The Impact of Information Quality of Antimicrobial Susceptibility Test Report on the Rational Antimicrobial Use: A Retrospective Study

References

• World Health Organization. Antimicrobial resistance[EB/OL]. Available from: https://www.who.int/health-topics/antimicrobial-resistance. Accessed October 20, 2023.
   Google Scholar
• Allison MG, Heil EL, Hayes BD. Appropriate antibiotic therapy. Emerg Med Clin North Am. 2017;35(1):25–42. doi:10.1016/j.emc.2016.08.003
   PubMed Web of Science ®Google Scholar
• Machowska A, Stalsby LC. Drivers of irrational use of antibiotics in Europe. Int J Environ Res Public Health. 2018;16(1):27. doi:10.3390/ijerph16010027
   PubMed Web of Science ®Google Scholar
• Kerremans JJ, Verboom P, Stijnen T, et al. Rapid identification and antimicrobial susceptibility testing reduce antibiotic use and accelerate pathogen-directed antibiotic use. J Antimicrob Chemother. 2008;61(2):428–435. doi:10.1093/jac/dkm497
   PubMed Web of Science ®Google Scholar
• Wellcome Trust London E. Four diagnostic strategies for better-targeted antibiotic use.[M/OL]. Available from: https://wellcomecollection.org/works/javqbjc4. Accessed October 20, 2023.
   Google Scholar
• Gajdacs M. The continuing threat of methicillin-resistant staphylococcus aureus. Antibiotics. 2019;8(2):52. doi:10.3390/antibiotics8020052
   Web of Science ®Google Scholar
• Jorgensen JH, Ferraro MJ. Antimicrobial susceptibility testing: a review of general principles and contemporary practices. Clin Infect Dis. 2009;49(11):1749–1755. doi:10.1086/647952
   PubMed Web of Science ®Google Scholar
• Li S, Han Z, He J, et al. Society for translational medicine expert consensus on the use of antibacterial drugs in thoracic surgery. J Thorac Dis. 2018;10(11):6356–6374. doi:10.21037/jtd.2018.10.108
   PubMed Web of Science ®Google Scholar
• Cunney RJ, Smyth EG. The impact of laboratory reporting practice on antibiotic utilisation. Int J Antimicrob Agents. 2000;14(1):13–19. doi:10.1016/S0924-8579(99)00144-2
   PubMed Web of Science ®Google Scholar
• Ghalayini M, Antoun J, Moacdieh N. Too much or too little? Investigating the usability of high and low data displays of the same electronic medical record. Health Informatics J. 2018;26:246051812.
   Web of Science ®Google Scholar
• Campo L, Mylotte JM. Use of microbiology reports by physicians in prescribing antimicrobial agents. Am J Med Sci. 1988;296(6):392–398. doi:10.1097/00000441-198812000-00005
   PubMed Web of Science ®Google Scholar
• Simpson CW, Prusak L. Troubles with information overload—Moving from quantity to quality in information provision. Int J Inf Manage. 1995;15(6):413–425. doi:10.1016/0268-4012(95)00045-9
   Web of Science ®Google Scholar
• Shaughnessy AF, Slawson DC, Bennett JH. Becoming an information master: a guidebook to the medical information jungle. J Fam Pract. 1994;39(5):489–499.
   PubMed Web of Science ®Google Scholar
• Jackson TW, Farzaneh P. Theory-based model of factors affecting information overload. Int J Inf Manage. 2012;32(6):523–532. doi:10.1016/j.ijinfomgt.2012.04.006
   Web of Science ®Google Scholar
• Batini C, Scannapieco M. Data and Information Quality: Dimensions, Principles and Techniques //Batini C, Scannapieco M. Data and Information Quality: Dimensions, Principles and Techniques. Cham: Springer International Publishing; 2016:E413–E418.
   Google Scholar
• Diekema DJ, Lee K, Raney P, et al. Accuracy and appropriateness of antimicrobial susceptibility test reporting for bacteria isolated from blood cultures. J Clin Microbiol. 2004;42(5):2258–2260. doi:10.1128/JCM.42.5.2258-2260.2004
   PubMed Web of Science ®Google Scholar
• Juran J. Juran on Planning for Quality. New York: Simon and Schuster; 1988.
