Skip to main content
Download PDF

Would they accept it? An interview study to identify barriers and facilitators to user acceptance of a prescribing advice service

BMC Health Services Research volume 22, Article number: 514 (2022) Cite this article

Abstract

Objectives

Few studies have explored the factors influencing user uptake of interventions designed to enhance therapeutic drug monitoring (TDM). This study aimed to identify barriers and facilitators to acceptance of a pilot intervention, the TDM Advisory Service (the Service), that provided prescribing advice for the antibiotic, vancomycin at an Australian public hospital.

Methods

A sample of prescribers and pharmacists who had interacted with the Service (n = 10), and a sample who had not (n = 13), participated in semi-structured interviews. Interviews were transcribed verbatim and analysed independently by two researchers for emerging themes. The Theoretical Domains Framework (TDF) was used to synthesise barriers and facilitators to Service acceptance.

Results

Key barriers reported by participants who had interacted with the Service aligned with two TDF domains: ‘Social Influences’ (prescribing hierarchy) and ‘Environmental Context and Resources’ (accessibility of dose advice). For participants who had not interacted with the Service, key barriers aligned with two TDF domains: ‘Knowledge’ (uncertainty of Service processes) and ‘Environmental Context and Resources’ (accessibility of dose advice). Key facilitators for both participant groups aligned with ‘Beliefs about Consequences’ (improved prescribing and patient outcomes) and ‘Environmental Context and Resources’ (accessibility of dose advice). A novel domain, ‘Trust’, was identified.

Conclusions

Independent of participant interaction with the Service, knowledge of Service processes, perceived beneficial outcomes, improved accessibility, and trust in Service capabilities were key determinants of acceptance. This evidence can be used to inform the adoption of strategies to adapt and enhance integration of the Service into clinical workflow.

Peer Review reports

Background

Therapeutic drug monitoring (TDM) is the process by which biological samples (e.g. blood samples) are collected from a patient and analysed to determine the concentration of drug present to help guide subsequent dosing regimens [1]. The overall aim is to individualise drug dosing for each patient to achieve the therapeutic effect and/or avoid adverse effects. Vancomycin, a glycopeptide antibiotic, is the gold standard therapy for serious methicillin-resistant Staphylococcus aureus (MRSA) infections [2]. TDM is recommended for vancomycin due to its narrow therapeutic range, significant interpatient pharmacokinetic variability and associated concentration-dependent adverse effects, including nephrotoxicity [3,4,5]. Given that mortality from MRSA septicaemia and vancomycin-induced acute kidney injury is approximately 40 and 15%, respectively [6, 7], optimal dosing to limit the incidence of these outcomes is imperative. Despite prescriber awareness of available guidelines for vancomycin dosing and TDM [8, 9], audits of vancomycin therapy demonstrate suboptimal practices [9,10,11]. Of particular concern is the observed failure to adjust dosing appropriately in response to TDM [11]. Interventions designed to improve uptake of TDM-informed dose advice, including pharmacist-led initiatives [12], education programs [13] and clinical decision support systems [14], have had variable success. This variability is likely related to how readily interventions were integrated into user workflow.

Studies that have explored factors influencing uptake of TDM interventions have identified barriers including poor coordination of TDM processes, time constraints, perceived lack of user competency and the prescribing hierarchy [8, 15, 16]. While these findings are useful, systematically identifying barriers and facilitators to uptake using a theory-based framework provides an evidence-based approach to enhance intervention design and achieve successful integration into routine workflow [17, 18]. The Theoretical Domains Framework (TDF; Table 1) [19] has been successfully employed to understand barriers and facilitators to uptake of clinical interventions, such as best practices for management of nasogastric tubes [20], and to design targeted strategies to enhance implementation [20,21,22].

Table 1 The Theoretical Domains Framework with domain definitions and associated constructs
Full size table

In 2018, a TDM Advisory Service (the Service) was piloted at an Australian hospital [23]. The Service combined clinical expertise, alongside dose prediction software, to provide vancomycin dose advice to prescribers. Prior to piloting, the TDF was used to identify anticipated barriers to prescriber acceptance of dose prediction software recommendations [24]. Limited knowledge of software capabilities, as well as concerns about the impact on workload and patient outcomes were key barriers identified [24]. Multifaceted education strategies were implemented to address these barriers and support piloting of the Service. An interrupted time series analysis revealed that the Service increased the proportion of therapy spent in the target range [23], but provided limited information about how and why the Service was used. This current study aimed to identify barriers and facilitators to user acceptance of the Service to inform its adaptation and sustained use.

