Literature DB >> 34950966

[Learning from coding data-surgical treatment of benign prostatic syndrome : Big data for BPS].

Nadine Binder1, J Franz2, A Sigle2, C Gratzke2, A Miernik2.   

Abstract

Benign prostatic syndrome (BPS) is one of the most common urological diseases. Currently, there are numerous surgical methods to treat BPS. The digitalisation of medicine enables new study approaches in healthcare research using digital data from individual treatment pathways. In the present work, BPS-specific longitudinal trend analyses were performed. Treatment-related figures, both with regard to the therapy methods and predefined patient cohorts, could be examined after validating the datasets. This meant that information on relevant characteristics of surgical BPS treatment could be read and calculations made that reflect the overall impact of these processes. In the future, it is expected that increasingly comprehensive, higher-quality digital datasets on different clinical pictures will be available for analytical purposes. Intensification of research projects in this field is desirable. The results thus obtained enable further optimisation steps of certain treatment actions and provide important key figures for the strategy development of a medical facility.
© 2021. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.

Entities:  

Keywords:  Big data; Databases; Digitalization; Healthcare research; Trend Analysis

Mesh:

Year:  2021        PMID: 34950966     DOI: 10.1007/s00120-021-01739-7

Source DB:  PubMed          Journal:  Urologe A        ISSN: 0340-2592            Impact factor:   0.639


  1 in total

Review 1.  Current Treatment for Benign Prostatic Hyperplasia.

Authors:  Arkadiusz Miernik; Christian Gratzke
Journal:  Dtsch Arztebl Int       Date:  2020-12-04       Impact factor: 5.594

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.