Healthcare data frequently lack the specificity level needed to achieve clinical and operational objectives such as optimising bed management. Pneumonia is a disease of importance as it accounts for more bed days than any other lung disease and has a varied aetiology. The condition has a range of SNOMED CT concepts with different levels of specificity.
Objective
This study aimed to quantify the importance of the specificity of an SNOMED CT concept, against well-established predictors, for forecasting length of stay for pneumonia patients.
Method
A retrospective data analysis was conducted of pneumonia admissions to a tertiary hospital between 2011 and 2021. For inclusion, the primary diagnosis was a subtype of bacterial or viral pneumonia, as identified by SNOMED CT concepts. Three linear mixed models were constructed. Model One included known predictors of length of stay. Model Two included the predictors in Model One and SNOMED CT concepts of lower specificity. Model Three included the Model Two predictors and the concepts with higher specificity. Model performances were compared.
Results
Sex, ethnicity, deprivation rank and Charlson Comorbidity Index scores (age-adjusted) were meaningful predictors of length of stay in all models. Inclusion of lower specificity SNOMED CT concepts did not significantly improve performance (ΔR2 = 0.41%, p = .058). SNOMED CT concepts with higher specificity explained more variance than each of the individual predictors (ΔR2 = 4.31%, p < .001).
Conclusion
SNOMED CT concepts with higher specificity explained more variance in length of stay than a range of well-studied predictors.
Implications
Accurate and specific clinical documentation using SNOMED CT can improve predictive modelling and the generation of actionable insights. Resources should be dedicated to optimising and assuring clinical documentation quality at the point of recording.
Abd-ElrazekMAEltahawiAAAbd ElazizMH, et al. (2021) Predicting length of stay in hospitals intensive care unit using general admission features. Ain Shams Engineering Journal12(4): 3691–3702. DOI: 10.1016/j.asej.2021.02.018.
2.
AlonsoVSantosJVPintoM, et al. (2020a) Health records as the basis of clinical coding: Is the quality adequate? A qualitative study of medical coders’ perceptions. Health Information Management Journal, 49: 28–37. DOI: 10.1177/1833358319826351.
3.
AlonsoVSantosJVPintoM, et al. (2020b) Problems and Barriers during the Process of Clinical Coding: a Focus Group Study of Coders’ Perceptions. Journal of Medical Systems, 44: 1–8. DOI: 10.1007/S10916-020-1532-X.
4.
BahrmannABennerLChristM, et al. (2019) The Charlson Comorbidity and Barthel Index predict length of hospital stay, mortality, cardiovascular mortality and rehospitalization in unselected older patients admitted to the emergency department. Aging Clinical and Experimental Research, 31: 1233–1242. DOI: 10.1007/s40520-018-1067-x.
5.
BaniasadiTKahnoujiKDavaridolatabadiN, et al. (2019) Factors affecting length of stay in Children Hospital in Southern Iran. BMC Health Services Research, 19: 1. DOI: 10.1186/s12913-019-4799-1.
6.
BatesDMächlerMBolkerB, et al. (2015) Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software67(1): 11. DOI: 10.18637/jss.v067.i01.
7.
BatiniCScannapiecoM (2016) Data and Information Quality: Dimensions, Priniciples, and Techniques. In Data-Centric Systems and Applications. 1st ed. Cham: Springer International Publishing. DOI: 10.1007/978-3-319-24106-7.
8.
BensonTGrieveG (2016) Principles of Health Interoperability. 3rd ed. Cham: Health Information Technology Standards. Springer. DOI: 10.1007/978-3-319-30370-3.
9.
BruceMSmithJ (2020) Length of stay among multi-ethnic psychiatric inpatients in the United Kingdom. Comprehensive Psychiatry102: 152201. DOI: 10.1016/j.comppsych.2020.152201.
10.
CabreMBolivarIPeraG, et al. (2004) Factors influencing length of hospital stay in community-acquired pneumonia: a study in 27 community hospitals. Epidemiology and Infection132(5): 821–829. DOI: 10.1017/S0950268804002651.
11.
ChalmersJCamplingJEllsburyG, et al. (2017) Community-acquired pneumonia in the United Kingdom: a call to action. Pneumonia9(1): 15. DOI: 10.1186/s41479-017-0039-9.
12.
CharlsonMSzatrowskiTPPetersonJ, et al. (1994) Validation of a combined comorbidity index. Journal of Clinical Epidemiology47(11): 1245–1251. DOI: 10.1016/0895-4356(94)90129-5.
13.
CirilloDNunez CarpinteroIValenciaA (2021) Artificial intelligence in cancer research: learning at different levels of data granularity. Molecular Oncology15(4): 817–829. DOI: 10.1002/1878-0261.12920.
14.
CoevoetVFressonJVieuxR, et al. (2013) Socioeconomic deprivation and hospital length of stay: A new approach using area-based socioeconomic indicators in multilevel models. Medical Care51(6): 548–554. DOI: 10.1097/MLR.0b013e3182928f84.
