Abstract
In many hospitals all over the world there is an acute lack of physicians; in addition, doctors who are working in hospitals are overwhelmed by the number of patients and other administrative duties which they must do. Due to the specific of the work many operations/procedures/activities must be done manually and there are no automated systems which could improve the quality of the medical service and the efficient usage of the physician’s time. In this paper we propose a service system designed to help physicians to automate the work of registering patient clinical observations into the patient clinical observation sheet. The procedure of registering observations can be time consuming in some situations due to the numerous parameters which must be registered. The proposed system uses voice to text conversion engine to register the observations; thus, doctors spend much less time to review the clinical observations and eventually make corrections if necessary.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang, H., Wu, Q., Domingo-Ferrer, J.: FRR: fair remote retrieval of outsourced private medical records in electronic health networks. J. Biomed. Inform. 50, 226–233 (2014)
Capterra: Electronic Medical Records (EMR) Software (2018). https://www.capterra.com/electronic-medical-records-software/
Reisenwitz, C.: EHR Software Industry User Report, Capterra Research (2015). https://www.capterra.com/electronic-medical-records-software/user-research
Shanley, L.A., et al.: Structure and function of observation units in children’s hospitals: a mixed-methods study. Acad. Paediatr. 15(5), 518–525 (2015)
AHIMA: Requirements for the disclosure of protected health information. J. AHIMA 84(11) (2013). http://library.ahima.org/PB/Disclosure#.WprkVdhJmM8
Borangiu, T., Polese, F.: Introduction to the special issue on exploring service science for data-driven service design and innovation. Serv. Sci. INFORMS 9(4), v–x (2017). https://doi.org/10.1287/serv.2017.0195
Vocapia Research: Speech to Text Services (2018). http://www.vocapia.com/speech-to-text-services.html
IBM Watson: Speech to Text (2018). https://www.ibm.com/watson/services/speech-to-text/
Google: Cloud Speech API. Speech to text conversion powered by machine learning (2018). https://cloud.google.com/speech/
RAD Studio 10.2.3: (2018). https://www.embarcadero.com/products/rad-studio
Todd, C., Johnson Jr., N.L.: Chapter 3 – Kerberos server authentication. In: Hack Proofing Windows 2000 Server, pp. 63–104 (2001)
De Clercq, J., Grillenmeier, G.: Chapter 6 – Kerberos. In: Microsoft Windows Security Fundamentals, pp. 303–408 (2007)
Russell, D.: High-level security architectures and the Kerberos system. Comput. Netw. ISDN Syst. 19(3–5), 201–214 (1990)
Code::Blocks: http://www.codeblocks.org/license
AutoHotKey: https://autohotkey.com/docs/AutoHotkey.htm
Acknowledgments
This scientific work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS/CCCDI - UEFISCDI, project number PN-III-P2-2.1-PED-2016-1336, within PNCDI III.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Anton, F., Borangiu, T., Raileanu, S., Iacob, I., Anton, S. (2018). Managing Patient Observation Sheets in Hospitals Using Cloud Services. In: Satzger, G., Patrício, L., Zaki, M., Kühl, N., Hottum, P. (eds) Exploring Service Science. IESS 2018. Lecture Notes in Business Information Processing, vol 331. Springer, Cham. https://doi.org/10.1007/978-3-030-00713-3_27
Download citation
DOI: https://doi.org/10.1007/978-3-030-00713-3_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00712-6
Online ISBN: 978-3-030-00713-3
eBook Packages: Computer ScienceComputer Science (R0)