Creating a data warehouse to support monitoring of NSQHS blood management standard from EMR data
Abstract Background Blood management is an important aspect of healthcare and vital for the well-being of patients. For effective blood management, it is essential to determine the quality and documentation of the processes for blood transfusions in the Electronic Medical Records (EMR) system. The E...
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BMC
2024-11-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | https://doi.org/10.1186/s12911-024-02732-8 |
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| author | David Cheng-Zarate James Burns Cathy Ngo Agnes Haryanto Gregory Duncan David Taniar Michael Wybrow |
| author_facet | David Cheng-Zarate James Burns Cathy Ngo Agnes Haryanto Gregory Duncan David Taniar Michael Wybrow |
| author_sort | David Cheng-Zarate |
| collection | DOAJ |
| description | Abstract Background Blood management is an important aspect of healthcare and vital for the well-being of patients. For effective blood management, it is essential to determine the quality and documentation of the processes for blood transfusions in the Electronic Medical Records (EMR) system. The EMR system stores information on most activities performed in a digital hospital. As such, it is difficult to get an overview of all data. The National Safety and Quality Health Service (NSQHS) Standards define metrics that assess the care quality of health entities such as hospitals. To produce these metrics, data needs to be analysed historically. However, data in the EMR is not designed to easily perform analytical queries of the kind which are needed to feed into clinical decision support tools. Thus, another system needs to be implemented to store and calculate the metrics for the blood management national standard. Methods In this paper, we propose a clinical data warehouse that stores the transformed data from EMR to be able to identify that the hospital is compliant with the Australian NSQHS Standards for blood management. Firstly, the data needed was explored and evaluated. Next, a schema for the clinical data warehouse was designed for the efficient storage of EMR data. Once the schema was defined, data was extracted from the EMR to be preprocessed to fit the schema design. Finally, the data warehouse allows the data to be consumed by decision support tools. Results We worked with Eastern Health, a major Australian health service, to implement the data warehouse that allowed us to easily query and supply data to be ingested by clinical decision support systems. Additionally, this implementation provides flexibility to recompute the metrics whenever data is updated. Finally, a dashboard was implemented to display important metrics defined by the National Safety and Quality Health Service (NSQHS) Standards on blood management. Conclusions This study prioritises streamlined data modeling and processing, in contrast to conventional dashboard-centric approaches. It ensures data readiness for decision-making tools, offering insights to clinicians and validating hospital compliance with national standards in blood management through efficient design. |
| format | Article |
| id | doaj-art-1d20b96f0bb74c248e94820e848de7f1 |
| institution | Kabale University |
| issn | 1472-6947 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj-art-1d20b96f0bb74c248e94820e848de7f12024-11-24T12:29:03ZengBMCBMC Medical Informatics and Decision Making1472-69472024-11-0124111410.1186/s12911-024-02732-8Creating a data warehouse to support monitoring of NSQHS blood management standard from EMR dataDavid Cheng-Zarate0James Burns1Cathy Ngo2Agnes Haryanto3Gregory Duncan4David Taniar5Michael Wybrow6Faculty of Information Technology, Monash UniversityEastern HealthEastern HealthFaculty of Information Technology, Monash UniversityFaculty of Medicine, Nursing and Health Sciences, Monash UniversityFaculty of Information Technology, Monash UniversityFaculty of Information Technology, Monash UniversityAbstract Background Blood management is an important aspect of healthcare and vital for the well-being of patients. For effective blood management, it is essential to determine the quality and documentation of the processes for blood transfusions in the Electronic Medical Records (EMR) system. The EMR system stores information on most activities performed in a digital hospital. As such, it is difficult to get an overview of all data. The National Safety and Quality Health Service (NSQHS) Standards define metrics that assess the care quality of health entities such as hospitals. To produce these metrics, data needs to be analysed historically. However, data in the EMR is not designed to easily perform analytical queries of the kind which are needed to feed into clinical decision support tools. Thus, another system needs to be implemented to store and calculate the metrics for the blood management national standard. Methods In this paper, we propose a clinical data warehouse that stores the transformed data from EMR to be able to identify that the hospital is compliant with the Australian NSQHS Standards for blood management. Firstly, the data needed was explored and evaluated. Next, a schema for the clinical data warehouse was designed for the efficient storage of EMR data. Once the schema was defined, data was extracted from the EMR to be preprocessed to fit the schema design. Finally, the data warehouse allows the data to be consumed by decision support tools. Results We worked with Eastern Health, a major Australian health service, to implement the data warehouse that allowed us to easily query and supply data to be ingested by clinical decision support systems. Additionally, this implementation provides flexibility to recompute the metrics whenever data is updated. Finally, a dashboard was implemented to display important metrics defined by the National Safety and Quality Health Service (NSQHS) Standards on blood management. Conclusions This study prioritises streamlined data modeling and processing, in contrast to conventional dashboard-centric approaches. It ensures data readiness for decision-making tools, offering insights to clinicians and validating hospital compliance with national standards in blood management through efficient design.https://doi.org/10.1186/s12911-024-02732-8Blood managementClinical data warehouseDashboardEMR |
| spellingShingle | David Cheng-Zarate James Burns Cathy Ngo Agnes Haryanto Gregory Duncan David Taniar Michael Wybrow Creating a data warehouse to support monitoring of NSQHS blood management standard from EMR data BMC Medical Informatics and Decision Making Blood management Clinical data warehouse Dashboard EMR |
| title | Creating a data warehouse to support monitoring of NSQHS blood management standard from EMR data |
| title_full | Creating a data warehouse to support monitoring of NSQHS blood management standard from EMR data |
| title_fullStr | Creating a data warehouse to support monitoring of NSQHS blood management standard from EMR data |
| title_full_unstemmed | Creating a data warehouse to support monitoring of NSQHS blood management standard from EMR data |
| title_short | Creating a data warehouse to support monitoring of NSQHS blood management standard from EMR data |
| title_sort | creating a data warehouse to support monitoring of nsqhs blood management standard from emr data |
| topic | Blood management Clinical data warehouse Dashboard EMR |
| url | https://doi.org/10.1186/s12911-024-02732-8 |
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