OCC MPI Health & Medical MPI Duplication Analysis

This activity has students review recent registrations at the General Hospital for potential duplicate records and then categorize and analyze the causes for the duplicates. Students are then asked to make recommendations for decreasing duplicate record formation

Knowledge Activity: MPI Duplication Analysis
(Informatics)
Prerequisites
1. It is highly recommended the following activities are completed prior to this activity:
•
Query – Basic Orientation
•
Query – Advanced Orientation
Student instructions
1. If you have questions about this activity, please contact your instructor for assistance.
2. You will use the EHR Go queries tool to complete this activity. Your instructor has
provided you with a link to the MPI Duplication Analysis activity. Click on 2: Launch EHR
to begin this activity.
3. You’ve been granted access to certain data in the EHR to complete this activity. The
approved data has already been linked to the queries tool in the EHR. Refer to any
suggested resources to complete this activity.
4. Document your answers directly on this activity document as you complete the activity.
When you are finished, you will save this activity document to your device and upload
this activity document with your answers to your Learning Management System (LMS).
The activity
Background information
The General Hospital HIIM department head has asked you to review recent registrations for
potential duplicate records and analyze the cause for the duplicates. The Hospital would like to
initiate efforts to prevent duplicates from being created. The first step is to identify the causes
of the duplicates in the master patient index (MPI).
A master patient index is an index of an organization’s known patients. These patients’ visits are
linked together by a medical record number, or another single identifier. The medical record
number is the most common link. Management of an MPI involves identifying and categorizing
information from the database index. (AHIMA, (Updated September 2010)). Review the
Fundamentals for Building a Master Patient Index resource, found under 1: Overview &
Resources along with this activity document, for more information on MPIs.
The General Hospital’s Master Patient Index (MPI) is interfaced with the EHR. Both share a
common SQL database. The patient fields tracked in the MPI are: first name, middle name, last
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name, date of birth (DOB), gender, medical record number (MRN), and social security number
(SSN). Each new registration record is compared to existing MPI records with a matching
algorithm. MPI records that exceed the duplicate threshold are flagged. The Hospital’s duplicate
resolution software labels potential duplicates with a match code and the duplicate records are
periodically reviewed and resolved manually.
Questions
Categorize causes of duplicate records
To prepare for the General Hospital duplicate MPI audit, read the Challenges in MPI Duplicate
Resolution resource, found under 1: Overview & Resources along with this activity document.
Then answer the following questions.
For questions 1-6 below, identify the frequency of the duplicate causes found on page 5 of the
MPI study. Round to the nearest percent.
1.
2.
3.
4.
5.
6.
7.
Middle name discrepancy
SSN discrepancy
First name discrepancy
Last name discrepancy
DOB discrepancies
Gender discrepancies
How can the percentages of discrepancies total over 100%?
Identify the common issues causing duplicates in the MPI study.
8. Main cause of middle name (MN) duplicates was:
9. Main cause of SSN duplicates was:
10. The most frequent DOB discrepancy was:
Duplicate Analysis
Analyze the example duplicate record entries listed below and identify the cause(s) of the
duplicate.
Debora Suzanne Sullivan, 11/09/1998, Female, MRN8176512, SSN 765-90-8979
Debra Suzanne Sullivan, 9/11/1998, Female, MRN8175612, SSN XXX-XX-XXXX
For questions 11-17 below, indicate whether each of the following data elements are the cause
of the discrepancy, and if yes, identify the source of the discrepancy.
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11. First name
12. Middle name
13. Last name
14. DOB
15. Gender
16. MRN
17. SSN
Next, run the MPI query in EHR Go by following the steps below.
Part 1: Generate the Dataset Using the Queries Tool
Access the EHR queries tool and corresponding dataset under 2: Launch EHR. Click the New
Session button to launch the EHR.
On the Queries tab, select New to initiate a new query.
•
•
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First, Name the query ‘MPI Duplication Analysis’
Then enter the following query rule:
Query rule(s): ‘Duplicate Threshold’ equal ‘Y’
Select Add rule to add another rule and select OR as the logic
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Then enter the following second rule:
Query rule(s): ‘Duplicate Threshold’ equal ‘N’
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Select Query to generate the results. There should be 526 total records. If not, review
the screenshot above to ensure your query is setup correctly. If necessary, adjust and
re-run the query.
Select Export Results to Excel to export your dataset for further analysis.
•
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Part 2: Analyze the Data in Microsoft Excel®
Open the exported file in Microsoft Excel®.
In order to analyze the possible duplicates, use the Sort function.
•
Select Data then Sort. Enter ‘Duplicate Threshold’ in the Sort by field. Then select ‘Z to A’
for the Order field. Then click OK.
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Those with “Y” (yes) as the Duplicate Threshold will now be listed first in the spreadsheet.
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Of those that are possible duplicates (Y’s), group the possible duplicates together for analysis. To
do so, build on the previous sort by incorporating the ‘Possible match code’ data.
•
Go to Data then Sort. Your previous sort parameters will still be listed. Choose Add level
and select ‘Possible match code’ as the column and ‘A to Z’ as the Order then OK.
The resulting spreadsheet will have the possible duplicate entries grouped together for further
review. For example, the possible match code of “C” results are highlighted – a potential
duplicate pair requiring further analysis.
Review all possible duplicate matches in the resulting spreadsheet and answer the following
questions. Round to the nearest percent when asked for a percentage.
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18. How many of the results are possible duplicates?
19. What percentage of the registrations are potential duplicates?
20. How many of the potential duplicate pairs have first name discrepancies? What is the
percentage of first name discrepancies in the duplicate pairs?
21. What is the most common cause of first name discrepancies?
22. How many of the potential duplicate pairs have middle name discrepancies? What is the
percentage of middle name discrepancies in the duplicate pairs?
23. What is the most common cause of middle name discrepancies?
24. How many of the potential duplicates have last name discrepancies? What is the
percentage of last name discrepancies?
