چکیده (انگلیسی):

Background:Analysis of encounter data relevant to the diagnostic process sourced from routine electronic medical
record (EMR) databases represents a classic example of the concept of alearning healthcare system (LHS). By collecting
International Classification of Primary Care (ICPC) coded EMR data as part of the Transition Project from Dutch and
Maltese databases (using the EMR TransHIS), data mining algorithms can empirically quantify the relationships of all
presentingreasons for encounter (RfEs)and recorded diagnostic outcomes. We have specifically looked at newepisodes
of care (EoC)for two urinary system infections: simple urinary tract infection (UTI, ICPC code: U71) and pyelonephritis
(ICPC code: U70).
Methods:Participating family doctors (FDs) recorded details of all their patient contacts in an EoC structure using the
ICPC, including RfEs presented by the patient, and the FDs’ diagnostic labels. The relationships between RfEs and
episode titles were studied using probabilistic and data mining methods as part of the TRANSFoRm project.
Results:The Dutch data indicated that the presence of RfE’s“Cystitis/Urinary Tract Infection”, “Dysuria”, “Fear of UTI”,
“Urinary frequency/urgency”, “Haematuria”, “Urine symptom/complaint, other”are all strong, reliable, predictors for the
diagnosis“Cystitis/Urinary Tract Infection”. The Maltese data indicated that the presence of RfE’s“Dysuria”, “Urinary
frequency/urgency”, “Haematuria”are all strong, reliable, predictors for the diagnosis“Cystitis/Urinary Tract Infection”.
The Dutch data indicated that the presence of RfE’s“Flank/axilla symptom/complaint”, “Dysuria”, “Fever”, “Cystitis/
Urinary Tract Infection”, “Abdominal pain/cramps general”are all strong, reliable, predictors for the diagnosis
“Pyelonephritis”. The Maltese data set did not present any clinically and statistically significant predictors for
pyelonephritis.
Conclusions:We describe clinically and statistically significant diagnostic associations observed between UTIs and
pyelonephritis presenting as a new problem in family practice, and all associated RfEs, and demonstrate that the
significant diagnostic cues obtained are consistent with the literature. We conclude that it is possible to generate
clinically meaningful diagnostic evidence from electronic sources of patient data.