Patient Health Record Linkage Methods
Patient Health Record
Linkage Methods
Toan C. Ong, PhD
Agenda
▪
Definition and objectives of record linkage
▪
Linkage process
▪
Linkage variables
▪
Linkage methods
Definition
▪
Record linkage: a process to determine if two or
more records belong to the same individual
Objectives
Data de-duplication versus data enrichment using record-linkage
CURL Honest Broker
Site B
Site A
(U of Colorado)
CU Record Linkage (CURL) Data Flow
Site A
Source data
Color code:
Hashed PHI
Clear-text PHI
CURL Key Master
Linkage
Configurations
Hashed
data
Clean
and
profile
data
Load,
block and
profile
hashed
data
Review
Hashed
Data
Generate
Key
Link
data
Linked data
Data transfer
Site B
Source data
Hashed
data
Clean
and
profile
data
Review
Hashed
Data
Hash key
NWI
NWI = Network identifier
Hash
Data
Hash
Data
www.curl.center
Linkage variables
Linkability measures
▪
Missing data ratio
▪
Unique data ratio
▪
Group size
▪
Information amount
▫
Shannon entropy
▫
Maximum theoretical entropy
▫
Percent of Maximum theoretical entropy
Linkability measures
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Data normalization
▪
First and last names: non-alphabetical
characters, including spaces, were removed.
▪
Middle name: shortened to middle initial.
▪
Gender: all genders were converted to one of
three possible values: F, M, or NULL.
▪
Date of birth: converted to YYYYMMDD format.
▪
SSN4: Extract last 4 digits from full SSN.
Data hashing
▪
Data encryption/hashing
▫
Advanced encryption standard (AES) methods
▫
One-way hashing methods
▫
Bloom filters, locality-sensitivity hashing
Dice coefficient =
Deterministic linkage methods
Probabilistic linkage methods
w
i
: Weight of linkage variable i – Value Range: 0-1
d : Edit distance – Value Range: 0-100
▪
Expectation Maximization (EM) for optimal weights
▪
Edit distance
Linkage classification
▪
Two classes
•
match_score ≥ threshold
Matches
•
match_score < threshold
Non-Matches
▪
Two classes
•
match_score ≥ threshold
Matches
•
match_score < threshold
Non-Matches
Validation method
▪
Sample: Potential linkages
▪
Adjudicators:
▫
Two reviewers
▫
One tie-breaker
▪
Validation result
▫
True match: >=2 yeses
▫
Incorrect match: >=2 nos OR 1 no + 1 maybe
▫
Undecided: >= 2 maybes
Linkage methods
Hybrid linkage methods
Summary
▪
CURL = CU Record Linkage
▪
Linkage data
▫
Linkability measures
▫
Data normalization
▫
Data hashing
▪
Linkage method
▫
Deterministic
▫
Probabilistic
▫
Hybrid
Funding
▪
D2V
▪
CDIFund
▪
PCORI
Acknowledgement
▪
Michael Kahn
▪
Lisa Schilling
▪
Chan Voong
▪
Bethany Kwan
▪
Jenna Reno
▪
Chris Uhrich
▪
Gali Baler
▪
Tessa Crume
▪
Lindsey Duca
▪
Jessica Toth
▪
Hossein Esteri
▪
Ibrahim Lazrig
▪
Andrew Hill
▪
Will Carter
▪
Rachel Zucker
▪
Kimberly Muller
▪
Doreen Molk
▪
James Roberts
Thank You!
Explore the methods and processes involved in linking patient health records to ensure data accuracy and integrity. Learn about objectives, data de-duplication, encryption, data normalization, and linkage variables. Discover CU Record Linkage (CURL) data flow and key quality measures. Dive into data hashing techniques for security and confidentiality.
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Presentation Transcript
Patient Health Record Linkage Methods Toan C. Ong, PhD
Agenda Definition and objectives of record linkage Linkage process Linkage variables Linkage methods
Definition Record linkage: a process to determine if two or more records belong to the same individual
Objectives Data de-duplication versus data enrichment using record-linkage
CU Record Linkage (CURL) Data Flow Hashed PHI Clear-text PHI Color code: (U of Colorado) Linkage Configurations CURL Key Master Generate Key Hash key Site A Clean and profile data Review Hashed Data Hash Data Hashed data Site A Source data Site B Clean and profile data Data transfer Review Hashed Data Hash Data Hashed data Site B Source data NWI CURL Honest Broker Load, block and profile hashed data Link data Linked data NWI = Network identifier
Linkability measures Missing data ratio Unique data ratio Group size Information amount Shannon entropy Maximum theoretical entropy Percent of Maximum theoretical entropy
Linkability measures Results of key quality and linkability measures using the 300K corrupted synthetic data set
Data normalization First and last names: non-alphabetical characters, including spaces, were removed. Middle name: shortened to middle initial. Gender: all genders were converted to one of three possible values: F, M, or NULL. Date of birth: converted to YYYYMMDD format. SSN4: Extract last 4 digits from full SSN.
Data hashing Data encryption/hashing Advanced encryption standard (AES) methods One-way hashing methods Bloom filters, locality-sensitivity hashing Dice coefficient =
Deterministic linkage methods Exact match Michael = Michael Micheal Michael Fuzzy match Micheal Michael M240 = M240 Key match - Key = 3 FN initial + birth year Mic2010 = Mic2010
Probabilistic linkage methods wi: Weight of linkage variable i Value Range: 0-1 d : Edit distance Value Range: 0-100 Expectation Maximization (EM) for optimal weights Edit distance
Linkage classification Two classes match_score threshold Matches match_score < threshold Non-Matches Two classes match_score threshold Matches match_score < threshold Non-Matches
Validation method Sample: Potential linkages Adjudicators: Two reviewers One tie-breaker Validation result True match: >=2 yeses Incorrect match: >=2 nos OR 1 no + 1 maybe Undecided: >= 2 maybes
Summary CURL = CU Record Linkage Linkage data Linkability measures Data normalization Data hashing Linkage method Deterministic Probabilistic Hybrid
Funding D2V CDIFund PCORI
Acknowledgement Michael Kahn Lisa Schilling Chan Voong Bethany Kwan Jenna Reno Chris Uhrich Gali Baler Tessa Crume Lindsey Duca Jessica Toth Hossein Esteri Ibrahim Lazrig Andrew Hill Will Carter Rachel Zucker Kimberly Muller Doreen Molk James Roberts
