Trends in delivery hospitalizations and the impact of ICD-9-CM to ICD-10-CM-PCS transition in Portugal between 2010 and 2018
Background: Hospital discharge data are essential for maternal health surveillance, clinical research, and healthcare resource allocation. In 2017, Portuguese hospitals transitioned from the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) to the Internation...
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2025-01-01
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| Series: | Informatics in Medicine Unlocked |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352914825000140 |
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| author | Catarina de Paraíso Camarinha Maria Miguel Gomes Oliveira Cecília Elias Miguel de Araújo Nobre Leonor Bacelar Costa Nicolau Cristina Furtado Andreia Silva da Costa Paulo Jorge da Silva Nogueira |
| author_facet | Catarina de Paraíso Camarinha Maria Miguel Gomes Oliveira Cecília Elias Miguel de Araújo Nobre Leonor Bacelar Costa Nicolau Cristina Furtado Andreia Silva da Costa Paulo Jorge da Silva Nogueira |
| author_sort | Catarina de Paraíso Camarinha |
| collection | DOAJ |
| description | Background: Hospital discharge data are essential for maternal health surveillance, clinical research, and healthcare resource allocation. In 2017, Portuguese hospitals transitioned from the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) to the International Classification of Diseases, 10th edition, Clinical Modification and Procedure Coding System (ICD-10-CM/PCS), impacting the recording of delivery hospitalizations. This study examines trends in delivery hospitalizations from 2010 to 2018 and assesses the impact of the ICD-10-CM/PCS transition. Methods: We conducted a register-based observational cross-sectional analysis using data from the National Hospital Discharge Database, covering delivery hospitalizations in public hospitals from January 1, 2010, to December 31, 2018. Delivery episodes were identified using diagnosis codes, normal delivery codes, diagnosis-related group (DRG) codes, and procedure codes. Statistical analyses included descriptive statistics, interrupted time series with segmented regression, and Prophet forecasting models to evaluate trends and the impact of the coding transition. Results: A total of 673,978 delivery hospitalizations were recorded. The transition from ICD-9-CM to ICD-10-CM/PCS in 2017 had minimal overall impact on delivery trends. DRG codes consistently identified the majority of delivery episodes, with outcome of delivery codes and selected procedure codes showing varying trends. An increase in episodes identified by normal delivery codes and a significant decrease in episodes identified by procedure codes was observed immediately after the ICD-10 transition (p < 0.001). The Prophet model indicated improved forecast accuracy for procedure codes when including the ICD-10 transition variable. Conclusion: The transition to ICD-10-CM/PCS had a limited impact on overall delivery hospitalization trends but significantly affected procedure coding. These findings underscore the importance of considering coding system changes in healthcare data analyses. Further research should incorporate private hospital data and continuously monitor coding practices to ensure reliable health data for research and policy-making. |
| format | Article |
| id | doaj-art-40dead2ae3604d71b7b295e5401aac6e |
| institution | DOAJ |
| issn | 2352-9148 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
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| series | Informatics in Medicine Unlocked |
