Health Insurance Hospital Registry

This article was first published in the IIB Bulletin, Vol 1, Issue 4: Co-Author: Varsha, GS1 India
https://iib.gov.in/IIB/Articles/IIB%20Bulletin%20Q4%202014-15.pdf

Poor data impacts many areas in the healthcare system. One of the areas that has an impact on Healthcare Analytics is the way hospitals are identified and stored in the various databases. In the case of the Insurance Industry, each Insurer has their own naming convention for Hospitals. For example, Table 1 shows that five different Insurers can name the same hospital in 5 different ways in their databases.

Table 1

Database A	Database B	Database C	Database D	Database E
ABC Hospital	The ABC Hospital & Emergency Services	ABC Hospitals Pvt. Ltd	ABC Hospital Group	ABC Group of Hospitals

In the above illustration one cannot be certain if all the names are referring to the same entity or if they are all different entities, without painstaking manual intervention. Using the list as it is would not give a clear picture of the number of claims, average claims, top diseases in a period in a particular Hospital, total insurance claims paid per Insurer to the hospital, and many more such statistics.

To overcome this issue it is recommended to identify each entity (hospital) with a standard and unique number. Think of it as a mailing address: an identifier for a single location in the world that is globally unique to that location. No other organization, agency, or affiliate can use it to identify their locations, but all parties can and should use it to identify that location.

The Standard adopted globally to identify a location using a unique and unambiguous number is a GS1 Global Location Numbers (GLNs) based on the GS1 System of Standards. Utilizing a GLN can help improve data integrity. In turn, it will help reduce cost and time spent on data cleaning and making it more reliable.

Such a system enables global and unique identification of products and locations, as well as the continuous, automatic update (i.e., synchronizing) of standardized information across all stakeholders. Unique identification provide the necessary foundation for achieving the best results when using complementary applications like automatic data capture, e-commerce, electronic record management, etc.

Insurance Information Bureau of India has undertaken a project to identify each Hospital in the Health Insurance Providers Network. GS1 India would allocate a GLN to each hospital, which is a unique, 13-digit number for a specific location. Implementing GLNs simplifies the exchange of information and provides the opportunity to manage accurate and authenticated data more effectively.

The GLN, or the globally unique ID would not only identify a specific location, but also provide the link to the information pertaining to it (i.e., a database holding the GLN attributes such as postal address and GPS co-ordinates of the location, services offered at that location, key contact person at that location etc.). This is a key advantage of using a globally unique identifier because all information can be held and maintained centrally in a database or registry reducing the effort required to maintain and communicate information between multiple parties on a national or global basis.

This enables various stakeholders to simply reference a GLN in communications, as opposed to manually entering all of the necessary party/location information. Using a GLN to reference party/location information promotes efficiency, precision and accuracy in communicating and sharing location information.

Figure 1

Several countries like UK, Australia, Austria, North America etc. use GLN’s in their procurement processes to enable efficiency and transparency to deliver better patient care.

The use of GLNs provides a method of identifying locations that are:

·         Unique: with a simple structure, facilitating processing and transmission of data;

·         Multi sectoral: the non-significant characteristic of the GLN allows any location to be identified - regardless of its activity

·         International: location numbers are unique worldwide.

By identifying hospitals with GLNs enables interoperability with other GS1 Healthcare Registries in the world, building global visibility of Indian healthcare facilities, services and capabilities for international patients

However, the most immediate impact of the Unique Identification would be on the quality of Analytics. Only when hospitals are properly identified, logged and data generated on health aspects from them are reliable, can any meaningful analysis be carried out. A list of unique hospitals will be beneficial to Hospitals, Insurers, Govt. Agencies and also the Public.

·         Claims payment can be accelerated

·         Fast, reliable and relevant Analytics

·         Geography based trends, patterns of disease occurrence, cost patterns, etc.

·         Footprint is visible

·         Will aid in the Fraud Analytics efforts of IRDAI

Ministry of Health and Family Welfare is working on standardizing treatment procedures and costing templates. Efforts are being made by IRDAI-FICCI to categorize hospitals. Unique Hospitals would complement all of these projects as well.

