TY - GEN
T1 - ProPath - A guideline based software for the implementation into the medical environment
T2 - 2014 IEEE Canada International Humanitarian Technology Conference, IHTC 2014
AU - Klausner, S.
AU - Entacher, K.
AU - Kranzer, S.
AU - Sönnichsen, A.
AU - Flamm, M.
AU - Fritsch, G.
N1 - Conference code: 113212
Cited By :3
Export Date: 14 December 2023
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Information Quality in eHealth (2011) Information Quality in E-Health: 7th Conference of the Workgroup Human-Computer Interaction and Usability Engineering of the Austrian Computer, Programming and Software Engineering), p. 732. , Auflage: 2011., A. Holzinger and K.-M. Simonic, Eds. Heidelberg; New York: Springer; Von Grlitz, P.G., (2004) TELEPOLlS: Vernetzte Medizin, , edition: 1., Aufl. Hannover: Heise Zeitschriften Verlag; Marckmann, G., Diagnose per Computer?: Eine Ethische Bewertung Medizinischer Expertensysteme, , Auflage: I. Koln: Deutscher ArzteVerlag; Pauker, S.G., Gorry, G.A., Kassirer, J.P., Schwartz, W.B., Towards the simulation of clinical cognition: Taking a present illness by computer (1976) The American Journal of Medicine, 60 (7), pp. 981-996. , Jun; Miller, R.A., Jr., H.E.P., Myers, J.D., INTERNIST-I, an experimental computer-based diagnostic consultant for general internal medicine (1985) Computer-Assisted Medical Decision Making, pp. 139-158. , J. A. R. M.D and S. T. M.D, Eds. Springer New York; Weiss, S.M., Kulikowski, C.A., AmareJ, S., Safir, A., A modelbased method for computer-aided medical decision-making (1978) Artificial Intelligence, 2 (1-2), pp. 145-172. , Aug; Shortliffe, E.H., Scott, A.C., Bischoff, M.B., Campbell, A.B., Van Melle, W., Jacobs, C.D., An expert system for oncology protocol management (1984) Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project, pp. 653-665; Tsumoto, S., Tanaka, H., Primerose: Probabilistic rule induction method based on rough sets and resampling methods (1995) Computational Intelligence, 2 (2), pp. 389-405. , May; Matsumura, Y., Matsunaga, T., Hata, R., Kimura, M., Matsumura, H., Consultation system for diagnoses of headache and facial pain: RHINOS (1986) Inform Health Soc Care, 2 (2), pp. 145-157. , Jan; Zhou, Z., Ackerman, E., Gatewood, L., An expert system for simulation of coronary heart disease risk factor interventions (1991) Proceedings of the Annual Symposium on Computer Application in Medical Care, p. 674; Lambay, F.A., Maria, L.B., XNEOr: Development and evaluation of an expert system to improve the quality and cost of decision-making in neuro-oncology (1994) Proceedings i the ... Annual Symposium on Computer Application [Sic] in Medical Care. Symposium on Computer Applications in Medical Care, pp. 678-683; Lau, F., Kuziemsky, C., Price, M., Gardner, J., A review on systematic reviews of health information system studies (2010) Journal of the American Medical Informatics Association, 17 (6), pp. 637-645; Lundsgaarde, H.P., Evaluating medical expert systems (1987) Social Science & Medicine, 24 (10), pp. 805-819; Marin, R., Taboada, M., Mira, J., Delgado, A., Macia, M., Pereira, M., Rapid prototyping in graphic interface development for medical expert systems (1992) Engineering in Medicine and Biology Society, 1992 14th Annual International Conference of the IEEE, 3, pp. 840-841; Alexandrou, D.A., Skitsas, I.E., Mentzas, G.N., A holistic environment for the design and execution of self-adaptive clinical pathways (2011) Information Technology in Biomedicine, IEEE Transactions on, 15 (1), pp. 108-118; Preston, S., Markar, S., Baker, C., Soon, Y., Singh, S., Low, D., Impact of a multidisciplinary standardized clinical pathway on perioperative outcomes in patients with oesophageal cancer (2013) British Journal of Surgery, 100 (1), pp. IOS-II2; Yang, X., Han, R., Guo, Y., Bradley, J., Cox, B., Dickinson, R., Kitney, R., Modelling and perfonnance analysis of clinical pathways using the stochastic process algebra PEP A (2012) BMC Bioinformatics, 13, p. S4; Romeyke, T., Stummer, H., Clinical pathways as instruments for risk and cost management in hospitals - A discussion paper (2012) Global Journal of Health Science, 4 (2); BQLL Prfioperative Diagnostik, , http://bmg.gv.at/cms/home/attachments/3/5/6/CHI063/CMS1334240501976/report_bundesqualitaetsleitlinieJ)raeop.pdf, [Online]. Available at [Accessed: 02-Nov-2013]; OGARI-Osterreichischen Gesellschaft