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Background: The International Classification for Primary Care (ICPC) standard aims to facilitate simultaneous and longitudinal comparisons of clinical primary care practice within and across country borders; it is also used for administrative purposes. This study evaluates the use of the original ICPC-1 and the more complete ICPC-2 Norwegian versions in electronic patient records., Methods: We performed a retrospective study of approximately 1.5 million ICPC codes and diagnoses that were collected over a 16-year period at 12 primary care sites in Norway. In the first phase of this period (transition phase, 1992-1999) physicians were allowed to not use an ICPC code in their practice while in the second phase (regular phase, 2000-2008) the use of an ICPC code was mandatory. The ICPC codes and diagnoses defined a problem event for each patient in the PROblem-oriented electronic MEDical record (PROMED). The main outcome measure of our analysis was the percentage of problem events in PROMEDs with inappropriate (or missing) ICPC codes and of diagnoses that did not map the latest ICPC-2 classification. Specific problem areas (pneumonia, anaemia, tonsillitis and diabetes) were examined in the same context., Results: Codes were missing in 6.2% of the problem events; incorrect codes were observed in 4.0% of the problem events and text mismatch between the diagnoses and the expected ICPC-2 diagnoses text in 53.8% of the problem events. Missing codes were observed only during the transition phase while incorrect and inappropriate codes were used all over the 16-year period. The physicians created diagnoses that did not exist in ICPC. These 'new' diagnoses were used with varying frequency; many of them were used only once. Inappropriate ICPC-2 codes were also observed in the selected problem areas and for both phases., Conclusions: Our results strongly suggest that physicians did not adhere to the ICPC standard due to its incompleteness, i.e. lack of many clinically important diagnoses. This indicates that ICPC is inappropriate for the classification of problem events and the clinical practice in primary care.

To address the problem of extracting structured information from pathology reports for research purposes in the STRIDE Clinical Data Warehouse, we adapted the ChartIndex Medical Language Processing system to automatically identify and map anatomic and diagnostic noun phrases found in full-text pathology reports to SNOMED CT concept descriptors. An evaluation of the system's performance showed a positive predictive value for anatomic concepts of 92.3% and positive predictive value for diagnostic concepts of 84.4%. The experiment also suggested strategies for improving ChartIndex's performance coding pathology reports.

The increasing availability of complex temporal clinical records collected today has prompted the development of new methods that extend classical machine learning and data mining approaches to time series data. In this work, we develop a new framework for classifying the patient's time-series data based on temporal abstractions. The proposed STF-Mine algorithm automatically mines discriminative temporal abstraction patterns from the data and uses them to learn a classification model. We apply our approach to predict HPF4 test orders from electronic patient health records. This test is often prescribed when the patient is at the risk of Heparin induced thrombocytopenia (HIT). Our results demonstrate the benefit of our approach in learning accurate time series classifiers, a key step in the development of intelligent clinical monitoring systems.