title: Extracting antipsychotic polypharmacy data from electronic health records: developing and evaluating a novel process.
abstract: BACKGROUND
  Antipsychotic prescription information is commonly derived from structured fields in clinical health records. However, utilising diverse and comprehensive sources of information is especially important when investigating less frequent patterns of medication prescribing such as antipsychotic polypharmacy (APP). This study describes and evaluates a novel method of extracting APP data from both structured and free-text fields in electronic health records (EHRs), and its use for research purposes.
  
  
  METHODS
  Using anonymised EHRs, we identified a cohort of patients with serious mental illness (SMI) who were treated in South London and Maudsley NHS Foundation Trust mental health care services between 1 January and 30 June 2012. Information about antipsychotic co-prescribing was extracted using a combination of natural language processing and a bespoke algorithm. The validity of the data derived through this process was assessed against a manually coded gold standard to establish precision and recall. Lastly, we estimated the prevalence and patterns of antipsychotic polypharmacy.
  
  
  RESULTS
  Individual instances of antipsychotic prescribing were detected with high precision (0.94 to 0.97) and moderate recall (0.57-0.77). We detected baseline APP (two or more antipsychotics prescribed in any 6-week window) with 0.92 precision and 0.74 recall and long-term APP (antipsychotic co-prescribing for 6 months) with 0.94 precision and 0.60 recall. Of the 7,201 SMI patients receiving active care during the observation period, 338 (4.7 %; 95 % CI 4.2-5.2) were identified as receiving long-term APP. Two second generation antipsychotics (64.8 %); and first -second generation antipsychotics were most commonly co-prescribed (32.5 %).
  
  
  CONCLUSIONS
  These results suggest that this is a potentially practical tool for identifying polypharmacy from mental health EHRs on a large scale. Furthermore, extracted data can be used to allow researchers to characterize patterns of polypharmacy over time including different drug combinations, trends in polypharmacy prescribing, predictors of polypharmacy prescribing and the impact of polypharmacy on patient outcomes.
authors.affiliation: Department of Psychological Medicine, King's College London, Institute of Psychiatry, Psychology, and Neuroscience, London, UK. Giouliana.kadra@kcl.ac.uk.
authors.forename: Giouliana
authors.initials: G
authors.lastname: Kadra
authors.affiliation: Department of Psychological Medicine, King's College London, Institute of Psychiatry, Psychology, and Neuroscience, London, UK. robert.stewart@kcl.ac.uk.|South London and Maudsley NHS Foundation Trust, London, UK. robert.stewart@kcl.ac.uk.
authors.forename: Robert
authors.initials: R
authors.lastname: Stewart
authors.affiliation: South London and Maudsley NHS Foundation Trust, London, UK. Hitesh.Shetty@slam.nhs.uk.
authors.forename: Hitesh
authors.initials: H
authors.lastname: Shetty
authors.affiliation: Department of Psychological Medicine, King's College London, Institute of Psychiatry, Psychology, and Neuroscience, London, UK. Richard.G.Jackson@slam.nhs.uk.|South London and Maudsley NHS Foundation Trust, London, UK. Richard.G.Jackson@slam.nhs.uk.
authors.forename: Richard G
authors.initials: RG
authors.lastname: Jackson
authors.affiliation: Department of Computer Science, The University of Sheffield, Sheffield, UK. m.a.greenwood@sheffield.ac.uk.
authors.forename: Mark A
authors.initials: MA
authors.lastname: Greenwood
authors.affiliation: Department of Computer Science, The University of Sheffield, Sheffield, UK. angus.roberts@sheffield.ac.uk.
authors.forename: Angus
authors.initials: A
authors.lastname: Roberts
authors.affiliation: Department of Psychological Medicine, King's College London, Institute of Psychiatry, Psychology, and Neuroscience, London, UK. chin-kuo.chang@kcl.ac.uk.
authors.forename: Chin-Kuo
authors.initials: CK
authors.lastname: Chang
authors.affiliation: South London and Maudsley NHS Foundation Trust, London, UK. james.maccabe@kcl.ac.uk.|Department of Psychosis Studies, King's College London, Institute of Psychiatry, Psychology, and Neuroscience, London, UK. james.maccabe@kcl.ac.uk.
authors.forename: James H
authors.initials: JH
authors.lastname: MacCabe
authors.affiliation: Department of Psychological Medicine, King's College London, Institute of Psychiatry, Psychology, and Neuroscience, London, UK. richard.hayes@kcl.ac.uk.
authors.forename: Richard D
authors.initials: RD
authors.lastname: Hayes
mesh_terms: D000328:Adult; D014150:Antipsychotic Agents / Q000627:therapeutic use*; D057286:Electronic Health Records / Q000592:standards / Q000706:statistics & numerical data*; D006801:Humans; D008131:London / Q000453:epidemiology; D001523:Mental Disorders / Q000188:drug therapy* / Q000453:epidemiology; D019338:Polypharmacy; D010818:Practice Patterns, Physicians' / Q000706:statistics & numerical data; D015995:Prevalence
chemical_list: D014150:Antipsychotic Agents