fur Anfisthesiologie, Reanimation und lntensivmedizin, OGARI-Leitlinie Prfioperative Evaluierung, , http://www.oegari.at/web_filesldateiarchiv12051Quellleitlinie%20Prfioperative%20PatientInnenevaluierung%20Juni%2020II.pdf, [Online]. Available at [Accessed: 28-Nov-2013]; Flamm, M., Fritsch, G., Seer, J., Panisch, S., Sonnichsen, A.C., Nonadherence to guidelines for preoperative testing in a secondary care hospital in Austria: The economic impact of unnecessary and double testing (2011) Eur J Anaesthesiol, 28 (12), pp. 867-873. , Dec; Klausner, S., Eine Applikation zur Unterstiitzung der prfioperativen Befundung (2009) Proceedings der FiNuT, Presented at the Frauen in Naturwissenschaft und Technik, Trier; Fritsch, G., Flamm, M., Hepner, D., Panisch, S., Seer, J., Soennichsen, A., Abnormal pre-operative tests, pathologic findings of medical history, and their predictive value for peri operative complications (2012) Acta Anaesthesiologica Scandinavica, 56 (3), pp. 339-350; Cebulla, C., (2013) Prfioperatives Anfimiemanagement-wer Ist Zustfindig?, , no. Klinik 0212013, May; Arztliches Zentrum fiir Qualitfit in der Medizin Praxishilfen Kreuzschmerz-Versorgungsleitlinien.De, , http://www.versorgungsleitlinien.de/praxishilfen/ksJ)raxislindex_html, [Online]. Available at: [Accessed: 22-Apr-2014]
PY - 2014
Y1 - 2014
N2 - Over the last decades, the amount of medical information has been growing rapidly. Online platforms such as patient's and doctor's blogs and forums and medical databases are widely and easily accessible to medical professionals as well as to the public. However, researching, filtering and evaluating the quality of this often overwhelming amount of data remains a challenge. Moreover, existing guidelines in the medical context are extensive and hardly applicable in the clinical context since reading and translation into clinical practice is time consuming [1][2]. Due to growing critical awareness among patients towards their medical treatment, there is an increased demand from internists, general practitioners, and other specialists, to explain medical conditions, treatment options and procedures in a more comprehensive fashion. In addition this discussion should be supported by the current state of clinical research. Expert systems could provide valuable support to fulfill these needs. Initial prototypes of expert systems in the inpatient arena were already implemented in the 1960's in the context of clinical trials [3]. The main goal of these systems was to improve medical care by assisting in the medical decision process. However, most of these systems did not remain in clinical practice for a prolonged period of time. In most cases, the user interface of the software was too complex for daily use. Appropriate application and a detailed insight into these systems requires a lot of handbook knowledge. Therefore the initial hurdles for the integration of software into specific clinical application, faced by the potential users were too cumbersome. The main purpose of the project ProPath was to eliminate these issues and at the same time provide optimal clinical practice for the health care system in a variety of medical topics. Both in the outpatient and inpatient scenario, there is an increasing demand to support communication and to improve the distribution of published knowledge and the application of practical experiences within the medical field. The main challenge to achieve that objective is to design an intuitive, user friendly software product that can be integrated into the current standard network environments. An example of successful implementation of a medical information system into clinical practice is the PROP system [4]. It is a medical decision support system, which has been designed, developed and implemented in Austria in the course of Reformpoolprojekt, in order to optimize the preoperative process. Since 2008, it is applied by general practicioners, pediatricians, clinicians and internists, in the state of Salzburg and was externally evaluated by the Paracelsus Medical University (PMU) in Salzburg. This paper provides an overview on how acquired knowledge can be utilized to reduce the complexity of designing and implementing clinical pathways (ProPath), supported by medical information or expert systems. Finally, statistical results evaluating PROP user-behavior are described. © 2014 IEEE.
