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Exploring ICD-10 Data What Impact on Trends Analysis? Anne Elixhauser, Marguerite Barrett, Katie Fingar, Kevin Heslin, Zeynal Karaca, Pam Owens, Claudia Steiner, Carol Stocks NAHDO Annual Meeting October 27, 2016

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Page 1: Exploring ICD-10 Data What Impact on Trends Analysis? · Exploring ICD-10 Data What Impact on Trends Analysis? ... Alpha characters are in the middle of ranges ... Ill-defined conditions

Exploring ICD-10 Data

What Impact on Trends Analysis?

Anne Elixhauser, Marguerite Barrett, Katie Fingar, Kevin Heslin,

Zeynal Karaca, Pam Owens, Claudia Steiner, Carol Stocks

NAHDO Annual Meeting

October 27, 2016

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Purpose of these Analyses

• Examine the potential for doing trends analysis

covering the ICD transition period

• Understand shifts in diagnosis and procedure codes

• Compare ICD-9 period to ICD-10 period: how

discharges are assigned to

– Service lines

– Diagnosis chapters

– Procedure chapters

– CCS (groupings of ICD codes)

– MS-DRGs

2

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Results Reported from Three Projects

• ICD-10 Methods Report

► Literature review and review of HCUP tools

• Dually Coded Data Analysis

► Based on a small dataset from Washington state

• HCUP ICD-10 Data

► Based on quarterly data from 12 states (submitting data

for Oct-Dec 2015)

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Project #1—ICD-10 Methods Report:

Impact on Research Using Admin Data

• Report summarizes some

of the effects of transition

to ICD-10

– Compares ICD-9 and ICD-10

– Changes in coding rules

– Coding differences

– Translation tools

– GEMS

– Trends

– Resources for researchers

http://www.hcup-us.ahrq.gov/

reports/methods/2016-02.pdf

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Project #1—ICD-10 Methods Report:

Diagnosis Coding

• More combination codes (ICD-10 contains more

information in a single code)

► DM with complications + manifestations =

one code in ICD-10 rather than multiple codes in ICD-9

• No consistency in the meaning of alpha characters

► First character D = benign neoplasm and blood disorders

► Alpha characters are in the middle of ranges

o C43, C4A, C44, C45 (Neoplasms)

► Final character is more consistent but not entirely

o A = initial encounter (S46) or A=initial counter for closed

fracture (S82)

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Project #1—ICD-10 Methods Report:

Procedure Coding

• Uses standard PCS terminology to reduce ambiguity

► But does not use medical record terms, e.g., PTCA

• Each character has a different meaning; differs by section of ICD-10

► 3rd character for breast biopsy = root operation (excision)

► 3rd character for radiation Rx = modality (brachytherapy)

• Diagnosis information is not included in PCS

ICD-9: 86.22 excision of wound, infection, burn translates to

ICD-10: OHB excision skin and breast or

OJB excision subcutaneous tissue (no mention of condition)

► Cannot use PCS to identify patient cohorts

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Project #1—ICD-10 Methods Report:

Summary of Boyd et al.

Relationship

between ICD-9

and ICD-10 codes

Description

Percent

of codes

Identity One ICD-9 code matches to one ICD-10 code 28%

Class-to-subclass One ICD-9 code gets mapped to multiple ICD-10

codes

22%

Subclass-to-class Multiple ICD-9 code get mapped to a single ICD-

10 code

12%

Convoluted Complex mapping where multiple similar ICD-9

codes get mapped to multiple ICD-10 codes but

in a complex way

36%

No mapping What it sounds like 1%

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Project #1—ICD-10 Methods Report: Use DX

and PR Groupings for Trends Analysis?