Methods

Study setting

Semi-structured interviews were undertaken (May 2019–January 2020) with prescribers and pharmacists to elicit their opinions of and experiences with the Service. The study was conducted at a 360-bed metropolitan Australian public hospital. An onsite pathology service provides vancomycin results 24 h-a-day, 7 days-a-week. All clinical areas, except the emergency department and outpatient clinics, use an electronic medication management system (eMMS).

Clinical context

Prior to piloting the Service, vancomycin dosing was based on trough concentrations and was supported by internal guidelines and the Antimicrobial Stewardship (AMS) team. At the study site, most patients on vancomycin receive a consult from AMS, including review by a pharmacist and/or an ID physician. Medications are prescribed by both junior and senior prescribers in Australian hospitals, with the majority of order-entry undertaken by juniors. Pharmacists do not have prescribing rights but play an important advisory role in the prescribing process. Process maps describing vancomycin dosing and TDM processes before (Supplementary Fig. 1) and after (Supplementary Fig. 2) introduction of the Service are provided.

TDM advisory Service

The aim of the Service was to improve compliance with antimicrobial guidelines at the institution [9] and to transition from trough- to AUC-guided dosing as per updated international consensus guidelines [25]. Service design was guided by previous research [26,27,28,29,30,31] and the expertise of a multidisciplinary committee, which included clinical pharmacologists, pathologists, infectious diseases and microbiology specialists, pharmacists, intensive care physicians and senior hospital scientists. It was tailored to accommodate local infrastructure and staffing limitations and was approved by formal governance processes.

The Service was staffed by Clinical Pharmacologists (three staff specialists and a registrar) and two senior hospital scientists. Support was provided by the AMS team. Prior to piloting, education sessions about vancomycin guidelines and Service processes were provided to all medical and surgical teams via different forums, including large scale presentations and small team meetings [23]. Attendance of education sessions was not enforced.

The Service operated on weekdays (9 am-5 pm) and provided advice after the first dose had been administered. Loading doses were prescribed with no input from the Service. Patients receiving intravenous vancomycin, regardless of infection, were identified daily from electronic prescriptions or referrals. Once identified, relevant patient information, including demographics and laboratory results, was collected and dose prediction software used to generate an individualised dose prediction to achieve an AUC24/MIC of 400–600 for the next 72 h of dosing. Dose predictions were generated and reviewed daily by the senior hospital scientists. After consultation with a clinical pharmacologist, dose advice was provided to clinical teams via electronic dose reports published in the patient pathology records every 72 h, or more frequently if needed. Doctors in the treating team were contacted by phone and/or page if a dose change was recommended. Acceptance of dose advice was at the discretion of the treating team.

The Service was piloted in July 2018 and transitioned to a referral basis in March 2020. Given the known lag between health system implementation and uptake, it was anticipated that not all healthcare professionals would have been aware of, or interacted directly with, the Service at the time of the study. Thus, participants were defined as either those who had not interacted (not received dose advice) with the Service, and those who had interacted (received dose advice) with the Service.

Data collection materials

An interview guide (Supplementary File 1) was designed with input from human factors (MB), implementation (NT) and clinical pharmacology (JC, SS, RY) researchers and a clinical pharmacologist (RD). Pilot interviews were conducted with three research students and a clinical pharmacology registrar to ensure clarity.

Two process maps, describing vancomycin dosing and TDM processes before and after introduction of the Service, and an example dose report were provided to participants during interviews (Supplementary Figs. 1–3). Process maps were designed with input from clinical pharmacology researchers, a clinical pharmacologist, and the hospital TDM Committee.

