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Preparing Nursing Home Data from Multiple Sites for Clinical Research – A Case Study Using Observational Health Data Sciences and Informatics

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Zeitschriftentitel: eGEMs (Generating Evidence & Methods to improve patient outcomes)
Personen und Körperschaften: Boyce, Richard D., Handler, Steven M., Karp, Jordan F., Perera, Subashan, Reynolds, III, Charles F.
In: eGEMs (Generating Evidence & Methods to improve patient outcomes), 4, 2016, 1, S. 21
Medientyp: E-Article
Sprache: Unbestimmt
veröffentlicht:
Ubiquity Press, Ltd.
Schlagwörter:
author_facet Boyce, Richard D.
Handler, Steven M.
Karp, Jordan F.
Perera, Subashan
Reynolds, III, Charles F.
Boyce, Richard D.
Handler, Steven M.
Karp, Jordan F.
Perera, Subashan
Reynolds, III, Charles F.
author Boyce, Richard D.
Handler, Steven M.
Karp, Jordan F.
Perera, Subashan
Reynolds, III, Charles F.
spellingShingle Boyce, Richard D.
Handler, Steven M.
Karp, Jordan F.
Perera, Subashan
Reynolds, III, Charles F.
eGEMs (Generating Evidence & Methods to improve patient outcomes)
Preparing Nursing Home Data from Multiple Sites for Clinical Research – A Case Study Using Observational Health Data Sciences and Informatics
Microbiology (medical)
Immunology
Immunology and Allergy
author_sort boyce, richard d.
spelling Boyce, Richard D. Handler, Steven M. Karp, Jordan F. Perera, Subashan Reynolds, III, Charles F. 2327-9214 Ubiquity Press, Ltd. Microbiology (medical) Immunology Immunology and Allergy http://dx.doi.org/10.13063/2327-9214.1252 <jats:p>Introduction: A potential barrier to nursing home research is the limited availability of research quality data in electronic form. We describe a case study of converting electronic health data from five skilled nursing facilities to a research quality longitudinal dataset by means of open-source tools produced by the Observational Health Data Sciences and Informatics (OHDSI) collaborative.Methods: The Long-Term Care Minimum Data Set (MDS), drug dispensing , and fall incident data from five SNFs were extracted, translated, and loaded into version 4 of the OHDSI common data model. Quality assurance involved identifying errors using the Achilles data characterization tool and comparing both quality measures and drug exposures in the new database for concordance with externally available sources.Findings: Records for a total 4,519 patients (95.1%) made it into the final database. Achilles identified 10 different types of errors that were addressed in the final dataset. Drug exposures based on dispensing were generally accurate when compared with medication administration data from the pharmacy services provider. Quality measures were generally concordant between the new database and Nursing Home Compare for measures with a prevalence ≥ 10%. Fall data recorded in MDS was found to be more complete than data from fall incident reports.Conclusions: The new dataset is ready to support observational research on topics of clinical importance in the NH including patient-level prediction of falls. The extraction, translation, and loading process enabled the use of OHDSI data characterization tools that improved the quality of the final dataset.</jats:p> Preparing Nursing Home Data from Multiple Sites for Clinical Research – A Case Study Using Observational Health Data Sciences and Informatics eGEMs (Generating Evidence & Methods to improve patient outcomes)
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series eGEMs (Generating Evidence & Methods to improve patient outcomes)
source_id 49
title Preparing Nursing Home Data from Multiple Sites for Clinical Research – A Case Study Using Observational Health Data Sciences and Informatics
title_unstemmed Preparing Nursing Home Data from Multiple Sites for Clinical Research – A Case Study Using Observational Health Data Sciences and Informatics
title_full Preparing Nursing Home Data from Multiple Sites for Clinical Research – A Case Study Using Observational Health Data Sciences and Informatics
title_fullStr Preparing Nursing Home Data from Multiple Sites for Clinical Research – A Case Study Using Observational Health Data Sciences and Informatics
title_full_unstemmed Preparing Nursing Home Data from Multiple Sites for Clinical Research – A Case Study Using Observational Health Data Sciences and Informatics
title_short Preparing Nursing Home Data from Multiple Sites for Clinical Research – A Case Study Using Observational Health Data Sciences and Informatics
title_sort preparing nursing home data from multiple sites for clinical research – a case study using observational health data sciences and informatics
topic Microbiology (medical)
Immunology
Immunology and Allergy
url http://dx.doi.org/10.13063/2327-9214.1252
publishDate 2016
physical 21
description <jats:p>Introduction: A potential barrier to nursing home research is the limited availability of research quality data in electronic form. We describe a case study of converting electronic health data from five skilled nursing facilities to a research quality longitudinal dataset by means of open-source tools produced by the Observational Health Data Sciences and Informatics (OHDSI) collaborative.Methods: The Long-Term Care Minimum Data Set (MDS), drug dispensing , and fall incident data from five SNFs were extracted, translated, and loaded into version 4 of the OHDSI common data model. Quality assurance involved identifying errors using the Achilles data characterization tool and comparing both quality measures and drug exposures in the new database for concordance with externally available sources.Findings: Records for a total 4,519 patients (95.1%) made it into the final database. Achilles identified 10 different types of errors that were addressed in the final dataset. Drug exposures based on dispensing were generally accurate when compared with medication administration data from the pharmacy services provider. Quality measures were generally concordant between the new database and Nursing Home Compare for measures with a prevalence ≥ 10%. Fall data recorded in MDS was found to be more complete than data from fall incident reports.Conclusions: The new dataset is ready to support observational research on topics of clinical importance in the NH including patient-level prediction of falls. The extraction, translation, and loading process enabled the use of OHDSI data characterization tools that improved the quality of the final dataset.</jats:p>
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author Boyce, Richard D., Handler, Steven M., Karp, Jordan F., Perera, Subashan, Reynolds, III, Charles F.
