Maxim Topaz1,2,3, Kenneth Lai4, Neil Dhopeshwarkar5, Diane L Seger5,4, Roee Sa'adon6, Foster Goss7, Ronen Rozenblum5,8, Li Zhou5,8,4. 1. Brigham and Women's Hospital, Boston, MA, USA. mtopaz80@gmail.com. 2. Harvard Medical School, Boston, MA, USA. mtopaz80@gmail.com. 3. , 93 Worcester st., Wellesley Gateway, Suite 2030I, Wellesley, MA, 02481, USA. mtopaz80@gmail.com. 4. Partners HealthCare System, Wellesley, MA, USA. 5. Brigham and Women's Hospital, Boston, MA, USA. 6. Treato Ltd., Or Yehuda, Israel. 7. University of Colorado, Aurora, CO, USA. 8. Harvard Medical School, Boston, MA, USA.
Abstract
INTRODUCTION: Large databases of clinician reported (e.g., allergy repositories) and patient reported (e.g., social media) adverse drug reactions (ADRs) exist; however, whether patients and clinicians report the same concerns is not clear. OBJECTIVES: Our objective was to compare electronic health record data and social media data to better understand differences and similarities between clinician-reported ADRs and patients' concerns regarding aspirin and atorvastatin. METHODS: This pilot study explored a large repository of electronic health record data and social media data for clinician-reported ADRs and patients concerns for two common medications: aspirin (n = 31,817 ADRs accessible in clinical data; n = 19,186 potential ADRs accessible in social media data) and atorvastatin (n = 15,047 ADRs accessible in clinical data; n = 23,408 potential ADRs accessible in social media data). RESULTS: We found that the most frequently reported ADRs matched the most frequent patients' concerns. However, several less frequently reported reactions were more prevalent on social media (i.e., aspirin-induced hypoglycemia was discussed only on social media). Overall, we found a relatively strong positive and statistically significant correlation between the frequency ranking of reactions and patients' concerns for atorvastatin (Pearson's r = 0.61, p < 0.001) but not for aspirin (Pearson's r = 0.1, p = 0.69). CONCLUSION: Future studies should develop further natural language methods for a more detailed data analysis (i.e., identifying causality and temporal aspects in the social media data).
INTRODUCTION: Large databases of clinician reported (e.g., allergy repositories) and patient reported (e.g., social media) adverse drug reactions (ADRs) exist; however, whether patients and clinicians report the same concerns is not clear. OBJECTIVES: Our objective was to compare electronic health record data and social media data to better understand differences and similarities between clinician-reported ADRs and patients' concerns regarding aspirin and atorvastatin. METHODS: This pilot study explored a large repository of electronic health record data and social media data for clinician-reported ADRs and patients concerns for two common medications: aspirin (n = 31,817 ADRs accessible in clinical data; n = 19,186 potential ADRs accessible in social media data) and atorvastatin (n = 15,047 ADRs accessible in clinical data; n = 23,408 potential ADRs accessible in social media data). RESULTS: We found that the most frequently reported ADRs matched the most frequent patients' concerns. However, several less frequently reported reactions were more prevalent on social media (i.e., aspirin-induced hypoglycemia was discussed only on social media). Overall, we found a relatively strong positive and statistically significant correlation between the frequency ranking of reactions and patients' concerns for atorvastatin (Pearson's r = 0.61, p < 0.001) but not for aspirin (Pearson's r = 0.1, p = 0.69). CONCLUSION: Future studies should develop further natural language methods for a more detailed data analysis (i.e., identifying causality and temporal aspects in the social media data).
Authors: Sanjit S Jolly; Janice Pogue; Kimberly Haladyn; Ron J G Peters; Keith A A Fox; Alvaro Avezum; Bernard J Gersh; Hans Jurgen Rupprecht; Salim Yusuf; Shamir R Mehta Journal: Eur Heart J Date: 2008-09-26 Impact factor: 29.983
Authors: Maxim Topaz; Diane L Seger; Sarah P Slight; Foster Goss; Kenneth Lai; Paige G Wickner; Kimberly Blumenthal; Neil Dhopeshwarkar; Frank Chang; David W Bates; Li Zhou Journal: J Am Med Inform Assoc Date: 2015-11-17 Impact factor: 4.497
Authors: Jeffrey R Curtis; Lang Chen; Phillip Higginbotham; W Benjamin Nowell; Ronit Gal-Levy; James Willig; Monika Safford; Joseph Coe; Kaitlin O'Hara; Roee Sa'adon Journal: Arthritis Res Ther Date: 2017-03-07 Impact factor: 5.156
Authors: Lucie M Gattepaille; Sara Hedfors Vidlin; Tomas Bergvall; Carrie E Pierce; Johan Ellenius Journal: Drug Saf Date: 2020-08 Impact factor: 5.606