1. Accurate, Customizable Matching: The Heart of the NYC Master Child Index
Andrew Borthwick, Ph.D.
Martin Buechi, Ph.D.
ChoiceMaker Technologies
Phone:
Vikki Papadouka, Ph.D.
Paul Schaeffer, M.P.A.
Deborah Walker, Ph.D.
Alexandra Ternier, M.P.H.
NYC Department of Health
Phone:

2. Goals of Talk Background
Overview of NYC DOH Master Child Index project
History of ChoiceMaker’s use at NYC DOH
ChoiceMaker 2.0
ChoiceMaker 2.0 improvements over 1.0
ChoiceMaker’s new “ClueMaker” programming language

3. Master Child Index (MCI)
Integrates the New York Citywide Immunization Registry (CIR) with other child health databases starting with LeadQuest (LQ) to produce a comprehensive Child Health Registry
Benefits:
Improved surveillance
Information sharing with providers
Identification of children in need of immunizations and/or lead tests
Information sharing among different DOH programs (e.g. addresses, providers, etc.)
Improved data quality

4. CIR and LQ
CIR contains 2 million records, and >13 million immunization events
1250 pediatric provider sites report
~170,000 immunization events reported monthly
also loads Vital records: ~125,000 children/year
LeadQuest contains 1.9 million records and 4 million blood tests
60 laboratories report
~39,000 test results reported monthly

5. MCI system
Records in LQ and CIR will be linked with each other through the MCI
New records will be checked against MCI first
ChoiceMaker on front end
Duplicates caught before they enter LQ
CIR
MCI

6. Accurate Matching Is Key to MCI
Matching system correlates records in the 2 databases
data from systems can be linked
Matching not only prevents duplicates from entering the system but also reduces duplication problem in existing databases
creates more complete, less fragmented records and hence increases usefulness of data
Matching allows for approximate searches
more successful identification of records by doctors and DOH staff who are doing lookups
Accurate matching is the heart of the MCI

7. Record Matching Challenges
No unique IDs: SSN not allowed
Incorrectly submitted data: Borthwick vs. Borthwich
Data change over time: names change, addresses change
Variations in data received: Andrew vs. Andy
Incomplete data: fields aren’t filled in or are filled in with generic values
Data in wrong fields: first and last names reversed
Large volume of data: need for automation

8. The MCI’s Record Matching Solution
ChoiceMaker™ 2.0 (formerly known as “MEDD”)
Identifies and merges fragmented database records
Searches databases for approximate matches, decides if same child, and merges if appropriate
Links related records across multiple databases (MCI-CIR-LQ)

9. ChoiceMaker 1.0’s Use by CIR
Used successfully in Batch mode by the CIR since 1999
Merged over 700,000 records
Accuracy measured at 99.7% in tests supervised by NYC DOH

10. ChoiceMaker 1.0 vs. 2.0 Both ChoiceMaker 1.0 and 2.0 are:
wholistic - take into account simultaneously many aspects of the record
modular: clues can be added and taken out depending on the data
based on an Artificial Intelligence technique called “maximum entropy modeling” that lets the system learn from examples (hand scored by people)

11. ChoiceMaker 1.0 vs. 2.0
Improvements in ChoiceMaker 2 vs. ChoiceMaker 1
Handles “stacked” data (more than one value for a field)
Is written in Java (vs. C++), which makes it more portable
Includes “Blocking” component
Includes a new programming language called “ClueMaker” for writing clues
Can be called online from MCI, CIR, and LQ applications, not just in batch mode

12. Technology: Production Matching
Search Record
Blocking
Many Possible Matches
Maximum Entropy Matching
Match Probabilities of Likely Matches
Non-Match
Match Probability
Match
Low
High
Intermediate
Human Review

13. ClueMaker Clues Encode business rules for matching
Take a pair of records and suggest that they match or differ
Written in a Java-based language, ClueMaker™
Importance, or weight, of each clue determined by maximum entropy training
Clues can also be used as filters (rules)
Clue weights are combined to get probability
ClueMaker is new to ChoiceMaker with version 2.0

14. Generic clues Do first names match?
clue mFirstNames {
match same(r.firstName); }
Do first names match approximately using “phonetic matches” such as Soundex?
clue mSoundexFirstNames {
match same(Soundex.soundex(r.firstName));
Do uncommon first names match?
clue mFrequencyFirstName {
match foreach(int freq : {0, 1, 2, 3};
same(r.firstName) &&
Maps.lookupInt("firstFreq", q.firstName) == freq);

15. Healthcare Clue
Do we have indication of a twin—matching last names and birthdates, but different first names and consecutive medical record numbers?
clue dTwin {
differ
same(r.last_name) && same(r.birthday) && different(r.first_name) && valid(q.medical_rcrd) && valid(m.medical_rcrd) && Math.abs(q.medical_rcrd – m.medical_rcrd) == 1; }

16. Clues for Peculiarities of Data
Do month and year of birth match and the record comes from facility XYZ?
Due to some error in their system, they always report the day of birth as '1'.
clue mDobXyz {
match
same(DateUtils.MonthAndYear(r.dob)) && ("XYZ" == q.facility || "XYZ" == m.facility); }

17. ClueMaker: Built for Stacked Data
Stacked data: multiple values for single field
Examples:
Stacked first names: Name, nickname, misspelling, middle name that grandma prefers and reports as first name, etc.
Stacked addresses: address history; some doctors may continue to report old address
Stacking of data improves matching accuracy
ChoiceMaker and ClueMaker built for stacking
// There exists a valid matching first name
clue mFirstNames {
match same(r.names.firstName); }

18. Benefits of ChoiceMaker for MCI
Integrates easily into DOH computing environment
Can capture peculiarities of data
Fast & inexpensive to modify if new peculiarity comes (e.g., new data provider with quirks)
Can easily create many clues
Designed to handle stacked data well
Bottom line: High accuracy