursement data suggested that reimbursements in most cases were less than claims. The payment system included features which, in effect, reimbursed only part of the cost, particularly when the claims were higher. This suggested that there was an in-built system of co-payments in reimbursements. To capture the elements of this system that characterized actual Figure 2: Break-up of Reimbursements Room 19% Medicines 16% Diagnostics 27% Doctor's Fees 38% Vol. 27, No. 4, October-December Vikalpa

10 Table 8: Descriptive Statistics for Two Years Claims Data (1st April 1997 to 31st March 1999) Days Statistics Cases Average Maximum Minimum Stan. Dev. Days from Policy to Illness Days from Illness to Admission Hospitalization Days Days from Claim to Settlement Insurance Statistics Sum Insured of Claimant Premium Paid by Claimant Total Family Insurance Amount Total Family Premium Total Persons Insured in the Family Age of the Claimant Amount Claimed Room Charges Claimed Doctor Charges Claimed Diagnostic Charges Claimed Medical Charges Claimed Total Amount Claimed Amount Reimbursed Room Charges Doctor Charges Diagnostic Charges Medicine Charges Total Amount Reimbursed Medical Reference Fees (Paid for 284 Cases) All figures except days, age, and the number of persons and cases are in rupees. payments, we estimated a general payment system in the following way: Reimbursement = a + b Claims + Error Term where a was the portion of payment that was unrelated to incurred costs assumed to be equal to zero in the present system of payments; b was referred as demand side cost sharing parameter* indicating the portion of incurred costs claimed by the policyholder that was reimbursed in the payment system. A full claim-based reimbursement could be characterized by setting a = 0 and b = 1. A mixed system has in-built co-payment and cost sharing elements, where a = 0 and 0 < b < 1. Using the above equation, we could estimate these elements. The estimated equation is as follows: In the US where providers are reimbursed by insurance companies, this parameter is defined as supply side cost sharing parameter (Ellis and McGuire, 1986; Newhouse, 1996). Reimbursement = Claims SE = (0.010) R 2 = Table 9: Correlation Matrix Claims Room Doctor Diagnostics Medicines Panel A Room Doctor Diagnostics Medicines Panel B Reimbursements Room Doctor Diagnostics Medicines Vol. 27, No. 4, October-December Vikalpa

11 In the above equation the constant was set to zero since there could not be any reimbursement.in the absence of claim. The estimated b was statistically significantly different from 1. It was clear that there was an in-built co-payment or co-insurance system in the payment system followed by the insurance company. It was further examined whether implicit co-insurance and co-payments remained constant at all levels of claims by adding a quadratic term in the above equation. The results are as follows: Reimbursement = Claims E-7 Claims 2 SE = (0.014) (6.1E-8) R 2 = The results suggested that implicit co-insurance and co-payments were significant at higher levels of claims. The coefficient of claims variable still remained significantly different from 1. It was clear from the above equation that, in practice, the payment system was a mixed system having implicit co-insurance and co-payments. Determinants of Claims and Reimbursements We attempted to find the determinants of amount claimed and amount reimbursed using regression analysis and identified the following independent variables: Hospitalization days Disease dummy for three groups of broad clas sification of disease Gender Age Self dummy for two groups (diseased is principal policyholder or other) Employed dummy (diseased is earning member or non-earning) Sum-insured as proxy for income group of dis eased The cross-section correlation matrix and regression results are provided in Table 10. We estimated two regression equations, one for the amount claimed and another for the amount reimbursed. Both these equations were significant at 5 per cent level of significance. Hospitalization days, disease dummy, age, and sum-insured were found significant at 5 per cent level of significance in both equations. Claims Settlement Management The next issue in our analysis was to find the number of days the insurance company had taken to reim- burse the claim and the number of cases where the claims were rejected and where reimbursements had been less than the claims submitted. We also discussed the reasons for rejecting the claims or reimbursements less than claims. The analysis of the data suggested that the insurance company on an average had taken about 121 days to settle the claim. In about one-fourth of the cases, either the case was rejected or reimbursements were less than the claims because of some errors. In about 75 per cent of claims, 100 per cent of reimbursements were given. The insurance company on an average spent about Rs 133 for referring the claims to qualified doctors for verification. In 46 per cent cases, medical reference was sought. Usually, the Mediclaim policy specifies charges which are excluded from the scope of the policy and if the claimant happens to include them in the claim, then the insurance company deducts that amount and reimburses the remaining amount. In about 30 per cent cases, the claims submitted included items which were not reimbursable such as charges for toothpaste, etc. The bills from the date of admission to the date of dispatch from the hospital could be included in the claim. If the bill bore a date before the date of admission or after the date of dispatch then it was not included in the reimbursement given to the claimant. In about one-fourth cases where reimbursements were less, the bills submitted did not belong to the period of hospitalization and were outside the dates of insurance policy. Some of the reasons were pre-existing diseases, claim outside the scope of the policy, unnecessary hospitalization etc. About 3 per cent of claims fell in this category. A summary of various other reasons for the mismatch between the amount claimed and reimbursed is provided in Table 11. A further analysis of medical reference cases suggested that the hospitalization days, amount claimed, and claim settlement days were statistically significantly different and higher for the cases that were referred to the medical doctor for further scrutiny. On an average, it took 24 more days in referred cases than the cases that were not referred to settle the claim. The claim settlement was still at 110 days for the cases that were not referred for further medical scrutiny (Table 12). One of the important conditions for the claimant to get the reimbursement was that the hospital where the claimant had received the treatment had to be registered. We found that 89 per cent of the hospitals were registered. In about 11 per cent cases, the Vol. 27, No. 4, October-December Vikalpa

