Skip to main content

Advertisement

Log in

Barriers and facilitators in using a Clinical Decision Support System for fall risk management for older people: a European survey

  • Research Paper
  • Published:
European Geriatric Medicine Aims and scope Submit manuscript

Key summary points

AbstractSection Aim

The aim of our study was to assess barriers and facilitators to CDSS use reported by European physicians treating older fallers and explore differences in their perceptions.

AbstractSection Findings

Our main findings were that a barrier to CDSS use is that physicians feel that complex geriatric patients need a physician’s clinical judgement and not the advice of a CDSS. Regional differences in barrier and facilitator perceptions occurred across Europe.

AbstractSection Message

Our main message is that when designing a CDSS for Geriatric falls patients, the patient’s medical complexity must be addressed whilst maintaining the doctor’s decision-making autonomy, and to increase successful CDSS implementation in Europe, regional differences in barrier perception should be overcome.

Abstract

Purpose

Fall-Risk Increasing Drugs (FRIDs) are an important and modifiable fall-risk factor. A Clinical Decision Support System (CDSS) could support doctors in optimal FRIDs deprescribing. Understanding barriers and facilitators is important for a successful implementation of any CDSS. We conducted a European survey to assess barriers and facilitators to CDSS use and explored differences in their perceptions.

Methods

We examined and compared the relative importance and the occurrence of regional differences of a literature-based list of barriers and facilitators for CDSS usage among physicians treating older fallers from 11 European countries.

Results

We surveyed 581 physicians (mean age 44.9 years, 64.5% female, 71.3% geriatricians). The main barriers were technical issues (66%) and indicating a reason before overriding an alert (58%). The main facilitators were a CDSS that is beneficial for patient care (68%) and easy-to-use (64%). We identified regional differences, e.g., expense and legal issues were barriers for significantly more Eastern-European physicians compared to other regions, while training was selected less often as a facilitator by West-European physicians. Some physicians believed that due to the medical complexity of their patients, their own clinical judgement is better than advice from the CDSS.

Conclusion

When designing a CDSS for Geriatric Medicine, the patient’s medical complexity must be addressed whilst maintaining the doctor’s decision-making autonomy. For a successful CDSS implementation in Europe, regional differences in barrier perception should be overcome. Equipping a CDSS with prediction models has the potential to provide individualized recommendations for deprescribing FRIDs in older falls patients.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Availability of data and materials

Data are available on request.

References

  1. de Vries M et al (2018) Fall-risk-increasing drugs: a systematic review and meta-analysis: I. Cardiovascular drugs. J Am Med Dir Assoc 19(4):371.e1-371.e9

    Article  Google Scholar 

  2. Seppala LJ et al (2018) Fall-risk-increasing drugs: a systematic review and meta-analysis: II. Psychotropics. J Am Med Dir Assoc 19:371

    PubMed  Google Scholar 

  3. Seppala LJ et al (2018) Fall-risk-increasing drugs: a systematic review and meta-analysis: III. Others. J Am Med Dir Assoc 19:371

    PubMed  Google Scholar 

  4. Gillespie S, Robertson LD, Gillespie MC, Sherrington WJ, Gates C, Clemson S, Lamb LM (2012) Interventions for preventing falls in older people living in the community. Cochrane Database Syst Rev 9:2012

    Google Scholar 

  5. Laflamme L, Monárrez-Espino J, Johnell K, Elling B, Möller J (2015) Type, number or both? A population-based matched case-control study on the risk of fall injuries among older people and number of medications beyond fall-inducing drugs. PLoS ONE 10(3):1–12

    Article  Google Scholar 

  6. Clemson L, Stark S, Pighills AC, Torgerson DJ, Sherrington C, Lamb SE (2019) Environmental interventions for preventing falls in older people living in the community. Cochrane Database Syst Rev 2:2019

    Google Scholar 

  7. Seppala LJ et al (2019) EuGMS task and finish group on fall-risk-increasing drugs (FRIDs): position on knowledge dissemination, management, and future research. Eur Geriatr Med 10(2):275–283

    Article  CAS  PubMed  Google Scholar 

  8. Bell HT, Steinsbekk A, Granas AG (2015) Factors influencing prescribing of fall-risk-increasing drugs to the elderly: a qualitative study. Scand J Prim Health Care 33(2):107–114