25. What is the most common cause of last name discrepancies?
26. How many of the potential duplicates have gender discrepancies?
27. What are the causes of the gender discrepancies?
28. How many of the potential duplicates have DOB discrepancies?
29. What is the most common cause of DOB discrepancies?
30. How many of the potential duplicates have mismatches of MRN?
31. What is the most common cause of mismatched MRN?
32. How many of the potential duplicates have mismatches of SSN?
33. What is the most common cause of mismatched SSN?
Blank and default fields are a common cause of duplicate record creation. Analyze the resulting
possible duplicates for blank and default fields. Use the sort function again to determine which
employees were responsible for entering the data containing blank or default entries.
•
•
•
Go to Data and Sort
Delete the existing level based on Possible match code (keep the Duplicate Threshold
criteria)
Add level and choose ‘Registered By’ as the column then ‘A to Z’ as the Order. Then click
OK.
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Of the employees listed below, how many blank or default (MRXXXXXX or XXX-XX-XXXX) entries
were each responsible for creating? If there were multiple blank or default entries for one
registration, only count that registration once.
34. Dvaughn Lane, LPN
35. Cora Zimmerman, MA
36. Kim Pham, LPN
37. Emily Garcia, MA
38. Clara Rojo, RHIT
39. Wanda Murray, RHIA
40. Tina Lin, HUC
41. Kevin Fanning, MA
42. Who created the most duplicate records by leaving default or blank values in the patient
registration data?
Recommendations for decreasing duplicate record formation
After analyzing the Hospital’s duplicate records, you are asked to provide recommendations for
improvement. Please review the MPI Duplicate Resolution resource included under 1: Overview
& Resources along with this activity document to assist in answering the questions below.
43. What else could the General Hospital implement to prevent duplicate records from
getting created?
44. What are potential negative impacts of duplicate records?
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45. What can HIIM professionals do to prevent and manage duplicate records?
46. What is an overlaid record?
47. What is the best way to prevent overlaid records?
48. Using the Challenges in MPI Duplicate Resolution resource found under 1: Overview &
Resources along with this activity document, list four data-related reasons for duplicate
records.
49. What are three strategies suggested by the Challenges in MPI Duplicate Resolution
resource to prevent duplicate records from being formed?
50. List four recommendations to provide to the General Hospital to help prevent duplicate
records from being created.
Submit your work
Document your answers directly on this activity document as you complete the activity. When
you are finished, save this activity document to your device and upload this activity document
with your answers to your Learning Management System (LMS). If you have any questions
about submitting your work to your LMS, please contact your instructor.
Learning objectives
1.
2.
3.
4.
5.
6.
7.
Identify the types of duplicate records found in an MPI.
Analyze common causes for duplicate records.
Identify strategies to address and prevent the creation of duplicate records in an MPI.
Analyze strategies for the management of information. (4)
Recommend compliance of health record content across the health system. (5)
Evaluate data dictionaries and data sets for compliance with governance standards. (5)
Facilitate training needs for a healthcare organization. (4)
References
AHIMA. Fundamentals for Building a Master Patient Index/Enterprise Master Patient Index.
Journal of AHIMA (Updated September 2010).
Haenke Just, B., Marc, D., Munns, M., & Sandefer, R. (2016). Why Patient Matching Is a
Challenge: Research on Master Patient Index (MPI) Data Discrepancies in Key Identifying
Fields. Washington D.C.: AHIMA.
Harris, S., & Houser, S. H. (2018). Double Trouble—Using Health Informatics to Tackle Duplicate
Medical Record Issues. Journal of AHIMA 89, no. 8, 20-23.
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Why Patient Matching Is a Challenge: Research on Master Patient Index (MPI) Data Discrepancies in Key Identifying Fields
Why Patient Matching Is a Challenge:
Research on Master Patient Index (MPI)
Data Discrepancies in Key Identifying Fields
by Beth Haenke Just, MBA, RHIA, FAHIMA; David Marc, MBS, CHDA; Megan Munns, RHIA;
and Ryan Sandefer, MA, CPHIT
Abstract
Patient identification matching problems are a major contributor to data integrity issues within
electronic health records. These issues impede the improvement of healthcare quality through health
information exchange and care coordination, and contribute to deaths resulting from medical errors.
Despite best practices in the area of patient access and medical record management to avoid duplicating
patient records, duplicate records continue to be a significant problem in healthcare.
This study examined the underlying causes of duplicate records using a multisite data set of 398,939
patient records with confirmed duplicates and analyzed multiple reasons for data discrepancies between
those record matches. The field that had the greatest proportion of mismatches (nondefault values) was
the middle name, accounting for 58.30 percent of mismatches. The Social Security number was the
second most frequent mismatch, occurring in 53.54 percent of the duplicate pairs. The majority of the
mismatches in the name fields were the result of misspellings (53.14 percent in first name and 33.62
percent in last name) or swapped last name/first name, first name/middle name, or last name/middle name
pairs.
The use of more sophisticated technologies is critical to improving patient matching. However, no
amount of advanced technology or increased data capture will completely eliminate human errors. Thus,
the establishment of policies and procedures (such as standard naming conventions or search routines) for
front-end and back-end staff to follow is foundational for the overall data integrity process. Training staff
on standard policies and procedures will result in fewer duplicates created on the front end and more
accurate duplicate record matching and merging on the back end. Furthermore, monitoring, analyzing
trends, and identifying errors that occur are proactive ways to identify data integrity issues.
