Literature DB >> 20819879

Subspecialisation and despecialisation in anatomical pathology.

Mark K Heatley1.   

Abstract

Subspecialisation in histopathology was anticipated to improve the quality of reporting, teaching and communication with the clinical team. Although there is little information available, there is a suggestion that subspecialised services are more expensive than services provided by general departments but are speedier, although even this improvement is now being compromised by requirements to doubly report some types of case. Departments considering adopting a subspecialist model should carefully consider the financial and organisational implications, and recognise that it is associated with reduced flexibility in case of vacancy or illness. Although individual pathologists report moving from subspecialist to more general practice with varying degrees of success (this was facilitated by general training, a brief period of subspecialisation, exposure to a general pool of straightforward cases from other specialities and a defined period of supervised retraining), there is no known example of an entire subspecialised department returning to provide a general service, and it is unclear as to how the difficulties likely to be encountered could be overcome. The original move to subspecialise was based on enthusiasm with little objective measurement of changes in cost or quality (or any input or output measure). Cost and quality associated with both general and subspecialist units are still poorly documented in the literature, and the need to establish baselines for these is a major challenge for pathologists. Further subspecialisation or any move to despecialise should be undertaken on the basis of such measures, which should be carefully monitored.

Mesh:

Year:  2010        PMID: 20819879     DOI: 10.1136/jcp.2010.079640

Source DB:  PubMed          Journal:  J Clin Pathol        ISSN: 0021-9746            Impact factor:   3.411


  2 in total

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

Authors:  Joanna L Conant; Pamela C Gibson; Janice Bunn; Abiy B Ambaye
Journal:  Acad Pathol       Date:  2017-07-06

2.  Impact of a deep learning assistant on the histopathologic classification of liver cancer.

Authors:  Amirhossein Kiani; Bora Uyumazturk; Pranav Rajpurkar; Alex Wang; Rebecca Gao; Erik Jones; Yifan Yu; Curtis P Langlotz; Robyn L Ball; Thomas J Montine; Brock A Martin; Gerald J Berry; Michael G Ozawa; Florette K Hazard; Ryanne A Brown; Simon B Chen; Mona Wood; Libby S Allard; Lourdes Ylagan; Andrew Y Ng; Jeanne Shen
Journal:  NPJ Digit Med       Date:  2020-02-26
  2 in total

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