Towards Responsible Medical Diagnostics Recommendation Systems
D. Schlör, and A. Hotho. (2022)cite arxiv:2209.03760Comment: 4th FAccTRec Workshop on Responsible Recommendation.
Abstract
The early development and deployment of hospital and healthcare information
systems have encouraged the ongoing digitization of processes in hospitals.
Many of these processes, which previously required paperwork and telephone
arrangements, are now integrated into IT solutions and require physicians and
medical staff to interact with appropriate interfaces and tools. Although this
shift to digital data management and process support has benefited patient care
in many ways, it requires physicians to accurately capture all relevant
information digitally for billing and documentation purposes, which takes a lot
of time away from actual patient care work. However, systematic collection of
healthcare data over a long period of time offers opportunities to improve this
process and support medical staff by introducing recommender systems. Based on
a practical working example, in this position paper, we will outline the design
of a responsible recommender system in the medical context from a technical,
application driven perspective and discuss potential design choices and
criteria with a specific focus on accountability, safety, and fairness.
Description
Towards Responsible Medical Diagnostics Recommendation Systems
%0 Generic
%1 schlor2022towards
%A Schlör, Daniel
%A Hotho, Andreas
%D 2022
%K ai ethical myown recommendation recsys
%T Towards Responsible Medical Diagnostics Recommendation Systems
%U http://arxiv.org/abs/2209.03760
%X The early development and deployment of hospital and healthcare information
systems have encouraged the ongoing digitization of processes in hospitals.
Many of these processes, which previously required paperwork and telephone
arrangements, are now integrated into IT solutions and require physicians and
medical staff to interact with appropriate interfaces and tools. Although this
shift to digital data management and process support has benefited patient care
in many ways, it requires physicians to accurately capture all relevant
information digitally for billing and documentation purposes, which takes a lot
of time away from actual patient care work. However, systematic collection of
healthcare data over a long period of time offers opportunities to improve this
process and support medical staff by introducing recommender systems. Based on
a practical working example, in this position paper, we will outline the design
of a responsible recommender system in the medical context from a technical,
application driven perspective and discuss potential design choices and
criteria with a specific focus on accountability, safety, and fairness.
@misc{schlor2022towards,
abstract = {The early development and deployment of hospital and healthcare information
systems have encouraged the ongoing digitization of processes in hospitals.
Many of these processes, which previously required paperwork and telephone
arrangements, are now integrated into IT solutions and require physicians and
medical staff to interact with appropriate interfaces and tools. Although this
shift to digital data management and process support has benefited patient care
in many ways, it requires physicians to accurately capture all relevant
information digitally for billing and documentation purposes, which takes a lot
of time away from actual patient care work. However, systematic collection of
healthcare data over a long period of time offers opportunities to improve this
process and support medical staff by introducing recommender systems. Based on
a practical working example, in this position paper, we will outline the design
of a responsible recommender system in the medical context from a technical,
application driven perspective and discuss potential design choices and
criteria with a specific focus on accountability, safety, and fairness.},
added-at = {2023-01-19T13:43:39.000+0100},
author = {Schlör, Daniel and Hotho, Andreas},
biburl = {https://www.bibsonomy.org/bibtex/21736d3ff2032d726a6e79a528e1f2918/daschloer},
description = {Towards Responsible Medical Diagnostics Recommendation Systems},
interhash = {5ccdc4763fbb227c510301af365b5081},
intrahash = {1736d3ff2032d726a6e79a528e1f2918},
keywords = {ai ethical myown recommendation recsys},
note = {cite arxiv:2209.03760Comment: 4th FAccTRec Workshop on Responsible Recommendation},
timestamp = {2023-01-19T13:43:39.000+0100},
title = {Towards Responsible Medical Diagnostics Recommendation Systems},
url = {http://arxiv.org/abs/2209.03760},
year = 2022
}