BACKGROUND AND OBJECTIVE: The clinical note documents the clinician's information collection, problem assessment, clinical management, and its used for administrative purposes. Electronic health records (EHRs) are being implemented in clinical practices throughout the USA yet it is not known whether they improve the quality of clinical notes. The goal in this study was to determine if EHRs improve the quality of outpatient clinical notes. MATERIALS AND METHODS: A five and a half year longitudinal retrospective multicenter quantitative study comparing the quality of handwritten and electronic outpatient clinical visit notes for 100 patients with type 2 diabetes at three time points: 6 months prior to the introduction of the EHR (before-EHR), 6 months after the introduction of the EHR (after-EHR), and 5 years after the introduction of the EHR (5-year-EHR). QNOTE, a validated quantitative instrument, was used to assess the quality of outpatient clinical notes. Its scores can range from a low of 0 to a high of 100. Sixteen primary care physicians with active practices used QNOTE to determine the quality of the 300 patient notes. RESULTS: The before-EHR, after-EHR, and 5-year-EHR grand mean scores (SD) were 52.0 (18.4), 61.2 (16.3), and 80.4 (8.9), respectively, and the change in scores for before-EHR to after-EHR and before-EHR to 5-year-EHR were 18% (p<0.0001) and 55% (p<0.0001), respectively. All the element and grand mean quality scores significantly improved over the 5-year time interval. CONCLUSIONS: The EHR significantly improved the overall quality of the outpatient clinical note and the quality of all its elements, including the core and non-core elements. To our knowledge, this is the first study to demonstrate that the EHR significantly improves the quality of clinical notes.

BACKGROUND AND OBJECTIVE: The outpatient clinical note documents the clinician's information collection, problem assessment, and patient management, yet there is currently no validated instrument to measure the quality of the electronic clinical note. This study evaluated the validity of the QNOTE instrument, which assesses 12 elements in the clinical note, for measuring the quality of clinical notes. It also compared its performance with a global instrument that assesses the clinical note as a whole. MATERIALS AND METHODS: Retrospective multicenter blinded study of the clinical notes of 100 outpatients with type 2 diabetes mellitus who had been seen in clinic on at least three occasions. The 300 notes were rated by eight general internal medicine and eight family medicine practicing physicians. The QNOTE instrument scored the quality of the note as the sum of a set of 12 note element scores, and its inter-rater agreement was measured by the intraclass correlation coefficient. The Global instrument scored the note in its entirety, and its inter-rater agreement was measured by the Fleiss κ. RESULTS: The overall QNOTE inter-rater agreement was 0.82 (CI 0.80 to 0.84), and its note quality score was 65 (CI 64 to 66). The Global inter-rater agreement was 0.24 (CI 0.19 to 0.29), and its note quality score was 52 (CI 49 to 55). The QNOTE quality scores were consistent, and the overall QNOTE score was significantly higher than the overall Global score (p=0.04). CONCLUSIONS: We found the QNOTE to be a valid instrument for evaluating the quality of electronic clinical notes, and its performance was superior to that of the Global instrument.

Data quality is an important aspect of empirical analysis. This paper compares three noise handling methods to assess the benefit of identifying and either filtering or editing problematic instances. We compare a 'do nothing' strategy with (i) filtering, (ii) robust filtering and (Hi) filtering followed by polishing. A problem is that it is not possible to determine whether an instance contains noise unless it has implausible values. Since we cannot determine the true overall noise level we use implausible val.ues as a proxy measure. In addition to the ability to identify implausible values, we use another proxy measure, the ability to fit a classification tree to the data. The interpretation is low misclassification rates imply low noise levels. We found that all three of our data quality techniques improve upon the 'do nothing' strategy, also that the filtering and polishing was the most effective technique for dealing with noise since we eliminated the fewest data and had the lowest misclassification rates. Unfortunately the polishing process introduces new implausible values. We believe consideration of data quality is an important aspect of empirical software engineering. We have shown that for one large and complex real world data set automated techniques can help isolate noisy instances and potentially polish the values to produce better quality data for the analyst. However this work is at a preliminary stage and it assumes that the proxy measures of lity are appropriate.

The control and management of prototyping is a neglected research area. Whilst there is now some acceptance of prototyping as a technique which can be used to enhance software development, there is little evidence available that gives practical assistance on its effective use. Most prototyping work concentrates on individual case studies and a qualitative judgment on how it can be used to improve some aspect of software development. This paper suggests how control can be formally applied. It contrasts the controlling mechanisms behind structured life cycle techniques with methods advocated for controlling prototyping. A model for controlling prototyping is described. This emphasises the essential need for formally defining methods of monitoring and terminating the prototyping process. It is suggested that by using this form of approach is it possible to properly organise and plan prototyping, either as part of an evolutionary or as part of a life-cycle development method.>