Recruitment

A targeted approach was used to recruit participants who were involved in vancomycin prescribing. Prescribers and pharmacists were invited via email or telephone to participate in the study. Initially, purposive recruitment was utilised, whereby healthcare professionals who had previous contact with the Service were invited to participate. Subsequently, a snowballing recruitment strategy was used, whereby participants recommended other colleagues involved in vancomycin prescribing. Individuals who had directly interacted with the Service, as well as those who had not interacted with the Service were recruited. Junior medical officers (JMOs), registrars, staff specialists and pharmacists were interviewed. JMOs were defined as interns (first-year postgraduate doctors) or residents (second-year postgraduate doctors). Registrars were defined as basic physician trainees or advanced trainees.

Data collection and analysis

Interviews were conducted face-to-face or via telephone. All were audio-taped and transcribed verbatim. Identifiable data were removed from transcripts before analysis. All transcripts were analysed independently by two investigators (RY, JC). First, interviews were read to become familiar with the data. Potential themes were then identified using an inductive approach. Interviews were conducted alongside analysis to confirm thematic saturation. Finally, two investigators (RY, JC) independently synthesised barriers and facilitators to acceptance of dose advice using the TDF. Barriers and facilitators reported by participants who had not interacted with the Service, and those who had, were coded separately. Investigators met periodically to discuss findings and resolve any discrepancies. TDF coding outcomes were discussed with a third researcher (NT) when consensus could not be reached.

Results

Interviews were conducted with prescribers (n = 17) and pharmacists (n = 6) from 25 clinical units. Some participants were affiliated with more than one team. Most participants were female (n = 12) and JMOs (n = 8). The reported experience with, and frequency of, vancomycin dosing and TDM varied (Table 2). Prescribers were reported to play key roles in the prescribing process; senior prescribers selecting the drug, while junior prescribers were responsible for “… physically prescribing vancomycin.” (P10, JMO). Of those who were aware of the pilot Service (n = 16), 10 had received dose advice. Despite not being aware of the Service, three additional participants reported having received dose advice via telephone from the Clinical Pharmacology registrar involved in the Service. Therefore, a total of 13 participants had received dose advice from the Service. The remaining 10 participants had not received dose advice from the Service. Regardless of interactions with the Service, participants described its role as providing “advice and information about dosing of vanc [omycin]” (P02, JMO). Barriers and facilitators reported by prescribers and pharmacists were similar, so are reported together.

Table 2 Participant demographics
Full size table

Barriers to acceptance of the pilot Service

For participants who had not interacted with the Service, seven TDF domains encapsulated barriers to acceptance of the Service (Table 3); ‘Knowledge’ and ‘Environmental Context and Resources’ were well represented. Five TDF domains captured the barriers to acceptance of the Service reported by participants who had interacted with the Service; ‘Social Influences’, and ‘Environmental Context and Resources’ were well represented. Barriers aligned with a novel TDF domain, ‘Trust’, were identified across both groups.

Table 3 Reported barriers to acceptance of the pilot Therapeutic Drug Monitoring Advisory Service (the Service) aligned with the domains of the Theoretical Domains Framework (TDF)
Full size table

Knowledge

Poor understanding of how the Service operated, including information collected and ability to cater for complex patients, was a key barrier to acceptance reported by participants who had not interacted with the Service: “… whether or not they’re on dialysis would be a big one, but then that would have to be plugged into the software recommendation as well” (P05, Registrar). Lack of awareness of the existence of the Service, as well as limited scientific knowledge to interpret terminology used in dose reports, were also noted. “I feel like I’ve prescribed vancomycin this year and this didn’t get used” (P20, Registrar).

Environmental context and resources

Accessibility of dose advice was a key concern across both groups. In particular, the lack of 24/7 decision support, dose report turnaround time and the effort required to source advice, were key barriers to acceptance of the Service. One registrar commented: “… it’s just how accessible the information is, and how much effort I would have to go to to source the information” (P18).

Participants who had not interacted with the Service identified poor communication between the Service and clinical teams as a potential hinderance to acceptance: “… with anything in healthcare, [it] comes down to how those results are communicated to a team” (P09, Registrar).

Social influences

For participants who had interacted with the Service, the prescribing hierarchy was reported to prevent acceptance. Advice provided by senior clinicians and clinical experts, including ID consultants, took precedence because “Ultimately, they [seniors] have the final say” (P01, JMO).