   Google Scholar
• Strong D, Lee Y, Wang R. Data Quality in Context. Commun ACM. 2002;2002:40.
   Google Scholar
• Batini C, Cappiello C, Francalanci C, et al. Methodologies for data quality assessment and improvement. ACM Comput Surv. 2009;41(3):1–52. doi:10.1145/1541880.1541883
   Web of Science ®Google Scholar
• Batini C. Data Quality Assessment//LIU L, ÖZSU M T. Encyclopedia of Database Systems. Boston, MA: Springer US; 2009:608–612.
   Google Scholar
• McGrath N, Foley B, Hurley C, et al. A multi-method quality improvement approach to systematically improve and promote the quality of national health and social care information. Health Inf Manag J. 2020;51(1):50–56. doi:10.1177/1833358320926422
   PubMedGoogle Scholar
• Wang RY, Strong DM. Beyond accuracy: what data quality means to data consumers. J Manag Inf Syst. 1996;12(4):5–33. doi:10.1080/07421222.1996.11518099
   Google Scholar
• Lee YW, Strong DM, Kahn BK, et al. AIMQ: a methodology for information quality assessment. Inf Manag. 2002;40(2):133–146. doi:10.1016/S0378-7206(02)00043-5
   Web of Science ®Google Scholar
• Batini C, Palmonari M, Viscusi G The many faces of information and their impact on information quality. Proceedings of ICIQ 2012: 17th International Conference on Information Quality; 2012.
   Google Scholar
• Sciacovelli L, Plebani M. The IFCC working group on laboratory errors and patient safety. Clin Chim Acta. 2009;404(1):79–85. doi:10.1016/j.cca.2009.03.025
   PubMed Web of Science ®Google Scholar
• Lu P, Liu Y, Toh H, et al. Epidemiology and antimicrobial susceptibility profiles of gram-negative bacteria causing urinary tract infections in the Asia-Pacific region: 2009–2010 results from the Study for Monitoring Antimicrobial Resistance Trends (SMART). Int J Antimicrob Agents. 2012;401:S37–S43. doi:10.1016/S0924-8579(12)70008-0
   Google Scholar
• Bai L, Meredith R, Burstein F. A data quality framework, method and tools for managing data quality in a health care setting: an action case study. J Decis Syst. 2018;27(sup1):1–11. doi:10.1080/12460125.2018.1460161
   Google Scholar
• Wang H, Ning Y, Chen H. Guideline and consensus: standardization of antibacterial susceptibility test report. Chin J Lab Med. 2016;39(01):18–22.
   Google Scholar
• China Antimicrobial Resistance Surveillance System. China antimicrobial resistance surveillance system technical program 2020 version[EB/OL]. Available from: http://www.carss.cn/Download/Details/657. Accessed October 20, 2023.
   Google Scholar
• China Antimicrobial Surveillance Network. China antimicrobial surveillance network technical program 2020 version[EB/OL]. Available from: http://www.chinets.com/Document. Accessed October 20, 2023.
   Google Scholar
• Clinical and Laboratory Standard Institute. Performance Standards for Antimicrobial Susceptibility Testing. 31st ed. Clinical And Laboratory Standard Institute; 2021.
   Google Scholar
• Byrd LW, Byrd TA Contrasting the dimensions of information quality in their effects on healthcare quality in hospitals. Proceedings of the 46th Annual Hawaii International Conference On System Sciences; 2013:2505–2514.
   Google Scholar
• Kurtzman ET, Greene J. Effective presentation of health care performance information for consumer decision making: a systematic review. Patient Educ Couns. 2016;99(1):36–43. doi:10.1016/j.pec.2015.07.030
   PubMed Web of Science ®Google Scholar
• Clinical And Laboratory Standard Institute. Performance Standards for Antimicrobial Susceptibility Testing. 29st ed. USA: Clinical And Laboratory Standard Institute; 2019.
