A Wider Perspective on Data Validation: A Multiregion Collaboration.

BACKGROUND: Data validation is important in maintaining the high-quality data necessary for trauma programs and research. Most existing guidance focuses on trauma center-level data validation, but validation from a broader level (region, state) may also be a helpful tool. OBJECTIVE: The purpose of this project is to improve data collection and submission at the local, regional, and state levels by performing logic-based data validation. METHODS: Logic edits were identified and accuracy rates were tracked quarterly, as measures were taken to improve accuracy. Following completion of Phase 1 of validation, Phase 2 was initiated to include both new fields and fields from Phase 1 that did not meet the accuracy goal. Data from Phase 2 were then compared with data from the state trauma registry. RESULTS: In both Phase 1 and Phase 2, five of the seven data fields validated reached 90% accuracy by the end of the respective project phase. The project facilitated registrar education and pursuit of data collection solutions in registry software. Systemic issues were identified at a higher level that had not been noticed at the trauma center level. DISCUSSION: Robust data validation is critical for an accurate trauma registry. Engaging higher-level organizations, like trauma regions, provides new perspective in data validation. CONCLUSION: This regional data validation approach provided additional value beyond usual center-level data validation.

Development of an expert derived ICD-AIS map for serious AIS3+ injury identification.

Objective: The objective of the mapping project was to develop an expert derived map between the International Statistical Classification of Diseases and Related Health Problems (ICD) clinical modifications (CM) and the Abbreviated Injury Scale (AIS) to be able to relate AIS severity to ICD coded data road traffic collision data in EU datasets. The maps were developed to enable the identification of serious AIS3+ injury and provide details of the mapping process for assumptions to be made about injury severity from mass datasets. This article describes in detail the mapping process of the International Classification of Diseases Ninth Revision, Clinical Modification (ICD-9-CM) and the International Classification of Diseases Tenth Revision, Clinical Modification (ICD-10-CM) codes to the Abbreviated Injury Scale 2005, Update 2008 (AIS08) codes to identify injury with an AIS severity of 3 or more (AIS3+ severity) to determine 'serious' (MAIS3+) road traffic injuries.Methods: Over 19,000 ICD codes were mapped from the following injury categories; injury ICD-9-CM (Chapter 17) codes between '800 and 999.9' and injury ICD-10-CM (Chapter 19) 'S' and 'T' prefixed codes were reviewed and mapped to an AIS08 category and then relate the severity to three groups; AIS3+, AIS < =2 and AIS 9 (no-map). The mapping was undertaken by ICD coding experts and certified AIS specialists from Europe, North America, Australia and Canada in face-to-face working groups and subsequent webinars between May 2014 and October 2015. During the process, the business rules were documented to define guidelines for the mapping process and enable inter-rater discrepancies to be resolved.Results: In total 2,504 ICD-9-CM codes were mapped to the AIS, of which 780 (31%) were assigned an AIS3+ severity. For the16,508 ICD-10-CM mapped codes a total of 2,323 (14%) were assigned an AIS3+ severity. Some 17% (n = 426) and 27% (n = 4,485) of ICD-9-CM and ICD-10-CM codes respectively were assigned to AIS9 (no-map) following the mapping process. It was evident there were 'problem' codes that could not be easily mapped to an AIS code to reflect severity. Problem maps affect the specificity of the map and severity when used to translate historical data in large datasets.Conclusions: The Association for the Advancement in Automotive Medicine, AAAM-endorsed expert-derived map offers a unique tool to road safety researchers to establish the number of MAIS3+ serious injuries occurring on the roads. The detailed process offered in this paper will enable researchers to understand the decision making and identify limitations when using the AIS08/ICD map on country-specific data. The results could inform protocols for dealing with problem codes to enable country comparisons of MAIS3+ serious injury rates.

Development of an expert based ICD-9-CM and ICD-10-CM map to AIS 2005 update 2008.

OBJECTIVE: This article describes how maps were developed from the clinical modifications of the 9th and 10th revisions of the International Classification of Diseases (ICD) to the Abbreviated Injury Scale 2005 Update 2008 (AIS08). The development of the mapping methodology is described, with discussion of the major assumptions used in the process to map ICD codes to AIS severities. There were many intricacies to developing the maps, because the 2 coding systems, ICD and AIS, were developed for different purposes and contain unique classification structures to meet these purposes. METHODS: Experts in ICD and AIS analyzed the rules and coding guidelines of both injury coding schemes to develop rules for mapping ICD injury codes to the AIS08. This involved subject-matter expertise, detailed knowledge of anatomy, and an in-depth understanding of injury terms and definitions as applied in both taxonomies. The official ICD-9-CM and ICD-10-CM versions (injury sections) were mapped to the AIS08 codes and severities, following the rules outlined in each coding manual. The panel of experts was composed of coders certified in ICD and/or AIS from around the world. In the process of developing the map from ICD to AIS, the experts created rules to address issues with the differences in coding guidelines between the 2 schemas and assure a consistent approach to all codes. RESULTS: Over 19,000 ICD codes were analyzed and maps were generated for each code to AIS08 chapters, AIS08 severities, and Injury Severity Score (ISS) body regions. After completion of the maps, 14,101 (74%) of the eligible 19,012 injury-related ICD-9-CM and ICD-10-CM codes were assigned valid AIS08 severity scores between 1 and 6. The remaining 4,911 codes were assigned an AIS08 of 9 (unknown) or were determined to be nonmappable because the ICD description lacked sufficient qualifying information for determining severity according to AIS rules. There were also 15,214 (80%) ICD codes mapped to AIS08 chapter and ISS body region, which allow for ISS calculations for patient data sets. CONCLUSION: This mapping between ICD and AIS provides a comprehensive, expert-designed solution for analysts to bridge the data gap between the injury descriptions provided in hospital codes (ICD-9-CM, ICD-10-CM) and injury severity codes (AIS08). By applying consistent rules from both the ICD and AIS taxonomies, the expert panel created these definitive maps, which are the only ones endorsed by the Association for the Advancement of Automotive Medicine (AAAM). Initial validation upheld the quality of these maps for the estimation of AIS severity, but future work should include verification of these maps for MAIS and ISS estimations with large data sets. These ICD-AIS maps will support data analysis from databases with injury information classified in these 2 different systems and open new doors for the investigation of injury from traumatic events using large injury data sets.

A survey of trauma registrars: job requirements, responsibilities, recruitment, and retention.

Study purpose was to describe trauma registrar job requirements, responsibilities, and recruitment/retention practices. An online survey was used. One-third required a high school diploma; two-thirds required a college degree. Most required skills were medical terminology (66%), database management (65%), anatomy (64%), Word (63%). Data responsibilities included abstracting, entry, coding, and validating. Few employers required certification. Twenty-six percent reported problems with recruitment, and 35% with retention. Salary and lack of advancement were primary reasons for employee turnover. Certifications were less relevant than skills; the primary focus was data management. Recommendations for recruitment/retention include job flexibility, educational opportunities, and recognition as a profession.