Herding: a new phenomenon affecting medical decision-making in multiple sclerosis care? Lessons learned from DIScUTIR MS

Gustavo Saposnik, Jorge Maurino, Angel P Sempere, Christian C Ruff, Philippe N Tobler, Gustavo Saposnik, Jorge Maurino, Angel P Sempere, Christian C Ruff, Philippe N Tobler

Abstract

Purpose: Herding is a phenomenon by which individuals follow the behavior of others rather than deciding independently on the basis of their own private information. A herding-like phenomenon can occur in multiple sclerosis (MS) when a neurologist follows a therapeutic recommendation by a colleague even though it is not supported by best practice clinical guidelines. Limited information is currently available on the role of herding in medical care. The objective of this study was to determine the prevalence (and its associated factors) of herding in the management of MS.

Methods: We conducted a study among neurologists with expertise in MS care throughout Spain. Participants answered questions regarding the management of 20 case scenarios commonly encountered in clinical practice and completed 3 surveys and 4 experimental paradigms based on behavioral economics. The herding experiment consisted of a case scenario of a 40-year-old woman who has been stable for 3 years on subcutaneous interferon and developed a self-limited neurological event. There were no new magnetic resonance imaging (MRI) lesions. Her neurological examination and disability scores were unchanged. She was advised by an MS neurologist to switch from interferon to fingolimod against best practice guidelines. Multivariable logistic regression analysis was conducted to evaluate factors associated with herding.

Results: Out of 161 neurologists who were invited to participate, 96 completed the study (response rate: 60%). Herding was present in 75 (78.1%), having a similar prevalence in MS experts and general neurologists (68.8% vs 82.8%; P=0.12). In multivariate analyses, the number of MS patients seen per week was positively associated with herding (odds ratio [OR] 1.08, 95% CI 1.01-1.14). Conversely, physician's age, gender, years of practice, setting of practice, or risk preferences were not associated with herding.

Conclusion: Herding was a common phenomenon affecting nearly 8 out of 10 neurologists caring for MS patients. Herding may affect medical decisions and lead to poorer outcomes in the management of MS.

Keywords: decision-making; disease-modifying therapy; herding; multiple sclerosis; neuroeconomics; risk aversion.

Conflict of interest statement

Disclosure The study was sponsored by the Sociedad Española de Neurologia (SEN) and funded by an operating grant from Roche Farma Spain. The sponsors were not involved in the design, execution, analysis, and interpretation or reporting of the results. Dr Gustavo Saposnik is supported by the Distinguished Clinicians Scientist Award from HSFC. Dr Jorge Maurino is an employee of Roche Farma Spain. Prof Philippe Tobler and Christian Ruff were funded by the Swiss National Science Foundation (PNT: PP00P1_150739, CRSII3_141965, and 00014_165884, CCR:105314_152891, CRSII3_141965, and 320030_143443). The authors report no other conflicts of interest in this work.

Figures

Figure 1
Figure 1
Prevalence of herding-like behavior according to specialty and volume of MS patients. Notes: (A) Herding-like behavior in MS specialists and general neurologists. (B) Prevalence of herding-like behavior by volume of MS patients seen per week (in terciles). Abbreviation: MS, multiple sclerosis.

