Performance of ICD-10-AM codes for quality improvement monitoring of hospital-acquired pneumonia in a haematology-oncology casemix in Victoria,Australia
Restricted accessResearch articleFirst published online May, 2024
Performance of ICD-10-AM codes for quality improvement monitoring of hospital-acquired pneumonia in a haematology-oncology casemix in Victoria,Australia
The Australian hospital-acquired complication (HAC) policy was introduced to facilitate negative funding adjustments in Australian hospitals using ICD-10-AM codes.
Objective
The aim of this study was to determine the positive predictive value (PPV) of the ICD-10-AM codes in the HAC framework to detect hospital-acquired pneumonia in patients with cancer and to describe any change in PPV before and after implementation of an electronic medical record (EMR) at our centre.
Method
A retrospective case review of all coded pneumonia episodes at the Peter MacCallum Cancer Centre in Melbourne, Australia spanning two time periods (01 July 2015 to 30 June 2017 [pre-EMR period] and 01 September 2020 to 28 February 2021 [EMR period]) was performed to determine the proportion of events satisfying standardised surveillance definitions.
Results
HAC-coded pneumonia occurred in 3.66% (n = 151) of 41,260 separations during the study period. Of the 151 coded pneumonia separations, 27 satisfied consensus surveillance criteria, corresponding to an overall PPV of 0.18 (95% CI: 0.12, 0.25). The PPV was approximately three times higher following EMR implementation (0.34 [95% CI: 0.19, 0.53] versus 0.13 [95% CI: 0.08, 0.21]; p = .013).
Conclusion
The current HAC definition is a poor-to-moderate classifier for hospital-acquired pneumonia in patients with cancer and, therefore, may not accurately reflect hospital-level quality improvement. Implementation of an EMR did enhance case detection, and future refinements to administratively coded data in support of robust monitoring frameworks should focus on EMR systems.
Implications
Although ICD-10-AM data are readily available in Australian healthcare settings, these data are not sufficient for monitoring and reporting of hospital-acquired pneumonia in haematology-oncology patients.
Australian Consortium for Classification Development (2019a) Australian Coding Standards. Sydney, NSW: Report, Independent Hospital Pricing Authority.
4.
Australian Consortium for Classification Development (2019b) The International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification (ICD-10-AM) - Tabular List. Sydney, NSW: Report, Independent Hospital Pricing Authority.
5.
BakerDQuinnB (2018) Hospital Acquired Pneumonia Prevention Initiative-2: Incidence of nonventilator hospital-acquired pneumonia in the United States. American Journal of Infection Control46(1): 2–7.
6.
BartleyDPanchasarpRBowenS, et al. (2021) How accurately is hospital acquired pneumonia documented for the correct assignment of a hospital acquired complication (HAC)?Infection, Disease & Health26(1): 67–71.
7.
BestWRKhuriSFPhelanM, et al. (2002) Identifying patient preoperative risk factors and postoperative adverse events in administrative databases: results from the Department of Veterans Affairs National Surgical Quality Improvement Program. Journal of the American College of Surgeons194(3): 257–266.
8.
BossuytPMReitsmaJBBrunsDE, et al. (2015) STARD 2015: An updated list of essential items for reporting diagnostic accuracy studies. British Medical Journal351: h5527. PMID: 26511519.
9.
de BruinJSSeelingWSchuhC (2014) Data use and effectiveness in electronic surveillance of healthcare associated infections in the 21st century: a systematic review. Journal of the American Medical Informatics Association21(5): 942–951.
10.
Commonwealth of Australia (2020) Addendum to National Health Reform Agreement 2020-2025. Report. Canberra, ACT: Australian Government.
11.
DoktorchikCLuMQuanH, et al. (2020) A qualitative evaluation of clinically coded data quality from health information manager perspectives. Health Information Management Journal49(1): 19–27.
12.
DröslerS (2011) Vergleich der Qualitätsmessung nach §137 SGB V mit Messungen aus Routinedaten. Deutsche Medizinische Wochenschrift126: S58–S58.
13.
DukeGJMoranJLBerstenAD, et al. (2021) Hospital-acquired complications: the relative importance of hospital- and patient-related factors. Medical Journal of Australia. Epub ahead of print 2022/01/01. DOI: 10.5694/mja2.51375.
