Explain the challenge of matching records among large datasets, with and without common unique iden9fiers Explore some of the available techniques to

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3 Explain the challenge of matching records among large datasets, with and without common unique iden9fiers Explore some of the available techniques to help link records Provide some examples where we use such these techniques Focus of the talk will be on prac9cal things you can implement in your projects

4 Many circumstances where informa9on needs to be linked between different systems May or may not be a common linking iden9fier that can be used to match records Challenges in medical research related to restric9ons on the use of protected health informa9on (PHI) Lack of a na9onal pa9ent iden9fier that would facilitate studies of popula9on health

5 We have data in Database A We have data in Database B How do we find the records in Database B that correspond to the records in Database A? How do we do this when there is not a common iden9fier between the two databases?

6 Unique iden9fier (UID) for a given pa9ent encounter OR case number for a surgical procedure Hospital account number for an admission Medical Record number for a pa9ent Enterprise Master Pa9ent Index for a system of hospitals with different informa9on systems Records in separate databases have the same UID One- to- one rela9onship between records in the datasets Lookup up record in one database with the UID Find all the corresponding records in the other database having the same UID Simple. But maybe not

7 Unique iden9fiers may not be unique Same pa9ent assigned mul9ple iden9fiers Medical Record Number Pa9ents may use another person s iden9fica9on card to gain access to medical service Emergency Room Pa9ents may be associated with another pa9ent s iden9fier by a clerk Similar names Char9ng may be done on the wrong pa9ent Linking iden9fier may be missing Manual entry of informa9on normally received by an electronic data feed AIMS Cases populated from an OR Scheduling Systems Manual pa9ent entry into an automated drug dispensing system

8 Medical records departments spend a large amount of 9me cleaning up these mistakes Once charted, removing incorrect informa9on from the electronic medical record can be challenging Whether doing research studies or managing the clinical chart, even when UIDs are present, probabilis9c methods will be needed Accuracy is very important when dealing with pa9ent care Small errors in matching typically do not affect the conclusions of research studies (when P very small) Sensi9vity analyses to explore the impact in mismatching

9 Uses descrip9ve informa9on to link records Pa9ent Name DOB SSN Address Not 100% certainty that records in two databases correspond to each other Probabili9es can be assigned for various matching criteria to weight the likelihood of matching From prior knowledge of the data Training the matching algorithms manually

10 Field Database A Match Database B Last Name Smith = Smith First Name John = John Middle Name J. missing DOB 01/03/1950 = 01/03/1950 Address 111 S Main Street 111 South Main St SSN ? MRN

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12 763,465 allegedly unique pa9ent entries in our anesthesia informa9on management system (AIMS) database (from the hospital ADT feed) Many duplicate names; some are for different individuals 107 Robert Smith s 88 James Williams 62 Michael Jones Only 1 Donna Sczerbowitcz If the person has a common name, more likely to be ambiguity Weights can be adjusted automa9cally for names based on their frequency in the database

13 Duplicate names and DOB 9 pts with the same DOB named Calvin Williams 7 pts with the same DOB named Thomas Kennedy 35,646 records with 1 duplicate name and DOB Many of these are the same pa9ent who have mul9ple MRN s assigned (but not all) Adding addi9onal informa9on to the matching criteria may allow resolu9on E.g., pa9ent s address

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15 Reduce the number of candidate matches to improve the probability of gehng a good match Don t use the en9re database for matches, but only reasonable candidates For matching AIMS records on a given date of surgery, only consider pa9ents in the hospital on that date For determining if a pa9ent is dead from the Social Security Death Master File, ignore all pa9ents returned as dead who had a lab test done aier the date of death Addi9onal informa9on about the record to be matched may suggest other strategies

16 Transform data to the same format These dates are the same January 1, Jan2013 These states are the same PA Pennsylvania These first names may or may not be the same William Bill Remove punctua9on from names John Q. Public John Q Public James Smith, III James Smith III Convert address components Street and St South and S

17 Name matching can be imprecise Misspellings Use of nicknames or preferred names may differ among databases Name changes (especially maiden vs. married name) Transposi9on of first and last names We have 723 such pa9ents in our AIMs where the names are switched and both records have the same DOB Many likely are transposi9ons of the same person

