Addressing the Overuse of Cultures to Optimize Patient Care
Authors: Valerie M. Vaughn, Julia E. Szymczak, Duane W. Newton, Mohamad G. Fakih
Journal: Annals of Internal Medicine (2019)

Supplement: STRIVE1 October 2019Addressing the Overuse of Cultures to Optimize Patient CareFREEValerie M. Vaughn, MD, MSc, Julia E. Szymczak, PhD, Duane W. Newton, PhD, and Mohamad G. Fakih, MD, MPHValerie M. Vaughn, MD, MScPatient Safety Enhancement Program, University of Michigan and Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, Michigan (V.M.V.)Search for more papers by this author, Julia E. Szymczak, PhDDepartment of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania (J.E.S.)Search for more papers by this author, Duane W. Newton, PhDClinical Microbiology Laboratory, Department of Pathology, Michigan Medicine, Ann Arbor, Michigan (D.W.N.)Search for more papers by this author, and Mohamad G. Fakih, MD, MPHCare Excellence, Ascension Healthcare, St. Louis, Missouri (M.G.F.)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/M18-3442 SectionsAboutVisual AbstractPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Microbiological testing helps clinicians identify pathogens and optimize treatment. However, infection may be misdiagnosed when a “positive” culture represents colonization or contamination. Misdiagnosis of infection may delay correct diagnosis, prompt inappropriate antibiotic use, and result in patient harm (1). Diagnostic stewardship can improve the appropriate use of microbiological tests and cultures. As diagnostic tests have proliferated, our reliance on tests for diagnosis has increased, even as our knowledge of test characteristics has decreased (1). Consideration of the mental models that contribute to overtesting can help us identify strategies to combat the “culture of culturing.”OvertestingReflexive TestingClinicians often over-rely on the perceived scientific objectivity of microbiological testing (2). Clinicians may reflexively initiate testing on the basis of a cursory evaluation of the patient, causing them to, for example, test for Clostridioides difficile when laxatives are the likely cause of a patient's symptoms (3). Reflexive testing may also happen because it is easier than careful history-taking in an environment where efficiency is prioritized and time is limited. Furthermore, social norms may lead to overculturing. For example, the “pan-culture” has been propagated by norms learned through the hidden curriculum of medical training and collectively reinforced in practice (4).More Data, More ComfortThe perceived objectivity of culture results may also provide comfort in the face of uncertainty (5). However, testing in low-yield settings, such as ordering blood cultures in patients receiving antibiotics, is more likely to result in false-positive results than helpful information. Grouped polymerase chain reaction–based panels exacerbate this problem. Rather than ordering 1 test, 15 are ordered. Grouped testing disregards the pretest probability, baseline prevalence, and sensitivity and specificity for individual tests. For example, gastrointestinal polymerase chain reaction panels have low yield in hospitalized patients and overdiagnose C difficile infection (6).Misunderstanding Normal Versus AbnormalClinicians may also misinterpret culture results. For example, findings of asymptomatic pyuria and bacteriuria often trigger inappropriate antibiotic use (7). If one falsely interprets a normal finding as abnormal, the subsequent management is not only unnecessary but potentially harmful.OvertreatingA “positive” culture may be difficult to ignore and diagnostic stewardship cannot eliminate all overculturing. We need strategies to combat the misconceptions that may lead to overtreatment.A “Positive” Test Result Equates With DiseaseTrue interpretation of a diagnostic test requires understanding pretest probability, baseline prevalence, specificity, and sensitivity. Having this level of knowledge for a single diagnostic test is difficult; for every test, Herculean. Furthermore, when interpreting results, providers may not have access to all information (for example, the reason a culture was ordered). In this scenario, clinicians may assume good reasons for testing and treat a “positive” culture with antibiotics, even if the patient currently lacks an indication for treatment. Although improved communication could help, cognitive biases further influence test interpretation even when all information is available. The microbiology laboratory can help clinicians by using the most appropriate diagnostic tests and framing results in a manner that informs decision-making, such as “no MRSA present” or “This result may represent asymptomatic bacteriuria.”Every Diagnosis Requires Medical TreatmentPhysicians may assume patients desire antibiotic treatment. Even when patients and providers recognize that antibiotics are unlikely to be helpful, providers may feel they should do something.SolutionsThe Appendix Table shows sociobehavioral strategies and system nudges to help clinicians avoid overuse of microbiological testing.Appendix Table. Mental Models, Potential Strategies, and Practical Examples to Reduce Overuse of Microbiological TestsSociobehavioral StrategiesSociobehavioral strategies are directed at the capacity of an individual clinician to make decisions or the social context in which decision making occurs. Often, these strategies require education, a change in social norms, and time. First, we must recognize the urge to culture and identify the reason behind it—are we treating the patient or provider uncertainty?Second, we must address the social norms underpinning the culture of culturing by modeling and modifying