Facilitators to acceptance of the Service

For both participant groups two TDF domains, ‘Beliefs about Consequences’ and ‘Environmental Context and Resources’, captured key facilitators to acceptance of the Service (Table 4). Facilitators that aligned with a novel TDF domain, ‘Trust’, were also identified.

Table 4 Reported facilitators to acceptance of the pilot Therapeutic Drug Monitoring Advisory Service (the Service) aligned with the domains of the Theoretical Domains Framework (TDF)
Full size table

Beliefs about consequences

Across both groups, participants reported that acceptance of the Service was facilitated by a belief that the Service’s advice would result in better prescribing outcomes: “… the idea is that you’re achieving better therapeutic drug monitoring and individual dosing for patients so that you can achieve therapeutic levels more efficiently and for [a] greater period of time, as well as trying to mitigate those risks of developing toxicity of vancomycin” (P02, JMO). Additionally, the Service was identified by both groups as resulting in increased ease and efficiency of vancomycin prescribing, including reduced workload, attributed to not needing to collect specifically timed blood samples or interpret laboratory information. One registrar noted: “… if you don’t have to take trough levels, that’s pretty handy” (P20). Improved confidence and decision-making were also aligned with the dose advice.

Environmental context and resources

Both groups described efficient communication between the Service and medical teams, as well as easy access to online dose reports, as facilitators to acceptance of the Service. One JMO commented: “‘What should my next dose be?’, I would make that information as easily accessible as possible so that people don’t have to rummage through” (P04).

A novel TDF domain, ‘Trust’

Interview analysis revealed barriers and facilitators that aligned with a novel TDF domain, ‘Trust’ (Supplementary Table 1). Both participant groups reported trust in the Service due to the perception that those operating the Service were vancomycin prescribing experts: “I trust the expertise of the department” (P23, Staff Specialist). Participants who had interacted with the Service associated the Service with “another level of security … for the patients and for yourself” (P01, JMO).

A barrier to acceptance of the Service for both participant groups was a lack of trust in its ability to cater for every patient as it was “… a bit removed” (P19, Pharmacist) from the bedside: “… the person who’s writing this [the dose report] probably hasn’t seen the patient in person as well, so it’d be a bit hard to trust them just based on this alone” (P16, Registrar). Lack of trust in the Service was particularly evident when prescribing for complex patients. A pharmacist noted: “… it’s hard for me then to recommend it again until we work out the system for critically ill” (P21).

Discussion

Limited work has been undertaken to understand user integration of TDM interventions into routine workflow [8, 15, 16]. This study applied the TDF to synthesise barriers and facilitators to user uptake of a pilot TDM Service. Key barriers and facilitators to acceptance of the Service aligned with the TDF domains of ‘Knowledge’, ‘Beliefs about Consequences’, ‘Environmental Context and Resources’, and ‘Social Influences’. A novel TDF domain, ‘Trust’, was also identified.

The TDF domains identified here are similar to those identified prior to piloting of the Service [24], but the specific barriers and facilitators that align with the domains differ. This is consistent with a recent systematic review that revealed ongoing assessment of the dynamic needs of end-users is necessary to support the design, implementation and sustainability of hospital-based interventions [32]. Before piloting, barriers aligned with ‘Knowledge’ included concerns about not having sufficient scientific knowledge to interpret dose advice [24]. In the current study, although this concern was reported by some participants who had not interacted with the Service, a dominant barrier was a lack of understanding of Service processes. Similarly, concerns about the potential negative impact of the Service on workload reported before piloting [24] were not identified in the current study. Rather, the Service was reported to have positive impacts on workload, making prescribing decisions “so much easier” (P08, JMO). The shift in barriers and facilitators to acceptance of the Service reinforces the value of ongoing evaluation, particularly as an intervention moves from hypothetical to actual.

Integration of the pilot Service into current vancomycin prescribing processes was welcomed by most interview participants. However, the prescribing hierarchy, as was reported by both prescribers and pharmacists, appeared to be a dominant barrier to acceptance by participants who had interacted with the Service. Although doctors are central to medication decision-making, interprofessional relationships have a strong influence on this process [33,34,35]. If sustained acceptance of the Service by junior doctors is to be achieved, senior prescribers must support the Service. Tailored education strategies may help overcome the apparent reluctance of some senior prescribers to accept dose advice provided by the Service [26]. Understanding why they may choose to override dose advice is also necessary. This finding emphasises that intervention design and implementation must consider all members of the clinical team, and their influence on one another.

