Problem
Case Study: SNOMED to ICD-9-CM
In September 2004 AHIMA signed a contract with the National Library of Medicine to validate the SNOMED to ICD-9-CM rules-based, reimbursement use-case map being created by SNOMED for inclusion in the Unified Medical Language System. AHIMA has been involved for several years in the SNOMED Mapping Working Group.
The alpha data map consisting of 500 mapped concepts was received by AHIMA in late March 2005. AHIMA decided to validate the map by reproducing it using the guidelines and heuristics provided by the working group.
Six AHIMA validators were assigned to validate approximately 150 concepts each. The work of each validator overlapped with the work of two other validators. This was done so AHIMA could examine internal inter-rater reliability (IRR) statistics for training and learning purposes since this was a new project.
AHIMA developed a mapping workflow document for the validators from the SNOMED International Alpha Testing documentation. This was done to specify the decision steps when mapping a concept. The workflow document and MS Access tool were tested prior to training, using a small number of concepts.
Training for the map validators consisted of a two-hour Web seminar. The validators were provided with the AHIMA workflow document, the SNOMED Alpha Test documentation, and the references and resources as outlined in the SNOMED Alpha Test documentation. Several of the testing concepts were selected, and maps were determined collaboratively and documented in the tool using the workflow and documentation.
Following the training, the database and the related documents were placed on an internal AHIMA server supporting version control. AHIMA validators checked out the database to perform validation on their assigned concepts, checking it back in when they had completed their work. Database use was reserved using a shared calendar.
Following completion of the validation work, AHIMA proceeded with data analysis. SAS statistical software was chosen for this analysis since it is anticipated that future validation databases may consist of close to 10,000 concepts, each potentially having multiple maps.
The validation data were loaded into SAS, and inter-rater reliability statistics between AHIMA validator pairs were run for ICD-9-CM code assignment, map category, and map rule. Overall, the results were good, usually with an inter-rater reliability greater than 70 percent. Variances in the mapping were most often due to misunderstandings of the instructions provided and the difficulty in determining when some terms are specific or nonspecific for reimbursement or subjectivity in classifying a concept that is not indexed in ICD nor commonly encountered for statistical aggregation or reimbursement reporting. These data will be used to refine AHIMA's training and education for the future validation efforts.
In addition to results detailed in a report delivered to both SNOMED International and NLM, AHIMA drew the main conclusions from its work:
The specification of the guidelines and heuristics for data mapping must be very detailed to enable understanding and reproducibility.
HIM professionals are well-suited to data mapping because of their training in health information and related processes.
Task: Summarize the use and beneficial aspects of data mapping in the above case study. When do you think using a data map would be beneficial and what other types of data can be mapped?