What do patients learn about psychotropic medications on the web? A natural language processing study.
Background: Low rates of medication adherence remain a major challenge across psychiatry. In part, this likely reflects patient concerns about safety and adverse effects, accurate or otherwise. We therefore sought to characterize online information about common psychiatric medications in terms of po...
| Published in: | Journal of Affective Disorders Vol. 260; pp. 366 - 372 |
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| Main Authors: | , , , |
| Format: | research tables/charts Journal Article |
| Published: |
Elsevier B.V.
Jan2020
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=141606835&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141606835 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01650327 3M9 jtl: Journal of Affective Disorders issn: 01650327 maglogo: N pubinfo: dt: Jan2020 vid: 260 pid: 1004 pub: Elsevier B.V. artinfo: ui: 141606835 141606835 NLM31539672 141606835 10.1016/j.jad.2019.09.043 NLM31539672 141606835 ppf: 366 ppct: 6 formats: tig: atl: What do patients learn about psychotropic medications on the web? A natural language processing study. aug: au: Hart, Kamber L. Perlis, Roy H. McCoy, Thomas H. McCoy, Thomas H Jr affil: Center for Quantitative Health, Massachusetts General Hospital, 185 Cambridge Street, 6th Floor, Boston, MA 02114, USA sug: subj: Internet Psychotropic Drugs Therapeutic Use Consumer Health Information Natural Language Processing Antipsychotic Agents Therapeutic Use Antimanic Agents Therapeutic Use Antidepressive Agents Therapeutic Use Male Female Medication Compliance Psychiatry Trends Funding Source Human Male Female ab: Background: Low rates of medication adherence remain a major challenge across psychiatry. In part, this likely reflects patient concerns about safety and adverse effects, accurate or otherwise. We therefore sought to characterize online information about common psychiatric medications in terms of positive and negative sentiment.Methods: We applied a natural language processing tool to score the sentiment expressed in web search results for 51 psychotropic medications across 3 drug classes (antidepressants, antipsychotics, and mood stabilizers), as a means of seeing if articles referencing these medications were generally positive or generally negative in tone. We compared between medications of the same class, and across medication classes.Results: Across 12,733 web search results, significant within-class differences in positive (antidepressants: F(24,2682) = 2.97, p < 0.001; antipsychotics: F(16,4029) = 3.25, p < 0.001; mood stabilizers: F(8,2371) = 6.88, p < 0.001) and negative sentiment (antidepressants: F(24,6282) = 11.17, p < 0.001; antipsychotics: F(16, 4029) = 12.13, p < 0.001; mood stabilizers: F(8, 2371) = 13.28, p < 0.001) were identified. Among these were significantly greater negative sentiment for the antidepressants sertraline, duloxetine, venlafaxine, and paroxetine, and for the antipsychotics, quetiapine and risperidone. Conversely, lithium preparations and valproate exhibited less negative sentiment than other mood stabilizing medications.Limitations: While these results provide a novel means of comparing medications, the present analyses cannot be linked to individual patient consumption of this information, or to its influence on their future clinical interactions.Conclusions: Overall, a subset of psychotropic medications were associated with significantly more negative sentiment. Characterizing these differences may allow clinicians to anticipate patient willingness to initiate or continue medications. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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