Publication | Open Access
Applying multiple methods to assess the readability of a large corpus of medical documents.
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Citations
12
References
2013
Year
Document ProcessingLarge CorpusQuestioned Document ExaminationDocument ReadabilityCorpus LinguisticsText MiningNatural Language ProcessingMultiple MethodsPrimary CareDocument AnalysisComputational LinguisticsStructured DocumentMedical DocumentsLanguage StudiesContent AnalysisOutcomes ResearchElectronic Health RecordMedical Language ProcessingClinical DataNursingDischarge SummariesPatient SafetyPatient-centered OutcomeText ProcessingMedicinePatient ExperienceLinguisticsHealth InformaticsEmergency Medicine
Medical documents provided to patients at the end of an episode of care, such as discharge summaries and referral letters, serve as an important vehicle to convey critical information to patients and families. Increasingly, healthcare institutions are also experimenting with granting patients direct electronic access to other types of clinical narratives that are not typically shared unless explicitly requested, such as progress notes. While these efforts have great potential to improve information transparency, their value can be severely diminished if patients are unable to read and thus unable to properly interpret the medical documents shared to them. In this study, we approached the problem by contrasting the 'readability' of two types of medical documents: referral letters vs. other genres of narrative clinician notes not explicitly intended for direct viewing by patients. To establish a baseline for comparison, we also computed readability scores of MedlinePlus articles - exemplars of fine patient education materials carefully crafted for lay audiences. We quantified document readability using four different measures. Differences in the results obtained through these measures are also discussed.
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