   Google Scholar
• Urquhart J. Patient non-compliance with drug regimens: measurement, clinical correlates, economic impact. Eur Heart J. 1996;17(Suppl A):8–15. doi:10.1093/eurheartj/17.suppl_A.8
   PubMed Web of Science ®Google Scholar
• Villadsen A, Wulff J. Are you 110% sure? Modeling of fractions and proportions in strategy and management research. Strateg Organ. 2019;19:1029010728.
   Web of Science ®Google Scholar
• Papke LE, Wooldridge JM. Econometric methods for fractional response variables with an application to 401(k) plan participation rates. J Appl Econ. 1996;11(6):619–632. doi:10.1002/(SICI)1099-1255(199611)11:6<619::AID-JAE418>3.0.CO;2-1
   Web of Science ®Google Scholar
• Greene WH. Econometric Analysis. 7th ed. Prentice Hall; 1951.
   Google Scholar
• Gallani S, Krishnan R. Applying the Fractional Response Model to Survey Research in Accounting. Social Science Electronic Publishing; 2015.
   Google Scholar
• Yang CC, Fong Y, Lin LC, et al. The age-adjusted Charlson comorbidity index is a better predictor of survival in operated lung cancer patients than the Charlson and Elixhauser comorbidity indices. Eur J Cardiothorac Surg. 2018;53(1):235–240. doi:10.1093/ejcts/ezx215
   PubMed Web of Science ®Google Scholar
• Juneja YM, Thakar AB. Clinical evaluation of Basti administered by Basti Putak (Pressure method), Enema pot method (Gravity fed method), and syringe method in Kshinashukra (Oligozoospermia). Ayu. 2011;32(2):234–240. doi:10.4103/0974-8520.92593
   PubMedGoogle Scholar
• Sciacovelli L, Aita A, Padoan A, et al. Performance criteria and quality indicators for the post-analytical phase. Clin Chem Lab Med. 2016;54(7):1169–1176. doi:10.1515/cclm-2015-0897
   PubMed Web of Science ®Google Scholar
• Keenan G, Yakel E, Dunn LK, et al. Challenges to nurses’ efforts of retrieving, documenting, and communicating patient care information. J Am Med Inform Assoc. 2013;20(2):245–251. doi:10.1136/amiajnl-2012-000894
   PubMed Web of Science ®Google Scholar
• Morgan MS. Perceptions of a medical microbiology service: a survey of laboratory users. J Clin Pathol. 1995;48(10):915–918. doi:10.1136/jcp.48.10.915
   PubMed Web of Science ®Google Scholar
• Noble MA, Rennie R. Combined international external quality assessment results of medical laboratory performance and reporting of samples with known antimicrobial resistance. DIAGNOSIS. 2018;5(3):161–166. doi:10.1515/dx-2018-0020
   Web of Science ®Google Scholar
• Arts D, de Keizer NF, Scheffer GJ. Defining and improving data quality in medical registries: a literature review, case study, and generic framework. J Am Med Inform Assoc. 2002;9(6):600–611. doi:10.1197/jamia.M1087
   PubMed Web of Science ®Google Scholar
• Granato PA. The impact of same-day tests versus traditional overnight testing. Diagn Microbiol Infect Dis. 1993;16(3):237–243. doi:10.1016/0732-8893(93)90116-O
   PubMed Web of Science ®Google Scholar
• Chengalur-Smith IN, Ballou DP, Pazer HL. The impact of data quality information on decision making: an exploratory analysis. IEEE Trans Knowl Data Eng. 1999;11(6):853–864. doi:10.1109/69.824597
   Web of Science ®Google Scholar
• Graham M, Walker DA, Haremza E, et al. RCPAQAP audit of antimicrobial reporting in Australian and New Zealand laboratories: opportunities for laboratory contribution to antimicrobial stewardship. J Antimicrob Chemother. 2019;74(1):251–255. doi:10.1093/jac/dky398
   PubMed Web of Science ®Google Scholar