A simple illustration may be seen in Table 2 where the outlier analysis throws out more meaningful results when the hospital is correctly identified.

Table 2

	Cost of treatment for Disease type Cholera
	Database A	Database B	Database C	Database D	Database E
	ABC Hospital	The ABC Hospital & Emergency Services	ABC Hospitals Pvt. Ltd	ABC Hospital Group	ABC Group of Hospitals
Claim Paid 1	16,016	2,093	33,115	24,299	39,113
Claim Paid 2	16,577	27,929	22,919	19,366	26,343
Claim Paid 3	12,122	23,767	30,916	29,279	26,000
Claim Paid 4	16,134	25,958	31,108	21,147	15,500
Claim Paid 5	10,280	15,981	1,99,400	26,828	25,000
Average claim paid per hospital	14,226	19,146	63,492	24,184	26,391
Overall Average claim paid	29,488
Highlight Outliers where Claim paid or amount claimed is above/below +/- 50% of the average for the hospital
	Database A	Database B	Database C	Database D	Database E
	ABC Hospital	The ABC Hospital & Emergency Services	ABC Hospitals Pvt. Ltd	ABC Hospital Group	ABC Group of Hospitals
Claim Paid 1	-	Outlier	Outlier	-	-
Claim Paid 2	-	-	Outlier	-	-
Claim Paid 3	-	-	Outlier	-	-
Claim Paid 4	-	-	Outlier	-	-
Claim Paid 5	-	-	Outlier	-	-
If the Hospital is identified as the same hospital in all databases, the average claim paid will be Rs 29,488/- across all 25 claims.
	Database A	Database B	Database C	Database D	Database E
	ABC Hospital	ABC Hospital	ABC Hospital	ABC Hospital	ABC Hospital
Claim Paid 1	-	Outlier	-	-	-
Claim Paid 2	-	-	-	-	-
Claim Paid 3	Outlier	-	-	-	-
Claim Paid 4	-	-	-	-	-
Claim Paid 5	Outlier		Outlier	-	-

Health Insurance vs Macro and Socio-Economic Indicators

This article was first published in the IIB Bulletin, Vol 1, Issue 4: Co-Author: Vishnu Vardhan, Syed Md. Ismail

https://iib.gov.in/IIB/Articles/IIB%20Bulletin%20Q4%202014-15.pdf

Inference

It may be inferred from the graph and the GLM analysis that higher the Literacy rate and the GDP per Capita, higher will be the Health Insurance Premiums in the State. In a research done at the Indian Institute of Management, Ahmedabad (Bhat & Jain, 2006) it was found that the purchase of health insurance is related to the awareness and knowledge about insurance and also the income of the household. Almost a decade later, our findings suggest the same.

Reference

Bhat, R. & Jain, N., “Factoring affecting the demand for insurance in a micro health insurance scheme”, Indian Institute of Management, Ahmedabad, Working Paper No. 2006-07-02, 29p

Underwriting in the Health Insurance Business

This interview was first published in the IIB Bulletin, Vol 1, Issue 4
https://iib.gov.in/IIB/Articles/IIB%20Bulletin%20Q4%202014-15.pdf

Dhanasar (Danny) Ramjit is the Chief Executive Officer of MediGuide America, which provides remote Medical Second Opinions to its Members and their local treating physicians. Danny is a seasoned Insurance Executive and Actuary with experience in diversified areas- Life, Accident & Health and in companies like AXIS Global Accident & Health, American Life Insurance Company, New York Life Insurance Company, Manulife Financial and Cigna, in various capacities.

With a bachelor’s degree in Physics and Mathematics, Danny believes that everyone should study Physics as it is principles based and makes logic and reasoning a habit. Originally from Guyana, Danny moved to the United States of America in 1980 and studied Actuarial Science. He is a Fellow of the Society of Actuaries (FSA) and a Member of the American Academy of Actuaries (MAAA). In addition, Danny became a Fellow of the Institute of Actuaries of India (FIAI) in 1999 and he is also a Fellow of the Actuarial Institute of Taiwan. In a career spanning more than 25 years, Danny has worked in the USA, Latin America, India, China, Japan, South Korea, South East Asia, the UK, Central and Eastern Europe, Western Europe and the Middle East.

