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(MySQL) Created our own dataset, and merged those 7 different datasets. Applied different topics like stored procedure, multiple joins, and use of the indexes for better results.

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napsterninad20/Health-Insurance-Claim-System

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Health Insurance Claim System

Overview

Planned to design a real world health insurance claims database which involves information pertaining to the members, providers, claims, address, claim status, claim payment and coverage, and generated our own dataset which had 7 tables (described in the further sections). We formulated a relational database using MySQL Workbench and populated all 7 tables with approximately 100 rows each table.

Project Goal

Main goal is to extract reports for the healthcare insurance officials which can enable them to analyse and present insights to the mangeorial officials, and to understand the entire Database Design process from scratch resulting into a development of a real world database and data visualization system for the health insurance companies.

Logical Database Design (Entity Relationship Diagram)

Data Visualizations (Exploratory Data Analysis)

Queries to get the results

1) Reporting top 20 members along with provider details, billed amount and approved amount associated and having approved_amount greater than $2200 - Virtual Table/View

Use health_insurance_claims_database;

create view top_20_claim_approved_customers as (select m.member_first_name, m.member_last_name, s.claim_status, p.provider_first_name, p.provider_last_name, p.network, p.practice_name, cp.billed_amount, cp.approved_amount, cp.net_payment from ((((member m inner join claim c on m.claim_id = c.claim_id) inner join status s on s.status_id = c.status_id) inner join provider p on p.claim_id = m.claim_id) inner join claim_payment cp on cp.claim_id = c.claim_id) where cp.approved_amount > 2200 order by cp.approved_amount desc limit 20);

select * from top_20_claim_approved_customers;

2) Reporting distinct members (member demographic information) who have their claims in progress - Stored Procedure

use health_insurance_claims_database;

DELIMITER //

CREATE PROCEDURE get_member_claim_status_data( IN claim_status_in VARCHAR(50) )

BEGIN select m.member_first_name, m.member_last_name, m.member_dob, m.gender, m.claim_id, s.claim_status, s.type from ((member m inner join claim c on m.claim_id = c.claim_id) inner join status s on s.status_id = c.status_id) where s.claim_status = claim_status_in order by m.member_first_name;

END //

DELIMITER ;

CALL get_member_claim_status_data('paid');

3) Finding member subscribed to a coverage plan having address ID between 2000 to 5000 - Indexing

use health_insurance_claims_database;

select distinct m.member_first_name, m.member_last_name, cp.coverage_name, m.address_id from (coverage cp inner join member m on m.member_id = cp.member_id) where m.address_id between 2000 and 5000;

create index member_index on member(address_id);

select distinct m.member_first_name, m.member_last_name, cp.coverage_name, m.address_id from (coverage cp inner join member m on m.member_id = cp.member_id) where m.address_id between 2000 and 5000;

drop index member_index on member;

Additional topics covered

Performance of the queries is improved using below operations,

  • Creating Views for Queries with Multiple Join Operations
  • Stored procedures for adding a centralized business logic
  • Use of Indexes for optimizing table traversals

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(MySQL) Created our own dataset, and merged those 7 different datasets. Applied different topics like stored procedure, multiple joins, and use of the indexes for better results.

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