Keywords: patient matching, patient identity, data integrity, master patient index (MPI), enterprise
master patient index (EMPI), health information exchange (HIE), data discrepancy, data quality
Introduction
Deaths due to medical errors are the third leading cause of death in the United States, killing about
400,000 patients every year and more than 1,000 people each day, according to one estimate.1 A different
study estimated that 195,000 deaths occur each year because of medical errors, with 10 of 17 being the
result of identity errors or “wrong patient errors.”2 These substantial problems are exacerbated as the
healthcare system continues to consolidate and grow. The number of patient records in healthcare
providers’ databases is growing rapidly. Industry experts estimate that the average healthcare
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organization’s electronic health record (EHR) system has an 8 percent to 12 percent rate of duplicate
records. A RAND Corporation report on duplicate records indicates that the average duplicate record rate
for US healthcare organizations is 8 percent and is higher in large systems (15 to 16 percent).3 If we
translate these percentages into real numbers, a database with approximately 1 million records, or unique
patients, may include up to 120,000 duplicate records. In terms of price, these duplicate records cost
healthcare organizations about $96 per duplicate record, according to a research study conducted at
Children’s Medical Center Dallas.4 The study also showed that in 4 percent of cases involving confirmed
duplicate records, clinical care was negatively affected. Delays in initiating treatment in the emergency
room were a common issue. Further, care quality issues included duplicated tests due to lack of access to
previous test results and delays in surgery due to lack of patient history and physical reports. On average,
repeated tests or treatment delays added $1,100 to the cost of the patient’s care. Moreover, nearly 11
percent of duplicates are associated with bad debt.
EHR technologies have significantly improved patient safety and care coordination in recent years;
however, some unforeseen difficulties and unintended consequences have arisen alongside these
advances, including challenges associated with accurate patient matching. Despite these difficulties, the
United States is experiencing an explosion in EHR adoption and use. According to 2013 estimates, the
rate of certified EHR adoption among US acute-care hospitals was 94 percent, a 22 percent jump since
2011. Moreover, among acute-care hospitals, the rate of adoption of a basic EHR system increased from
9.4 percent in 2008 to 59.4 percent in 2013—a five-fold increase.5 This rampant adoption, spurred by the
Health Information Technology for Economic and Clinical Health (HITECH) Act, part of the American
Recovery and Reinvestment Act of 2009, has increased master patient index (MPI) data conversion
activities, which often exacerbate issues of duplicate patient records.
According to preliminary findings from the Care Connectivity Consortium, a health system using
standard technology is able to identify only 10 percent of all matches.6 To address this issue, among other
identity issues, in September 2013 the Office of the National Coordinator for Health Information
Technology (ONC) launched a patient matching initiative to help “improve patient matching across
disparate systems.”7 ONC’s final report, published in February 2014, outlined patient matching
challenges and recommended changes and opportunities for the healthcare system to improve patient
matching and, as a result, duplicate patient resolution. Recommendations were based on a national
sampling of healthcare organizations, health information exchanges (HIEs), and EHR vendors. Their
recommendations included establishing standardized patient identifying attributes, following existing data
exchange standards, providing duplicate reporting capabilities, expanding best practices for patient
matching, and developing and disseminating educational and training material regarding verification of
accurate patient identity attributes.8
Patient matching provides the ability to match a unique individual with a unique set of data in a
healthcare database or data set. In hospitals, this database is called the MPI or enterprise master patient
index (EMPI). In the greater healthcare arena, the MPIs for large integrated delivery networks, health
information organizations, accountable care organizations, HIEs, and others are more complex and have
additional layers of identifiers. Within an organization, different facilities may have different medical
record numbers (MRNs) for the same patient, each of which was assigned at a specific facility at which
the patient was seen, such as an inpatient facility or a clinic. To add another layer, enterprise identification
numbers (EIDs) link the various MRNs for one patient to a single umbrella record. Despite best practices
in the area of patient access and medical record management as a method to avoid duplicating patient
records, including scrubbing the MPI, using probabilistic and mathematical algorithmic tools to identify
and select the correct patient record, adopting technology and educating stakeholders, and monitoring
performance, duplicate records continue to be a major problem for healthcare. Reasons that duplicate
records continue to plague healthcare systems include varying methods of matching patient records;
departmental system silos; lack of data standardization; lack of policies, procedures, and data ownership;
frequently changing demographic data; multiple required data points needed for record matching; and
default and null values in key identifying fields.
Why Patient Matching Is a Challenge: Research on Master Patient Index (MPI) Data Discrepancies in Key Identifying Fields
Despite the extent of patient matching challenges, very little research has been conducted to date on
the prevalence of duplicate medical records and the underlying causes of those duplicates. Just Associates
Inc. worked in collaboration with the College of St. Scholastica to conduct research on confirmed
duplicate records. The purpose of the study was to examine the underlying causes of duplicate records.
The study’s objectives were to provide categorization of data discrepancies on key patient-identifying
data fields and to analyze the data for additional patterns that may lead to conclusions about what caused
the duplicates to be created.
Methodology
The study was performed in 2014. A total of 398,939 confirmed duplicate pairs were analyzed from
10 different states, spanning more than 100 data cleanup projects. The data came from a variety of
settings, including academic medical centers, community-based organizations, integrated delivery
networks, a health information organization, and a children’s medical center. The average size of the
databases analyzed in this study was 1,813,201 unique patient records, with the median being closer to 1
million. A duplicate pair was defined as a single patient with two distinct records, and the study was
limited to patients that had only two distinct records. Patients that had more than two records (multiples)
were excluded from this study because of the complexity of examining records with more than one
duplicate. The duplicate records were identified by a variety of MPI record-matching algorithms, with the
majority of possible duplicates being identified by intermediate algorithms because the majority of
cleanup projects that occurred during the time of the study used intermediate algorithms.9 (See Appendix
A.)
However, only confirmed duplicates were included in the study. Each algorithm identified duplicate
records by referencing and comparing key demographic data fields. These fields included name, date of
birth (DOB), gender, and Social Security number (SSN). Many intermediate and advanced algorithms
also used telephone number and address in their comparison. The algorithms used in this analysis ranged
from basic to advanced, with the majority being intermediate. All potential duplicate records identified by
these various algorithms were evaluated and confirmed by patient identity experts, with the duplicate
validity decision checked to ensure quality. The underlying causes of duplication were determined by
examining mismatches between data elements commonly used in record-matching algorithms that existed
in duplicate pairs. The following six data elements were examined for mismatches between duplicate
pairs: first name (FN), middle name (MN), last name (LN), gender, SSN, and DOB.
