Background: In the era of novel therapies, the first line and subsequent lines of therapies are rapidly evolving in the treatment of patients with HL in order to optimize disease control and reduce long-term health risks. HSCT is often used following treatment failure, which is associated with cost and morbidity. We define and describe a cohort of patients with HL from 2009-2013 in the Massachusetts All Payer Claims Database (MA APCD) who received HSCT. Methods: We defined a cohort of patients with HL who underwent HSCT from among the 7,613 cases with ICD-9 diagnostic codes for HL. To identify HSCT cases, we developed an iteratively refined algorithm using ICD-9 diagnostic and procedure codes, dates of service, and length of stay. Each potential HSCT case was reviewed by an oncologist prior to final inclusion. Reasons for exclusion were not HL, no HSCT, and remote history of HSCT. Results: Before applying our algorithm, we identified 695 HL patients with any ICD-9 code related to HSCT. Our algorithm reduced this to 178 potential cases of patients with HL who underwent HSCT. After clinical review, 128 (72%) were true cases. Cases were excluded for no HL (n = 28), no HSCT (n = 2), and history of HSCT (n = 20). Of the 128 patients with HL who received HSCT, the median age was 40 years (q1 = 26, q3 = 52) and 50% were female. 80% (N = 102) received autologous HSCT, 6% (N = 8) received allogeneic HSCT, 2% (N = 2) received both, and 13% (N = 16) received HSCT of undetermined type based on the claims data. Conclusions: We successfully developed and refined an algorithm to help identify HSCT among patients with HL within a claims database. While clinical review was required to determine true cases, our algorithm substantially reduced the amount of case reviews required. This algorithm and process can be applied to identify the use of HSCT in other hematological malignancies. Identifying true cases of HSCT from claims databases allows us to better describe patterns for treatment failure in HL, follow younger patients who are not routinely captured in other databases, and estimate cost of care. Future directions of this analysis include following patients across different insurers and estimating cost based on site and payer type.