12 Table 10: Cross-section Regression Results of Claims and Reimbursements Pearson Correlation Matrix of Variables HD D Age Self Emp SI AC AR HD D G Age Self Employment SI AC AR Regression Results: Dependent Variable: Amount Claimed, Multiple R: 0.512, Squared Multiple R: 0.262, Adjusted Squared Multiple R: 0.253, F-ratio: (Prob 0.000) Independent Variables Coefficient Std. Error t-value Prob. (2-tailed) Constant Hospitalization Days Disease (Dummy) Gender (Dummy) Age Self (Dummy) Employed (Dummy) Sum-insured Dependent Variable: Amount Reimbursed Multiple R: 0.547, Squared Multiple R: Adjusted Squared Multiple R: F-ratio: (Prob 0.000) Effect Coefficient Std. Error t-value Prob. (2-tailed) Constant Hospitalization Days Disease (Dummy) Gender Age Self (Dummy) Employed (Dummy) Sum-insured Disease (dummy) variable is 1 for non-communicable diseases and 0 for others. Self dummy is 1 for principal policyholder and 0 for others members of the family. Employed dummy is 1 if diseased is earning member and 0 for others. Sum-insured is proxy for income variable. information whether the facility was registered was not specifically stated in the claim file. In many of these cases, the claims had been reimbursed and it was assumed that since the claim was settled, the facility was registered (Table 13). Implications The growth in the number of policies and premia collected has implications for the management of the Mediclaim scheme and problems of adverse selection Vol. 27, No. 4, October-December Vikalpa