The causes of mismatches between specific data elements in duplicate pairings were further analyzed.
The frequency of the following underlying causes of mismatches in the six data elements described above
were evaluated:
•
•
•
•
Mismatches between any of the six fields that were a result of a blank entry in one record
Mismatches in the MN field that were a result of the middle initial entered in one record and the
complete middle name entered in the other record
Mismatches in the SSN field that were a result of default values entered in one record
Mismatches due to typographical errors in the FN, LN, or SSN field
In addition to identifying mismatches between duplicate pairings, additional analyses were conducted
to identify swapping between the name fields and DOB subcomponents. Therefore, an analysis was
conducted to identify duplicate pairs where values between the following fields were swapped: first and
middle name, first and last name, middle and last name, month and day, month and year, and day and
year. Five matching data fields were required for the duplicate pair (two records) to be validated as
belonging to the same patient. The primary data fields included in the decision making process were LN,
FN, MN, DOB, gender, SSN, telephone number, and address. Additional confirmatory fields such as alias
name or guarantor may have also been used.
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Statistical Methods
The R statistical software package was used to analyze this data set. The frequency of discrepancies
between data elements of duplicate pairs was determined by developing IF-THEN statements and
counting the frequency of each output. For example, to determine if there were discrepancies in the FN of
two records of a duplicate pair, the following statement was developed: “IF the first name in record 1
matches first name in record 2 THEN label as TRUE ELSE label as FALSE.” The IF-THEN statements
resulted in the Boolean output of TRUE or FALSE. The frequencies of TRUE and FALSE outputs for
each data field were calculated to determine the overall occurrence of discrepancies. Also, the proportions
of FALSE occurrences were calculated to determine the percentage of duplicate pairs where there were
discrepancies in a specific data field.
FOR-LOOP statements were used to identify character discrepancies in the FN, LN, and SSN. The
FN, LN, and SSN of duplicate pairs were aligned when the data elements had the same number of
characters. Figure 1 depicts the method that was adopted to determine the number of character
discrepancies between data elements of duplicate pairs. In Figure 1, “Sandefer” was the LN in one record
while “Sandefre” was the LN entered in the other record of a duplicate pair. The two records were aligned
and positional mismatches were identified. The number of character discrepancies was determined by
identifying the number of positional letter differences between the two records. In this example, there are
two letter discrepancies. Green arrows indicate position matches between the characters, and red arrows
indicate discrepancies. Again, the R statistical software package was utilized to make this comparison.
To determine the frequency of duplicate pairs that were the result of typographical errors, the
frequencies of positional character discrepancies were calculated for the FN, LN, and SSN of duplicate
pairs where the number of characters in that data field was the same in both records. Typographical errors
were equated by aligning the characters of the FN, LN, and SSN in the duplicate pairs and counting the
number of character discrepancies between these fields. Typographical errors were defined as instances in
which a duplicate pair had one or two characters that differed between the FN, LN, or SSN. A onecharacter discrepancy was likely due to a single typographical error. A two-character discrepancy was
likely due to transposition, such as “Michael” entered in one record and “Micheal” entered in the
duplicate record.
Further analytical breakdown was performed on pairs of records in which a single data field was
discrepant across the pair and on pairs of records in which only two data fields had discrepancies. These
more detailed analyses identified more specifically the types of discrepancies that occurred, such as
misspellings, data entry errors (transpositions or typographical errors), or complete differences in the data
values (such as “Johnson” as the LN on one record and “Smith” on its duplicate record).
As in any research study, it is important to note the limitations of the method. Some margin of error
may have occurred during the analysis, both in the way the statistical code was run and in the validity
decisions made by patient identity experts. In addition, comparisons were made at the end of the analysis
to the results of an unpublished 2007 Just Associates study in which the statistics were calculated in a
similar but not identical manner.10 Unfortunately, very little research has been conducted to date on this
topic and similar topics, which made it difficult to perform meta-analysis and compare these results to
those of previous studies. In addition, the majority of duplicate records were identified via an intermediate
algorithm. If an advanced algorithm had been applied, more data discrepancies would have been
identified and discrepancy rates would have been higher, which could have resulted in significantly
different results of the study. (Refer to Appendix A for more information on algorithms.) Lastly, in the
analysis, three or more character or digit differences in a name or SSN field led to the mismatches being
defined as different records when in fact they could have potentially been records of the same person.
Results
A variety of counts and percentages were calculated to determine the most prevalent types of data
discrepancies. Table 1 depicts the most prevalent single and combination data discrepancies identified in
the study.
Why Patient Matching Is a Challenge: Research on Master Patient Index (MPI) Data Discrepancies in Key Identifying Fields
The MN was the data element that had the greatest proportion of discrepancies between duplicate
pairs. It was discrepant in 58.1 percent (231,566) of the duplicate pairs. The SSN was discrepant in 53.4
percent (213,003) of the duplicate pairs. FN discrepancies occurred in 22.6 percent (90,035) of the
duplicate pairs, and LN discrepancies occurred in 23.7 percent (94,661) of the duplicate pairs. DOB
discrepancies occurred in 6.2 percent (24,781) of the duplicate pairs. Discrepancies in gender occurred the
least frequently, accounting for 6.0 percent (34,014) of duplicate pairs (see Table 2).
When limiting the analysis to just the duplicate pairs that had discrepancies in the MN, 62.0 percent
(143,504) of the MN discrepancies were due to a blank entry in one record of a duplicate pair.
Additionally, 20.3 percent (47,097) of the MN discrepancies were a result of the middle initial entered in
one record and the full MN entered in the other record. The FN was swapped with the MN in 1.6 percent
(3,595) of the duplicate pairs that had discrepancies in the MN (see Table 2).
