SSuN: Diagnostic coding
SSuN Call #4
Nov 6, 2008
Nov. 6 – Call Agenda
1. Diagnoses to capture in SSuN Cycle 2
2. File structure for collecting data
3. System for coding diagnoses with
minimum burden and changes to local
systems
4. Mapping local codes to a common
system
What Diagnoses to
Capture? (1)
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Syphilis
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Primary, secondary, early latent, unknown
latent, late latent, late with symptoms
Gonorrhea
Chlamydia
Genital Herpes
Genital Warts
Trichomoniasis
Nongonococcal Urethritis (NGU)
Muco-purulent cervicitis (MPC)
What Diagnoses to
Capture? (2)
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Pelvic Inflammatory Disease (PID)
Epididymitis
Chancroid
Lymphogranuloma venereum (LGV)
Granuloma Inguinale
Hepatitis

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HepB acute, HepB chronic
HIV/AIDS
No STD diagnosed
What Diagnoses to
Capture? (3)
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Consider:
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Bacterial vaginosis (BV)
Candidiasis
Scabies
Pediculosis
Contact to STD
Pregnancy
Normal exam/diagnosis
Other
Other Issues to Consider
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How much specificity do we need?
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Diagnostic file vs. lab file

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Do we need anatomic site information in both
files?
Any other diagnoses to consider?
Questions & Comments?
STD clinic diagnostic coding
File structure
Flat file vs. relational file
Flat file structure
Relational file structure
Individual variable for each
diagnosis
One diagnosis variable linked by
record number
Easily analyzable
Must create an analytic file to
flatten diagnoses
Uses more storage space
Uses less space
Can have empty cells
Has a value for each diagnosis
Long record
Compact records
SSuN Cycle 1 Method
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Relational Database
Lab data (test results) is linked by
Event ID
Multiple events linked to 1 patient
Currently only collect:
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GC test type
GC anatomic site
GC test result
Chlamydial co-infection
MSM Prevalence Monitoring
Project Method
 Flat file structure
 Individual variables for each diagnosis
 Often diagnosis variables incomplete
and result variables are used
 Currently collect test results for:
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GC
CT
Syphilis
HIV
STD Clinic Lab file
Patient ID
No.
Event date
Test Type
Collection
site
Test Result
Patient 1
Jan 1, 2008
GC Culture
Rectum
Positive
Patient 1
Jan 1, 2008
NAAT - CT
Urine
Negative
Patient 1
Jan 1, 2008
HIV
Blood
Positive
Patient 1
Jan 1, 2008
RPR
Blood
1:64
Patient 2
Jan 3, 2008
HSV-2 serology
Blood
Positive
Patient 2
Jan 3, 2008
Darkfield
Penis
Negative
Patient 1
April 1, 2008
RPR
Blood
1:4
• One patient can have several different lab tests done at a single visit
• One patient can have several different visits
STD Clinic diagnostic code file
Patient ID No.
Event date
Diagnosis
Patient 1
Jan 1, 2008
Gonorrhea
Patient 1
Jan 1, 2008
Acute HIV infection
Patient 1
Jan 1, 2008
Primary syphilis
Patient 2
Jan 3, 2008
Genital herpes
Patient 1
April 1, 2008
Normal exam
• One patient can have several diagnoses at a single visit
File Structure –
Discussion Topics
 Are there significant advantages to
using the relational file structure?
• More efficient use of space
• More flexible to add diagnosis codes
 What method do sites favor?
• Flat vs. relational file structure
Questions & Comments?
STD clinic diagnostic coding
Coding options
What’s currently happening
at the local level?
 In house diagnostic codes (5 sites)
 Some or all ICD-9 codes (3 sites)
 Check box, yes/no for each
diagnosis (3 sites)
 Some or all universal codes from
STD*MIS (3 sites)
Options for coding
1. ICD-9 codes (modified)
2. ICD-10 codes (modified)
3. Snomed Clinical Terms (CT)
4. Universal codes (e.g., STD*MIS)
5. Create new coding classifications
6. Individual variables for each diagnosis
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Check box, yes/no
ICD-9 vs. ICD-10
ICD-9
ICD-10
3-5 characters in length
3-7 characters in length
~13,000 codes
~68,000 available codes
Digit 1 is alpha or numeric
Digit 1 is alpha
Digits 2-5 are numeric
Digits 2-3 are numeric; 4-7
are alpha or numeric
Limited space for adding new Flexible for adding new
codes
codes
Less detailed
Very specific
Reasons to postpone
using ICD-10
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Planned deadline for national transition
is 2011
Has not been piloted in U.S.
68,000 codes
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No sites are currently using ICD-10
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compared to 13,000 ICD-9 codes
Some use ICD-9
American Academy of Family
Physicians discourages use
SNOMED CT
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What is it?
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Input system as opposed to “output system” like
ICD-9 or ICD-10
Used for documentation of clinical care
Better if linked to a classification system
Disadvantages
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Less than 30% of ICD-9 codes can be mapped
to Snomed CT
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Difficult to manually assign codes
Strings of digits in length 6-18
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No logic to string, unlike ICD-9
Sample Snomed to ICD-10
SNOMED-CT
SNOMED-CT Concept
Description
ICD-10-CM
Code
ICD-10-CM Code
Description
11530004
Brittle diabetes
E10.9
Type 1 diabetes mellitus
without complication
190371008
Type I diabetes mellitus - poor
control
E10.9
Type 1 diabetes mellitus
without complication
190392008
Type II diabetes mellitus - poor
control
E11.9
Type 2 diabetes mellitus
without complication
371055001
Type I diabetes mellitus with
ketoacidosis
E10.10
Type 1 diabetes mellitus
with ketoacidosis
without coma
190334006
Diabetes mellitus, juvenile type,
with ketoacidotic coma
E10.11
Type 1 diabetes mellitus
with ketoacidosis
with coma
395204000
Hyperosmolar non-ketotic state
in type 2 diabetes mellitus
E11.00
Type 2 diabetes mellitus
with etc..
Universal Codes from STD*MIS
Advantages/Disadvantages to
Universal Codes
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Advantages:
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Used in 3 sites
Less complex than ICD-9 or Snomed
Already standardized and collected at
CDC
Disadvantages:
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Might not be specific enough
Additional codes may be needed
Advantages/Disadvantages to
Creating a New Coding System
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Advantages:
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Flexible
Wouldn’t require modification of an
established coding system
Disadvantages:
Not based on a current system
More time to create and standardize
Issues with piloting
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Individual Diagnosis
Variables
• One variable for each diagnosis
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Check box, yes/no
• Many variables
• Longer record length
• Not as easily modified
Coding System –
Discussion Topics
 A standardized/structured coding system
would work best
 Will have to modify coding system
regardless of what we choose
 Best options:
• ICD-9?
• STD*MIS universal codes?
• Create new coding system?
 What coding system do sites favor?
STD clinic diagnostic coding
Mapping
Mapping Relationships
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Many to one:
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ICD-9 codes may be too specific
Need to collapse several codes into 1
code
One to many:
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What if our codes are more specific
than the sites collect?
How do we map a local site from one
to many?
Questions & Comments?