TY - GEN
T1 - Delivering Health Intelligence For Healthcare Services
AU - Murray, Michael
AU - Macedo, Mario
AU - Glynn, Carole
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - The systems barrier for clinical information interoperability and standards has now evolved from a technology barrier to a semantic barrier. The processes to gather clinical data and to build clinical information and knowledge cannot be fully implemented, owing to semantic dissonances and limited data normalization. According to [1], "Just over a half of entered codes were appropriate for a given scenario and about a quarter were omitted." This is a significant data and financial gap for healthcare provision. Huge amount of addition to the financial cost, lack of data integration and loss of information affects the ability to maintain standards in clinical care delivery and patient outcomes. This paper proposes that the solution to these issues is an augmented network of clinical note taking, where coding is automatically generated by an AI system as clinicians write their clinical notes. The system (AI-KEN) offers enhanced web support that is integrated to local clinical systems, whereby clinical notes are prompted by suggested predictive text options in real time. The anticipated benefits include reducing financial loss for acute services, support for clinical standard maintenance and enhanced advancements for clinical practice and research in real time.
AB - The systems barrier for clinical information interoperability and standards has now evolved from a technology barrier to a semantic barrier. The processes to gather clinical data and to build clinical information and knowledge cannot be fully implemented, owing to semantic dissonances and limited data normalization. According to [1], "Just over a half of entered codes were appropriate for a given scenario and about a quarter were omitted." This is a significant data and financial gap for healthcare provision. Huge amount of addition to the financial cost, lack of data integration and loss of information affects the ability to maintain standards in clinical care delivery and patient outcomes. This paper proposes that the solution to these issues is an augmented network of clinical note taking, where coding is automatically generated by an AI system as clinicians write their clinical notes. The system (AI-KEN) offers enhanced web support that is integrated to local clinical systems, whereby clinical notes are prompted by suggested predictive text options in real time. The anticipated benefits include reducing financial loss for acute services, support for clinical standard maintenance and enhanced advancements for clinical practice and research in real time.
KW - artificial-intelligence
KW - clinical-decision-support-system
KW - healthcare
UR - http://www.scopus.com/inward/record.url?scp=85078895684&partnerID=8YFLogxK
U2 - 10.1109/DDP.2019.00026
DO - 10.1109/DDP.2019.00026
M3 - Conference contribution
AN - SCOPUS:85078895684
T3 - Proceedings - 2019 1st International Conference on Digital Data Processing, DDP 2019
SP - 88
EP - 91
BT - Proceedings - 2019 1st International Conference on Digital Data Processing, DDP 2019
A2 - Robles, Ramiro Smano
A2 - Losket, Pavel
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st International Conference on Digital Data Processing, DDP 2019
Y2 - 15 November 2019 through 17 November 2019
ER -