Positive perceptions of, and trust in, clinical interventions facilitate their uptake by healthcare professionals [36, 37]. Dual coding of barriers and facilitators to ‘Trust’ and the TDF domains of ‘Social Influences’, ‘Beliefs about Consequences’, ‘Knowledge’, and ‘Beliefs about Capabilities’ highlights the complexity of trust, but also provides natural levers to enhance trust in the Service. For example, both groups of participants reported concerns about the ability of the Service to provide dose advice for complex patients (‘Beliefs about Consequences’), expressing apprehension that relevant patient characteristics were not considered. They preferred instead to consult clinical experts, such as ID consultants. ‘Knowledge’ of Service processes would have provided reassurance as an ID consultant was involved in Service operations. Understanding the capability of the Service to cater for patients in the intensive care unit [27] could also overcome these concerns. Insufficient knowledge has been reported previously to prevent uptake of clinical interventions [16]. Strategies that harness the facilitators identified in this study could build trust in the Service to support sustained uptake by end-users.

Although our study was one of the first to examine acceptance of a TDM Service in-depth, the key barriers and facilitators identified are not unique to TDM interventions. Rather, they align with those reported in work identifying factors that influence prescribing [38,39,40,41]. This finding suggests that the end-users and context of use of an intervention are critical to ensuring an intervention is accepted and used, more so than the intervention itself. Following on from this, previously reported strategies addressing contextual barriers could be employed to improve acceptance of different prescribing interventions. For example, ensuring appropriate information technology infrastructure is available at different sites is essential to minimise barriers associated with accessibility (‘Environmental Context and Resources’) and facilitate incorporation of digital interventions into the workflow of users [42, 43]. Similarly, ensuring drug-specific requirements (e.g. timing of monitoring) are known and addressed will facilitate uptake. Overall, our study indicates that understanding the specific context of the dosing decision, and addressing context-related barriers is key in supporting prescribing behaviour change.

Only two groups of healthcare professionals, prescribers and pharmacists, were interviewed. However, participants were drawn from 25 clinical units, and different levels of seniority, thereby representing a strength of the study. The TDF was not used to devise interview questions, thus interviews may not have revealed outcomes relating to all domains. However, this design ensured questions captured outcomes beyond the TDF, such as trust. Using the TDF provides a theoretical basis for the selection of strategies to support uptake of the pilot Service [17, 19, 44]. Although this study was conducted at a single-centre, and evaluated a vancomycin-targeted intervention, insights provided by the TDF may prove useful when implementing TDM interventions at other institutions.

This study showed that barriers to acceptance of a TDM advisory service included uncertainty of Service processes, the prescribing hierarchy, and potential poor accessibility of dose advice. Key facilitators included perceived improvements in prescribing and patient outcomes and easy access to the dose advice. Trust was identified as a key factor, suggesting that strategies to build trust in the Service will facilitate user acceptance. The evidence gathered will be used to inform the design and implementation of strategies to adapt and enhance integration of the Service into clinical workflow.

Availability of data and materials

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Abbreviations

AMS:

Antimicrobial stewardship

ICU:

Intensive care unit

ID:

Infectious diseases

JMO:

Junior medical officer

MRSA:

Methicillin-resistant Staphylococcus aureus

NR:

Nil report

TDF:

Theoretical domains framework

TDM:

Therapeutic drug monitoring

References

  1. Gross AS. Best practice in therapeutic drug monitoring. Br J Clin Pharmacol. 1998;46(2):95–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Choo EK, Garro AC, Ranney ML, Meisel ZF, Morrow GK. Qualitative research in emergency care part i: research principles and common applications. Acad Emerg Med. 2015;22(9):1096–102.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Kang JS, Lee MH. Overview of therapeutic drug monitoring. Korean J Intern Med. 2009;24(1):1–10.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Begg EJ, Barclay ML, Kirkpatrick CJ. The therapeutic monitoring of antimicrobial agents. Br J Clin Pharmacol. 1999;47(1):23–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Darko W, Medicis JJ, Smith A, Guharoy R, Lehmann DE. Mississippi mud no more: cost-effectiveness of pharmacokinetic dosage adjustment of vancomycin to prevent nephrotoxicity. Pharmacotherapy. 2003;23(5):643–50.