author_facet Boyce, Richard D., Handler, Steven M., Karp, Jordan F., Perera, Subashan, Reynolds, III, Charles F., Boyce, Richard D., Handler, Steven M., Karp, Jordan F., Perera, Subashan, Reynolds, III, Charles F.
author_sort boyce, richard d.
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description <jats:p>Introduction: A potential barrier to nursing home research is the limited availability of research quality data in electronic form. We describe a case study of converting electronic health data from five skilled nursing facilities to a research quality longitudinal dataset by means of open-source tools produced by the Observational Health Data Sciences and Informatics (OHDSI) collaborative.Methods: The Long-Term Care Minimum Data Set (MDS), drug dispensing , and fall incident data from five SNFs were extracted, translated, and loaded into version 4 of the OHDSI common data model. Quality assurance involved identifying errors using the Achilles data characterization tool and comparing both quality measures and drug exposures in the new database for concordance with externally available sources.Findings: Records for a total 4,519 patients (95.1%) made it into the final database. Achilles identified 10 different types of errors that were addressed in the final dataset. Drug exposures based on dispensing were generally accurate when compared with medication administration data from the pharmacy services provider. Quality measures were generally concordant between the new database and Nursing Home Compare for measures with a prevalence ≥ 10%. Fall data recorded in MDS was found to be more complete than data from fall incident reports.Conclusions: The new dataset is ready to support observational research on topics of clinical importance in the NH including patient-level prediction of falls. The extraction, translation, and loading process enabled the use of OHDSI data characterization tools that improved the quality of the final dataset.</jats:p>
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spelling Boyce, Richard D. Handler, Steven M. Karp, Jordan F. Perera, Subashan Reynolds, III, Charles F. 2327-9214 Ubiquity Press, Ltd. Microbiology (medical) Immunology Immunology and Allergy http://dx.doi.org/10.13063/2327-9214.1252 <jats:p>Introduction: A potential barrier to nursing home research is the limited availability of research quality data in electronic form. We describe a case study of converting electronic health data from five skilled nursing facilities to a research quality longitudinal dataset by means of open-source tools produced by the Observational Health Data Sciences and Informatics (OHDSI) collaborative.Methods: The Long-Term Care Minimum Data Set (MDS), drug dispensing , and fall incident data from five SNFs were extracted, translated, and loaded into version 4 of the OHDSI common data model. Quality assurance involved identifying errors using the Achilles data characterization tool and comparing both quality measures and drug exposures in the new database for concordance with externally available sources.Findings: Records for a total 4,519 patients (95.1%) made it into the final database. Achilles identified 10 different types of errors that were addressed in the final dataset. Drug exposures based on dispensing were generally accurate when compared with medication administration data from the pharmacy services provider. Quality measures were generally concordant between the new database and Nursing Home Compare for measures with a prevalence ≥ 10%. Fall data recorded in MDS was found to be more complete than data from fall incident reports.Conclusions: The new dataset is ready to support observational research on topics of clinical importance in the NH including patient-level prediction of falls. The extraction, translation, and loading process enabled the use of OHDSI data characterization tools that improved the quality of the final dataset.</jats:p> Preparing Nursing Home Data from Multiple Sites for Clinical Research – A Case Study Using Observational Health Data Sciences and Informatics eGEMs (Generating Evidence & Methods to improve patient outcomes)
spellingShingle Boyce, Richard D., Handler, Steven M., Karp, Jordan F., Perera, Subashan, Reynolds, III, Charles F., eGEMs (Generating Evidence & Methods to improve patient outcomes), Preparing Nursing Home Data from Multiple Sites for Clinical Research – A Case Study Using Observational Health Data Sciences and Informatics, Microbiology (medical), Immunology, Immunology and Allergy
title Preparing Nursing Home Data from Multiple Sites for Clinical Research – A Case Study Using Observational Health Data Sciences and Informatics
title_full Preparing Nursing Home Data from Multiple Sites for Clinical Research – A Case Study Using Observational Health Data Sciences and Informatics
title_fullStr Preparing Nursing Home Data from Multiple Sites for Clinical Research – A Case Study Using Observational Health Data Sciences and Informatics
title_full_unstemmed Preparing Nursing Home Data from Multiple Sites for Clinical Research – A Case Study Using Observational Health Data Sciences and Informatics
title_short Preparing Nursing Home Data from Multiple Sites for Clinical Research – A Case Study Using Observational Health Data Sciences and Informatics
title_sort preparing nursing home data from multiple sites for clinical research – a case study using observational health data sciences and informatics
title_unstemmed Preparing Nursing Home Data from Multiple Sites for Clinical Research – A Case Study Using Observational Health Data Sciences and Informatics
topic Microbiology (medical), Immunology, Immunology and Allergy
url http://dx.doi.org/10.13063/2327-9214.1252