Proactive Analysis of Specialized Databases

Transcription

1 Proactive Analysis of Specialized Databases Using Visual Analytics to Expose Patterns and Trends Presented by: Ronald Stanko, Chief Deputy Attorney General Robert Dintino, Senior Supervisory Special Agent A cooperative effort between Visual Analytics Inc. and the Pennsylvania Office of the Attorney General

2 What is the PDMP Program? A Prescription Drug Monitoring Program (PDMP) is operated by a state and used to collect data on Schedule II through V controlled substances from pharmacies that dispense prescriptions within the state. 39 States have PDMP 33 States are operational PDMPs are designed to support education, treatment, track trends, help with drug abuse prevention, and monitor for non-compliant behaviors of patients and practitioners.

3 What are Schedule II Drugs? Schedule II drugs have a high potential for abuse and severe dependence, but have a currently accepted medical use. Schedule II drugs include: Amphetamine (Dexedrine, Adderall, Obetrol) Fentanyl (Duragesic, Oralet, Actiq) Cocaine Methadone (Dolophine, Methadose, Amidone) Methamphetamine (Desoxyn, Crack, Speed) Methylphenidate (Concerta, Ritalin) Opium Oxycodone (OxyContin,Percocet, Endocet, Roxicodone) PCP DEA SCHEDULE II DRUG LISTING

4 Sample PDMP State Profiles* Michigan: Established: 1988 Monitors: Schedule II - V Volume: 15M annual Collected: Monthly Dispensers: 3,000 California: Established: 1939 Monitors: Schedule II, III Volume: 21M annual Collected: Monthly Dispensers: 155,000 Texas: Established: 1981 Monitors: Schedule II Volume: 3.3M annual Collected: Monthly Dispensers: 4,300 Pennsylvania : Established: 2002 Monitors: Schedule II Volume: 4.1M annual Collected: Monthly Dispensers: 3,500 *

5 Potential Abstract Use Scenario Use Visual Analytics and VisuaLinks to provide the first proactive analysis of the Schedule II PDMP data. Outcomes: Identify patterns of abuse Cross (state)border utilization Doctor shopping patterns Irregular filling patterns PDMP DATA

6 VisuaLinks Specialized Functionality Query Quickly identify unknown outliers in data Frequencies/Counts Combinations of values Ratios Expand Find indirect associations between different entities that would be missed using traditional methods. Indirect connections Non-obvious relationships Collusion among doctors/pharmacies View See data visually and spot anomalies faster Hot spots (locations/pharmacies/etc) Network diagrams Temporal & Geospatial patterns Report Produce high-quality, finished output

7 Analytical Models For PDMP Data SUBJECT PRESCRIPTION PHYSICIAN PHARMACY **

8 VisuaLinks Summary Summary of Doctors Seen: Identified an individual who was seen by 179 physicians within the time-frame of the Class II data. Summary of Prescriptions Filled: Identified an individual who was issued 367 prescriptions for Class II medications within the time-frame of the Class II data.

9 One-Many-Many This view shows the relationship between a SUBJECT (left), to PRESCRIPTION (middle), and the related PHYSICIAN (right) SUBJECT PRESCRIPTION PHYSICIAN

10 Doctor Shopping Pattern PHYSICIAN-A PHYSICIAN-B Target suspect is related to multiple doctors for the same prescription-types SUBJECT PHYSICIAN-C PHYSICIAN-D PHYSICIAN-E PHYSICIAN-F PHYSICIAN-G

11 Multiple Pharmacy Usage ECKERD GIANT PHARMACY Target suspect uses multiple pharmacies to fill his prescriptions SUBJECT ACME PHARMACY WALGREEN DRUG STORE CVS PHARMACY The structure of this pattern is virtually identical to the doctor-shopping pattern RITE-AID PHARMACY

12 Sample Temporal Pattern For The Prescription Fill Date JAN FEB Prescription filled multiple time during the same week MAR APR MAY JUN JUL AUG SEP OCT Even on Christmas Day! NOV DEC

13 Geo-spatial Patterns in PDMP Data System can expose Geo-spatial patterns where a subject is filling prescriptions at different locations to try and avoid detection. filter on drug-type filter on date/times filter on locations filter on distances

14 Visual Analytics Summary Quickly identifies the extremes in any data set. Establishing outliers Using any combination of values Easily detects high-value targets Adds-value to source data Geo-encoding addresses Calculating meta-data about locations/dates/etc Expandable to include additional data sources State welfare and benefits data Law enforcement sources DIG database access (Server to Server) Promote cross-border sharing of PDMP data Identify larger-scope networks and patterns

15 Expected Discoveries of the Schedule II Data Identification of potential drug abuse by individuals. Identification of patterns related to drug trafficking. Identification of potential over-prescribing by physicians. Ability to customize and modify data on the fly to provide more relevance of the data captured to investigators. Ability to analyze data relevant to specific regulatory regions of a state to develop targeted responses to patterns and findings within the Schedule II data. Ability to proactive work to reduce the misuse of Schedule II drugs. Proactive analysis capabilities which were not available before the use of VisuaLinks.

16 Federated Searches Using High Value Aggregated Data Sets

17 Concept Determine the value and benefits of cross checking PA AG PDMP data against Appriss MethCheck and PC HIDTA data sets

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21 Appriss MethCheck Data Pseudoephedrine sales database containing data compiled by pharmacies National scope Data can be organized by state Data contains consumer s name, driver s license or other identification, geocoded address, type of sale, geocoded location of sale

22 Retailers in the NPLEx network-march ,950 pharmacies

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24 Methodology and Process Starting Point A group of PA AG PDMP interesting persons Process Use Visual Analytics Software to quickly cross reference the interesting persons against multiple data sets

25 Pennsylvania AG PDMP Data Top 100 people by most doctors visited Top 100 people by number of prescriptions written

26 Sample Interesting Persons Amy Bina Juan Carmona Christopher Hazeleaf James Sarchiapone Julia Arrivello Louis Kopistecki

27 Appriss Cross Reference Top 100 people by most doctors visited 133 Results from PA 637 Results from DC, MD, NJ, NY, PA, VA Top 100 people by number of prescriptions 97 Results from PA 509 Results from DC, MD, NJ, NY, PA, VA

28 PC HIDTA Cross Reference Top 100 people by most doctors visited 1,013 Results from 14 data sources Top 100 people by number of prescriptions 919 Results

29 Next Steps?? Who in the result sets are the same? Entity Resolution Address Standardization Identify cross border activity and in-state hot spots Driver s License Analysis Review, Validation, and Commonality

30 Trending and Behavioral Analysis Correlation surrounding events Sporting events, holidays, concerts, etc Correlation around hospital admissions Overdoses, preferred drug trends

31 Summary There exists a great power potential in using federated searches against currently available data sources Further investigation against interesting persons would likely be beneficial for all involved agencies

32 Questions?