References

    1. Glimcher P, Fehr E. Neuroeconomics: Decision Making and the Brain. 2nd ed. San Diego, CA: Academic Press; 2014.
    1. Dijkstra IS, Pols J, Remmelts P, Brand PL. Preparedness for practice: a systematic cross-specialty evaluation of the alignment between postgraduate medical education and independent practice. Med Teach. 2015;37(2):153–161.
    1. Keynes JM. A Treatise on Money. London: Macmillan; 1930.
    1. Baddeley M. Herding, social influence and economic decision-making: socio-psychological and neuroscientific analyses. Philos Trans R Soc Lond B Biol Sci. 2010;365(1538):281–290.
    1. Noyes K, Weinstock-Guttman B. Impact of diagnosis and early treatment on the course of multiple sclerosis. Am J Manag Care. 2013;19(17 suppl):s321–s331.
    1. Sormani MP, Rio J, Tintore M, et al. Scoring treatment response in patients with relapsing multiple sclerosis. Mult Scler. 2013;19(5):605–612.
    1. Duquette P, Giacomini PS, Bhan V, Hohol M, Schecter R. Balancing early aggression against risk of progression in multiple sclerosis. Can J Neurol Sci. 2016;43(1):33–43.
    1. Woolf SH, Kuzel AJ, Dovey SM, Phillips RL., Jr A string of mistakes: the importance of cascade analysis in describing, counting, and preventing medical errors. Ann Fam Med. 2004;2(4):317–326.
    1. Saposnik G, Sempere AP, Raptis R, Prefasi D, Selchen D, Maurino J. Decision making under uncertainty, therapeutic inertia, and physicians’ risk preferences in the management of multiple sclerosis (DIScUTIR MS) BMC Neurol. 2016;16(1):58.
    1. Tramacere I, Del Giovane C, Salanti G, D’Amico R, Filippini G. Immunomodulators and immunosuppressants for relapsing-remitting multiple sclerosis: a network meta-analysis. Cochrane Database Syst Rev. 2015;2015(9):CD011381.
    1. Freedman MS, Selchen D, Arnold DL, et al. Canadian Multiple Sclerosis Working Group Treatment optimization in MS: Canadian MS Working Group updated recommendations. Can J Neurol Sci. 2013;40(3):307–323.
    1. Wattjes MP, Rovira A, Miller D, et al. MAGNIMS Study Group Evidence-based guidelines: MAGNIMS consensus guidelines on the use of MRI in multiple sclerosis – establishing disease prognosis and monitoring patients. Nat Rev Neurol. 2015;11(10):597–606.
    1. Prosperini L, Mancinelli CR, De Giglio L, De Angelis F, Barletta V, Pozzilli C. Interferon beta failure predicted by EMA criteria or isolated MRI activity in multiple sclerosis. Mult Scler. 2014;20(5):566–576.
    1. Correale J, Abad P, Alvarenga R, et al. Management of relapsing-remitting multiple sclerosis in Latin America: practical recommendations for treatment optimization. J Neurol Sci. 2014;339(1–2):196–206.
    1. García Merino A, Ramón Ara, Callizo J, Fernández Fernández O, Landete Pascual L, Moral Torres E, Rodríguez-Antigüedad Zarrantz A. Consensus statement on the treatment of multiple sclerosis by the Spanish Society of Neurology in 2016. Neurologia. 2016 May 5; Epub.
    1. Sormani MP, Gasperini C, Romeo M, et al. Assessing response to interferon-beta in a multicenter dataset of patients with MS. Neurology. 2016;87(2):134–140.
    1. Bermel RA, You X, Foulds P, et al. Predictors of long-term outcome in multiple sclerosis patients treated with interferon beta. Ann Neurol. 2013;73(1):95–103.
    1. Linder JA, Doctor JN, Friedberg MW, et al. Time of day and the decision to prescribe antibiotics. JAMA Intern Med. 2014;174(12):2029–2031.
    1. Danziger S, Levav J, Avnaim-Pesso L. Extraneous factors in judicial decisions. Proc Natl Acad Sci U S A. 2011;108(17):6889–6892.
    1. Björkstén KS, Bergqvist M, Andersén-Karlsson E, Benson L, Ulfvarson J. Medication errors as malpractice-a qualitative content analysis of 585 medication errors by nurses in Sweden. BMC Health Serv Res. 2016;16(1):431.
    1. Muchnik L, Aral S, Taylor SJ. Social influence bias: a randomized experiment. Science. 2013;341(6146):647–651.
    1. Prosperini L, Sacca F, Cordioli C, et al. Real-world effectiveness of natalizumab and fingolimod compared with self-injectable drugs in non-responders and in treatment-naïve patients with multiple sclerosis. J Neurol. 2016 Nov 22; Epub.
    1. Columbia Accident Investigation Board [homepage on the Internet] Washington, DC: National Aeronautics and Space Administration and the Government Printing Office; 2011. [Accessed January 17, 2017]. Available from: .
    1. Bordallo attends unveiling of painting depicting 1997 Korean Air Crash [webpage on the Internet] Guam: Official Guam Crash Site Center Korean Air Fit 801; [Accessed January 17, 2017]. Available from: .
    1. Woiceshyn J, Blades K, Pendharkar SR. Integrated versus fragmented implementation of complex innovations in acute health care. Health Care Manage Rev. 2017;42(1):76–86.
    1. Wohlauer M. Fragmented care in the era of limited work hours: a plea for an explicit handover curriculum. BMJ Qual Saf. 2012;21(suppl 1):i16–i18.
    1. Emanuel EJ, Ubel PA, Kessler JB, et al. Using behavioral economics to design physician incentives that deliver high-value care. Ann Intern Med. 2016;164(2):114–119.

Source: PubMed

3
Abonneren