18 Soundex Most well known phone9c algorithm Originally developed in 1918 For names pronounced in English Goal is to encode homophones so that names with minor spelling varia9ons will s9ll match 4 characters First character is the first leler of the name 3 numbers corresponding to consonants with similar sounds Included as a standard feature in most commercial databases

19 Name 1 Soundex Name 2 Soundex Result Roberts R163 Robertson R163 False match Epstein E123 Eps9en E123 Match Limita9ons Only first 3 consonants are encoded Not designed to work with non- English names Variants exist (e.g., for eastern European names) Can be useful in conjunc9on with other methods

20 Metaphone 1 More accurate phone9c encoding than Soundex for English names Open source Metaphone 2 Accounts for spelling peculiari9es in languages other than English Open source Metaphone 3 More advanced version of Metaphone 2 License fee

21 Name 1 DMP- 2 Name 2 DMP- 2 Result Roberts RPRTS Robertson RPRTS False match Epstein APSTN Eps9en APSTN Match Limita9ons Any vowel star9ng the name is encoded as A 5 characters are returned, 1 more than Soundex Like Soundex, doesn t work that well for words that are not names (e.g., drugs)

22 Levenshtein Calculates the minimum number of steps needed to change one name into the other # steps = edit distance Example Johnstone to Johnsons Edit distance = 3 Delete t and e and add an s Johnstones Johnsons Smaller the edit distance, the closer the names match Good for picking up transposi9ons, dele9ons, inser9ons Not restricted to matching names

23 Jaro- Winkler Similar to Levenshtein in that it looks at transforma9ons, but much more complicated Results in a frac9on from 0 to 1, with higher numbers represen9ng closer matches 1 = two strings are iden9cal 0 = there is no similarity

24 Source Name Target Name Jaro- Winkler HOWEJessicaS HOWIEJESSICAS HOWIEJessica HOWIEMARKE HOWIESTEPHEN HOWIESTEVENJ HOWIEMARK HOWIEKevinJ Names were normalized by concatena9ng the first, last, and middle names Punctua9on was removed Implementa9on of algorithm was case insensi9ve, so no need to convert Jessica S Howe best matches Jessica S Howie An i was dropped from the entered name

25 DOB is commonly available in datasets, and usually accurate But DOB can be entered incorrectly MM/DD/YYYY vs. DD/MM/YYYY Dates entered in universal 9me can present problems DOB of 4/30/1953 is represented as :00:00 In 2007, start and end of US daylight savings 9me changed Dates entered prior to 2007 in the overlap weeks will be off by 1 hour if UTC is converted to local 9me using the current parameters Simpler to just extract the mm/dd/yyyy from the DOB

26 Use as an iden9fier is generally frowned upon Issues related to iden9ty thei Some jurisdic9ons restrict its use (e.g., CA) Individuals may be assigned more than one SSN (e.g., if the original number is being used fraudulently) Rarely, the same SSN can be assigned to more than one person However, SSN is frequently available in hospital databases Needed for mortality studies querying the Death Master File from the Social Security Administra9on This has become increasingly difficult Discon9nua9on of sites previously providing free access Removal of access to deaths within 3 years of the request Discon9nua9on of public access to death data contributed by states

27 May be useful to dis9nguish individuals with the same name E.g., Father and Son May be used to limit targets for alempted matches (e.g., using zip code) Lots of issues related to non- standard formahng People change addresses

28 PHI will not be exposed in outside databases containing medical informa9on HIPAA May or may not be available in internal data sources Need to take great care to protect this informa9on Don t put it on your notebook or USB drive If moved to your desktop, be sure to use a highly encrypted par99on TrueCrypt is a good, free program that can be used Best not to remove this informa9on from the source database in clear text De- iden9fied data repositories as research sources Pa9ent iden9fies are encrypted or otherwise de- iden9fied Can match between data sources using the encrypted values

29 No na9onal person iden9fier to facilitate record linkage for studies of popula9on health Large amount of pushback from organiza9ons concerned about privacy Administra9on of a na9onal system to provide unique iden9fiers to all ci9zens would be extremely complicated and expensive Rochester Epidemiology Project is a singular excep9on Covers medical records of all pa9ents living in Olmsted County, MN since January 1, 1966 who have given permission to use their data for research Collabora9on between Olmsted Medical Center, the Mayo Clinic, and the Rochester Family Medicine Clinic >500,00 unique individuals as of 2010 Rich source of epidemiologic and longitudinal studies of health