A blank entry accounted for 58.2 percent (123,894) of the SSN discrepancies. Discrepancies in the
SSN as a result of a default value (e.g., 111-11-1111, 999-99-9999, 123-45-6789) accounted for 50.2
percent (106,965) of all SSN discrepancies. Duplicate pairs with SSN discrepancies often had a blank
entry for one record and a default value for the other record (see Table 2).
Of the duplicate pairs with discrepancies in the FN, 31.3 percent (28,174) were caused by a blank
entry. The LN was swapped with the FN in 7.8 percent (6,978) of duplicate pairs with FN discrepancies,
while the MN was swapped with the FN in 4.0 percent (3,595) of the duplicate pairs (see Table 2).
A blank entry accounted for only 5.1 percent (4,804) of the LN discrepancies. The FN was swapped
with the LN in 7.4 percent (6,978) of the duplicate pairs with LN discrepancies, while the MN was
swapped with the LN in 2.0 percent (1,933) of the duplicate pairs (see Table 2).
A blank entry accounted for 16.5 percent (3,972) of the gender discrepancies. Other discrepancies in
gender were a result of a discrepant gender being entered (see Table 2).
Of the duplicate pairs with discrepancies in the DOB, only 0.4 percent (103) were due to a blank
entry. The most frequent DOB discrepancy was with the day component, which differed in 48.3 percent
(11,977) of the pairs with DOB discrepancies. The month differed in 22.1 percent (5,486) of these pairs,
and the year differed in 40.0 percent (9,909) of the pairs. Swapped month and day accounted for 2.6
percent (635) of the duplicate pairs with discrepancies in the DOB. No duplicate pairs had a swapped
month and year or a swapped year and day (see Table 2).
As shown in Table 3, typographical errors that included misspellings and transpositions (as
determined by the count of duplicate pairs with one-character and two-character discrepancies) in the FN
accounted for 53.1 percent of duplicate pairs with discrepancies in the FN. Typographical errors in the LN
accounted for 33.6 percent of duplicate pairs with discrepancies in the LN.
FN and LN discrepancies were further analyzed to categorize the discrepancies as typographical
errors or misspellings rather than a different name. Table 3 portrays the frequency of the number of letter
discrepancies between the FN (n = 18,503) and LN (n = 77,912) fields of duplicate pairs when the fields
had the same number of letters. The percentage is calculated as the count of letter discrepancies divided
by the total number of discrepancies for that field. Three or more discrepancies in a name field was
defined as being a “different” name.
Finally, typographical errors in the SSN accounted for 7.8 percent of duplicate pairs with
discrepancies in the SSN (Table 4). More than 92 percent of duplicate pairs with SSN discrepancies
occurred with three or more digits being discrepant, indicating that the discrepancy is not likely to be the
result of a typographical error. Table 4 shows the frequency of the number of digit discrepancies between
aligned SSNs of duplicate pairs (n = 87,473). The percentage is calculated as the count of digit
discrepancies divided by the total number of discrepancies for that field. Three or more discrepancies in a
SSN field was defined as being a “different” SSN.
Figure 2 shows the frequency of duplicate pairs in which one of the key demographic data points was
discrepant. The highest percentage of single-field discrepancy was in the MN, accounting for 58.30
percent of the duplicate pairs. The second most prevalent discrepancy was SSN, with 53.54 percent. The
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LN was the only discrepancy in 23.84 percent of the pairs, while the FN was discrepant in 22.72 percent.
The DOB was discrepant in 6.30 percent of the pairs, and gender was discrepant in 6.32 percent.
Finally, Table 5 shows the frequency of duplicate pairs with co-occurring discrepancies where two or
more of the data fields were discrepant. For example, a duplicate pair of records for a patient may have a
discrepancy involving the MN and FN fields but also have a discrepancy in the SSN field. This pair of
records would be counted in the table in the FN/MN cell, the FN/SSN cell, and the MN/SSN cell. Again,
the highest co-occurring discrepancies are due to issues with the SSN, FN, MN, and LN fields.
Discussion
As noted above, there are numerous reasons for duplicate records. This study focused on data capture
and standardization with the goal of ascertaining the underlying cause of various types of data
discrepancies. Further explanation of data-related reasons for duplicate records is provided below:
•
•
•
•
Lack of data standardization. Demographic field names vary across the healthcare system
databases, and the fields typically do not have a one-to-one match. For example, one system
might have three different name fields: first name (FN), middle name (MN), and last name (LN).
Another system may have only one field for the name, allowing the user to enter the patient name
in any order: Megan L Munns; Munns, Megan L; Megan Munns; Munns, Megan; and so on.
Matching these fields is not an easy task, and name suffixes compound this problem.
Frequently changing demographic data. In today’s society, people commonly change their
name, addresses, and phone numbers and occasionally change their gender. Data collection also
can be a problem. For instance, the MN and SSN frequently are not collected. Human error,
policy gaps, and lack of standardization all contribute to data inaccuracy. In individual facilities
and in the healthcare industry as a whole, there is a lack of consistency in the data collected.
Required multiple demographic data points. To effectively match one patient with another,
several data points are necessary. If records do not have enough matching data points and patient
matching is based on limited data, there is a risk of overlaid records. An overlaid record occurs
when two different patients are associated with the same record.
Default and null values in key identifying fields. Having default values or no information in
fields such as SSN and MN can reduce an organization’s ability to accurately match patient
records.
Overall, this study demonstrates that duplicate patient record discrepancies are often due to a blank
entry or a default entry in one of the key identifying fields, with the majority being in MN and SSN fields.