13 Table 11: Claims Rejected or Less Reimbursements and Reasons Thereof Cases % No Error Found (Reimbursements = Claims) Errors Found (Reimbursements < Claims) Claim Rejected (No Reimbursement) Total Reason for Errors or Claim Rejection Charges not Claimable (Various Items) Bill Date Outside Hospitalization Dates Incorrect Bill and Errors in Calculation Claim Exceeds Sum Insured Pre-existing Disease Excluded from Scope of Policy Hospitalization not Necessary Duplicate Bills Claim Pending Other^ (Not Specified) Total or provider-induced demand, and falling premia per insured person. The study estimated that about onethird of the claim amount increase was due to the problem of adverse selection or provider-induced demand. Thus, the scheme is vulnerable to adverse selection and provider-induced moral hazard problem. At present, the insurance companies do not have any mechanism in place which would lessen such unintended consequences of insurance. The analysis of break-up of reimbursements suggested that about 38 per cent of the reimbursements were made towards doctor's fees. This was followed by diagnostic charges which accounted for about 25 per* cent. The correlation between these two claims was highest than other items of expenditures. This finding implies that the insurance claims are highly vulnerable to provider-induced use of resources. The implementation of the Mediclaim scheme is critically dependent on the efficiency and effectiveness of the delivery system of private medical services. The dependence of government-run insurance companies and all new private entrants in health insurance would remain significant on private for-profit sector. The public healthcare sector has grown over the years but the dependence of insurance companies on this sector is negligible. In our case study, no claimant has used the medical services of a government hospital. One of the problems with the private sector is that it has grown without any regulation. Dependence on totally unregulated for-profit private medical sector leads to high cost reimbursement-based insurance scheme. It is a well-known fact that hospitals have been overcharging patients specially those who have Mediclaim cover. At present, the four state-owned insurance companies are experiencing a high claim ratio in the region of 130 per cent in Mediclaim (Business Standard, 2001). We found that the insurance company took, on an average, 121 days to settle the claim. Given the demand side and supply side imperfections in private for-profit healthcare markets and absence of appropriate regulatory mechanisms in place, the IDRA's proposal to ensure payment settlement within seven days looks ambitious. The current claim management systems of government-run insurance companies do not match with the tasks that would be required in monitoring the diverse unregulated for-profit medical service providers. In the absence of standardization, treatment protocols, and price regulation, billings vary and this is not necessarily in keeping with any minimum hospital facility requirements. Settling the claims under these circumstances is a time-consuming task. The development of third party administrators is one way of divesting this responsibility from insurance Table 12: Characteristics of Cases Referred for Medical Reference Hospitalization Days Amount Claimed Claim Settlement Cases not Referred (337 Cases) 3.23 days Rs days Cases Referred (284 Cases) 5.35 days Rs days t-statistics Mann-Whitney U-test Statistic (Probability) (0.001) (0.000) (0.000) x 2 Approximation (d.f.= l) Vol. 27, No. 4, October-December Vikalpa

14 Table 13: Registration of Facility Registered Cases % Yes Not Specified No Total companies. However, the cost implications of this intervention from the consumer's point of view need to be examined. The regulations in this area are still in the nascent stage. The IRDA in collaboration with the Central Ministry of Health and Family Welfare also proposes to develop accreditation system for References Ansari, H (2000). "Regulatory Aspects in Health Insurance," Paper Presented at the Conference on Health Insurance, Ahmedabad: Indian Institute of Management, March Business Standard (2001). "IRDA to Look into Accreditation of Hospitals," August 2. Ellis, R P; Alam, M and Gupta, I (2000). "Health Insurance in India: Prognosis and Prospectus," Economic and Political Weekly, January 22, pp Ellis, R P and McGuire, T G (1996). "Hospital Response to Prospective Payment: Moral Hazard, Selection, and Practice-Style Effects," Journal of Health Economics, Vol 15, pp Garg, Charu (2000). "Implications of Current Experience of Health Insurance in India," Paper Presented at the Conference on Health Insurance, Ahmedabad: Indian Institute of Management, March GIC India (1995). "Health Insurance," International Workshop on Health Insurance in India, Bangalore: Indian Institute of Management, September hospitals. This may be the first step towards developing foundation for health insurance market to work effectively. However, the larger problem would be to develop appropriate incentive-based mechanisms and contracts which would be binding on the providers to provide appropriate care which critically depends on the mechanism of feedback of information to clinicians on treatment patterns relative to peers and clinical norms. The experiences suggest that there are many other stumbling blocks in the way of getting efficient and effective health insurance market in place. It remains to be seen how the scope of activities of the IRDA will get widened as we discover the complexities of the healthcare market in India. Krishnan, T N (1996). "Hospitalisation Insurance: A Proposal," Economic and Political Weekly, April 13. Kutzin, J (1995). "Experience with Organizational and Financing Reform of the Health Sector," SHS Paper 8, Geneva: World Health Organization. Mills, Anne (2000). "Health Insurance, Implications for the Demand and Supply of Health Services," Paper Presented at the Conference on Health Insurance, Ahmedabad: Indian Institute of Management, March Newhouse, J P (1996). "Reimbursing Health Plans and Health Providers: Selection versus Efficiency in Production," Journal of Economic Literature, Vol 34, No 3, pp Supakankunti, Siripen (2000). "Community Health Insurance in Thailand," Paper Presented at the Conference on Health Sector Reforms, Equity, Efficiency, and Sustainability, Dhaka-Bangladesh, July 4-6. Zervoudis, M and Karamchandani, A (2000). "The Monitor Group Study on Insurance," Business Today, March 22. Vol. 27, No. 4, October-December Vikalpa