    Article  PubMed  Google Scholar 

  6. Rybak MJ, Albrecht LM, Boike SC, Chandrasekar PH. Nephrotoxicity of vancomycin, alone and with an aminoglycoside. J Antimicrob Chemother. 1990;25(4):679–87.

    Article  CAS  PubMed  Google Scholar 

  7. Hanrahan TP, Kotapati C, Roberts MJ, et al. Factors associated with vancomycin nephrotoxicity in the critically ill. Anaesth Intensive Care. 2015;43(5):594–9.

    Article  CAS  PubMed  Google Scholar 

  8. Chan JOS, Baysari MT, Carland JE, Sandaradura I, Moran M, Day RO. Barriers and facilitators of appropriate vancomycin use: prescribing context is key. Eur J Clin Pharmacol. 2018;74(11):1523–9.

    Article  PubMed  Google Scholar 

  9. Carland JE, Stocker SL, Baysari MT, et al. Are vancomycin dosing guidelines followed? A mixed methods study of vancomycin prescribing practices. Br J Clin Pharmacol. 2021;87(11):4221-9.

  10. Morrison AP, Melanson SEF, Carty MG, Bates DW, Szumita PM, Tanasijevic MJ. What proportion of vancomycin trough levels are drawn too early?: Frequency and impact on clinical actions. Am J Clin Path. 2012;137(3):472–8.

    Article  CAS  PubMed  Google Scholar 

  11. Armstrong L, O'Neill E, Love B. Audit of vancomycin and gentamicin dosing and monitoring in Connolly Hospital Blanchardstown (CHB) 2014. BMC Proc. 2015;9(7):A21.

    Article  PubMed Central  Google Scholar 

  12. Smith AP, Millares-Sipin CA, James M, Cohen H. Impact of a pharmacist-initiated vancomycin monitoring program. Consult Pharm. 2016;31(9):505–10.

    Article  PubMed  Google Scholar 

  13. Li J, Udy AA, Kirkpatrick CM, Lipman J, Roberts JA. Improving vancomycin prescription in critical illness through a drug use evaluation process: a weight-based dosing intervention study. Int J Antimicrob Agents. 2012;39(1):69–72.

    Article  CAS  PubMed  Google Scholar 

  14. Pereboom M, Mulder IJ, Verweij SL, van der Hoeven RTM, Becker ML. A clinical decision support system to improve adequate dosing of gentamicin and vancomycin. Intl J Med Inform. 2019;124:1–5.

    Article  Google Scholar 

  15. Claeys KC, Hopkins TL, Brown J, Heil EL. Pharmacists’ perceptions of implementing a pharmacist-managed area under the concentration time curve-guided vancomycin dosing program at a large academic medical center. J Am Coll Clin Pharm. 2019;2(5):482–7.

    Article  Google Scholar 

  16. Newham R, Thomson AH, Semple Y, Dewar S, Steedman T, Bennie M. Barriers to the safe and effective use of intravenous gentamicin and vancomycin in Scottish hospitals, and strategies for quality improvement. Eur J Hosp Pharm. 2015;22(1):32–7.

    Article  Google Scholar 

  17. Michie S, Johnston M, Abraham C, et al. Making psychological theory useful for implementing evidence based practice: a consensus approach. Qual Saf Health Care. 2005;14(1):26–33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, Lowery JC. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Imp Sci. 2009;4(1):50.

    Google Scholar 

  19. Cane J, O'Connor D, Michie S. Validation of the theoretical domains framework for use in behaviour change and implementation research. Imp Sci. 2012;7:37.

    Google Scholar 

  20. Taylor N, Lawton R, Slater B, Foy R. The demonstration of a theory-based approach to the design of localized patient safety interventions. Imp Sci. 2013;8:123.