In a conversation with Dr. Nupur Pavan Bang of the Insurance Information Bureau of India, Danny talks about Underwriting in the Health Insurance Business.

What are the factors that an Underwriter should look at before deciding to write a particular risk?

Is there a need to cover the particular risk and is it estimable? Can the risk have a catastrophic financial impact on people? To be insurable, either the “timing” or “Severity” must be beyond the control of the Insured.

In the Health Insurance space, do you see any major gaps in terms of the products being offered?

In India, Health Insurance plans generally only cover “In-Hospital” treatment. At this point, the person is already sick. With healthcare costs spiraling out of control globally and medicine extending lives, there is a very great need for “preventative coverage”. Keep people healthy rather than trying to cure them when they are sick. Outpatient care is therefore as huge gap in the health coverages available in India.

What type of statistics would help a Health Insurance Underwriter?

One must understand the cost drivers. Important statistics include Medical Trend (utilization and Medical cost Inflation), geographical variations (Urban vs. Rural) and other good data to support the rate table structure (Age Groupings, Sums Insured, Gender variations). In the end, the Pure Premium for any given “Age, Sum Insured, Gender” cell must be supported by credible estimates of “average claims costs” for each such cell.

It is important that the data must be for the “benefits covered” by the policy. If a new benefit is being added or an existing benefit is being enhanced, then the necessary research must be done to find credible estimates of the additional costs. For provider networks, it must be made sure that the right cost differentials are taken into account in setting cost sharing parameters.

There is increasing concern regarding the Individual Health Premiums being more expensive than the Group Health Premiums. What do you think are the reasons for it?

Group Policies bring together large numbers participants under a single policy. Participants in the group subsidize each other and if the group is large enough and homogenous enough by occupation and Industry, then it can be treated as a “self-contained or 100% Credible” group for rating purposes. Individual anti-selection is removed if participation is 100%.

For Individual health plans, it is almost impossible to screen out “anti-selection” and so the “average claims cost per Individual” can be higher than that for a group. So, assuming that all health policies are priced to produce combined ratios not to exceed 100%, an Individual Plan can be expected to cost more than that for a large group.

On the expense side, “bulking of administrative” costs in Group policies and differences in “Commissions and other acquisition expenses” can result in lower expenses per participant in a group vis-à-vis an Individual plan.

As per the IRDAI Annual Report 2013-14, the Net Incurred Claims Ratio for individuals is 83% and group is 110%. If expenses are accounted for, both the categories would not be profitable. Why do you think is this happening?

I am glad you chose to look at “loss ratios” here. The only answer is that competition is driving group pricing below the so-called appropriate “Burning costs”. If loss ratios are at 110%, think of how high the combined ratios must be!

Underwriting in the Group Health Business is considered a black box of sorts. How can this scenario be changed? How is risk assessment done in other countries for Group Health Business?

This should not be. In an efficient marketplace, groups would be rated using a combination of that group’s own experience history and the “universe of all similar groups”. A credibility weighted actuarial approach is used which blends each group’s own experience and that of its “universe”.

The least result that can be expected from such an approach would be combined ratios of less than 100% for the group industry as a whole, while still maintaining some balance between “pure actuarial equity” and “100% social equity”.

Of course, one can charge all groups the same rate per participant (100% social) and this rate can be calculated in such a way that a combined ratio of less than 100% is arrived at.

If you were to import a key learning from your experience globally in Health Insurance, to India, what would that be?

Products should be priced and underwritten to pricing parameters using the sound actuarial principles espoused by the Institute of Actuaries of India and Global Actuarial and Insurance standards. This requires good data capturing and analysis by companies. The IIB can serve a very good role here by publishing Industry averages in broad terms (so as not to violate confidentiality of contributing companies) as benchmarks.  IIB should also publish Medical trend even if it’s a simplistic “Crude analysis” of the historical data.