DaghistaniTAElshawiRSakrS, et al. (2019) Predictors of in-hospital length of stay among cardiac patients: A machine learning approach. International Journal of Cardiology288: 140–147. DOI: 10.1016/j.ijcard.2019.01.046.
DelvauxNVaesBAertgeertsB, et al. (2020) Coding Systems for Clinical Decision Support: Theoretical and Real-World Comparative Analysis. JMIR Formative Research4(10): 11. JMIR Form Res. DOI: 10.2196/16094.
HickeyJGiardinoE (2016). In: HickeyJ v.BrosnanCA (eds), Evaluation of Health Care Quality for DNPs. 3rd ed. New York, NY: Springer Publishing Company. DOI: 10.1891/9780826131584.
24.
LeeDCornetRLauF, et al. (2013) A survey of SNOMED CT implementations. Journal of Biomedical Informatics46(1): 87–96. DOI: 10.1016/j.jbi.2012.09.006.
25.
LeeDde KeizerNLauF, et al. (2014) Literature review of SNOMED CT use. Journal of the American Medical Informatics Association, 21. e11–e19. DOI: 10.1136/AMIAJNL-2013-001636.
26.
LeeSHRuanS-YPanS-C, et al. (2019) Performance of a multiplex PCR pneumonia panel for the identification of respiratory pathogens and the main determinants of resistance from the lower respiratory tract specimens of adult patients in intensive care units. Journal of Microbiology, Immunology and Infection52(6): 920–928. DOI: 10.1016/j.jmii.2019.10.009.
27.
LequertierVWangTFondrevelleJ, et al. (2021) Hospital length of stay prediction methods: A Systematic Review. Medical Care, 59: 929–938. DOI: 10.1097/MLR.0000000000001596.
28.
LiskRUddinMParbhooA, et al. (2019) Predictive model of length of stay in hospital among older patients. Aging Clinical and Experimental Research31(7): 993–999. DOI: 10.1007/s40520-018-1033-7.
29.
MathijssenNMCVerburgHvan LeeuwenCCG, et al. (2016) Factors influencing length of hospital stay after primary total knee arthroplasty in a fast-track setting. Knee Surgery, Sports Traumatology, Arthroscopy24(8): 2692–2696. DOI: 10.1007/s00167-015-3932-x.
30.
McBrideSJLawleyMJLerouxH, et al. (2012) Using Australian Medicines Terminology (AMT) and SNOMED CT-AU to better support clinical research. Studies in Health Technology and Informatics, 178: 144. DOI: 10.3233/978-1-61499-078-9-144.
31.
MillettERCde StavolaBLQuintJK, et al. (2015) Risk factors for hospital admission in the 28 days following a community-acquired pneumonia diagnosis in older adults, and their contribution to increasing hospitalisation rates over time: a cohort study. BMJ Open5(12): e008737. DOI: 10.1136/bmjopen-2015-008737.
32.
MurdochDRO’BrienKLScottJAG, et al. (2009) Breathing New Life into Pneumonia Diagnostics. Journal of Clinical Microbiology47(11): 3405–3408. DOI: 10.1128/JCM.01685-09.
33.
NakagawaSSchielzethH (2013) A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods in Ecology and Evolution4(2): 133–142. DOI: 10.1111/j.2041-210x.2012.00261.x.
34.
National Information Board (2014) Personalised Health and Care 2020 Using Data and Technology to Transform Outcomes for Patients and Citizens A Framework for Action. UK Government. November 2014.
OnoTTamaiATakeuchiD, et al. (2010) Predictors of length of stay in a ward for demented elderly: gender differences. Psychogeriatrics10(3): 153–159. doi: 10.1111/j.1479-8301.2010.00328.x.
39.
OstropoletsAReichCRyanP, et al. (2020) Characterizing database granularity using SNOMED-CT hierarchy. In AMIA Annual Symposium Proceedings 2020. American Medical Informatics Association: 983
40.
ParkH-AYuS-JJungH (2021) Strategies for Adopting and Implementing SNOMED CT in Korea. Healthcare Informatics Research27(1): 3–10. DOI: 10.4258/hir.2021.27.1.3.
41.
RobertsLCheungR (2017) The aetiology of infections: The significance for hospital management. British Journal of Health Care Management, 23: 271–280
42.
RobertsLTimmisJNagrechaR, et al. (2016) Capturing causative agents in clinical coding. British Journal of Health Care Management, 22: 461–468 DOI: 10.12968/bjhc.2016.22.9.461.
43.
RobertsLLanesSKyteJ, et al. (2022) Acute pain assessments and records: a pilot study of digital transformation. British Journal of Nursing31(10): 541–548. DOI: 10.12968/bjon.2022.31.10.541.
YadavAUnalMArmstrongPR, et al. (2019) P806 Impact of patient age, gender and season of admission on length of stay in hospital for acute inflammatory bowel disease admissions. Journal of Crohn’s and Colitis13(Supplement_1): S525–S525. DOI: 10.1093/ecco-jcc/jjy222.930.
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