Identifier Field

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James M. Greenberg - One of the best experts on this subject based on the ideXlab platform.

  • Evaluation of Identifier Field agreement in linked neonatal records
    Journal of Perinatology, 2017
    Co-Authors: Eric S. Hall, Keith Marsolo, James M. Greenberg
    Abstract:

    Objective: To better address barriers arising from missing and unreliable Identifiers in neonatal medical records, we evaluated agreement and discordance among traditional and non-traditional linkage Fields within a linked neonatal data set. Study Design: The retrospective, descriptive analysis represents infants born from 2013 to 2015. We linked children’s hospital neonatal physician billing records to newborn medical records originating from an academic delivery hospital and evaluated rates of agreement, discordance and missingness for a set of 12 Identifier Field pairs used in the linkage algorithm. Results: We linked 7293 of 7404 physician billing records (98.5%), all of which were deemed valid upon manual review. Linked records contained a mean of 9.1 matching and 1.6 non-matching Identifier pairs. Only 4.8% had complete agreement among all 12 Identifier pairs. Conclusion: Our approach to selection of linkage variables and data formatting preparatory to linkage have generalizability, which may inform future neonatal and perinatal record linkage efforts.

  • Evaluation of Identifier Field agreement in linked neonatal records.
    Journal of perinatology : official journal of the California Perinatal Association, 2017
    Co-Authors: Eric S. Hall, Keith Marsolo, James M. Greenberg
    Abstract:

    To better address barriers arising from missing and unreliable Identifiers in neonatal medical records, we evaluated agreement and discordance among traditional and non-traditional linkage Fields within a linked neonatal data set. The retrospective, descriptive analysis represents infants born from 2013 to 2015. We linked children’s hospital neonatal physician billing records to newborn medical records originating from an academic delivery hospital and evaluated rates of agreement, discordance and missingness for a set of 12 Identifier Field pairs used in the linkage algorithm. We linked 7293 of 7404 physician billing records (98.5%), all of which were deemed valid upon manual review. Linked records contained a mean of 9.1 matching and 1.6 non-matching Identifier pairs. Only 4.8% had complete agreement among all 12 Identifier pairs. Our approach to selection of linkage variables and data formatting preparatory to linkage have generalizability, which may inform future neonatal and perinatal record linkage efforts.

Claude Castelluccia - One of the best experts on this subject based on the ideXlab platform.

Eric S. Hall - One of the best experts on this subject based on the ideXlab platform.

  • Evaluation of Identifier Field agreement in linked neonatal records
    Journal of Perinatology, 2017
    Co-Authors: Eric S. Hall, Keith Marsolo, James M. Greenberg
    Abstract:

    Objective: To better address barriers arising from missing and unreliable Identifiers in neonatal medical records, we evaluated agreement and discordance among traditional and non-traditional linkage Fields within a linked neonatal data set. Study Design: The retrospective, descriptive analysis represents infants born from 2013 to 2015. We linked children’s hospital neonatal physician billing records to newborn medical records originating from an academic delivery hospital and evaluated rates of agreement, discordance and missingness for a set of 12 Identifier Field pairs used in the linkage algorithm. Results: We linked 7293 of 7404 physician billing records (98.5%), all of which were deemed valid upon manual review. Linked records contained a mean of 9.1 matching and 1.6 non-matching Identifier pairs. Only 4.8% had complete agreement among all 12 Identifier pairs. Conclusion: Our approach to selection of linkage variables and data formatting preparatory to linkage have generalizability, which may inform future neonatal and perinatal record linkage efforts.

  • Evaluation of Identifier Field agreement in linked neonatal records.
    Journal of perinatology : official journal of the California Perinatal Association, 2017
    Co-Authors: Eric S. Hall, Keith Marsolo, James M. Greenberg
    Abstract:

    To better address barriers arising from missing and unreliable Identifiers in neonatal medical records, we evaluated agreement and discordance among traditional and non-traditional linkage Fields within a linked neonatal data set. The retrospective, descriptive analysis represents infants born from 2013 to 2015. We linked children’s hospital neonatal physician billing records to newborn medical records originating from an academic delivery hospital and evaluated rates of agreement, discordance and missingness for a set of 12 Identifier Field pairs used in the linkage algorithm. We linked 7293 of 7404 physician billing records (98.5%), all of which were deemed valid upon manual review. Linked records contained a mean of 9.1 matching and 1.6 non-matching Identifier pairs. Only 4.8% had complete agreement among all 12 Identifier pairs. Our approach to selection of linkage variables and data formatting preparatory to linkage have generalizability, which may inform future neonatal and perinatal record linkage efforts.

Keith Marsolo - One of the best experts on this subject based on the ideXlab platform.

  • Evaluation of Identifier Field agreement in linked neonatal records
    Journal of Perinatology, 2017
    Co-Authors: Eric S. Hall, Keith Marsolo, James M. Greenberg
    Abstract:

    Objective: To better address barriers arising from missing and unreliable Identifiers in neonatal medical records, we evaluated agreement and discordance among traditional and non-traditional linkage Fields within a linked neonatal data set. Study Design: The retrospective, descriptive analysis represents infants born from 2013 to 2015. We linked children’s hospital neonatal physician billing records to newborn medical records originating from an academic delivery hospital and evaluated rates of agreement, discordance and missingness for a set of 12 Identifier Field pairs used in the linkage algorithm. Results: We linked 7293 of 7404 physician billing records (98.5%), all of which were deemed valid upon manual review. Linked records contained a mean of 9.1 matching and 1.6 non-matching Identifier pairs. Only 4.8% had complete agreement among all 12 Identifier pairs. Conclusion: Our approach to selection of linkage variables and data formatting preparatory to linkage have generalizability, which may inform future neonatal and perinatal record linkage efforts.

  • Evaluation of Identifier Field agreement in linked neonatal records.
    Journal of perinatology : official journal of the California Perinatal Association, 2017
    Co-Authors: Eric S. Hall, Keith Marsolo, James M. Greenberg
    Abstract:

    To better address barriers arising from missing and unreliable Identifiers in neonatal medical records, we evaluated agreement and discordance among traditional and non-traditional linkage Fields within a linked neonatal data set. The retrospective, descriptive analysis represents infants born from 2013 to 2015. We linked children’s hospital neonatal physician billing records to newborn medical records originating from an academic delivery hospital and evaluated rates of agreement, discordance and missingness for a set of 12 Identifier Field pairs used in the linkage algorithm. We linked 7293 of 7404 physician billing records (98.5%), all of which were deemed valid upon manual review. Linked records contained a mean of 9.1 matching and 1.6 non-matching Identifier pairs. Only 4.8% had complete agreement among all 12 Identifier pairs. Our approach to selection of linkage variables and data formatting preparatory to linkage have generalizability, which may inform future neonatal and perinatal record linkage efforts.

Pars Mutaf - One of the best experts on this subject based on the ideXlab platform.