Table 1

Adaptations made to existing DDSS to create GPACSS

Overall designSMART-on-FHIR enabled EHR
  • Custom archive stores key files

  • RESTful interface.

Coordination and communicationUser interface
  • SMART-on-FHIR application (GPACSS FHIR app client, figure 2).

  • Interface allows user access to DDSS directly from patient record.

  • Choice to launch with no findings or with findings previously saved.

  • GPACSS ‘Coordinator’ application programming interface (API) saves the NLP output

  • Matching of UMLS codes in NLP output to DDSS findings

  • Send the matched flagged findings to the DDSS at launch (figure 2)

Natural language processingExtraction of findings
  • NLP: open source Apache cTAKES V.4.0.23

  • cTAKES default modules to handle sentence boundary detection, tokenisation, normalisation, tagging parts of speech, recognising named entities and negation.

  • cTAKES pretrained module to recognise UMLS concepts in text.

Mapping in DDSS
  • DDSS findings mapped within the DDSS to one or more UMLS and Human Phenotype Ontology codes.

  • Mapping strategy minimises false negatives in term capture while tolerating false positives (identifying information unrelated or irrelevant to the diagnostic process).

Display in DDSS
  • Findings identified by NLP display a flag icon.

  • Clicking the flag enables viewing of metadata.

  • DDSS, diagnostic decision support system; EHR, electronic health record; GPACSS, Genotype-Phenotype Archiving and Communication System with SimulConsult; UMLS, Unified Medical Language System.