Subspecialisation in cellular pathology in the DGH setting: the Warwick experience

Transition to Subspecialty Sign-Out at an Academic Institution and Its Advantages

Many pathology departments are introducing subspecialty sign-out in surgical pathology. In 2014, the University of Vermont Medical Center transitioned from general sign-out to partial subspecialty sign-out to include gastrointestinal and breast/cervix subspecialty benches; other specimens remained on general benches. Our experiences with the transition are described, including attending pathologist, trainee, support staff, and clinician satisfaction. A survey was e-mailed to all University of Vermont Medical Center anatomic pathology attendings, pathology trainees, pathologist assistants and grossing technicians, and clinicians who send surgical pathology specimens, immediately before and 1 year after transitioning to partial subspecialty sign-out. Quality assurance metrics were obtained for the 18 months prior to and following the transition. Gastrointestinal and breast/cervix attendings were more satisfied with partial subspecialty sign-out compared to those on the general benches. Overall, trainees were more satisfied with general sign-out because of the rotation schedule but preferred partial subspecialty sign-out due to improved teaching and more focused learning while on subspecialty benches. Clinicians remained very satisfied with our department and our reports; no differences were observed. Turnaround time was unchanged. After switching to partial subspecialty sign-out, there were significantly fewer discrepancies following multidisciplinary conference review for gastrointestinal and breast/cervix cases but remained the same for general cases. Fewer formal internal consults were performed after transitioning to partial subspecialty sign-out across all areas, but more notable for gastrointestinal and breast/cervix cases. Our data show improved quality assurance metrics and trainee education in a subspecialty sign-out setting compared to general sign-out setting.

Modeling complexity in pathologist workload measurement: the Automatable Activity-Based Approach to Complexity Unit Scoring (AABACUS)

Pathologists provide diagnoses relevant to the disease state of the patient and identify specific tissue characteristics relevant to response to therapy and prognosis. As personalized medicine evolves, there is a trend for increased demand of tissue-derived parameters. Pathologists perform increasingly complex analyses on the same 'cases'. Traditional methods of workload assessment and reimbursement, based on number of cases sometimes with a modifier (eg, the relative value unit (RVU) system used in the United States), often grossly underestimate the amount of work needed for complex cases and may overvalue simple, small biopsy cases. We describe a new approach to pathologist workload measurement that aligns with this new practice paradigm. Our multisite institution with geographically diverse partner institutions has developed the Automatable Activity-Based Approach to Complexity Unit Scoring (AABACUS) model that captures pathologists' clinical activities from parameters documented in departmental laboratory information systems (LISs). The model's algorithm includes: 'capture', 'export', 'identify', 'count', 'score', 'attribute', 'filter', and 'assess filtered results'. Captured data include specimen acquisition, handling, analysis, and reporting activities. Activities were counted and complexity units (CUs) generated using a complexity factor for each activity. CUs were compared between institutions, practice groups, and practice types and evaluated over a 5-year period (2008-2012). The annual load of a clinical service pathologist, irrespective of subspecialty, was ∼40,000 CUs using relative benchmarking. The model detected changing practice patterns and was appropriate for monitoring clinical workload for anatomical pathology, neuropathology, and hematopathology in academic and community settings, and encompassing subspecialty and generalist practices. AABACUS is objective, can be integrated with an LIS and automated, is reproducible, backwards compatible, and future adaptable. It can be applied as a robust decision support tool for the assessment of overall and targeted staffing needs as well as utilization analyses for resource allocation.