14.
El-SaedABalkhyHHWeberDJ (2013) Benchmarking local healthcare-associated infections: available benchmarks and interpretation challenges. Journal of Infection and Public Health6(5): 323–330.
15.
EwanVCWithamMDKiernanM, et al. (2017) Hospital-acquired pneumonia surveillance—an unmet need. The Lancet Respiratory Medicine5(10): 771–772.
16.
Fernando-CanavanLGustAHsuehA, et al. (2021) Measuring the economic impact of hospital-acquired complications on an acute health service. Australian Health Review45(2): 135–142.
17.
GeraedtsMSelbmannHK (2011) Benchmarking in der Gesundheitsversorgung: Fazit und Empfehlungen. Zeitschrift für Evidenz, Fortbildung und Qualität im Gesundheitswesen105(5): 412–416.
18.
Health Policy Analysis Pty Ltd (2013) Analysis of hospital-acquired diagnoses and their effect on case complexity and resource use - Final report. Sydney, NSW: Report, Australian Commission on Safety and Quality in Health Care.
19.
Holland-BillLXuHSørensenHT, et al. (2014) Positive predictive value of primary inpatient discharge diagnoses of infection among cancer patients in the Danish National Registry of Patients. Annals of Epidemiolology24(8): 593–597.
20.
HoranTCAndrusMDudeckMA (2008) CDC/NHSN surveillance definition of health care-associated infection and criteria for specific types of infections in the acute care setting. American Journal of Infection Control36(5): 309–332.
21.
HosseiniNKimiafarKMostafaviSM, et al. (2021) Factors affecting the quality of diagnosis coding data with a triangulation view: A qualitative study. International Journal of Health Planning and Management. Epub ahead of print 2021/05/27. DOI: 10.1002/hpm.3254.
22.
Independent Hospital Pricing Authority (2018) Pricing and Funding for Safety and Quality: Risk Adjustment Model for Hospital Acquired Complications - Version 3 March 2018. Report. Sydney, NSW: Independent Hospital Pricing Authority.
23.
Independent Hospital Pricing Authority (2020) Pricing and Funding for Safety and Quality: Risk Adjusted Model for Hospital Acquired Complications - March 2020. Report. Sydney, NSW: Independent Hospital Pricing Authority.
KlompasMKulldorffMPlattR (2008) Risk of misleading ventilator-associated pneumonia rates with use of standard clinical and microbiological criteria. Clinical Infectious Diseases46(9): 1443–1446.
26.
LawsonEHLouieRZingmondDS, et al. (2012) A comparison of clinical registry versus administrative claims data for reporting of 30-day surgical complications. Annals of Surgery256(6): 973–981.
27.
LexAGehlenborgNStrobeltH, et al. (2014) UpSet: Visualization of intersecting sets. IEEE Transactions on Visualization and Computer Graphics20(12): 1983–1992.
28.
LiLSunderlandNRathnayakeK, et al. (2020) Epidemiology of Sepsis in Australian Public Hospitals: A Mixed Methods, National Longitudinal Study (2013-2018). Sydney, NSW: Australian Commission on Safety and Quality in Health Care.
29.
MaassCKuskeSLessingC, et al. (2015) Are administrative data valid when measuring patient safety in hospitals? A comparison of data collection methods using a chart review and administrative data. International Journal for Quality in Health Care27(4): 305–313.
30.
MaassCSchleizWWeyermannM, et al. (2011) Krankenhaus-Routinedaten zur externen Qualitätssicherung? Vergleich von Qualitätsindikatoren anhand der Daten der gesetzlichen externen Qualitätssicherung (BQS) und Routinedaten. Deutsche Medizinische Wochenschrift136(9): 409–414.
31.
MichelJLNghiemHSJacksonTJ (2009) Using ICD-10-AM codes to characterise hospital-acquired complications. Health Information Management Journal38(3): 18–25.
32.
NaessensJMHuschkaTR (2004) Distinguishing hospital complications of care from pre-existing conditions. International Journal for Quality in Health Care16: i27–i35.
33.
National Health and Medical Research Council, Australian Research Council and the Australian Vice-Chancellor’s Committee (2007) National Statement on Ethical Conduct in Human Research 2007. Canberra, ACT: National Health and Medical Research Council. Updated May 2015.
34.
