Evaluation of adverse drug reaction formatting in drug information mobile phone applications
DOI:
https://doi.org/10.5195/jmla.2022.1251Keywords:
adverse drug reaction, clinical pharmacy information systems, drug information service,Abstract
Objective: To evaluate the differences in presentation (formatting) of adverse drug reaction (ADR) information within drug monographs in commonly used drug information (DI) mobile device applications.
Methods: A cross-sectional analysis of ADR formatting of twenty commonly prescribed oral medications within seven DI mobile applications was performed. Databases were assessed for ADR information, including presence of placebo comparisons, severity of ADR, onset of ADR, formatting of ADRs in percentile (quantitative) format or qualitative format, whether references were used to cite information, and whether ADRs are grouped by organ system. Data was collected by two study investigators and discrepancies were resolved via consensus.
Results: The seven DI mobile applications varied significantly on every analyzed ADR variable with the exception of ADR onset, which was absent in all databases. Significant differences were found for variables known to impact clinical judgment such as placebo comparisons and qualitative versus quantitative formatting. Placebo comparisons were most common among monographs in Lexicomp (30%) but were absent among monographs within other applications. Quantitative information was commonly used in most databases but was absent in Epocrates. Qualitative formatting was present in all Epocrates and Micromedex applications but absent in the majority of other applications. Substantial variations were also found in severity and grouping information.
Conclusion: Substantial variation in ADR formatting exists among the most common DI mobile applications. These differences may translate into alternative interpretations of medical information and thus impact clinical judgment. Health care librarians and clinicians should consider ADR formatting when choosing between DI applications.
References
World Health Organization. WHO draft guidelines for adverse event reporting and learning systems. Geneva, Switzerland: WHO; 2005.
Mack KA, Centers for Disease Control and Prevention (CDC). Drug-induced deaths - United States, 1999-2010. MMWR Suppl. 2013;62(3):161–3.
Miguel A, Azevedo LF, Araujo M, Pereira AC. Frequency of adverse drug reactions in hospitalized patients: a systematic review and meta-analysis. Pharmacoepidemiol Drug Saf. 2012;21(11):1139–54. DOI: https://doi.org/10.1002/pds.3309.
Schmiedl S, Rottenkolber M, Szymanski J, Drewelow B, Siegmund W, Hippius M, Farker K, Guenther IR, Hasford J, Thuermann PA. Preventable ADRs leading to hospitalization — results of a long-term prospective safety study with 6,427 ADR cases focusing on elderly patients. Expert opinion on drug safety. 2018;17(2):125-37. DOI: https://doi.org/10.1080/14740338.2018.1415322.
Beijer HJ, de Blaey CJ. Hospitalisations caused by adverse drug reactions (ADR): a meta-analysis of observational studies. Pharmacy world & science : PWS. 2002;24(2):46–54. DOI: https://doi.org/10.1023/a:1015570104121.
Formica D, Sultana J, Cutroneo PM, Lucchesi S, Angelica R, Crisafulli S, Ingrasciotta Y, Salvo F, Spina E, Trifirò G. The economic burden of preventable adverse drug reactions: a systematic review of observational studies. Expert Opin Drug Saf. 2018;17(7):681–95. DOI: https://doi.org/10.1080/14740338.2018.1491547.
Behera SK, Das S, Xavier AS, Velupula S, Sandhiya S. Comparison of different methods for causality assessment of adverse drug reactions. Int J Clin Pharm. 2018;40(4):903–10. DOI: https://doi.org/10.1007/s11096-018-0694-9.
Greenhalgh T, Kostopolou O, Harries C. Making decisions about medications and harms of medicines. BMJ. 2004;329(7456):47–50.
Bornstein BH, Emler AC. Rationality in medical decision making: a review of the literature on doctors' decision-making biases. J Eval Clin Pract. 2001;7(2):97–107. DOI: https://doi.org/10.1046/j.1365-2753.2001.00284.x.
Arimone Y, Begaud B, Miremont-Salame G, Fourrier-Reglat A, Moore N, Molimard M, Haramburu F. Agreement of expert judgement in causality assessment of adverse drug reactions. Eur J Clin Pharmacol. 2005;69(3):169–73. DOI: https://doi.org/10.1007/s00228-004-0869-2.
Arimone Y, Miremont-Salame G, Haramburu F, Molimard M, Moore N, Fourrier-Reglat A, Begaud B. Inter-expert agreement of seven criteria in causality assessment of adverse drug reactions. Br J Clin Pharmacol. 2007;64(4):482–8. DOI: https://doi.org/10.1111/j.1365-2125.2007.02937.x.
Franko OI, Tirrell TF. Smartphone app use among medical providers in ACGME training programs. J Med Syst. 2012;36(5):3135–9. DOI: https://doi.org/10.1007/s10916-011-9798-7.
Payne KB, Wharrad H, Watts K. Smartphone and medical related app use among medical students and junior doctors in the United Kingdom (UK): a regional survey. BMC Med Inform Decis Mak. 2012;12:121. DOI: https://doi.org/10.1186/1472-6947-12-121.