• Clinical Classification Software maps ICD into groups

– We hoped that using broad categories would ease mapping

across ICD-9 and ICD-10

• However … Some CCS procedure categories are not

populated with ICD-10 codes:

• 57 Creation/removal fistula/cannula for dialysis

• 68 Injection/ligation esophageal varices

• 140 Repair of OB laceration

• 143 Bunionectomy

• 151 Excision semilunar cartilage of knee

• 169 Debridement wound/burn/infection

• 206 Microscopic exam (bacterial smear, culture)

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Project #2—

Analysis of Dually Coded Data

• Had difficulty finding a dually coded dataset

• Most analyses rely on coding conversion based on

GEMs

– Rather than coders assigning ICD-9 and ICD-10 codes to

the same records

• Wanted to see impact of ICD-10 coding in practice

• Dually coded dataset from Washington state

– 2,665 inpatient discharge records that were dually coded

using both ICD-9-CM and ICD-10-CM/PCS

– 8 hospitals submitted data

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Project #2— Analysis of Dually Coded Data:

CCS Coding in the Same Records

Comparing ICD-9 and ICD-10 codes in

the same records

Frequency Overall

%

% with

coding

agreement

Diagnoses

DX code assigned in one system but not

the other

94 9.3

CCS assignment was the same 870 85.9 94.7

Different CCS coded 49 4.8 5.3

Procedures

PR code assigned in one system but not

the other

145 12.5

CCS assignment was the same 903 77.6 88.7

Different CCS coded 115 9.9 11.3

Suggests potential

issues with

process used by

coders

Excludes

potential

coding issues

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Project #2— Analysis of Dually Coded Data:

Causes of Different CCS Category Assignment

• Two major causes of differences between the CCS

assignment in the ICD-9-CM and ICD-10-CM/PCS

codes

► A code was recorded in I-9 and not in I-10, or vice versa

► Changes in coding based on differences in the coding

systems

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Project #2— Analysis of Dually Coded Data:

Differences in Coding Systems

• In some cases there is increased specificity in codes

– But in some cases less specificity

• More codes may be required

– Especially for procedures: multiple codes per operation

• Some ICD-10 codes have more detail (replace multiple

ICD-9 codes) so fewer codes on record

• Coding rules have changed

– For example, may be more difficult to identify rehab cases

• Some conditions reclassified to different categories

– Sarcoidosis was Chap. 1 infection, now Chap. 3 blood/

immunity

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Project #3—Analysis of ICD-10 HCUP Data:

Methods• Data from 12 States with ICD-10 data (Oct-Dec 2015)

• Outcomes:

– Number of diagnoses, procedures, and OR procedures

– Service lines (hierarchical, mutually exclusive):

• Maternal/neonatal

• Mental health/substance abuse disorders

• Injury

• Surgical

• Medical

– All-listed procedures grouped by PR chapter

– 1st-listed and 2ndary diagnoses grouped by DX chapter

– MS-DRGs

13

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Project #3— Analysis of ICD-10 HCUP Data:

Methods (cont’d)

• Examined quarter 4 of 2013, 2014 and 2015

• Calculated change during two periods:

– Percentage change from 2013–2014

– Percentage change from 2014–2015

• For all States (combined—pooled together)

• Frequency distributions by hospital

• Preliminary findings based on only one quarter of

ICD-10 in the field

14

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Project #3— Analysis of ICD-10 HCUP Data:

Number of Diagnoses & Procedures

-1.8

-2.7

5.3

16.0

-3.3

0.2

-20.0 -10.0 0.0 10.0 20.0

OR procedures (0.3)

Procedures (1.7)

Diagnoses (10.1)

Percentage Change in Mean Numberfrom Q4 2014–2015 Compared with Q4 2013–2014

Dia

gn

oses o

r P

roced

ure

s

(Mean

N in

2014)

Q4 2014–2015 Q4 2013–2014

15

• 2013-2014: 5%

increase in number of

DX per record

• 2014-2015: no change

• No real difference in

number of PR per

record between time

periods (expected

increase)

• 2014-2015: record

level analysis—16%

increase in number of

records with at least

one OR procedure

coded

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Project #3— Analysis of ICD-10 HCUP Data:

