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Factors Affecting Prescriber Implementation of Computer-Generated Medication Recommendations in the SENATOR Trial: A Qualitative Study

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Abstract

Background

The SENATOR trial intervention included the provision of computer-generated medication recommendations to physician prescribers caring for hospitalised older adults (≥ 65 years), with the aim of reducing in-hospital adverse drug reactions. Interim data analysis during the trial revealed that the prescriber implementation rates of the computer-generated STOPP/START recommendations were lower than expected across all six trial sites.

Aim

The aim of this qualitative study was to identify the factors affecting prescriber implementation of the medication recommendations in the SENATOR trial.

Methods

Semi-structured interviews were conducted with trial researchers and physician prescribers who were provided with SENATOR recommendations. Content analysis was used to identify the most relevant domains from the Theoretical Domains Framework (TDF) that affected recommendation uptake.

Results

Ten trial researchers and fourteen prescribers were interviewed across the six trial sites. Eight TDF domains were found to be most relevant in affecting prescriber implementation: ‘environmental context and resources’, ‘goals’, ‘intentions’, ‘knowledge’, ‘beliefs about consequences’, ‘memory, attention and decision processes’, ‘social/professional role and identity’, and ‘social influences’. Interviewees felt that there was often a disconnect between the time prescribers were reviewing the patient and the point at which the recommendations were provided. However, when recommendations were reviewed, prescriber inertia was highly pervasive, with a particular reluctance to make pharmacotherapy changes outside their own specialty. Implementation was facilitated by recommendations reaching a ‘decision-maker’, but this was often not possible as the software could not evaluate the entire clinical context of patients, and thus frequently produced recommendations of low clinical relevance.

Conclusion

This study has demonstrated that the clinical relevance of the SENATOR prescribing recommendations was a significant factor affecting their implementation. Whilst software refinement will be necessary to improve the quality of recommendations, future interventions will need to be multifaceted to overcome the complex prescriber specialty culture within the acute hospital environment.

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References

  1. Dalton K, O’Brien G, O’Mahony D, Byrne S. Computerised interventions designed to reduce potentially inappropriate prescribing in hospitalised older adults: a systematic review and meta-analysis. Age Ageing. 2018;47(5):670–8.

    PubMed  Google Scholar 

  2. O’Mahony D, O’Sullivan D, Byrne S, O’Connor MN, Ryan C, Gallagher P. STOPP/START criteria for potentially inappropriate prescribing in older people: version 2. Age Ageing. 2015;44(2):213–8.

    PubMed  Google Scholar 

  3. O’Connor MN, O’Sullivan D, Gallagher PF, Eustace J, Byrne S, O’Mahony D. Prevention of hospital-acquired adverse drug reactions in older people using screening tool of older persons’ prescriptions and screening tool to alert to right treatment criteria: a cluster randomized controlled trial. J Am Geriatr Soc. 2016;64(8):1558–66.

    PubMed  Google Scholar 

  4. Lewin S, Glenton C, Oxman AD. Use of qualitative methods alongside randomised controlled trials of complex healthcare interventions: methodological study. BMJ. 2009;10(339):b3496.

    Google Scholar 

  5. Oakley A, Strange V, Bonell C, Allen E, Stephenson J. Process evaluation in randomised controlled trials of complex interventions. BMJ. 2006;332(7538):413–6.

    PubMed  PubMed Central  Google Scholar 

  6. Lavan AH, O’Mahony D, Gallagher P, Fordham R, Flanagan E, Dahly D, et al. The effect of SENATOR (Software ENgine for the Assessment and optimisation of drug and non-drug Therapy in Older peRsons) on incident adverse drug reactions (ADRs) in an older hospital cohort—trial protocol. BMC Geriatr. 2019;19(1):40.

    PubMed  PubMed Central  Google Scholar 

  7. Braun V, Clarke V. Successful qualitative research: a practical guide for beginners. New York: Sage; 2013.

    Google Scholar 

  8. Tong A, Sainsbury P, Craig J. Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups. Int J Qual Health Care. 2007;19(6):349–57.

    PubMed  Google Scholar 

  9. Cane J, O’Connor D, Michie S. Validation of the theoretical domains framework for use in behaviour change and implementation research. Implement Sci. 2012;24(7):37.

    Google Scholar 

  10. Francis JJ, Johnston M, Robertson C, Glidewell L, Entwistle V, Eccles MP, et al. What is an adequate sample size? Operationalising data saturation for theory-based interview studies. Psychol Health. 2010;25(10):1229–45.

    PubMed  Google Scholar 

  11. Hsieh HF, Shannon SE. Three approaches to qualitative content analysis. Qual Health Res. 2005;15(9):1277–88.

    PubMed  Google Scholar 

  12. Atkins L, Francis J, Islam R, O’Connor D, Patey A, Ivers N, et al. A guide to using the Theoretical Domains Framework of behaviour change to investigate implementation problems. Implement Sci. 2017;12(1):77.