Because of the complex nature of record matching and the decreased capture of an accurate, valid SSN,
the MN is becoming ever more important to appropriately identify duplicate records. Additionally, name
mismatches arise repeatedly as a result of misspellings or transpositions, accounting for 53.14 percent of
all FN mismatches and 33.62 percent of all LN mismatches. Yet this analysis demonstrates that the MN
and SSN were discrepant in 21.88 percent of all discrepant records, which was the highest of any
multifield discrepancy (see Table 1). Furthermore, the record-matching algorithms in use today catch
duplicate records that have exact or similar data in key identifying fields, but the percentage of records
that match exactly is less than 5 percent of the discrepant records. Health information management
departments appear to be doing a better job of cleaning up the duplicates that match exactly in these key
identifying fields and/or registration areas are searching the MPI for patients before creating a new record.
Of further importance is the number of gender discrepancies, which is now slightly higher than DOB
discrepancies. Possible explanations for the number of gender discrepancies could be the increased
number of transgender patients and/or reference labs’ creation of shell records for lab patients without
capturing data in this field. However, most of the discrepancies identified consisted of elementary errors
Why Patient Matching Is a Challenge: Research on Master Patient Index (MPI) Data Discrepancies in Key Identifying Fields
identified by basic and intermediate algorithms. If advanced algorithms had been used to determine
potential matching records, these discrepancy patterns might have been significantly different.
Comparing this study’s results with those of the unpublished 2007 Just Associates study reveals some
drastic differences (see Table 6 and Table 7). In the current study, only about 5 percent of the duplicates
had no discrepancies in the six key identifying fields, whereas in 2007, nearly 18 percent had no data
discrepancies in these fields. Additionally, in the current study about 59 percent of the duplicate records
had two or more discrepancies in the key demographic data, compared with 48 percent in 2007. This
finding suggests that front-end registration processes are creating fewer exact-match duplicates, the exactmatch duplicates created are being actively fixed and therefore do not appear in this data set, or both.
Moreover, discrepancies in DOB fields have decreased substantially over the years, from 14.9 percent in
2007 to 6.2 percent in the current study. This improvement in data capture could be due in part to the
patient safety rules pertaining to accurate patient identification. Decreased capture of the SSN, which
often is the only unique identifier for a person, and decreased accuracy in name collection calls for the
industry to pay closer attention to data capture accuracy and the need for advanced algorithms that
tolerate multiple data discrepancies. Because the current study used a different statistical program to
compare the types of data discrepancies, not all of the changes identified above can be extrapolated to
indicate a change in data capture practices in the industry. However, the findings described in the first
three sentences of this paragraph are valid comparisons because they were computed using exact data
match programs in both studies.
So what does this information mean, and what can be done to address the issues presented by
duplicate patient records? Initial steps that can be taken to lessen the burden of duplicate records include
partnering with colleagues in patient access to establish standard policies and procedures, such as patient
searching protocols, standard name entry conventions, and questions that registrars can ask the patient in
order to determine if the patient has ever been to the facility or practice before. Capturing the full MN
would help substantially to verify the patient’s identity and is particularly useful to distinguish between
twins and patients with common names. Additionally, creating a searchable field to store the last four
digits of the patient’s SSN (separate from the full SSN field) would greatly improve duplicate record
prevention. Historically, patients have been hesitant to share their full SSN, and rightfully so with the rise
in identity thefts. Because of this, other industries such as banking and financial institutions have begun to
capture the last four digits of the SSN, which is useful in identity verification and gives customers ease of
mind knowing that they are not sharing their full SSN. This technique is commonly utilized in the finance
industry, and patients would be less hesitant to share the last four digits than they would be to share the
full SSN. This technique is not common in healthcare; however, some EHR vendors have added a
separate data field for the last four digits of the SSN, and at least one large HIE is using this field in its
patient matching algorithm.
In addition, staff should be trained and frequently assessed on their knowledge regarding identity data
capture and the consequences of inaccurate or incomplete data capture on duplicate record creation. It is
also imperative to track who is creating duplicate records and where those employees work. Tracking this
information can identify a need for additional training or changes to policy and procedures. Some EHRs
also offer the ability to track demographic changes to records. Tracking these reports and qualitychecking demographic changes for appropriateness may also trigger further training for staff, both
administrative and clinical. In addition, organizations should take steps to evaluate the underlying causes
of duplicate patient records, including an assessment of common data discrepancies, evaluation of people
and process augmentation, technology solutions, and ongoing MPI maintenance.
The ONC’s Patient Matching Initiative report clearly states that people and process improvements are
needed to improve patient identity data integrity.11 Technology is needed as well because errors will
always occur, and as databases get larger and larger, human manpower alone cannot solve this issue.
Effective tools to fix errors that occur are needed. Utilizing more advanced record-matching algorithms,
along with other technology solutions such as smart cards and biometrics, is key. Advanced algorithms
tolerate both multiple data entry errors and changes in a patient’s demographic data. The higher-quality
algorithms are able to limit the number of false positives that show up in a standard duplicate queue and
catch more real duplicates than are caught by basic or intermediate algorithms, minimizing false
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Perspectives in Health Information Management, Spring 2016
negatives.12 Algorithms should be continuously improved to review additional key fields for patient
matching. Currently, most algorithms utilize only standard demographics, such as name, DOB, gender,
SSN, and in some cases address and phone number. The use of additional fields can help to identify more
real duplicates. Some of these additional fields include previous names (to help detect overlays, as well as
to identify an existing record for that patient), insurance data, policy numbers, guarantor data, and next of
kin. Furthermore, front-end search algorithms could be improved by allowing searches using other
demographic fields (e.g., telephone number or the last four digits of the SSN) and by installing biometric
technologies or card readers.
Conclusion
To improve patient matching, increasing the use of more sophisticated technologies is critical. For
example, using biometrics, smart card readers, and advanced algorithms (or fine-tuned algorithm
matching criteria) can all help increase front-end and back-end matching. Record search algorithms in
scheduling and registration systems specifically are in need of major enhancements. For example, adding
last four digits of a SSN as a searchable field and adding complementary fields in the matching criteria,
such as telephone number, next of kin, guarantor, or insured, would find more potential duplicates and
could accommodate more data discrepancies. Apart from technological advances that can be
implemented, additional improvements can be made to the current state of demographic data capture. As
demonstrated in Table 6 and Table 7, MPI data discrepancies overall appear to be increasing, as more
duplicate records now have multiple data discrepancies than in the 2007 study. As seen in the results of
this study, the SSN and full MN had the lowest percentages of being a reliable demographic data element.
