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Measuring the Coding Quality of the Hospital Discharge Data Set in Belgium Measuring the Coding Quality of the Hospital Discharge Data Set in Belgium A.

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Presentation on theme: "Measuring the Coding Quality of the Hospital Discharge Data Set in Belgium Measuring the Coding Quality of the Hospital Discharge Data Set in Belgium A."— Presentation transcript:

1 Measuring the Coding Quality of the Hospital Discharge Data Set in Belgium Measuring the Coding Quality of the Hospital Discharge Data Set in Belgium A. Orban, MD L. Belmans, MD PCSI 2012 Avignon, Oct. 17th, 2012

2 1 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

3 2 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

4 3 Measuring Data Quality (1) Literature: gold standard = medical record audit Automated alternatives: PICQ TM Australia | ICD-10-AM HCAT Ireland | ICD-10-AM NCCI Edits, MUEs, CorrectCoder ® USA | CPT, HCPCS 3M TM Data Quality Editor 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,...

5 4 Measuring Data Quality (2) Data quality in Belgium: MoHs web based application Porta-Health checks technical characteristics of ICD codes yearly external audit by MoHs 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

6 5 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 and (ICD-10: I10.- and I11.-) codes to be combined e.g and (ICD-10: I12.- and N18.-) codes prohibited in Belgium e.g (ICD-10: R65.10) codes incompatible / unlikely with some parameters e.g (ICD-10: E66.-) with POA=NO

7 6 Methodology (1) Web platform: Belgian coding guidelines and FAQs only available in Dutch at this moment 10 voluntary participating institutions (= 17% of Flemish acute facilities) Hospital size (# beds)Participants 200– –6003 > 6003

8 7 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 using our dedicated coding alerts potential impact on DRG was left aside Restrictions: no gender and no age alerts (privacy) 6 months before3 months after

9 Results: individual use IdHospital size (# beds) Observation period (# wd) Days usedTotal pages viewed 1400– (71%)274 7> (53%) – (46%) – (8%) – (7%) – (7%) – (6%)59 2> (5%) – (5%)12 9> (5%)9 8 IdHospital size (# beds) Observation period (# wd) Days usedTotal pages viewed 1400– (71%)274 7> (53%) – (46%) – (8%) – (7%) – (7%) – (6%)59 2> (5%) – (5%)12 9> (5%)9 A B

10 Id #4, 5 and 9 didnt provide data Observed disparity: coding quality vs. case mix? This step only validates our coding alerts Results: coding alerts (1) IdHospital size (# beds) # inpatient stays # alerts triggered # stays triggering 1 alert 1400–60017, (2.42%) 2> 60048, ,064 (6.33%) 3200–40018,604451,487 (7.99%) 6400–60011, (4.91%) 7> 60036, (2.50%) 8200–4009, (1.57%) 10400–6007, (4.95%) 149, ,958 (4.66%) 9

11 Results: coding alerts (2) 75 coding alerts never triggered one coding alert triggered 1122 times in hospital #3

12 Number of triggered alerts seems to decrease Number of stays triggering an alert increases Results: pre–post comparison Id# inpatient stays # alerts triggered # stays triggering 1 alert signif. (t-test) 1before13, (2.30%) 1after4, (2.79%)P= before33, (2.45%) 7after2, (3.31%)P=

13 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 » 12

14 13 Questions ? Thank you Merci

15 14 Contact André J.B. ORBAN AZ Alma Hospital – Eeklo, Belgium Luc B.E. BELMANS RZ Heilig Hart Hospital – Tienen, Belgium


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