| spelling | doaj-art-40dead2ae3604d71b7b295e5401aac6e2025-08-20T03:15:47ZengElsevierInformatics in Medicine Unlocked2352-91482025-01-015310162610.1016/j.imu.2025.101626Trends in delivery hospitalizations and the impact of ICD-9-CM to ICD-10-CM-PCS transition in Portugal between 2010 and 2018Catarina de Paraíso Camarinha0Maria Miguel Gomes Oliveira1Cecília Elias2Miguel de Araújo Nobre3Leonor Bacelar Costa Nicolau4Cristina Furtado5Andreia Silva da Costa6Paulo Jorge da Silva Nogueira7EPI Task-Force FMUL, Faculdade de Medicina, Universidade de Lisboa, Avenida Professor Egas Moniz, 1649-028, Lisboa, Portugal; Área Disciplinar Autónoma de Bioestatística (Laboratório de Biomatemática), Faculdade de Medicina, Universidade de Lisboa, Avenida Professor Egas Moniz, 1649-028, Lisboa, Portugal; Corresponding author. EPI Task-Force FMUL, Faculdade de Medicina, Universidade de Lisboa, Avenida Professor Egas Moniz, 1649-028, Lisboa, Portugal.EPI Task-Force FMUL, Faculdade de Medicina, Universidade de Lisboa, Avenida Professor Egas Moniz, 1649-028, Lisboa, Portugal; Área Disciplinar Autónoma de Bioestatística (Laboratório de Biomatemática), Faculdade de Medicina, Universidade de Lisboa, Avenida Professor Egas Moniz, 1649-028, Lisboa, PortugalEPI Task-Force FMUL, Faculdade de Medicina, Universidade de Lisboa, Avenida Professor Egas Moniz, 1649-028, Lisboa, Portugal; Direção Geral de Saúde, Divisão de Saúde Sexual, Reprodutiva, Infantil e Juvenil, Alameda D. Afonso Henriques, 45, 1049-005, Lisboa, Portugal; Instituto de Saúde Ambiental, Faculdade de Medicina, Universidade de Lisboa, Avenida Professor Egas Moniz, 1649-028, Lisboa, PortugalEPI Task-Force FMUL, Faculdade de Medicina, Universidade de Lisboa, Avenida Professor Egas Moniz, 1649-028, Lisboa, Portugal; Clínica Universitária de Estomatologia, Faculdade de Medicina, Universidade de Lisboa, Avenida Professor Egas Moniz, 1649-028, Lisboa, PortugalEPI Task-Force FMUL, Faculdade de Medicina, Universidade de Lisboa, Avenida Professor Egas Moniz, 1649-028, Lisboa, Portugal; Área Disciplinar Autónoma de Bioestatística (Laboratório de Biomatemática), Faculdade de Medicina, Universidade de Lisboa, Avenida Professor Egas Moniz, 1649-028, Lisboa, Portugal; Instituto de Saúde Ambiental, Faculdade de Medicina, Universidade de Lisboa, Avenida Professor Egas Moniz, 1649-028, Lisboa, Portugal; Instituto de Medicina Preventiva e Saúde Pública, Faculdade de Medicina, Universidade de Lisboa, Avenida Professor Egas Moniz, 1649-028, Lisboa, PortugalEPI Task-Force FMUL, Faculdade de Medicina, Universidade de Lisboa, Avenida Professor Egas Moniz, 1649-028, Lisboa, Portugal; Instituto de Saúde Ambiental, Faculdade de Medicina, Universidade de Lisboa, Avenida Professor Egas Moniz, 1649-028, Lisboa, Portugal; Instituto de Medicina Preventiva e Saúde Pública, Faculdade de Medicina, Universidade de Lisboa, Avenida Professor Egas Moniz, 1649-028, Lisboa, Portugal; Unidade de Epidemiologia, Instituto de Medicina Preventiva e Saúde Pública, Faculdade de Medicina, Universidade de Lisboa. Avenida Professor Egas Moniz, 1649-028, Lisboa, Portugal; Instituto Nacional de Saúde Doutor Ricardo Jorge, Avenidade Padre Cruz, 1600-560, Lisboa, PortugalEPI Task-Force FMUL, Faculdade de Medicina, Universidade de Lisboa, Avenida Professor Egas Moniz, 1649-028, Lisboa, Portugal; Instituto de Saúde Ambiental, Faculdade de Medicina, Universidade de Lisboa, Avenida Professor Egas Moniz, 1649-028, Lisboa, Portugal; CIDNUR - Centro de Investigação, Inovação e Desenvolvimento em Enfermagem de Lisboa, Escola Superior de Enfermagem de Lisboa, Avenida Professor Egas Moniz, 1600-190, Lisboa, Portugal; CRC-W-Católica Research Centre for Psychological, Family and Social Wellbeing, Universidade Católica Portuguesa, Palma de Cima, 1649-023, Lisboa, PortugalEPI Task-Force FMUL, Faculdade de Medicina, Universidade de Lisboa, Avenida Professor Egas Moniz, 1649-028, Lisboa, Portugal; Área Disciplinar Autónoma de Bioestatística (Laboratório de Biomatemática), Faculdade de Medicina, Universidade de Lisboa, Avenida Professor Egas Moniz, 1649-028, Lisboa, Portugal; Instituto de Saúde Ambiental, Faculdade de Medicina, Universidade de Lisboa, Avenida Professor Egas Moniz, 1649-028, Lisboa, Portugal; Instituto de Medicina Preventiva e Saúde Pública, Faculdade de Medicina, Universidade de