    Article  PubMed  PubMed Central  Google Scholar 

  9. Strickland J (2015) Translational Biomedical Informatics: A Precision Medicine Perspective, ISBN : 9789811015038

  10. Monteiro L, Maricoto T, Solha I, Ribeiro-Vaz I, Martins C, Monteiro-Soares M (2019) Reducing potentially inappropriate prescriptions for older patients using computerized decision support tools—a systematic review (Preprint). J Med Internet Res 21:11

    Article  Google Scholar 

  11. Groshaus H, Boscan A, Khandwala F, Holroyd-Leduc J (2012) Use of clinical decision support to improve the quality of care provided to older hospitalized patients. Appl Clin Inform 3(1):94–102

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Meulendijk MC et al (2015) Computerized decision support improves medication review effectiveness: an experiment evaluating the STRIP assistant’s usability. Drugs Aging 32(6):495–503

    Article  PubMed  PubMed Central  Google Scholar 

  13. Nanji KC et al (2018) Medication-related clinical decision support alert overrides in inpatients. J Am Med Inform Assoc 25(5):476–481

    Article  PubMed  Google Scholar 

  14. Van de Velde S et al (2018) A systematic review of trials evaluating success factors of interventions with computerised clinical decision support. Implement Sci 13(1):1–11

    Article  Google Scholar 

  15. Grol R, Wensing M, Eccles M, Davis D (2005) Improving patient care: the implementation of change in health care, 2nd edn. Elsevier, Edinburgh

    Google Scholar 

  16. Jung M et al (2013) Attitude of physicians towards automatic alerting in computerized physician order entry systems: a comparative international survey. Methods Inf Med 52(2):99–108

    Article  CAS  PubMed  Google Scholar 

  17. Mulder-Wildemors LGM, Heringa M, Floor-Schreudering A, Jansen PAF, Bouvy ML (2020) Reducing Inappropriate drug use in older patients by use of clinical decision support in community pharmacy: a mixed-methods evaluation. Drugs Aging 37(2):115–123

    Article  PubMed  Google Scholar 

  18. Baysari MT, Westbrook JI, Egan B, Day RO (2013) Identification of strategies to reduce computerized alerts in an electronic prescribing system using a Delphi approach. Stud Health Technol Inform 192(1–2):8–12

    PubMed  Google Scholar 

  19. Boyd CM, Kent DM (2014) Evidence-based medicine and the hard problem of multimorbidity. J Gen Intern Med 29(4):552–553

    Article  PubMed  PubMed Central  Google Scholar 

  20. Chua AQ et al (2018) Psychosocial determinants of physician acceptance toward an antimicrobial stewardship program and its computerized decision support system in an acute care tertiary hospital. J Am Coll Clin Pharm 1(1):e1–e8

    Article  Google Scholar 

  21. Trinkley KE et al (2019) Clinician preferences for computerised clinical decision support for medications in primary care: a focus group study. BMJ Heal Care Inf 26(1):1–8

    Google Scholar 

  22. Lupiáñez-Villanueva F et al (2018) Digital agenda for europe benchmarking deployment of eHealth among General Practitioners Internal identification. https://data.europa.eu/doi/10.2759/511610

  23. UNSD (2016) UNSD—Standard country or area codes for statistical use (M49). In: 25. United Nations Statistics Division- Standard Country and Area Codes Classifications (M49). https://unstats.un.org/unsd/methodology/m49/

  24. Kux BR, Majeed RW, Ahlbrandt J, Röhrig R (2017) Factors influencing the implementation and distribution of Clinical Decision Support Systems (CDSS). Stud Health Technol Inform 243:127–131

    PubMed  Google Scholar 

  25. Lamb SE, Jørstad-Stein EC, Hauer K, Becker C (2005) Development of a common outcome data set for fall injury prevention trials: the prevention of falls network Europe consensus. J Am Geriatr Soc 53(9):1618–1622

    Article  PubMed  Google Scholar 

  26. Westerbeek L et al (2021) Barriers and facilitators influencing medication-related CDSS acceptance according to clinicians: a systematic review. Int J Med Inform 152:104506

    Article  PubMed  Google Scholar 

  27. Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q Manag Inf Syst 13(3):319–339

