Understanding patient

experiences from mining

primary care data

Centre for Health Informatics

Filippo Galgani

Adam Dunn

Margaret Williamson

Malcolm Gillies

Guy Tsafnat

General Practice EMRs

• Aim: measure quality of care for a range of conditions in a diverse

population using GP EMR data.

• Dataset: longitudinal data (2.5 million Australian patients) including

prescriptions, diagnoses, pathologies, referrals

• Patients’ journey: grouping patients by experience to detect relevant

patterns in data over time..

Big Data Problems

• Data collected to keep patient history:

– Dealing with missing information

– Inconsistency

– Combination of short text fields (not coded) and numerical

values

• Doctors’ time constraints make data entry inaccurate

• Progress notes not available (privacy issue)

• Patients may visit other practices (thus missing information)

• Events happen irregularly

Continuity of care

Reasons for Prescription

123571

162357

Some Reason Given

Reason Missing

1974 different for PPI prescriptions

GORD (Gastro-oesophageal Reflux Disease) 50842

Reflux - gastro-oesophageal 13596

Reflux oesophagitis 6285

GOR (Gastro-oesophageal Reflux) 6047

Gastritis 5755

Gastro-oesophageal Reflux 4356

… …

Textual inconsistency:

Natural Language Processing

gord

GORD

gord;

gord • Normalization of case

and punctuation

• Stopword Filtering

• Spelling Correction

Gastro-oesophageal

Reflux Disease Gastro-oesophageal

Reflux

oesophygitis oesophagitis

Textual inconsistency:

Natural Language Processing

• Lemmatization Oesophagitis ulcerative

Oesophagitis ulcerating

Oesophagitis

ulcer

• Acronym Expansion

• Synonyms

GORD

GORD (Gastro-oesophageal Reflux Disease)

Gastro-oesophageal Reflux Disease =

Reflux oesophagitis Gastro-oesophageal Reflux =

Reasons for Prescription

GORD (Gastro-oesophageal Reflux Disease) 50842

Reflux - gastro-oesophageal 13596

Reflux oesophagitis 6285

GOR (Gastro-oesophageal Reflux) 6047

Gastritis 5755

Gastro-oesophageal Reflux 4356

… …

GORD (Gastro-oesophageal Reflux Disease) 87217

NLP pipeline

1974 different for PPI prescriptions

123571

162357

Some Reason Given

Reason Missing

123571

162357

Some Reason Given

Reason Missing

Reasons for Prescription

?

Missing Information: Machine Learning Approach

Random set of PPI patients

annotated by experts wrt GORD

Grouping Patients by Journey

Conclusion

• Data mining on GP EMRs is challenging due to the

noisy, messy and sparse nature of the data

• Analyzing journeys is possible, it required:

– Temporal reasoning (infer missing events)

– Natural Language Processing (solve textual

inconsistencies)

– Machine Learning (predict missing information)

– Domain knowledge (for modeling)

Acknowledgment

• This research was funded by the Australian Department of Health

and Ageing through the NPS MedicineWise as part of the

MedicineInsight Program.

• I wish to express my gratitude to:

Malcolm Gillies and Margaret Williamson from NPS

Adam Dunn and Guy Tsafnat from UNSW

• Thank you for the attention