O'NeillEHumphreysH (2009) Use of surveillance data for prevention of healthcare-associated infection: risk adjustment and reporting dilemmas. Current Opinion in Infectious Diseases22(4): 359–363.
35.
Otero VarelaLDoktorchikCWiebeN, et al. (2021) Exploring the differences in ICD and hospital morbidity data collection features across countries: an international survey. BMC Health Services Research21(1): 308.
36.
PeasahSKMcKayNLHarmanJS, et al. (2013) Medicare non-payment of hospital-acquired infections: infection rates three years post implementation. Medicare & Medicaid Research Review3(3): E1–E13.
37.
RajaramRChungJWKinnierCV, et al. (2015) Hospital characteristics associated with penalties in the Centers for Medicare & Medicaid Services Hospital-Acquired Condition Reduction Program. Journal of the American Medical Association314(4): 375–383.
38.
Redondo-GonzálezOTeníasJMAriasÁ, et al. (2018) Validity and reliability of administrative coded data for the identification of hospital-acquired infections: an updated systematic review with meta-analysis and meta-regression analysis. Health Services Research53(3): 1919–1956.
39.
SeeIChangJGualandiN, et al. (2016) Clinical correlates of surveillance events detected by National Healthcare Safety Network pneumonia and lower respiratory infection definitions-Pennsylvania, 2011-2012. Infection Control & Hospital Epidemiology37(7): 818–824.
40.
ShepheardJLapizEReadC, et al. (2019) Reconciling hospital-acquired complications and CHADx+ in Victorian coded hospital data. Health Information Management Journal48(2): 76–86.
41.
SmibertOCSlavinMATehB, et al. (2020) Epidemiology and risks for infection following cytoreductive surgery and hyperthermic intra-peritoneal chemotherapy. Supportive Care in Cancer28(6): 2745–2752.
42.
SposatoKA (2020) Non-ventilator health care-associated pneumonia (NV-HAP): The infection preventionist's role in identifying NV-HAP. American Journal of Infection Control48(5S): A3–A6.
43.
StevensonKBKhanYDickmanJ, et al. (2008) Administrative coding data, compared with CDC/NHSN criteria, are poor indicators of health care-associated infections. American Journal of Infection Control36(3): 155–164.
44.
TehBWHarrisonSJWorthLJ, et al. (2015) Risks, severity and timing of infections in patients with multiple myeloma: a longitudinal cohort study in the era of immunomodulatory drug therapy. British Journal of Haematology171(1): 100–108.
45.
ValentineJHallLVerspoorK, et al. (2020a) Submission to the Independent Hospital Pricing Authority on the Pricing Framework for Australian Public Hospital Services 2021-22. ■■■: Independent Hospital Pricing Authority, 1–7.
46.
ValentineJCHallLSpelmanT, et al. (2020b) Burden and clinical outcomes of hospital-coded infections in patients with cancer: an 11-year longitudinal cohort study at an Australian cancer centre. Supportive Care in Cancer28(12): 6023–6034.
47.
ValentineJCHallLVerspoorKM, et al. (2021) Use of a Victorian statewide surveillance program to evaluate the burden of healthcare-associated Staphylococcus aureus bacteraemia and Clostridioides difficile infection in patients with cancer. Internal Medicine Journal. Epub ahead of print 2021/03/24. DOI: 10.1111/imj.1530110.1111/imj.15301.
48.
ValentineJCHallLVerspoorKM, et al. (2019) The current scope of healthcare-associated infection surveillance activities in hospitalized immunocompromised patients: a systematic review. International Journal of Epidemiology48(6): 1768–1782.
49.
van MourikMSMPerencevichENGastmeierP, et al. (2018) Designing surveillance of healthcare-associated infections in the era of automation and reporting mandates. Clinical Infectious Diseases66(6): 970–976.
50.
WabeNLiLLindemanR, et al. (2021) Evaluation of the accuracy of diagnostic coding for influenza compared to laboratory results: the availability of test results before hospital discharge facilitates improved coding accuracy. BMC Medical Informatics and Decision Making21(1): 168.
51.
WolfensbergerAMeierAHKusterSP, et al. (2018) Should International Classification of Diseases codes be used to survey hospital-acquired pneumonia?Journal of Hospital Infection99(1): 81–84.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
