Subscribe to RSS
Download PDF
DOI: 10.1055/s-0044-1786978
Factors Influencing Integration and Usability of Model-Informed Precision Dosing Software in the Intensive Care Unit
Funding M.G.C. is supported by a University of Queensland and the Australian National Health and Medical Research Council (NHMRC) (APP2002981) in the form of a Postgraduate Scholarship. J.A.R. is supported by an Australian National Health and Medical Research Council for a Centre of Research Excellence (APP2007007) and an Investigator Grant (APP2009736) as well as an Advancing Queensland Clinical Fellowship.
Abstract
Background Antimicrobial dosing in critically ill patients is challenging and model-informed precision dosing (MIPD) software may be used to optimize dosing in these patients. However, few intensive care units (ICU) currently adopt MIPD software use.
Objectives To determine the usability of MIPD software perceived by ICU clinicians and identify implementation barriers and enablers of software in the ICU.
Methods Clinicians (pharmacists and medical staff) who participated in a wider multicenter study using MIPD software were invited to participate in this mixed-method study. Participants scored the industry validated Post-study System Usability Questionnaire (PSSUQ, assessing software usability) and Technology Acceptance Model 2 (TAM2, assessing factors impacting software acceptance) survey. Semistructured interviews were used to explore survey responses. The framework approach was used to identify factors influencing software usability and integration into the ICU from the survey and interview data.
Results Seven of the eight eligible clinicians agreed to participate in the study. The PSSUQ usability scores ranked poorer than the reference norms (2.95 vs. 2.62). The TAM2 survey favorably ranked acceptance in all domains, except image. Qualitatively, key enablers to workflow integration included clear and accessible data entry, visual representation of recommendations, involvement of specialist clinicians, and local governance of software use. Barriers included rigid data entry systems and nonconformity of recommendations to local practices.
Conclusion Participants scored the MIPD software below the threshold that implies good usability. Factors such as availability of software support by specialist clinicians was important to participants while rigid data entry was found to be a deterrent.
Protection of Human and Animal Subjects
This study was approved by the Royal Brisbane and Women's Hospital Human Research Ethics committee (LNR/2020/QRBH/67100).
Publication History
Received: 28 October 2023
Accepted: 17 April 2024
Article published online:
16 May 2024
© 2024. Thieme. All rights reserved.
Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany
-
References
- 1 Roberts JA, Abdul-Aziz MH, Lipman J. et al; International Society of Anti-Infective Pharmacology and the Pharmacokinetics and Pharmacodynamics Study Group of the European Society of Clinical Microbiology and Infectious Diseases. Individualised antibiotic dosing for patients who are critically ill: challenges and potential solutions. Lancet Infect Dis 2014; 14 (06) 498-509
- 2 Roberts JA, Paul SK, Akova M. et al; DALI Study. DALI: defining antibiotic levels in intensive care unit patients: are current β-lactam antibiotic doses sufficient for critically ill patients?. Clin Infect Dis 2014; 58 (08) 1072-1083
- 3 Heil EL, Nicolau DP, Farkas A, Roberts JA, Thom KA. Pharmacodynamic target attainment for cefepime, meropenem, and piperacillin-tazobactam using a pharmacokinetic/pharmacodynamic-based dosing calculator in critically ill patients. Antimicrob Agents Chemother 2018; 62 (09) e01008-e01018
- 4 Jager NGL, Chai MG, van Hest RM, Lipman J, Roberts JA, Cotta MO. Precision dosing software to optimize antimicrobial dosing: a systematic search and follow-up survey of available programs. Clin Microbiol Infect 2022; 28 (09) 1211-1224
- 5 Chai MG, Cotta MO, Abdul-Aziz MH, Roberts JA. What are the current approaches to optimising antimicrobial dosing in the intensive care unit?. Pharmaceutics 2020; 12 (07) 638
- 6 Burton ME, Ash CL, Hill Jr DP, Handy T, Shepherd MD, Vasko MR. A controlled trial of the cost benefit of computerized Bayesian aminoglycoside administration. Clin Pharmacol Ther 1991; 49 (06) 685-694
- 7 Codina MS, Bozkir H, Jorda A. et al. Individualised antimicrobial dose optimisation: a systematic review and meta-analysis of randomised controlled trials. Clin Microbiol Infect 2023; 29 (07) 845-857
- 8 Neely MN, Kato L, Youn G. et al. Prospective trial on the use of trough concentration versus area under the curve to determine therapeutic vancomycin dosing. Antimicrob Agents Chemother 2018; 62 (02) e02042-e02017