Increasing data capture in these fields can also significantly increase matching possibilities.
No amount of advanced technologies or increased data capture will completely eliminate human
errors. Creating policies and procedures for front-end and back-end staff to follow is foundational for the
overall data integrity process. Training staff on standard policies and procedures will result in fewer
duplicates created on the front end and more accurate duplicate record matching and merging on the back
end. Furthermore, proactive ways to identify data integrity issues include monitoring, analyzing trends,
and identifying errors that occur. The sooner issues are detected, the sooner health information
management and registration leaders can address the concern.
Implications of ineffective patient matching are substantial because the MPI is the backbone of the
EHR. Severe patient-care issues can occur and resources are wasted when systems are inundated with
duplicate records. Patient safety is a major concern for many organizations, yet it is necessary to increase
awareness of the safety, legal, financial, and compliance concerns created by duplicate and overlaid
medical records. Additionally, the migration to value-based reimbursement may be the driver for
healthcare organizations to ensure that their MPI data are clean. If an organization’s data are not
sufficiently clean, risk-based contracting will create significant financial consequences that are likely to
capture the attention of C-suite executives. Lastly, a primary goal of the EHR Incentive Program, with its
criteria for meaningful use of EHRs, is to “improve quality, safety, efficiency, and reduce health
disparities.”13 Several of the meaningful use criteria have the number of “unique patients” as a
denominator. Duplicate records increase the number of “unique patients,” thereby making it more
challenging for providers to reach the minimum core criteria.
This research study was limited by the exclusion of multiples (defined as the existence of more than
two records for a patient), the fact that the majority of cases were identified by an intermediate algorithm,
and the lack of other published research studies to use as a basis for comparison. Further research is
needed to demonstrate the improvement in patient matching when advanced technologies are in place,
better data capture of other demographic identifiers such as full MN or last four digits of the SSN is
achieved, and multiple duplicate record sets are included in the analysis. Solid data demonstrating the
effectiveness of these possible solutions are needed to further educate healthcare leaders and health
information management professionals on patient matching challenges and opportunities for
improvement.
Why Patient Matching Is a Challenge: Research on Master Patient Index (MPI) Data Discrepancies in Key Identifying Fields
Beth Haenke Just, MBA, RHIA, FAHIMA, is the founder and CEO of Just Associates in Centennial,
CO.
David Marc, MBS, CHDA, is an assistant professor and director of the health informatics graduate
program at the College of St. Scholastica in Duluth, MN.
Megan Munns, RHIA, is an associate identity manager at Just Associates in Centennial, CO.
Ryan Sandefer, MA, CPHIT, is an assistant chair and professor in the Department of Health
Informatics and Information Management at the College of St. Scholastica in Duluth, MN.
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Perspectives in Health Information Management, Spring 2016
Notes
1. McCann, Erin. “Deaths by Medical Mistakes Hit Records.” Healthcare IT News, July 18,
2014. Available at http://www.healthcareitnews.com/news/deaths-by-medical-mistakeshit-records.
2. Smart Card Alliance. Effective Healthcare Identity Management: A Necessary First Step
for Improving U.S. Healthcare Information Systems. 2014. Available at
http://www.smartcardalliance.org/resources/pdf/Healthcare_Identity_Brief.pdf.
3. Hillestad, Richard, James H. Bigelow, Basit Chaudhry, Paul Dreyer, Michael D.
Greenberg, Robin C. Meili, M. Susan Ridgely, Jeff Rothenberg, and Roger Taylor.
Identity Crisis: An Examination of the Costs and Benefits of a Unique Patient Identifier
for the U.S. Health Care System. Santa Monica, CA: RAND Corp., 2008. Available at
http://www.rand.org/pubs/monographs/MG753.html.
4. Just Associates. “Studies in Success: Children’s Medical Center Dallas.” Available at
http://www.justassociates.com/Articles-MPI-Childrens-Medical-Center-Dallas.html.
5. Charles, Dustin, Meghan Gabriel, and Michael F. Furukawa. Adoption of Electronic
Health Record Systems among U.S. Non-federal Acute Care Hospitals: 2008–2013 (ONC
Data Brief No. 16). Office of the National Coordinator for Health Information
Technology, May 2014, p. 1. Available at
http://www.healthit.gov/sites/default/files/oncdatabrief16.pdf.
6. Heflin, Eric, Sid Thornton, and Reg Smith. “An Approach to Understanding and
Resolving Inter-organizational Patient Matching.” Presented at the Care Connectivity
Consortium, February 24, 2014, slide 7.
7. Stevens, Lee. “ONC Launches Patient Matching Initiative.” Health IT Buzz, September
11, 2013. Available at http://www.healthit.gov/buzz-blog/health-innovation/onclaunches-patient-matching-initiative/.
8. Morris, Genevieve, Greg Farnum, Scott Afzal, Carol Robinson, Jan Greene, and Chris
Coughlin. Patient Identification and Matching Final Report. Prepared for the Office of
the National Coordinator for Health Information Technology. February 7, 2014.
Available at
http://www.healthit.gov/sites/default/files/patient_identification_matching_final_report.p
df.
9. AHIMA. “Managing the Integrity of Patient Identity in Health Information Exchange
(Updated).” Journal of AHIMA 85, no.5 (May 2014): 60–65. Available at
http://library.ahima.org/xpedio/groups/public/documents/ahima/bok1_050658.hcsp?dDoc
Name=bok1_050658.
10. Heflin, Eric, Sid Thornton, and Reg Smith. “An Approach to Understanding and
Resolving Inter-organizational Patient Matching.”
