Transcript 28th PCSI - Medische Registratie

Avignon, Oct. 17th, 2012
PCSI 2012
Measuring the Coding Quality
of the
Hospital Discharge Data Set
in Belgium
A. Orban, MD
L. Belmans, MD
Background
• 1990: start of HDDS recording in Belgium
• 2008: new model: « minimum hospital data »
• single recordset integrating medical, nursing and
administrative data
• ICD-9-CM (APR-DRG) & Belgian coding guidelines
• transition to ICD-10/PCS planned for 2015
• Budget of acute hospitals
•
•
•
•
40% fee-for-service (clinicians)
40% prospective financing (“BFM”) ~ DRG
fixed fees for medication, medical imaging & biology ~ DRG
various income
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Goals
1. Determine the best tool to evaluate
the coding quality in Belgium
2. Analyse the correlation between the
accessibility of coding guidelines and
coding quality
2
Measuring Data Quality (1)
• Literature: gold standard = medical record audit
• Automated alternatives:
•
•
•
•
PICQTM
HCAT
NCCI Edits, MUEs, CorrectCoder®
3MTM Data Quality Editor




Australia | ICD-10-AM
Ireland | ICD-10-AM
USA | CPT, HCPCS
USA | ICD-9-CM
• Other conclusion:
ICD code attribution improves with the easy
availability for coders of all relevant clinical
information, coding guidelines and standards,
knowledge of the coding process, anatomy, ...
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Measuring Data Quality (2)
• Data quality in Belgium:
• MoH’s web based application ‘Porta-Health’
 checks technical characteristics of ICD codes
• yearly external audit by MoH’s official (MD)
 40 stays per hospital – 108 acute hospitals – 1.5M stays
= 0.3% of all inpatient stays
• very few studies about hospital data quality
• no figures about internal auditing or peer reviewing
• Neighbouring countries:
• increasing importance of ICD coded data
• data quality measuring: same methods and same issues
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Coding alerts
• 198 dedicated queries: « coding alerts »
• based on ICD-9-CM & Belgian coding guidelines
• covering every chapter of ICD
• Technical approach:
• codes strictly incompatible
e.g. 401.- and 402.- (ICD-10: I10.- and I11.-)
• codes to be combined
e.g. 403.- and 585.- (ICD-10: I12.- and N18.-)
• codes prohibited in Belgium
e.g. 995.93 (ICD-10: R65.10)
• codes incompatible / unlikely with some parameters
e.g. 278.0- (ICD-10: E66.-) with POA=NO
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Methodology (1)
• Web platform: www.medischeregistratie.be
• Belgian coding guidelines and FAQ’s
• only available in Dutch at this moment
• 10 voluntary participating institutions
(= 17% of Flemish acute facilities)
Hospital size (# beds)
Participants
200–400
4
400–600
3
> 600
3
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Methodology (2)
• Each participant = unique id
• Use of the web platform is monitored:
• number of workdays used
• total number of pages viewed
• Comparison pre–post access to web tool
6 months before
3 months after
• using our dedicated coding alerts
• potential impact on DRG was left aside
• Restrictions:
• no gender and no age alerts (privacy)
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Results: individual use
Id
Hospital size
(# beds)
Observation
period (# wd)
Days used
Total pages
viewed
1
400–600
63
45 (71%)
274
7
> 600
57
30 (53%)
243
5
200–400
57
26 (46%)
268
10
400–600
53
4 (8%)
17
8
200–400
57
4 (7%)
12
3
200–400
43
3 (7%)
15
4
200–400
63
4 (6%)
59
2
> 600
63
3 (5%)
14
6
400–600
57
3 (5%)
12
9
> 600
57
3 (5%)
9
A
B
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Results: coding alerts (1)
Id
Hospital size
(# beds)
# inpatient
stays
# alerts
triggered
# stays triggering
≥ 1 alert
1
400–600
17,390
40
421 (2.42%)
2
> 600
48,441
108
3,064 (6.33%)
3
200–400
18,604
45
1,487 (7.99%)
6
400–600
11,129
46
546 (4.91%)
7
> 600
36,130
56
903 (2.50%)
8
200–400
9,770
31
153 (1.57%)
10
400–600
7,764
48
384 (4.95%)
149,228
123
6,958 (4.66%)
• Id #4, 5 and 9 didn’t provide data
• Observed disparity: ≠ coding quality vs. ≠ case mix?
• This step only validates our coding alerts
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Results: coding alerts (2)
one coding alert triggered
1122 times in hospital #3
75 coding alerts
never triggered
Results: pre–post comparison
Id
# inpatient
stays
# alerts
triggered
# stays triggering
≥ 1 alert
1
before
13,236
33
305 (2.30%)
1
after
4,154
23
116 (2.79%)
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before
33,925
55
830 (2.45%)
7
after
2,205
24
73 (3.31%)
signif.
(t-test)
P=.0731
P=.0124
• Number of triggered alerts seems to decrease
• Number of stays triggering an alert increases
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Conclusions
• Easy accessibility of all needed information
might help to a better understanding of evolving
rules and standards  better coding quality
• Correlation between web tool usage and
reducing coding errors could not be established
• short observation period (summer)
• low number of respondents
• Lack of interest and/or motivation of participants
for a free and accessible tool is more alarming
« Coding is always an issue »
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Questions
?
Thank you
Merci
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Contact
André J.B. ORBAN
AZ Alma Hospital – Eeklo, Belgium
[email protected]
Luc B.E. BELMANS
RZ Heilig Hart Hospital – Tienen, Belgium
[email protected]
www.medischeregistratie.be
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