    Google Scholar 

  21. Debono D, Taylor N, Lipworth W, et al. Applying the Theoretical Domains Framework to identify barriers and targeted interventions to enhance nurses’ use of electronic medication management systems in two Australian hospitals. Imp Sci. 2017;12(1):42.

    Google Scholar 

  22. Fuller C, Besser S, Savage J, McAteer J, Stone S, Michie S. Application of a theoretical framework for behavior change to hospital workers’ real-time explanations for noncompliance with hand hygiene guidelines. Am J Infect Cont. 2014;42(2):106–10.

    Article  Google Scholar 

  23. Stocker SL, Carland JE, Reuter SE, et al. Evaluation of a pilot vancomycin precision dosing advisory service on target exposure attainment using an interrupted time series analysis. Clin Pharmacol Ther. 2021;109(1):212–21.

    Article  CAS  PubMed  Google Scholar 

  24. Carland JE, Elhage T, Baysari MT, et al. Would they trust it? An exploration of psychosocial and environmental factors affecting prescriber acceptance of computerised dose-recommendation software. Br J Clin Pharmacol. 2021;87(3):1215–33.

    Article  PubMed  Google Scholar 

  25. 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 Pediatric Infectious Diseases Society, and the Society of Infectious Diseases Pharmacists. Am J Health Syst Pharm. 2020;77(11):835–64.

    Article  PubMed  Google Scholar 

  26. Van Dort BA, Baysari MT, Carland JE, et al. Education to improve vancomycin use: the perspectives of educators and education recipients. Intern Med J. 2020;50(5):565–72.

    Article  PubMed  Google Scholar 

  27. Cunio CB, Uster DW, Carland JE, et al. Towards precision dosing of vancomycin in critically ill patients: an evaluation of the predictive performance of pharmacometric models in ICU patients. Clin Micro Infect. 2021;27(5):p783.e7-783.e14.

  28. Roydhouse SA, Carland JE, Debono DS, et al. Accuracy of documented administration times for intravenous antimicrobial drugs and impact on dosing decisions. Br J Clin Pharmacol. 2021;87(11):4273–82.

    Article  CAS  PubMed  Google Scholar 

  29. Shingde RV, Graham GG, Reuter SE, Carland JE, Day RO, Stocker SL. Comparison of the area under the curve for vancomycin estimated using compartmental and noncompartmental methods in adult patients with normal renal function. Ther Drug Monit. 2019;41(6):726–31.

    Article  CAS  PubMed  Google Scholar 

  30. Shingde RV, Reuter SE, Graham GG, et al. Assessing the accuracy of two Bayesian forecasting programs in estimating vancomycin drug exposure. J Antimicrob Chemother. 2020;75(11):3293–302.

    Article  CAS  PubMed  Google Scholar 

  31. 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.

  32. Geerligs L, Rankin NM, Shepherd HL, Butow P. Hospital-based interventions: a systematic review of staff-reported barriers and facilitators to implementation processes. Imp Sci. 2018;13(1):36.

    Google Scholar 

  33. Creswick N, Westbrook JI. Who do hospital physicians and nurses go to for advice about medications? A social network analysis and examination of prescribing error rates. J Patient Saf. 2015;11(3):152–9.

    Article  PubMed  Google Scholar 

  34. Chan B, Reeve E, Matthews S, et al. Medicine information exchange networks among healthcare professionals and prescribing in geriatric medicine wards. Br J Clin Pharmacol. 2017;83(6):1185–96.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Charani E, Castro-Sanchez E, Sevdalis N, et al. Understanding the determinants of antimicrobial prescribing within hospitals: the role of “prescribing etiquette”. Clin Infect Dis. 2013;57(2):188–96.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Mills PR, Weidmann AE, Stewart D. Hospital staff views of prescribing and discharge communication before and after electronic prescribing system implementation. Int J Clin Pharm. 2017;39(6):1320–30.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Yamada J, Potestio ML, Cave AJ, et al. Using the theoretical domains framework to identify barriers and enablers to pediatric asthma management in primary care settings. J Asthma. 2018;55(11):1223–36.

    Article  PubMed  Google Scholar 

  38. Cadogan CA, Ryan C, Francis JJ, et al. Improving appropriate polypharmacy for older people in primary care: selecting components of an evidence-based intervention to target prescribing and dispensing. Imp Sci. 2015;10:161.

