Abstract
Vancomycin is one of the most prescribed antibiotics in pediatric intensive care units (PICU) in US hospitals. However, a detailed understanding of workflow and information flow among various stakeholders regarding vancomycin treatment processes in clinical settings is lacking. We conducted direct observations and informant interviews to develop the mapping of key processes and information flow for vancomycin treatment, with an emphasis on therapeutic drug monitoring (TDM) dose adjustment decision-making. A health information technology (HIT) sociotechnical framework was used to identify EHR related safety concerns. A total of 27 vancomycin treatment activities were observed over a 60-h duration including infusion administration, infusion completion, trough concentration blood draw and therapeutic decision making processes. Workflow and information flow mappings revealed (1) deviations between the documented timestamp used for TDM decision making and the actual time the tasks executed and (2) the lack of information flow regarding infusion completion and interruption. Missing features, insufficient usability and lack of integration with workflow and communication in the EHR were deemed safety gaps that may affect the accuracy of therapeutic decisions. Our case study identified gaps in information flow among clinical team members via EHR in TDM processes to provide insights for the improvement of the EHR system for antibiotic treatment purposes. In particular, the potential harm of the missing, uncertain, and inaccurate documented TDM task times warrant further investigations.
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References
Brogan TV, Thurm C, Hersh AL, et al. Variability in antibiotic use across PICUs. Pediatr Crit Care Med. 2018;19(6):519-527.
Sosnin N, Curtis N, Cranswick N, Chiletti R, et al. Vancomycin is commonly under-dosed in critically ill children and neonates. Br J Clin Pharmacol. 2019;85(11):2591–2598.
Ringenberg T, Robinson C, Meyers R, et al. Achievement of Therapeutic Vancomycin Trough Serum Concentrations with Empiric Dosing in Neonatal Intensive Care Unit Patients, Pediatr. Infect. Dis. J. 2015;34 (7): 742-747.
Rybak MJ, Le J, Lodise TP, et al. Therapeutic monitoring of vancomycin for serious methicillin-resistant Staphylococcus aureus infections: A revised consensus guideline and review by the American Society of Health-System Pharmacists, the Infectious Diseases Society of America, the Pediatr. Am J Heal Pharm. 2020;77(11):835-864.
Morrison AP, Melanson SE, Carty MG, et al. What proportion of vancomycin trough levels are drawn too early? Am J Clin Pathol. 2012;137(3):472-478.
Peyko V, Friedman JM. Novel approach to vancomycin level monitoring: impact of a multidisciplinary monitoring system on timing of vancomycin levels. Am J Heal Pharm. 2018;75(3):121-126.
Krukas A, Franklin ES, Bonk C, et al. Identifying safety hazards associated with intravenous vancomycin through the analysis of patient safety event reports. Pennsylvania Patient Safety Authority. 2020;2(1):31-47.
Holstiege J, Mathes T, Pieper D. Effects of computer-aided clinical decision support systems in improving antibiotic prescribing by primary care providers: A systematic review. J Am Med Informatics Assoc. 2014;22(1):236-242.
Gonzalez D, Rao GG, Bailey SC, et al. Precision dosing: Public health need, proposed framework , and anticipated impact. Clin Transl Sci. 2017;10:443-454.
Das M, Eichner J. Challenges and barriers to clinical decision support (CDS) design and implementation experienced in the Agency for Healthcare Research and Quality CDS Demonstrations. Rockville (MD): Agency Healthc Res Qual; 2010:29. Report No.: 10–0064-EF. Contract No.: 290–04–0016. Prepared for the AHRQ National Resource Center for Health Information Technology.
Rawson TM, Moore LS, Charani E, et al. A systematic review of clinical decision support systems for antimicrobial management: are we failing to investigate these interventions appropriately? Clin Microbiol Infect. 2017;23(8):524-532.
Unertl KM, Weinger MB, Johnson KB, et al. Describing and modeling workflow and information flow in chronic disease care. J Am Med Informatics Assoc. 2009; 16(6):826-836.
Casper GR, Karsh BT, Calvin KL, et al. Designing a technology enhanced practice for home nursing care of patients with congestive heart failure. AMIA Annu Symp Proc. 2005:116–120.
Steckowych K, Smith M. Work flow process mapping to characterize office-based primary care medication use and safety : A conceptual approach. Res Soc Adm Pharm. 2019;15:378-386.
Medlock S, Wyatt JC, Patel VL, et al. Modeling information flows in clinical decision support: Key insights for enhancing system effectiveness. J Am Med Informatics Assoc. 2016;23(5):1001-1006.
Steckowych K, Smith M. Primary care workflow process mapping of medication-related activities performed by non-provider staff : A pilot project’s approach. Res Soc Adm Pharm. 2019;15(9):1107-1117.
Singh H, Sittig DF. Measuring and improving patient safety through health information technology : The Health IT Safety Framework. BMJ Qual Saf. 2016;25:226-232.
Sittig DF, Singh H. A new socio-technical model for studying health information technology in complex adaptive healthcare systems. Qual Saf Health Care. 2010;19(3):68-74.
Gooch P, Roudsari A. Computerization of workflows, guidelines, and care pathways: A review of implementation challenges for process-oriented health information systems. J Am Med Informatics Assoc. 2011;18(6):738-748.
Cheng CH, Goldstein MK, Geller E, et al. The Effects of CPOE on ICU workflow: an observational study. AMIA Annu Symp Proc. 2003:150–154.
Horsky J, McColgan K, Pang JE, et al. Complementary methods of system usability evaluation: Surveys and observations during software design and development cycles. J Biomed Inform. 2010;43(5):782-790.
Khairat S, Marc D, Crosby W, et al. Reasons for physicians not adopting clinical decision support systems: Critical analysis. J Med Internet Res. 2018;20(4).
Or C, Dohan M, Tan J. Understanding critical barriers to implementing a clinical information system in a nursing home through the lens of a socio-technical perspective. J Med Syst. 2014;38(9).
Nakajima K, Masuda S, Nakajima S. Exploring ways to capture and facilitate work-as-done that interact with health information technology. In: Braithwaite J, Wears RL, Hollnagel E, eds. Resilient Health Care, Volume 3: Reconciling Work-as-Imagined and Work-as-Done. 1st ed. London, UK: Taylor & Francis Group. 2016:236-246.
Braithwaite J, Wears RL, Hollnagel E. Conclusion: Pathways Towards Reconciling WAI and WAD. In: Braithwaite J, Wears RL, Hollnagel E, eds. Resilient Health Care, Volume 3: Reconciling Work-as-Imagined and Work-as-Done. 1st ed. London, UK: Taylor & Francis Group; 2017:171-175.
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This study was supported in part by College of Engineering and the Regenstrief Center for Healthcare Engineering at Purdue University.
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Tung, TH., DeLaurentis, P., Sinner, J.A. et al. Physical and information workflow mapping of vancomycin therapeutic drug management: A single site case study revealing potential gaps in the process. J Med Syst 45, 104 (2021). https://doi.org/10.1007/s10916-021-01784-x
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DOI: https://doi.org/10.1007/s10916-021-01784-x