11. Morris, Genevieve, Greg Farnum, Scott Afzal, Carol Robinson, Jan Greene, and Chris
Coughlin. Patient Identification and Matching Final Report. Prepared for the Office of
the National Coordinator for Health Information Technology.
12. Just Associates. “Studies in Success: Epic with IDOptimize.” Available at
http://www.justassociates.com/Articles-Epic-with-IDOptimize.html.
13. HealthIT.gov. “Meaningful Use Definition & Objectives.” Available at
http://www.healthit.gov/providers-professionals/meaningful-use-definition-objectives.
Why Patient Matching Is a Challenge: Research on Master Patient Index (MPI) Data Discrepancies in Key Identifying Fields
Figure 1
Example of Character Matching Method Used to Compare Name Fields of Duplicate Records
Note: Green arrows indicate position matches between the characters. Red arrows indicate
discrepancies.
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Perspectives in Health Information Management, Spring 2016
Figure 2
Percentage of Duplicate Pairs with a Mismatch in the SSN, MN, LN, FN, DOB, or Gender Field
(n = 398,939)
Abbreviations: DOB = date of birth, FN = first name, MN = middle name, LN = last name, SSN = Social Security
number.
Why Patient Matching Is a Challenge: Research on Master Patient Index (MPI) Data Discrepancies in Key Identifying Fields
Table 1
Percentage of Discrepant Pairs by Discrepancy Category
Discrepancy Type
MN
SSN
MN + SSN
LN
LN + SSN
LN + MN + SSN
LN + MN
SSN + DOB
FN + MN
MN + SSN + DOB
FN
DOB
FN + MN + SSN
FN + SSN
MN + DOB
LN + FN + MN
LN + FN
Gender
Other
Total without discrepancies
Total with discrepancies
Duplicate Pair
Counts by
Percentage of
Discrepancy
Total Duplicates
Category
(n = 398,939)
29,396
7.37%
75,115
18.83%
87,304
21.88%
21,362
5.35%
7,158
1.79%
7,102
1.78%
25,510
6.39%
3,097
0.78%
30,753
7.71%
2,764
0.69%
10,148
2.54%
5,925
1.49%
6,573
1.65%
6,636
1.66%
6,025
1.51%
14,175
3.55%
4,787
1.20%
854
0.21%
34,343
8.61%
19,912
4.99%
379,027
95.01%
Abbreviations: DOB = date of birth, FN = first name, MN = middle name, LN = last name, SSN = Social Security
number.
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Perspectives in Health Information Management, Spring 2016
Table 2
Proportion of Mismatches between Data Discrepancy Subtypes
Data
Element
Percentage of
Total
Duplications
(n = 398,939)
Percentage of Duplicate Pairs with Discrepancy by Data
Element
First
Name
22.6%
FN–MN swap: 4.0%
FN–LN swap: 7.8%
FN blank in one record: 31.3%
Middle
Name
58.1%
FN–MN swap: 1.6%
LN–MN swap: 0.8%
MN blank in one record: 62.0%
Middle initial in one record and full MN in the other record: 20.3%
Last
Name
23.7%
FN–LN swap: 7.4%
LN–MN swap: 2.0%
LN blank in one record: 5.1%
Gender
6.0%
Gender blank in one record: 16.5%
SSN
53.4%
Default SSN: 50.2%
SSN blank in one record: 58.2%
DOB
6.2%
DOB blank in one record: 0.4%
Month does not match: 22.1%
Day does not match: 48.3%
Year does not match: 40.0%
Month and day swapped: 2.6%
Month and year swapped: 0%
Day and year swapped: 0%
Abbreviations: DOB = date of birth, FN = first name, MN = middle name, LN = last name, SSN = Social Security
number.
Why Patient Matching Is a Challenge: Research on Master Patient Index (MPI) Data Discrepancies in Key Identifying Fields
Table 3
Frequency of Letter Discrepancies in First Name and Last Name
Number of Discrepancies
Field
1
2
3
4
5
6
7
>8
First Name (n = 18,503)
Count
6,753
3,078
1,394
2,026
2,186
1,948
725
393
Percentage
36.50
16.64
7.53
10.95
11.81
10.53
3.92
2.12
Last Name (n = 77,912)
Count
17,771
8,419
9,071
8,464
6,225
10,468
12,815
4,679
Percentage
22.81
10.81
11.64
10.86
7.99
13.44
16.45
6.01
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Perspectives in Health Information Management, Spring 2016
Table 4
Frequency of Digit Discrepancies in Social Security Number (n = 87,473)
Number of Discrepancies
1
2
3
4
5
6
7
8
9
Count
4,712
2,087
3,837
794
1,842
7,058
17,845
24,874
24,424
Percentage
5.4
2.4
4.9
0.9
2.1
8.1
20.4
28.4
27.9
Why Patient Matching Is a Challenge: Research on Master Patient Index (MPI) Data Discrepancies in Key Identifying Fields
Table 5
Percentages of Co-occurring Mismatched Fields
FN
MN
LN
Gender
SSN
DOB
FN
–
15.3
6.8
2.1
5.4
0.8
MN
15.3
–
13.6
4.2
28.7
3.1
LN
6.8
13.6
–
1.4
5.4
0.9
Gender
2.1
4.2
1.4
–
2.6
0.4
SSN
5.4
28.7
5.4
2.6
–
1.8
DOB
0.8
3.1
0.9
0.4
1.8
–
Abbreviations: DOB = date of birth, FN = first name, MN = middle name, LN = last name, SSN = Social Security
number.
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Perspectives in Health Information Management, Spring 2016
Table 6
Comparisons of the Causes of Duplicate Patient Records in an Unpublished 2007 Study by Just
Associates and the Current Study
Type of Discrepancy
DOB discrepancies
FN–LN swap
FN–MN swap
Gender discrepancies
SSN discrepancies
Two or more data
discrepancies
No data discrepancies
across six key identifying
fields
Percentage of
Total Pairs (n =
244,356) in the
2007 Study
14.9