    PubMed  PubMed Central  Google Scholar 

  13. Anrys P, Boland B, Degryse JM, De Lepeleire J, Petrovic M, Marien S, et al. STOPP/START version 2-development of software applications: easier said than done? Age Ageing. 2016;45(5):589–92.

    PubMed  Google Scholar 

  14. Craig P, Dieppe P, Macintyre S, Michie S, Nazareth I, Petticrew M. Developing and evaluating complex interventions: the new Medical Research Council guidance. BMJ. 2008;29(337):a1655.

    Google Scholar 

  15. O'Mahony D, Gudmundsson A, Soiza RL, Petrovic M, Cruz-Jentoft AJ, Cherubini A, et al. Prevention of adverse drug reactions in hospitalized older patients with multi-morbidity and polypharmacy: the SENATOR randomized controlled clinical trial. Age Ageing. 2020;49(4):605–14. https://doi.org/10.1093/ageing/afaa072

    Article  PubMed  Google Scholar 

  16. Lee EK, Mejia AF, Senior T, Jose J. Improving patient safety through medical alert management: an automated decision tool to reduce alert fatigue. AMIA Annu Symp Proc. 2010;2010:417–21.

    PubMed  PubMed Central  Google Scholar 

  17. Coombes ID, Stowasser DA, Coombes JA, Mitchell C. Why do interns make prescribing errors? A qualitative study. Med J Aust. 2008;188(2):89–94.

    PubMed  Google Scholar 

  18. Lewis PJ, Ashcroft DM, Dornan T, Taylor D, Wass V, Tully MP. Exploring the causes of junior doctors’ prescribing mistakes: a qualitative study. Br J Clin Pharmacol. 2014;78(2):310–9.

    PubMed  PubMed Central  Google Scholar 

  19. Spinewine A, Schmader KE, Barber N, Hughes C, Lapane KL, Swine C, et al. Appropriate prescribing in elderly people: how well can it be measured and optimised? Lancet. 2007;370(9582):173–84.

    PubMed  Google Scholar 

  20. Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ. 2005;330(7494):765.

    PubMed  PubMed Central  Google Scholar 

  21. Thursky K. Use of computerized decision support systems to improve antibiotic prescribing. Expert Rev Anti Infect Ther. 2006;4(3):491–507.

    PubMed  Google Scholar 

  22. Moxey A, Robertson J, Newby D, Hains I, Williamson M, Pearson SA. Computerized clinical decision support for prescribing: provision does not guarantee uptake. J Am Med Inform Assoc. 2010;17(1):25–33.

    PubMed  PubMed Central  Google Scholar 

  23. Stevenson KB, Barbera J, Moore JW, Samore MH, Houck P. Understanding keys to successful implementation of electronic decision support in rural hospitals: analysis of a pilot study for antimicrobial prescribing. Am J Med Qual. 2005;20(6):313–8.

    PubMed  Google Scholar 

  24. Baysari MT, Westbrook JI, Richardson KL, Day RO. The influence of computerized decision support on prescribing during ward-rounds: are the decision-makers targeted? J Am Med Inform Assoc. 2011;18(6):754–9.

    PubMed  PubMed Central  Google Scholar 

  25. Roumie CL, Elasy TA, Wallston KA, Pratt S, Greevy RA, Liu X, et al. Clinical inertia: a common barrier to changing provider prescribing behavior. Jt Comm J Qual Patient Saf. 2007;33(5):277–85.

    PubMed  Google Scholar 

  26. Anderson K, Freeman C, Rowett D, Burrows J, Scott I, Rigby D. Polypharmacy, deprescribing and shared decision-making in primary care: the role of the accredited pharmacist. J Pharm Pract Res. 2015;45(4):446–9.

    Google Scholar 

  27. Jones MI, Greenfield SM, Bradley CP. Prescribing new drugs: qualitative study of influences on consultants and general practitioners. BMJ. 2001;323(7309):378–81.

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Cullinan S, Fleming A, O’Mahony D, Ryan C, O’Sullivan D, Gallagher P, et al. Doctors’ perspectives on the barriers to appropriate prescribing in older hospitalized patients: a qualitative study. Br J Clin Pharmacol. 2015;79(5):860–9.

    PubMed  PubMed Central  Google Scholar 

  29. Martin P, Tamblyn R, Benedetti A, Ahmed S, Tannenbaum C. Effect of a pharmacist-led educational intervention on inappropriate medication prescriptions in older adults: the D-PRESCRIBE randomized clinical trial. JAMA. 2018;320(18):1889–98.

    PubMed  PubMed Central  Google Scholar 

  30. Tannenbaum C, Martin P, Tamblyn R, Benedetti A, Ahmed S. Reduction of inappropriate benzodiazepine prescriptions among older adults through direct patient education: the EMPOWER cluster randomized trial. JAMA Intern Med. 2014;174(6):890–8.

    PubMed  Google Scholar 

  31. Gordon M, Darbyshire D, Baker P. Non-technical skills training to enhance patient safety: a systematic review. Med Educ. 2012;46(11):1042–54.