    Google Scholar 

  39. Sargent L, McCullough A, Del Mar C, Lowe J. Using theory to explore facilitators and barriers to delayed prescribing in Australia: a qualitative study using the Theoretical Domains Framework and the Behaviour Change Wheel. BMC Fam Pract. 2017;18(1):20.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Fleming A, Bradley C, Cullinan S, Byrne S. Antibiotic prescribing in long-term care facilities: a qualitative, multidisciplinary investigation. BMJ Open. 2014;4(11):e006442.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Cadogan CA, Ryan C, Francis JJ, et al. Development of an intervention to improve appropriate polypharmacy in older people in primary care using a theory-based method. BMC Health Serv Res. 2016;16(1):661.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Frymoyer A, Schwenk HT, Zorn Y, et al. Model-informed precision dosing of vancomycin in hospitalized children: implementation and adoption at an Academic Children's Hospital. Front Pharmacol. 2020;11:551.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Rawson TM, Moore LSP, Hernandez B, 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):524–32.

    Article  CAS  PubMed  Google Scholar 

  44. French SD, Green SE, O’Connor DA, et al. Developing theory-informed behaviour change interventions to implement evidence into practice: a systematic approach using the Theoretical Domains Framework. Imp Sci. 2012;7(1):38.

    Google Scholar 

Download references

Acknowledgements

The authors would like to thank the members of the St. Vincent’s Hospital TDM Committee, for their input in the development of the process maps; Ms. Bethany Van Dort, for providing interview training; and Dr. Maurizio Stefani, for assisting with pilot interviews and for recommending potential interview participants.

Funding

None to declare.

Author information

Authors and Affiliations

  1. Department of Clinical Pharmacology and Toxicology, St Vincent’s Hospital, 390 Victoria Street, Darlinghurst, NSW, 2010, Australia

    Rachel Constance Yager, Sophie Lena Stocker, Richard Osborne Day & Jane Ellen Carland

  2. School of Medical Sciences, University of NSW, Kensington, NSW, Australia

    Rachel Constance Yager & Jane Ellen Carland

  3. School of Population Health, University of NSW, Kensington, NSW, Australia

    Natalie Taylor

  4. St Vincent’s Clinical Campus, University of NSW, Kensington, NSW, Australia

    Sophie Lena Stocker, Richard Osborne Day & Jane Ellen Carland

  5. School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia

    Sophie Lena Stocker

  6. Biomedical Informatics and Digital Health, School of Medical Sciences, Charles Perkins Centre, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia

    Melissa Therese Baysari

Authors
  1. Rachel Constance Yager
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Natalie Taylor
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sophie Lena Stocker
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Richard Osborne Day
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Melissa Therese Baysari
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jane Ellen Carland
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

JEC, SLS and ROD conceived the original concept. JEC, NT, SLS, MTB and ROD designed the methodology. RY, JEC and NT collected the data and conducted the analyses. All authors critically revised the manuscript. The author(s) read and approved the final manuscript.

Corresponding author

Correspondence to Jane Ellen Carland.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the St Vincent’s Hospital Human Research Ethics Committee (2019/ETH08756). Written consent was obtained from all participants. Participation was voluntary. No reimbursement was provided.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Supplementary File 1.

Interview guide. Supplementary Fig. 1. Process map depicting vancomycin TDM processes before implementation of the pilot Service. Tools used during interviews. Supplementary Fig. 2. Process map depicting vancomycin TDM processes after implementation of the pilot Service. Supplementary Fig. 3. A dose report provided for healthcare professionals by the pilot Service. Supplementary Table 1. Barriers and facilitators aligned with the novel domain, Trust.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yager, R.C., Taylor, N., Stocker, S.L. et al. Would they accept it? An interview study to identify barriers and facilitators to user acceptance of a prescribing advice service. BMC Health Serv Res 22, 514 (2022). https://doi.org/10.1186/s12913-022-07927-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12913-022-07927-1

Keywords

  • Vancomycin
  • Therapeutic drug monitoring
  • Theoretical domains framework
  • Consultative service
  • Dose prediction software

BMC Health Services Research

ISSN: 1472-6963

Contact us