- 9 Zhang Y, Wang T, Zhang D. et al. Therapeutic drug monitoring coupled with Bayesian forecasting could prevent vancomycin-associated nephrotoxicity in renal insufficiency patients: a prospective study and pharmacoeconomic analysis. Ther Drug Monit 2020; 42 (04) 600-609
- 10 Sabourenkov PE, McLeay RC. 1599. AUC24 vancomycin Bayesian-based dosing: increasing therapeutic target attainment with decreased TDM cost. Open Forum Infect Dis 2019; 6: S583-S583
- 11 van Lent-Evers NA, Mathôt RA, Geus WP, van Hout BA, Vinks AA. Impact of goal-oriented and model-based clinical pharmacokinetic dosing of aminoglycosides on clinical outcome: a cost-effectiveness analysis. Ther Drug Monit 1999; 21 (01) 63-73
- 12 Imani S, Alffenaar JW, Cotta MO. et al. Therapeutic drug monitoring of commonly used anti-infective agents: a nationwide cross-sectional survey of Australian hospital practices. Int J Antimicrob Agents 2020; 56 (06) 106180
- 13 Cresswell KM, Bates DW, Sheikh A. Ten key considerations for the successful implementation and adoption of large-scale health information technology. J Am Med Inform Assoc 2013; 20 (e1): e9-e13
- 14 Mosch LK, Poncette AS, Spies C. et al. Creation of an evidence-based implementation framework for digital health technology in the intensive care unit: qualitative study. JMIR Form Res 2022; 6 (04) e22866
- 15 Meyfroidt G. How to implement information technology in the operating room and the intensive care unit. Best Pract Res Clin Anaesthesiol 2009; 23 (01) 1-14
- 16 Kaplan B, Harris-Salamone KD. Health IT success and failure: recommendations from literature and an AMIA workshop. J Am Med Inform Assoc 2009; 16 (03) 291-299
- 17 Liberati EG, Ruggiero F, Galuppo L. et al. What hinders the uptake of computerized decision support systems in hospitals? A qualitative study and framework for implementation. Implement Sci 2017; 12 (01) 113
- 18 Irwin AD, Coin LJM, Harris PNA. et al. Optimising treatment outcomes for children and adults through rapid genome sequencing of sepsis pathogens. a study protocol for a prospective, multi-centre trial (DIRECT). Front Cell Infect Microbiol 2021; 11: 667680
- 19 Ivankova NV, Creswell JW, Stick SL. Using mixed-methods sequential explanatory design: from theory to practice. Field Methods 2006; 18: 3-20
- 20 Ancker JS, Benda NC, Reddy M, Unertl KM, Veinot T. Guidance for publishing qualitative research in informatics. J Am Med Inform Assoc 2021; 28 (12) 2743-2748
- 21 Rayhan RU, Zheng Y, Uddin E, Timbol C, Adewuyi O, Baraniuk JN. Administer and collect medical questionnaires with Google documents: a simple, safe, and free system. Appl Med Inform 2013; 33 (03) 12-21
- 22 Sauro J, Lewis JR. Standardized Usability Questionnaires. Quantifying the User Experience: Practical Statistics for User Research, 2nd Edition. 2016: 185-248
- 23 Venkatesh V, Davis FD. A theoretical extension of the Technology Acceptance Model: four longitudinal field studies. Manage Sci 2000; 46: 186-204
- 24 Gale NK, Heath G, Cameron E, Rashid S, Redwood S. Using the framework method for the analysis of qualitative data in multi-disciplinary health research. BMC Med Res Methodol 2013; 13: 117
- 25 Kantasiripitak W, Van Daele R, Gijsen M, Ferrante M, Spriet I, Dreesen E. Software tools for model-informed precision dosing: how well do they satisfy the needs?. Front Pharmacol 2020; 11: 620
- 26 Cresswell KM, Lee L, Mozaffar H, Williams R, Sheikh A. NIHR ePrescribing Programme Team. Sustained user engagement in health information technology: the long road from implementation to system optimization of computerized physician order entry and clinical decision support systems for prescribing in hospitals in England. Health Serv Res 2017; 52 (05) 1928-1957
- 27 Baysari M, Chan J, Carland J, Stocker S, Moran M, Day R. Usability of reports generated by a computerised dose prediction software. Stud Health Technol Inform 2018; 252: 27-32
- 28 Nanji KC, Garabedian PM, Shaikh SD. et al. Development of a perioperative medication-related clinical decision support tool to prevent medication errors: an analysis of user feedback. Appl Clin Inform 2021; 12 (05) 984-995
- 29 Sauro J, Lewis JR. Chapter 8 - Standardized usability questionnaires. In: Sauro J, Lewis JR. eds. Quantifying the User Experience, 2nd ed. Boston: Morgan Kaufmann; 2016: 185-248
- 30 Kumar AA, Burgard M, Stacey S. et al. An evaluation of the user-friendliness of Bayesian forecasting programs in a clinical setting. Br J Clin Pharmacol 2019; 85 (10) 2436-2441
- 31 Lanckohr C, Boeing C, De Waele JJ. et al. Antimicrobial stewardship, therapeutic drug monitoring and infection management in the ICU: results from the international A- TEAMICU survey. Ann Intensive Care 2021; 11 (01) 131
- 32 Hou J, Marriott D, Cattaneo D. et al. Therapeutic drug monitoring practices of anti-infectives: an Asia-wide cross-sectional survey. Front Pharmacol 2022; 13: 992354