    PubMed  Google Scholar 

  32. Pohontsch NJ, Heser K, Loffler A, Haenisch B, Parker D, Luck T, et al. General practitioners’ views on (long-term) prescription and use of problematic and potentially inappropriate medication for oldest-old patients—a qualitative interview study with GPs (CIM-TRIAD study). BMC Fam Pract. 2017;18(1):22.

    PubMed  PubMed Central  Google Scholar 

  33. Ekdahl AW, Hellström I, Andersson L, et al. Too complex and time-consuming to fit in!. Physicians' experiences of elderly patients and their participation in medical decision making: a grounded theory study BMJ Open. 2012;2:e001063.

    Google Scholar 

  34. Prosser H, Almond S, Walley T. Influences on GPs’ decision to prescribe new drugs-the importance of who says what. Fam Pract. 2003;20(1):61–8.

    PubMed  Google Scholar 

  35. Dalton K, O’Mahony D, O’Sullivan D, O’Connor MN, Byrne S. Prescriber implementation of STOPP/START recommendations for hospitalised older adults: a comparison of a pharmacist approach and a physician approach. Drugs Aging. 2019;36(3):279–88.

    PubMed  Google Scholar 

  36. Campbell NC, Murray E, Darbyshire J, Emery J, Farmer A, Griffiths F, et al. Designing and evaluating complex interventions to improve health care. BMJ. 2007;334(7591):455–9.

    PubMed  PubMed Central  Google Scholar 

  37. Lawton J, Jenkins N, Darbyshire J, Farmer A, Holman R, Hallowell N. Understanding the outcomes of multi-centre clinical trials: a qualitative study of health professional experiences and views. Soc Sci Med. 2012;74(4):574–81.

    PubMed  Google Scholar 

  38. Birken SA, Presseau J, Ellis SD, Gerstel AA, Mayer DK. Potential determinants of health-care professionals’ use of survivorship care plans: a qualitative study using the theoretical domains framework. Implement Sci. 2014;15(9):167.

    Google Scholar 

  39. Dyson J, Lawton R, Jackson C, Cheater F. Does the use of a theoretical approach tell us more about hand hygiene behaviour? The barriers and levers to hand hygiene. J Infect Prev. 2011;12(1):17–24.

    Google Scholar 

  40. Oates J. Use of Skype in interviews: the impact of the medium in a study of mental health nurses. Nurse Res. 2015;22(4):13–7.

    PubMed  Google Scholar 

  41. Hanna P. Using internet technologies (such as Skype) as a research medium: a research note. Qual Res. 2012;12(2):239–42.

    Google Scholar 

  42. Pearson SA, Moxey A, Robertson J, Hains I, Williamson M, Reeve J, et al. Do computerised clinical decision support systems for prescribing change practice? A systematic review of the literature (1990–2007). BMC Health Serv Res. 2009;28(9):154.

    Google Scholar 

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Acknowledgements

The authors wish to thank all the interviewees who kindly agreed to participate in this study. In addition, the authors would like to acknowledge the SENATOR research teams at each of the six trial sites who facilitated the conduct of this study. Finally, the authors thank Jessica Coyne for her contribution to this work.

Author information

Authors and Affiliations

  1. Pharmaceutical Care Research Group, School of Pharmacy, University College Cork, Cork, Ireland

    Kieran Dalton & Stephen Byrne

  2. Department of Medicine, University College Cork, Cork, Ireland

    Denis O’Mahony

  3. Geriatric Medicine, Cork University Hospital, Cork, Ireland

    Denis O’Mahony

  4. School of Pharmacy and Biomolecular Sciences, Royal College of Surgeons in Ireland, Dublin, Ireland

    Shane Cullinan

Authors
  1. Kieran Dalton
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  2. Denis O’Mahony
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  3. Shane Cullinan
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  4. Stephen Byrne
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Corresponding author

Correspondence to Kieran Dalton.

Ethics declarations

Ethical Approval

Ethical approval for this study was granted by the Clinical Research Ethics Committee of the Cork Teaching Hospitals, Cork, Ireland. As well as this, local ethical approval was also granted at each RCT site where English was not the first language of participants (sites 3–6 in Table 1), with participant information sheets and consent forms also translated into the participants’ native language.

Funding

This qualitative study received funding from the SENATOR project, which was supported by the European Commission’s Seventh Framework Programme FP7/2007-2013 under Grant agreement number 305930. Kieran Dalton and Denis O’Mahony received funding for their normal roles within the SENATOR project, but no extra funding was received for their work on this qualitative study. The Commission had no part in the design of this study, the collection, analysis and interpretation of the data, the writing of the report, or the decision to submit the article for publication. This research was also supported by funding from a Study within a Trial (SWAT) award from the Heath Research Board Trials Methodology Research Network (HRB-TMRN).

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Dalton, K., O’Mahony, D., Cullinan, S. et al. Factors Affecting Prescriber Implementation of Computer-Generated Medication Recommendations in the SENATOR Trial: A Qualitative Study. Drugs Aging 37, 703–713 (2020). https://doi.org/10.1007/s40266-020-00787-6

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  • DOI: https://doi.org/10.1007/s40266-020-00787-6

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