Lisboa, Avenida Professor Egas Moniz, 1649-028, Lisboa, PortugalBackground: Hospital discharge data are essential for maternal health surveillance, clinical research, and healthcare resource allocation. In 2017, Portuguese hospitals transitioned from the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) to the International Classification of Diseases, 10th edition, Clinical Modification and Procedure Coding System (ICD-10-CM/PCS), impacting the recording of delivery hospitalizations. This study examines trends in delivery hospitalizations from 2010 to 2018 and assesses the impact of the ICD-10-CM/PCS transition. Methods: We conducted a register-based observational cross-sectional analysis using data from the National Hospital Discharge Database, covering delivery hospitalizations in public hospitals from January 1, 2010, to December 31, 2018. Delivery episodes were identified using diagnosis codes, normal delivery codes, diagnosis-related group (DRG) codes, and procedure codes. Statistical analyses included descriptive statistics, interrupted time series with segmented regression, and Prophet forecasting models to evaluate trends and the impact of the coding transition. Results: A total of 673,978 delivery hospitalizations were recorded. The transition from ICD-9-CM to ICD-10-CM/PCS in 2017 had minimal overall impact on delivery trends. DRG codes consistently identified the majority of delivery episodes, with outcome of delivery codes and selected procedure codes showing varying trends. An increase in episodes identified by normal delivery codes and a significant decrease in episodes identified by procedure codes was observed immediately after the ICD-10 transition (p < 0.001). The Prophet model indicated improved forecast accuracy for procedure codes when including the ICD-10 transition variable. Conclusion: The transition to ICD-10-CM/PCS had a limited impact on overall delivery hospitalization trends but significantly affected procedure coding. These findings underscore the importance of considering coding system changes in healthcare data analyses. Further research should incorporate private hospital data and continuously monitor coding practices to ensure reliable health data for research and policy-making.http://www.sciencedirect.com/science/article/pii/S2352914825000140Delivery hospitalizationsICD-9-CMICD-10-CMCoding transitionMaternal health surveillancePortugal |
| spellingShingle | Catarina de Paraíso Camarinha Maria Miguel Gomes Oliveira Cecília Elias Miguel de Araújo Nobre Leonor Bacelar Costa Nicolau Cristina Furtado Andreia Silva da Costa Paulo Jorge da Silva Nogueira Trends in delivery hospitalizations and the impact of ICD-9-CM to ICD-10-CM-PCS transition in Portugal between 2010 and 2018 Informatics in Medicine Unlocked Delivery hospitalizations ICD-9-CM ICD-10-CM Coding transition Maternal health surveillance Portugal |
| title | Trends in delivery hospitalizations and the impact of ICD-9-CM to ICD-10-CM-PCS transition in Portugal between 2010 and 2018 |
| title_full | Trends in delivery hospitalizations and the impact of ICD-9-CM to ICD-10-CM-PCS transition in Portugal between 2010 and 2018 |
| title_fullStr | Trends in delivery hospitalizations and the impact of ICD-9-CM to ICD-10-CM-PCS transition in Portugal between 2010 and 2018 |
| title_full_unstemmed | Trends in delivery hospitalizations and the impact of ICD-9-CM to ICD-10-CM-PCS transition in Portugal between 2010 and 2018 |
| title_short | Trends in delivery hospitalizations and the impact of ICD-9-CM to ICD-10-CM-PCS transition in Portugal between 2010 and 2018 |
| title_sort | trends in delivery hospitalizations and the impact of icd 9 cm to icd 10 cm pcs transition in portugal between 2010 and 2018 |
| topic | Delivery hospitalizations ICD-9-CM ICD-10-CM Coding transition Maternal health surveillance Portugal |
| url | http://www.sciencedirect.com/science/article/pii/S2352914825000140 |
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