title: The Potential of Twitter as a Data Source for Patient Safety.
abstract: Error-reporting systems are widely regarded as critical components to improving patient safety, yet current systems do not effectively engage patients. We sought to assess Twitter as a source to gather patient perspective on errors in this feasibility study.
  
  
  
  We included publicly accessible tweets in English from any geography. To collect patient safety tweets, we consulted a patient safety expert and constructed a set of highly relevant phrases, such as "doctor screwed up." We used Twitter's search application program interface from January to August 2012 to identify tweets that matched the set of phrases. Two researchers used criteria to independently review tweets and choose those relevant to patient safety; a third reviewer resolved discrepancies. Variables included source and sex of tweeter, source and type of error, emotional response, and mention of litigation.
  
  
  
  Of 1006 tweets analyzed, 839 (83%) identified the type of error: 26% of which were procedural errors, 23% were medication errors, 23% were diagnostic errors, and 14% were surgical errors. A total of 850 (84%) identified a tweet source, 90% of which were by the patient and 9% by a family member. A total of 519 (52%) identified an emotional response, 47% of which expressed anger or frustration, 21% expressed humor or sarcasm, and 14% expressed sadness or grief. Of the tweets, 6.3% mentioned an intent to pursue malpractice litigation.
  
  
  
  Twitter is a relevant data source to obtain the patient perspective on medical errors. Twitter may provide an opportunity for health systems and providers to identify and communicate with patients who have experienced a medical error. Further research is needed to assess the reliability of the data.
authors.affiliation: From the Johns Hopkins University School of Medicine, Baltimore, Maryland.
authors.forename: Atul
authors.initials: A
authors.lastname: Nakhasi
authors.affiliation: University of Michigan Medical School, Ann Arbor, Michigan.
authors.forename: Sarah G
authors.initials: SG
authors.lastname: Bell
authors.affiliation: From the Johns Hopkins University School of Medicine, Baltimore, Maryland.
authors.forename: Ralph J
authors.initials: RJ
authors.lastname: Passarella
authors.affiliation: Departments of Computer Science and Human Language Technology Center of Excellence.
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Paul
authors.affiliation: Departments of Computer Science and Human Language Technology Center of Excellence.
authors.forename: Mark
authors.initials: M
authors.lastname: Dredze
authors.affiliation: From the Johns Hopkins University School of Medicine, Baltimore, Maryland.|Anesthesiology and Critical Care Medicine.|Surgery, Johns Hopkins University, Baltimore, Maryland and Armstrong Institute for Patient Safety.|Quality, Johns Hopkins University, Baltimore, Maryland.
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Pronovost
mesh_terms: D001290:Attitude; D003625:Data Collection / Q000379:methods*; D004644:Emotions; D006801:Humans; D016247:Information Storage and Retrieval; D019300:Medical Errors / Q000523:psychology; D061214:Patient Safety; D015203:Reproducibility of Results; D061108:Social Media