A computable biomedical knowledge object for calculating in‐hospital mortality for patients admitted with acute myocardial infarction
Abstract Introduction Quality indicators play an essential role in a learning health system. They help healthcare providers to monitor the quality and safety of care delivered and to identify areas for improvement. Clinical quality indicators, therefore, need to be based on real world data. Generati...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-10-01
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| Series: | Learning Health Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/lrh2.10388 |
| _version_ | 1797657911169646592 |
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| author | Rosemarie Sadsad Gema Ruber Johnson Zhou Steven Nicklin Guy Tsafnat |
| author_facet | Rosemarie Sadsad Gema Ruber Johnson Zhou Steven Nicklin Guy Tsafnat |
| author_sort | Rosemarie Sadsad |
| collection | DOAJ |
| description | Abstract Introduction Quality indicators play an essential role in a learning health system. They help healthcare providers to monitor the quality and safety of care delivered and to identify areas for improvement. Clinical quality indicators, therefore, need to be based on real world data. Generating reliable and actionable data routinely is challenging. Healthcare data are often stored in different formats and use different terminologies and coding systems, making it difficult to generate and compare indicator reports from different sources. Methods The Observational Health Sciences and Informatics community maintains the Observational Medical Outcomes Partnership Common Data Model (OMOP). This is an open data standard providing a computable and interoperable format for real world data. We implemented a Computable Biomedical Knowledge Object (CBK) in the Piano Platform based on OMOP. The CBK calculates an inpatient quality indicator and was illustrated using synthetic electronic health record (EHR) data in the open OMOP standard. Results The CBK reported the in‐hospital mortality of patients admitted for acute myocardial infarction (AMI) for the synthetic EHR dataset and includes interactive visualizations and the results of calculations. Value sets composed of OMOP concept codes for AMI and comorbidities used in the indicator calculation were also created. Conclusion Computable biomedical knowledge (CBK) objects that operate on OMOP data can be reused across datasets that conform to OMOP. With OMOP being a widely used interoperability standard, quality indicators embedded in CBKs can accelerate the generation of evidence for targeted quality and safety management, improving care to benefit larger populations. |
| first_indexed | 2024-03-11T17:52:23Z |
| format | Article |
| id | doaj.art-d2948d6dee1c443eb523fb062746bdd3 |
| institution | Directory Open Access Journal |
| issn | 2379-6146 |
| language | English |
| last_indexed | 2024-03-11T17:52:23Z |
| publishDate | 2023-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | Learning Health Systems |
| spelling | doaj.art-d2948d6dee1c443eb523fb062746bdd32023-10-18T03:55:06ZengWileyLearning Health Systems2379-61462023-10-0174n/an/a10.1002/lrh2.10388A computable biomedical knowledge object for calculating in‐hospital mortality for patients admitted with acute myocardial infarctionRosemarie Sadsad0Gema Ruber1Johnson Zhou2Steven Nicklin3Guy Tsafnat4Evidentli Sydney New South Wales AustraliaEvidentli Sydney New South Wales AustraliaEvidentli Sydney New South Wales AustraliaEvidentli Sydney New South Wales AustraliaEvidentli Sydney New South Wales AustraliaAbstract Introduction Quality indicators play an essential role in a learning health system. They help healthcare providers to monitor the quality and safety of care delivered and to identify areas for improvement. Clinical quality indicators, therefore, need to be based on real world data. Generating reliable and actionable data routinely is challenging. Healthcare data are often stored in different formats and use different terminologies and coding systems, making it difficult to generate and compare indicator reports from different sources. Methods The Observational Health Sciences and Informatics community maintains the Observational Medical Outcomes Partnership Common Data Model (OMOP). This is an open data standard providing a computable and interoperable format for real world data. We implemented a Computable Biomedical Knowledge Object (CBK) in the Piano Platform based on OMOP. The CBK calculates an inpatient quality indicator and was illustrated using synthetic electronic health record (EHR) data in the open OMOP standard. Results The CBK reported the in‐hospital mortality of patients admitted for acute myocardial infarction (AMI) for the synthetic EHR dataset and includes interactive visualizations and the results of calculations. Value sets composed of OMOP concept codes for AMI and comorbidities used in the indicator calculation were also created. Conclusion Computable biomedical knowledge (CBK) objects that operate on OMOP data can be reused across datasets that conform to OMOP. With OMOP being a widely used interoperability standard, quality indicators embedded in CBKs can accelerate the generation of evidence for targeted quality and safety management, improving care to benefit larger populations.https://doi.org/10.1002/lrh2.10388common data modelhealthcare quality and safetylearning health systemsOHDSIOMOPquality indicators |
| spellingShingle | Rosemarie Sadsad Gema Ruber Johnson Zhou Steven Nicklin Guy Tsafnat A computable biomedical knowledge object for calculating in‐hospital mortality for patients admitted with acute myocardial infarction Learning Health Systems common data model healthcare quality and safety learning health systems OHDSI OMOP quality indicators |
| title | A computable biomedical knowledge object for calculating in‐hospital mortality for patients admitted with acute myocardial infarction |
| title_full | A computable biomedical knowledge object for calculating in‐hospital mortality for patients admitted with acute myocardial infarction |
| title_fullStr | A computable biomedical knowledge object for calculating in‐hospital mortality for patients admitted with acute myocardial infarction |
| title_full_unstemmed | A computable biomedical knowledge object for calculating in‐hospital mortality for patients admitted with acute myocardial infarction |
| title_short | A computable biomedical knowledge object for calculating in‐hospital mortality for patients admitted with acute myocardial infarction |
| title_sort | computable biomedical knowledge object for calculating in hospital mortality for patients admitted with acute myocardial infarction |
| topic | common data model healthcare quality and safety learning health systems OHDSI OMOP quality indicators |
| url | https://doi.org/10.1002/lrh2.10388 |
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