Richard CA, Hastings JF, Bryant JE. Pharmacy students' preference for using mobile devices in a clinical setting for practice-related tasks. Am J Pharm Educ. 2015;79(2):22. DOI: https://doi.org/10.5688/ajpe79222.
Aungst TD. Medical applications for pharmacists using mobile devices. Ann Pharmacother. 2013;47(7–8):1088–95. DOI: https://doi.org/10.1345/aph.1S035.
Mosa AS, Yoo I, Sheets L. A systematic review of healthcare applications for smartphones. BMC Med Inform Decis Mak. 2012;12:67. DOI: https://doi.org/10.1186/1472-6947-12-67.
Bui T. Framing effects on physicians' judgement and decision making. Psychol Rep. 2015;117(2):508–22. DOI: https://doi.org/10.2466/13.PR0.117c20z0.
Gong J, Zhang Y, Yang Z, Huang Y, Feng J, Zhang W. The framing effect in medical decision-making: a review of the literature. Psychol Health Med. 2013;18(6):645–53. DOI: https://doi.org/10.1080/13548506.2013.766352.
O'Connor AM, Boyd NF, Tritchler DL, Kriukov Y, Sutherland H, Till JE. Eliciting preferences for alternative cancer drug treatments. The influence of framing, medium, and rater variables. Med Decis Making. 1985;5(4):453–63. DOI: https://doi.org/10.1177/0272989x8500500408.
Perneger TV, Agoritsas T. Doctors and patients susceptibility to framing bias: a randomized trial. J Gen Intern Med. 2011;26(12):1411–7. DOI: https://doi.org/10.1007/s11606-011-1810-x.
O'Donohughe AC, Sullivan HW, Aikin KJ. Randomized study of placebo and framing information in direct-to-consumer print advertisements for prescription drugs. Ann Behav Med. 2014;48(3):311–22. DOI: https://doi.org/10.1007/s12160-014-9603-1.
Peters E, Sol Hart P, Fraenkel L. Informing patients: the influence of numeracy, framing, and format of side effect information on risk perceptions. Med Decis Making. 2011;31(3):432–6. DOI: https://doi.org/10.1177/0272989X10391672.
Sinayev A, Peters E, Tusler M, Fraenkel L. Presenting numeric information with percentages and descriptive risk labels: a randomized trial. Med Decis Making. 2015;35(8):937–47. DOI: https://doi.org/10.1177/0272989X15584922.
McConachie SM, Volgyi D, Moore H, Giuliano CA. Evaluation of adverse drug reaction formatting in drug information databases. J Med Libr Assoc. 2020;108(4):598–604. DOI: https://doi.org/10.5195/jmla.2020.983.
Park SK, Purnell MC, Freeman MK, Reese RV, Varga, S. Preference and frequency of mobile phone app use for drug information among student pharmacists. J Pharm Technol. 2017;3(33):87–95.
US Food and Drug Administration. Guidance for industry adverse reactions section of labeling for human prescription drug and biological products — content and format, 2006. Available at: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/adverse-reactions-section-labeling-human-prescription-drug-and-biological-products-content-and. Accessed June 4, 2021.
McConachie SM, Giuliano CA, Mohammad I, Kale-Pradhan PB. Adverse drug reactions in drug information databases: does presentation affect interpretation? J Med Libr Assoc. 2020;108(1):76–83. DOI: https://doi.org/10.5195/jmla.2020.748.
Naranjo CA, Busto U, Sellers EM, Sandor P, Ruiz I, Roberts EA, Janecek E, Domecq C, Greenblatt DJ. A method for estimating the probability of adverse drug reactions. Clin Pharmacol Ther. 1981;30(2):239–45. DOI: https://doi.org/10.1038/clpt.1981.154.
Lipkus IM. Numeric, verbal, and visual formats of conveying health risks: suggested best practices and future recommendations. Med Decis Making. 2007;27(5):696–713. DOI: https://doi.org/10.1177/0272989X07307271.
Clauson KA, Marsh WA, Polen HH, Seamon MJ, Ortiz BI. Clinical decision support tools: analysis of online drug information databases. BMC Med Inform Decis Mak. 2007;7:7. DOI: https://doi.org/10.1186/1472-6947-7-7.
US Food and Drug Administration. Policy for device software functions and mobile medical applications. Available at: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/medical-device-data-systems-medical-image-storage-devices-and-medical-image-communications-devices. Accessed March 10, 2021.
US Food and Drug Administration. Part III: drugs and foods under the 1938 act and its amendments. Available at: https://www.fda.gov/about-fda/fdas-evolving-regulatory-powers/part-iii-drugs-and-foods-under-1938-act-and-its-amendments. Accessed January 28, 2020.
Additional Files
Published
Issue
Section
License
Copyright (c) 2022 Sean M McConachie, Dena Berri, Jewel Konja, Christopher A Giuliano
This work is licensed under a Creative Commons Attribution 4.0 International License.
