Mining primary care EMRs

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Mining primary care EMRs

  1. 1. Understanding patientexperiences from miningprimary care dataCentre for Health InformaticsFilippo GalganiAdam DunnMargaret WilliamsonMalcolm GilliesGuy Tsafnat
  2. 2. General Practice EMRs• Aim: measure quality of care for a range of conditions in a diversepopulation using GP EMR data.• Dataset: longitudinal data (2.5 million Australian patients) includingprescriptions, diagnoses, pathologies, referrals• Patients’ journey: grouping patients by experience to detect relevantpatterns in data over time..
  3. 3. Big Data Problems• Data collected to keep patient history:– Dealing with missing information– Inconsistency– Combination of short text fields (not coded) and numericalvalues• Doctors’ time constraints make data entry inaccurate• Progress notes not available (privacy issue)• Patients may visit other practices (thus missing information)• Events happen irregularly
  4. 4. Continuity of care
  5. 5. Reasons for Prescription123571162357Some Reason GivenReason Missing1974 different for PPI prescriptionsGORD (Gastro-oesophageal Reflux Disease) 50842Reflux - gastro-oesophageal 13596Reflux oesophagitis 6285GOR (Gastro-oesophageal Reflux) 6047Gastritis 5755Gastro-oesophageal Reflux 4356… …
  6. 6. Textual inconsistency:Natural Language ProcessinggordGORDgord;gord• Normalization of caseand punctuation• Stopword Filtering• Spelling CorrectionGastro-oesophagealReflux DiseaseGastro-oesophagealRefluxoesophygitis oesophagitis
  7. 7. Textual inconsistency:Natural Language Processing• LemmatizationOesophagitis ulcerativeOesophagitis ulceratingOesophagitisulcer• Acronym Expansion• SynonymsGORDGORD (Gastro-oesophageal Reflux Disease)Gastro-oesophageal Reflux Disease=Reflux oesophagitis Gastro-oesophageal Reflux=
  8. 8. Reasons for PrescriptionGORD (Gastro-oesophageal Reflux Disease) 50842Reflux - gastro-oesophageal 13596Reflux oesophagitis 6285GOR (Gastro-oesophageal Reflux) 6047Gastritis 5755Gastro-oesophageal Reflux 4356… …GORD (Gastro-oesophageal Reflux Disease) 87217NLP pipeline1974 different for PPI prescriptions123571162357Some Reason GivenReason Missing
  9. 9. 123571162357Some Reason GivenReason MissingReasons for Prescription?
  10. 10. Missing Information: Machine Learning ApproachRandom set of PPI patientsannotated by experts wrt GORD
  11. 11. Grouping Patients by Journey
  12. 12. Conclusion• Data mining on GP EMRs is challenging due to thenoisy, messy and sparse nature of the data• Analyzing journeys is possible, it required:– Temporal reasoning (infer missing events)– Natural Language Processing (solve textualinconsistencies)– Machine Learning (predict missing information)– Domain knowledge (for modeling)
  13. 13. Acknowledgment• This research was funded by the Australian Department of Healthand Ageing through the NPS MedicineWise as part of theMedicineInsight Program.• I wish to express my gratitude to:Malcolm Gillies and Margaret Williamson from NPSAdam Dunn and Guy Tsafnat from UNSW• Thank you for the attention

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