    Article  Google Scholar 

  28. Lunney GH (1970) Using analysis of variance with a dichotomous dependent variable: an empirical study 1. J Educ Meas 7(4):263–269

    Article  Google Scholar 

  29. Lapane KL, Waring ME, Schneider KL, Dubé C, Quilliam BJ (2008) A mixed method study of the merits of e-prescribing drug alerts in primary care. J Gen Intern Med 23(4):442–446

    Article  PubMed  PubMed Central  Google Scholar 

  30. Tsopra R, Jais JP, Venot A, Duclos C (2014) Comparison of two kinds of interface, based on guided navigation or usability principles, for improving the adoption of computerized decision support systems: application to the prescription of antibiotics. J Am Med Inf Assoc 21(E2):107–116

    Article  Google Scholar 

  31. Sirajuddin AM, Osheroff JA, Sittig DF, Chuo J, Velasco F, Collins DA (2009) Implementation pearls from a new guidebook on improving medication use and outcomes with clinical decision support. Effective CDS is essential for addressing healthcare performance improvement imperatives. J Healthc Inf Manag 23(4):38–45

    PubMed  PubMed Central  Google Scholar 

  32. Gagnon MP, Nsangou ÉR, Payne-Gagnon J, Grenier S, Sicotte C (2014) Barriers and facilitators to implementing electronic prescription: a systematic review of user groups’ perceptions. J Am Med Informatics Assoc 21(3):535–541

    Article  Google Scholar 

  33. Mooijaart SP et al (2015) Evidence-based medicine in older patients: How can we do better? Neth J Med 73(5):211–218

    CAS  PubMed  Google Scholar 

  34. Herrera AP, Snipes SA, King DW, Torres-Vigil I, Goldberg DS, Wenberg AD (2010) Disparate inclusion of older adults in clinical trials: priorities and opportunities for policy and practice change. Am J Public Health 100(SUPPL. 1):105–112

    Article  Google Scholar 

  35. Hofstede G, Hofstede GJ, Minkov M (2010) Cultures and organisation, software of the mind, intercultural cooperation and its importance. ISBN: 978-0-07-177015-6

  36. Singler K, Holm EA, Jackson T, Robertson G, Müller-Eggenberger E, Roller RE (2015) European postgraduate training in geriatric medicine: data of a systematic international survey. Aging Clin Exp Res 27(5):741–750

    Article  PubMed  Google Scholar 

  37. Michel JP et al (2008) Europe-wide survey of teaching in geriatric medicine. J Am Geriatr Soc 56(8):1536–1542

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We would like to thank Martine Conreur, Cristina Robijns-Campregher, Anna Hofer, and Pavel Dobes for their help with the translation of the surveys.

Funding

This work was supported by The Clementine Brigitta Maria Dalderup Fund of the Amsterdam University Fund [grant number 8040] and Aging & Later Life innovation grant, Amsterdam Public Health (APH) [2018].

Author information

Authors and Affiliations

Authors

Consortia

Contributions

KJP, AJL, LJS, MP, ET, JR, MACM, FL, HT, KS, SH, GB, BI, YM, TM, NV, and JCMW contributed to the study conception and design and the material preparation. Data collection was performed by KJP, MP, ET, JR, MACM, FL, HT, KS, SH, GB, BI, YM, TM, and NV. Data analysis was performed by KJP, SM, AJL, YL, and NV. The first draft of the manuscript was written by KJP and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Kim J. Ploegmakers.

Ethics declarations

Conflict of interest

Sirpa Hartikainen has received lecture fee from Astellas Pharma.

Ethical approval

The Medical Ethical Committee of the Academic Medical Centre of the University of Amsterdam reviewed this study and ruled that no ethical approval was required (W18_285#18.331); this study was approved by the Ethical Committees of the Jagiellonian University in Poland and the Ghent University in Belgium.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher's Note

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

Supplementary Information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ploegmakers, K.J., Medlock, S., Linn, A.J. et al. Barriers and facilitators in using a Clinical Decision Support System for fall risk management for older people: a European survey. Eur Geriatr Med 13, 395–405 (2022). https://doi.org/10.1007/s41999-021-00599-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41999-021-00599-w

Keywords

Navigation