Download Detection of improper costs/requests

Detection of improper costs/requests
N2 - Pilot Analytical Projekt
Information on the results
Ing. Vladimír Šolc, DMS
STYRAX Consulting, a.s.
Detection of improper costs/requests
Content
‣ Aims and Objectives
‣ What was done
‣ How
‣ Results
Detection of improper costs/requests
Aims and Objectives of
the Pilot Analytical Project
Detection of improper costs/requests
Aims
‣ Examine the ability to use the information system
with elements of artificial intelligence for revision
activity in the VZP CR
‣ Determine the applicability of technologies and
tools with existing data sources
Detection of improper costs/requests
Objectives
‣ Analyze health care data of VZP clients from 2 regions
(approx. 1,2 mil. clients) with time period analyzed 4
years (from 2008 to 2011) and total financial value
about 73 bil. CZK
‣ Identify illegally billed health care
‣ Define outputs applicable in revision system
Detection of improper costs/requests
The focus of the pilot project
The potential impact
on quality of care
and amount
of fees
Forecasts
OLAP
Models
Analytical
checks
Basic checks
“core IS”
DATA
Abilities
of the classical methods
Detection of improper costs/requests
What was done
Detection of improper costs/requests
Relations tracked during pilot project
(basic checks)
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Detection of improper costs/requests
Relations tracked during pilot project
(analytical checks)
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3
3
Detection of improper costs/requests
Relations tracked during pilot project
Detection of improper costs/requests
Basic checks - examples
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concurrent hospitalizations
frequency
concurrent procedures
missing procedures
care after death
…
Detection of improper costs/requests
Analytical checks - examples
duration of therapy, procedures times
additionally reported procedures
complicated hospitalizations
laboratory and prescription without doctor
strange geographical distances
ordinary and extraordinary sequences of
procedures
‣ not wanted relations between providers
‣ …
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Detection of improper costs/requests
How
Detection of improper costs/requests
Segmentation
sorting into groups
of similar cases
(Cluster Method)
Associative
analysis
Scoring and
(Market basket
analysis,
Social network
Predictive models
usual x unusual
wanted x not wanted
possible consequences
(Neural networks,
Decision trees)
Classification
machine learning
…Experts…
realistic x not realistic
Feedback
reaction proposal
analysis)
Detection of improper costs/requests
Clustering - groups of providers
Detection of improper costs/requests
Clustering - groups of providers
Usual providers (91% providers)
```
Detection of improper costs/requests
Clustering - Care without “reason”
Groups
1,9,17
are
delaye
d
Detection of improper costs/requests
items in group
Clustering - Care without “reason”
Detection of improper costs/requests
Forecast of time with doctor
Detection of improper costs/requests
Forecast of time with doctor
Detection of improper costs/requests
Forecast of time with doctor
high success rate of
forecast (model)
Detection of improper costs/requests
Tools which was used
• SQL - language
• Cursor-oriented database language
• Data-quality tools
• Data-mining tools
• Reporting tools
Detection of improper costs/requests
Results
Detection of improper costs/requests
Unusual situations characteristics
Detection of improper costs/requests
Unusual situations characteristics
Detection of improper costs/requests
Unusual situations characteristics
Detection of improper costs/requests
Detected without assignment
Frequent traffic between
departments of the same hospital
(own transport service)
Deal of more GPs
(cross-capitation)
Regularity in the excessive scale
(burning warts)
Unfair cooperation of specialists with
complement
Copy-paste of healthcare data
Circumventing of tenders
(geographic checks)
Laboratories that can generate
maximum examinations
(hospitals)
The narrow range of
services and forwarding
(ORL)
... and many others
Detection of improper costs/requests
Results in numbers
‣ During pilot project was analyzed more than
300 mil. records (procedures, …)
‣ System was identified
suspicious care with value more than 1 bil. points
‣ System was detected a lot of suspicious situations
which was not in original assignment
‣ Pilot project length was approx. 3 months and
main analytic and mining part takes approx. 1 month
Detection of improper costs/requests
Discussion
Detection of improper costs/requests
Thank you for your attention
[email protected]
www.styrax.cz