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
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
|
-
|
-
|
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