3 results for Journal article, Audit of health data captured routinely in primary healthcare for the clinical decision support system PREDICT (PREDICT CVD-4)

  • Audit of health data captured routinely in primary healthcare for the clinical decision support system PREDICT (PREDICT CVD-4)

    Riddell, Tania; Kenealy, Timothy; Wells, Linda; Jackson, Rodney; Broad, Joanna (2008)

    Journal article
    The University of Auckland Library

    Computerised clinical decision support systems require health data to be captured in an explicit, structured way. However, traditional patient medical records contain data that is recorded in multiple ways using coding systems, free text, medical jargon and idiosyncratic abbreviations. To be meaningful, data transferred either automatically or manually from medical records to a clinical decision support program must accurately reflect data held in the patient medical record. Aims To assess the accuracy of health data captured routinely in primary care practice by PREDICT-CVD, a clinical decision support program for supporting the assessment and management of cardiovascular disease risk. Methods Data saved in the PREDICT clinical decision support system were audited against the same patients’ data held within an electronic patient management system. The audit was conducted in three general practices in Auckland, New Zealand. Within each practice the sample included all Maori patient records and a random sample of non-Maori patient records that made the total up to 100 per practice (n=300). Results We found good agreement between the data stored within PREDICT and that held within the patient management system. For 12 of the 27 variables examined there was perfect agreement. The most common disagreements, in order of frequency, were for smoking, diabetes and ethnicity recordings. Overall, there were 70 observations where data were recorded in PREDICT (but not in the patient management system), compared to 21 occurrences where data were recorded in the patient management system (but not in PREDICT). Conclusions Health data captured routinely in general practice by the clinical decision support system PREDICT were found to be highly consistent with data held in electronic patient records. However, the use of PREDICT-CVD improved the completeness of cardiovascular risk factor documentation.

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  • Audit of health data captured routinely in primary healthcare for the clinical decision support system PREDICT (PREDICT CVD-4)

    Riddell, T; Kenealy, T; Wells, S; Jackson, R; Broad, J (2008)

    Journal article
    The University of Auckland Library

    Computerised clinical decision support systems require health data to be captured in an explicit, structured way. However, traditional patient medical records contain data that is recorded in multiple ways using coding systems, free text, medical jargon and idiosyncratic abbreviations. To be meaningful, data transferred either automatically or manually from medical records to a clinical decision support program must accurately reflect data held in the patient medical record. Aims To assess the accuracy of health data captured routinely in primary care practice by PREDICT-CVD, a clinical decision support program for supporting the assessment and management of cardiovascular disease risk. Methods Data saved in the PREDICT clinical decision support system were audited against the same patients’ data held within an electronic patient management system. The audit was conducted in three general practices in Auckland, New Zealand. Within each practice the sample included all Maori patient records and a random sample of non-Maori patient records that made the total up to 100 per practice (n=300). Results We found good agreement between the data stored within PREDICT and that held within the patient management system. For 12 of the 27 variables examined there was perfect agreement. The most common disagreements, in order of frequency, were for smoking, diabetes and ethnicity recordings. Overall, there were 70 observations where data were recorded in PREDICT (but not in the patient management system), compared to 21 occurrences where data were recorded in the patient management system (but not in PREDICT). Conclusions Health data captured routinely in general practice by the clinical decision support system PREDICT were found to be highly consistent with data held in electronic patient records. However, the use of PREDICT-CVD improved the completeness of cardiovascular risk factor documentation.

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  • Audit of health data captured routinely in primary healthcare for the clinical decision support system PREDICT (PREDICT CVD-4)

    Riddell, T; Kenealy, T; Wells, S; Jackson, R; Broad, J (2008)

    Journal article
    The University of Auckland Library

    Computerised clinical decision support systems require health data to be captured in an explicit, structured way. However, traditional patient medical records contain data that is recorded in multiple ways using coding systems, free text, medical jargon and idiosyncratic abbreviations. To be meaningful, data transferred either automatically or manually from medical records to a clinical decision support program must accurately reflect data held in the patient medical record. Aims To assess the accuracy of health data captured routinely in primary care practice by PREDICT-CVD, a clinical decision support program for supporting the assessment and management of cardiovascular disease risk. Methods Data saved in the PREDICT clinical decision support system were audited against the same patients’ data held within an electronic patient management system. The audit was conducted in three general practices in Auckland, New Zealand. Within each practice the sample included all Maori patient records and a random sample of non-Maori patient records that made the total up to 100 per practice (n=300). Results We found good agreement between the data stored within PREDICT and that held within the patient management system. For 12 of the 27 variables examined there was perfect agreement. The most common disagreements, in order of frequency, were for smoking, diabetes and ethnicity recordings. Overall, there were 70 observations where data were recorded in PREDICT (but not in the patient management system), compared to 21 occurrences where data were recorded in the patient management system (but not in PREDICT). Conclusions Health data captured routinely in general practice by the clinical decision support system PREDICT were found to be highly consistent with data held in electronic patient records. However, the use of PREDICT-CVD improved the completeness of cardiovascular risk factor documentation.

    View record details