Service Lines -- Overall

1.1

-1.4

-2.1

2.3

-1.1

-2.7

4.6

-1.7

3.0

0.9

-6.0 -4.0 -2.0 0.0 2.0 4.0 6.0

Medical(46.3%)

Surgical(20.2%)

Injury(4.6%)

Mental health/substance use

(6.8%)

Maternal/neonatal (22.1%)

Percentage Change in Service Line Mix from Q4 2014–2015 Compared with Q4 2013–2014

Se

rvic

e L

ine

(P

erc

en

t in

20

14

)

Q4 2014–2015 Q4 2013–2014

16

• Small change in

maternal/neonatal

records

• Similar pattern for

MHSU and injury

• Surgical records ↑ by

4.6% from 2014-2015

• Records showing

only a medical

problem ↓ by 2.7%

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Project #3—Analysis of ICD-10 HCUP Data:

Service Lines – by Hospital

17

Medical

• Less difference

Surgery

• Blue line to right = ↑ in number of

surgical records from 2014-2015

compared to 2013-2014

• Blue line wider = less concordance

between 2014-2015

-100 -50 0 50 100 150 200

Percentage Change

0

10

20

30

40

50

Perc

enta

ge o

f H

ospitals

Q4 2015 / 2014Q4 2014 / 2013

Service line by Hospital

4: Surgical

-100 -50 0 50 100 150 200

Percentage Change

0

10

20

30

40

50

Perc

enta

ge o

f H

ospitals

Q4 2015 / 2014Q4 2014 / 2013

Service line by Hospital

5: Medical

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Project #3—Analysis of ICD-10 HCUP Data:

All-listed Procedures – Body System

-9.5

5.8

18.8

14.4

2.9

9.4

6.8

-20.2

-2.7

-6.2

-50 -25 0 25 50 75

Female genital organs (1.6%)

Urinary system (2.2%)

Male genital organs (2.2%)

Integumentary system (2.4%)

Respiratory system (2.6%)

Nervous system (3.2%)

Musculoskeletal system (9.5%)

Digestive system (9.8%)

Obstetric (11.8%)

Cardiovascular system (17.8%)

Miscellaneous (35.5%)

Percentage Change in Procedure Mix from Q4 2014–2015 Compared with Q4 2013–2014

Pro

ce

du

re C

ha

pte

r (P

erc

en

t in

20

14

)

Q4 2014–2015 Q4 2013–2014

150

143.6

18

6.2% ↓ in miscellaneous

procedures

20.2% ↓ in OB procedures

9.5% ↓ in urinary

Balanced by large ↑ in

female genital procedures

18.8% ↑ in skin

14.4% ↑ in respiratory

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Project #3—Analysis of ICD-10 HCUP Data:

All-listed Procedures -- by Hospital

-100 -50 0 50 100 150 200

Percentage Change

0

10

20

30

40

50

Perc

enta

ge o

f H

ospitals

Q4 2015 / 2014Q4 2014 / 2013

PR Chapter by Hospital

12: Operations on the female genital organs

-100 -50 0 50 100 150 200

Percentage Change

0

10

20

30

40

50

Perc

enta

ge o

f H

ospitals

Q4 2015 / 2014Q4 2014 / 2013

PR Chapter by Hospital

13: Obstetrical procedures

-100 -50 0 50 100 150 200

Percentage Change

0

10

20

30

40

50

Perc

enta

ge o

f H

ospitals

Q4 2015 / 2014Q4 2014 / 2013

PR Chapter by Hospital

10: Operations on the urinary system

Downward shift in OB procedures

Not quite as large a shift in urinary

procedures

Large increase in female genital procedures

with more variability across hospitals

(no diagnostic info in procedure codes)

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Project #3—Analysis of ICD-10 HCUP Data:

Principal Diagnosis – by ICD Chapter

-4.0

2.6

-31.5

5.9

-3.0

-3.5

1.1

9.0

6.6

3.2

-1.0

-7.3

-0.2

0.8

1.4

1.8

-40 -30 -20 -10 0 10 20

Blood (1.2%)

Skin (1.7%)

Ill-defined conditions (2.4%)

