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Coding paediatric outpatient data to provide health planners with information on children with chronic conditions and
Elizabeth Craig1, Neal Kerr1, Gabrielle McDonald1
1NZ Child and Youth Epidemiology Service, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand.
Insights
Coding outpatient data for children with chronic conditions and disabilities (CCD) in New Zealand is feasible but resource-intensive. This approach can inform health planners, despite challenges with workload and documentation quality.
Area of Science:
- Health Informatics
- Paediatric Health Services Research
- Clinical Coding Systems
Background:
- Limited data exists on children with chronic conditions and disabilities (CCD) in New Zealand, partly due to a lack of diagnostic coding in hospital outpatient settings.
- Accurate health data is crucial for effective health planning and resource allocation for paediatric populations.
Purpose of the Study:
- To explore the feasibility of coding paediatric outpatient data to gather information on children with CCD.
- To assess the utility of diagnostic coding systems for understanding the health needs of children with chronic conditions in an outpatient setting.
Main Methods:
- Thirty-seven clinicians across six District Health Boards (DHBs) participated in a 12-week coding trial.
- Two coding systems, International Classification of Diseases and Related Health Problems, 10th Edition, Australian Modification (ICD-10-AM) and Systematised Nomenclature of Medicine Clinical Terms (SNOMED-CT), were trialled.
- Methods included a mix of paper and electronic approaches, with dual coding by clinicians and professional coders for a sample of events.
Main Results:
- A total of 2,604 outpatient events were coded using ICD-10-AM and 693 using SNOMED-CT.
- Developmental and behavioural disorders were prevalent, accounting for 34% of ICD-10-AM coded events and 40% of SNOMED-CT coded events.
- Clinicians reported concerns regarding workload, especially with paper-based methods, while coders noted issues with documentation quality and adherence to guidelines.
Conclusions:
- Coded outpatient data offers a potentially rich source of information for health planners regarding children with CCD.
- The coding process is resource-intensive, necessitating a cost-benefit analysis against the expense of managing health services with incomplete data.
- Improvements in documentation and workflow integration are essential for successful implementation of outpatient data coding for paediatric populations.
Aim:
In New Zealand, there is a paucity of information on children with chronic conditions and disabilities (CCD). One reason is that many are managed in hospital outpatients where diagnostic coding of health-care events does not occur. This study explores the feasibility of coding paediatric outpatient data to provide health planners with information on children with CCD.
Methods:
Thirty-seven clinicians from six District Health Boards (DHBs) trialled coding over 12 weeks. In five DHBs, the International Classification of Diseases and Related Health Problems, 10th Edition, Australian Modification (ICD-10-AM) and Systematised Nomenclature of Medicine Clinical Terms (SNOMED-CT) were trialled for 6 weeks each. In one DHB, ICD-10-AM was trialled for 12 weeks. A random sample (30%) of ICD-10-AM coded events were also coded by clinical coders. A mix of paper and electronic methods were used.
Results:
In total 2,604 outpatient events were coded in ICD-10-AM and 693 in SNOMED-CT. Dual coding occurred for 770 (29.6%) ICD-10-AM events. Overall, 34% of ICD-10-AM and 40% of SNOMED-CT events were for developmental and behavioural disorders. Chronic medical conditions were also common. Clinicians were concerned about the workload impacts, particularly for paper-based methods. Coder's were concerned about clinician's adherence to coding guidelines and the poor quality of documentation in some notes.
Conclusion:
Coded outpatient data could provide planners with a rich source of information on children with CCD. However, coding is also resource intensive. Thus its costs need to be weighed against the costs of managing a much larger health budget using very limited information.
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