the expectations of those around us. Social norms are difficult to change, but certain strategies may help. For example, a 2-question rapid-algorithm combating organizational norms and common myths reduced inappropriate urine culturing and improved diagnosis of catheter-associated urinary tract infection (8). Other potential strategies include structured “time-outs,” checklists, mindfulness, cognitive behavioral training, public commitment, improving handoffs, and ensuring clinicians are comfortable questioning test utility (8, 9).System NudgesIn the chaotic world of clinical decision making, time pressure may trump best intentions. Fortunately, well-designed nudges can be used to systematically improve testing and treatment without usurping physician autonomy. Examples include automatic rejection of inappropriate cultures, removing overused cultures from order sets, strategic defaults, framing or selectively hiding culture results, and suggesting easy-to-order alternatives when inappropriate cultures are suspected (3, 10).ConclusionUnderstanding the mental models and social contexts that drive overuse of microbiological testing is key to reducing overdiagnosis and overtreatment of infection. With a combination of sociobehavioral fixes and well-designed system nudges, we can reduce indiscriminate culturing, limit antibiotic overuse, and improve patient outcomes.References1. Committee on Diagnostic Error in Health Care; Board on Health Care Services; Institute of Medicine; The National Academies of Sciences, Engineering, and Medicine. Improving Diagnosis in Health Care. Washington, DC: National Academies Pr; 2016. Google Scholar2. Goldstein Jutel A, Conrad P. Putting a Name to It: Diagnosis in Contemporary Society. Baltimore: Johns Hopkins Univ Pr; 2014. Google Scholar3. White DR, Hamilton KW, Pegues DA, et al. The impact of a computerized clinical decision support tool on inappropriate Clostridium difficile testing. Infect Control Hosp Epidemiol. 2017;38:1204-8. [PMID: 28760168] doi:10.1017/ice.2017.161 CrossrefMedlineGoogle Scholar4. Gabbay J, leMay A. Evidence based guidelines or collectively constructed “mindlines?” Ethnographic study of knowledge management in primary care. BMJ. 2004;329:1013. [PMID: 15514347] CrossrefMedlineGoogle Scholar5. Fox R. Training for Uncertainty. Cambridge: Harvard Univ Pr; 1957. Google Scholar6. Murphy CN, Fowler RC, Iwen PC, et al. Evaluation of the BioFire FilmArray® GastrointestinalPanel in a midwestern academic hospital. Eur J Clin Microbiol Infect Dis. 2017;36:747-54. [PMID: 27957599] doi:10.1007/s10096-016-2858-7 CrossrefMedlineGoogle Scholar7. Trautner BW, Bhimani RD, Amspoker AB, et al. Development and validation of an algorithm to recalibrate mental models and reduce diagnostic errors associated with catheter-associated bacteriuria. BMC Med Inform Decis Mak. 2013;13:48. [PMID: 23587259] doi:10.1186/1472-6947-13-48 CrossrefMedlineGoogle Scholar8. Naik AD, Skelton F, Amspoker AB, et al. A fast and frugal algorithm to strengthen diagnosis and treatment decisions for catheter-associated bacteriuria. PLoS ONE. 2017;12:e0174415. [PMID: 28350833] doi:10.1371/journal.pone.0174415 CrossrefMedlineGoogle Scholar9. McDonald EG, Saleh RR, Lee TC. Mindfulness-based laboratory reduction: reducing utilization through trainee-led daily ‘Time outs'. Am J Med. 2017;130:e241-4. [PMID: 28161348] doi:10.1016/j.amjmed.2017.01.011 CrossrefMedlineGoogle Scholar10. Shirley D, Scholtz H, Osterby K, et al. Optimizing inpatient urine culture ordering practices using the electronic medical record: a pilot study. Infect Control Hosp Epidemiol. 2017;38:486-8. [PMID: 28025951] doi:10.1017/ice.2016.301 CrossrefMedlineGoogle Scholar Comments0 CommentsSign In to Submit A Comment Author, Article, and Disclosure InformationAffiliations: Patient Safety Enhancement Program, University of Michigan and Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, Michigan (V.M.V.)Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania (J.E.S.)Clinical Microbiology Laboratory, Department of Pathology, Michigan Medicine, Ann Arbor, Michigan (D.W.N.)Care Excellence, Ascension Healthcare, St. Louis, Missouri (M.G.F.)Disclaimer: The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Centers for Disease Control and Prevention, the American Hospital Association, or the Department of Veterans Affairs.Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M18-3442.Corresponding Author: Valerie M. Vaughn, MD, MSc, 2800 Plymouth Road, Building 16, Room 472C, Ann Arbor, MI 48105; e-mail, [email protected]edu.Current Author Addresses: Dr. Vaughn: 2800 Plymouth Road, Building 16, Room 472C, Ann Arbor, MI 48109.Dr. Szymczak: Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Blockley Hall, Room 708, 423 Guardian Drive, Philadelphia, PA 19104-4302.Dr. Newton: Michigan Medicine, Department of Pathology, 2800 Plymouth Road, Building 35, Ann Arbor, MI 48109-2800.Dr. Fakih: 19251 Mack Avenue, Suite 190, Grosse Pointe Woods, MI 48236.Author Contributions: Conception and design: V.M. VaughnDrafting of the article: V.M. Vaughn, J.E. Szymczak, D.W. NewtonCritical revision of the article for important intellectual content: V.M. Vaughn, J.E. Szymczak, D.W. Newton, M.G. FakihFinal approval of the article: V.M. Vaughn, J.E. Szymczak, D.W. Newton, M.G. FakihAdministrative, technical, or logistic support: V.M. VaughnThis article is part of the Annals supplement “States Targeting Reduction in Infections via Engagement (STRIVE),” which was supported by the Health Research & Educational Trust and built upon work made possible by the Centers for Disease Control and Prevention, STRIVE Program (contract 200-2015-88275). 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