Nervous system (2.5%)

Endocrine, nutritional, metabolic (3.7%)

Genitourinary system (4.0%)

Neoplasms (4.3%)

Infectious, parasitic (4.9%)

Musculoskeletal system (6.4%)

Mental Illness (6.8%)

Injury and poisoning (7.8%)

Respiratory system (8.6%)

Digestive system (8.8%)

Perinatal (10.8%)

Pregnancy, childbirth, puerperium (11.3%)

Circulatory system (14.1%)

Percentage Change in Diagnosis Mix from Q4 2014–2015 Compared with Q4 2013–2014

Dia

gn

osis

Ch

ap

ter

(Perc

en

t in

2014)

Q4 2014–2015 Q4 2013–2014

20

Decreases in:

• Respiratory system

• Ill-defined conditions

Increases in:

• Musculoskeletal

• Infectious (but less

than 2013-2014)

• Nervous

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Project #3—Analysis of ICD-10 HCUP Data:

Secondary Diagnoses – by ICD Chapter

-8.5

3.7

-3.9

1.6

-2.4

1.3

-19.7

-5.3

-10.8

11.3

46.0

-2.0-3.6

11.9

-2.0

-1.7

-6.9

-30 -20 -10 0 10 20 30 40 50

Skin (1.2%)

Perinatal (1.5%)

Injury and poisoning (2.3%)

Neoplasms (2.3%)

Blood (3.7%)

Pregnancy, childbirth, puerperium (4.0%)

Infectious, parasitic (4.1%)

Musculoskeletal system (4.2%)

Nervous system (4.7%)

Respiratory system (5.1%)

Residual codes (5.2%)

Genitourinary system (5.8%)

Digestive system (6.3%)

Ill-defined conditions (6.4%)

Mental Illness (9.2%)

Endocrine, nutritional, metabolic (15.1%)

Circulatory system (18.3%)

Percentage Change in Diagnosis Mix from Q4 2014–2015 Compared with Q4 2013–2014

Dia

gn

osis

Ch

ap

ter

(Perc

en

t in

2014)

Q4 2014–2015 Q4 2013–2014

21

Decreases in:

• Infectious (19.7%)

• Nervous (10.8%)

• Skin (8.5%)

• Circulatory (6.9%)

Increases in:

• Ill-defined conditions

• Residual

• Respiratory

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Project #3—Analysis of ICD-10 HCUP Data:

MS-DRGs

% of cases assigned to

less severe DRGs

2013-2014: 10%

2014-2015: 24%

% of cases assigned to

more severe DRGs

2013-2014: 53%

2014-2015: 38%

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Project #3—Analysis of ICD-10 HCUP Data:

Conclusions

• Seeing some shifts in diagnoses and procedures

with the transition from ICD-9 to ICD-10

• Even at broad service lines

• Pattern of DRG assignment seemed to change with

ICD-10: increase in assignment of less severe DRGs

• Need to continue exploration

► More quarters of data

► More States

• Caution when interpreting trends

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Conclusion:

One Way to Deal with Trends

For the

foreseeable

future, we will

demarcate the

transition to

ICD-10 in our

trends

analyses

Page 25: Exploring ICD-10 Data What Impact on Trends Analysis? · Exploring ICD-10 Data What Impact on Trends Analysis? ... Alpha characters are in the middle of ranges ... Ill-defined conditions

Examples of Trend Lines

Page 26: Exploring ICD-10 Data What Impact on Trends Analysis? · Exploring ICD-10 Data What Impact on Trends Analysis? ... Alpha characters are in the middle of ranges ... Ill-defined conditions

More examples—

3 quarters of ICD-10 data

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More work to do

• Continuing analyses on how to handle CY 2015 data

in terms of creating our databases

► 3 quarters of ICD-9 and 1 quarter of ICD-10

• Open questions still being considered

► National estimates?

► Trends?

► How to structure our national databases for CY 2015

o 3 quarter of ICD-9, 4th quarter ICD-10

• What help do you need?