30 One- way hash based on common pa9ent iden9fiers will create a non- colliding (unique) iden9fier However same issues related to errors in the iden9fiers as previously discussed Such hashes cannot be reversed to recover the original informa9on if an appropriate encryp9on method is used MD5 has been broken, so don t use it hsp:// Some commercial AIMS are swll using MD5 to encrypt passwords Clear text passwords are not stored, just the hash However, if a hacker gets access to the PW hash table, many can be reversed SHA- 1 and SHA- 2 have been shown to have collisions (different messages with the same digest), so are theore9cally subject to alack SHA- 1 is not recommended, as it has been broken in limited circumstances SHA- 2 has not been broken, but compromise of SHA- 1 would likely lead to breaking SHA- 2, as the same cryptographic method is used SHA- 3 is a new hashing algorithm, expected to become a FIPS standard in 2014

31 If two organiza9ons wish to share informa9on, they can use the same method of hashing so that the resul9ng records can be matched while s9ll maintaining privacy This is the same basis used to store passwords Within an organiza9on, database of de- iden9fied informa9on can also be created using the hashed value as the unique iden9fier However, even if a highly secure cryptographic hashing method is used, re- iden9fica9on is s9ll possible using other informa9on Unusual procedure Public repor9ng of circumstances surrounding an event

32 CREATE FUNCTION [dbo].[hashiden9fier] sql_variant ) RETURNS varchar(22) AS BEGIN varchar(20) varchar(20) END = ' D89dskd7dlsjdj41%%' = as varchar(20)) RETURN + cast(@iden9fier as varchar)) as bigint))

33 Consider a pa9ent with the following iden9fiers: Name: John Q. Public DOB: 01/01/1950 MRN: Create a composite iden9fier consis9ng of the string LastName- FirstName- MI- DOB- = Public- John- Q HashIden9fier(@UID) = [ ] If the same hashing process is applied to the data from another database, the records can then be matched using the this value Hashing techniques are also useful to hide the iden9fy of care providers while preserving the ability to analyze data by provider Anesthesiologist #7 from the 1985 Slogoff and Keats study of myocardial ischemia (Anesthesiology 1985; 62:107-14)

34 Automated reconcilia9on of controlled substances dispensed from Pyxis drug carts and our AIMS and no9fica9on of providers whose records are out of balance Pyxis records cover the en9re hospitaliza9on Link based on 9ming of transac9ons and the when cases are done and the MRN in Pyxis and our AIMS Use pa9ent name to link when the MRN is missing or incorrect 1. Scope limited 1 st to pa9ents the provider took care of in the AIMS 2. Try exact match of LastName+FirstName+MiddleName 3. Try match using Jaro- Winkler and distance >0.90 and first ini9al of Last and First name agree 4. Unsuccessful links then matched using all pa9ents who had cases in the AIMS on the DOS, repea9ng steps 2 and 3

35 Matching billing records to AIMS records For research, oien need the billed CPT4, anesthesia CPT, and ICD9 codes for cases Import labor epidurals into our ACGME repor9ng system, not documented using our AIMS Coders add the unique AIMS case_id to the billing system Historical data before 2012 did not have this informa9on They make transcrip9on errors What we do: First link using the MRN and the DOS If cannot match, then use the pa9ent name and the DOS Limit matches to cases where the anesthesia alending on the billing voucher was involved in the care of the pa9ent Same process using Jaro- Winkler as described on the previous slide

36 Contribu9ons of data to MPOG and AQI Not implemented yet, but planned Will one- way hash provider iden9fiers to prevent iden9fica9on Collabora9ve research studies Where provider heterogeneity must be considered in the analysis Hash provider iden99es when sharing data outside our ins9tu9on Hash pa9ent iden99es when there is a need to iden9fy mul9ple cases on the same pa9ent re-

37 Even where unique iden9fiers between databases are supposed to exist, the process may fail Linking methods are needed for both tradi9onal and big data projects Mul9ple techniques oien need to be applied sequen9ally in descending order of accuracy One- way hashing of iden9fiers can be used to link informa9on from different databases sharing common elements of PHI without compromising the PHI, provided that secure methods are used

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