AB - Over the last decades, the amount of medical information has been growing rapidly. Online platforms such as patient's and doctor's blogs and forums and medical databases are widely and easily accessible to medical professionals as well as to the public. However, researching, filtering and evaluating the quality of this often overwhelming amount of data remains a challenge. Moreover, existing guidelines in the medical context are extensive and hardly applicable in the clinical context since reading and translation into clinical practice is time consuming [1][2]. Due to growing critical awareness among patients towards their medical treatment, there is an increased demand from internists, general practitioners, and other specialists, to explain medical conditions, treatment options and procedures in a more comprehensive fashion. In addition this discussion should be supported by the current state of clinical research. Expert systems could provide valuable support to fulfill these needs. Initial prototypes of expert systems in the inpatient arena were already implemented in the 1960's in the context of clinical trials [3]. The main goal of these systems was to improve medical care by assisting in the medical decision process. However, most of these systems did not remain in clinical practice for a prolonged period of time. In most cases, the user interface of the software was too complex for daily use. Appropriate application and a detailed insight into these systems requires a lot of handbook knowledge. Therefore the initial hurdles for the integration of software into specific clinical application, faced by the potential users were too cumbersome. The main purpose of the project ProPath was to eliminate these issues and at the same time provide optimal clinical practice for the health care system in a variety of medical topics. Both in the outpatient and inpatient scenario, there is an increasing demand to support communication and to improve the distribution of published knowledge and the application of practical experiences within the medical field. The main challenge to achieve that objective is to design an intuitive, user friendly software product that can be integrated into the current standard network environments. An example of successful implementation of a medical information system into clinical practice is the PROP system [4]. It is a medical decision support system, which has been designed, developed and implemented in Austria in the course of Reformpoolprojekt, in order to optimize the preoperative process. Since 2008, it is applied by general practicioners, pediatricians, clinicians and internists, in the state of Salzburg and was externally evaluated by the Paracelsus Medical University (PMU) in Salzburg. This paper provides an overview on how acquired knowledge can be utilized to reduce the complexity of designing and implementing clinical pathways (ProPath), supported by medical information or expert systems. Finally, statistical results evaluating PROP user-behavior are described. © 2014 IEEE.
KW - Clinical Pathways
KW - Medical Information/Expert Systems
KW - Application programs
KW - Artificial intelligence
KW - Behavioral research
KW - Bioinformatics
KW - Complex networks
KW - Decision support systems
KW - Expert systems
KW - Medical applications
KW - Medical computing
KW - Patient treatment
KW - Product design
KW - Social networking (online)
KW - User interfaces
KW - Clinical application
KW - Clinical pathways
KW - General practitioners
KW - Medical decision making
KW - Medical decision support system
KW - Medical information
KW - Medical professionals
KW - User-friendly-software
KW - Medical information systems
U2 - 10.1109/IHTC.2014.7147551
DO - 10.1109/IHTC.2014.7147551
M3 - Conference contribution
BT - 2014 IEEE Canada International Humanitarian Technology Conference - (IHTC)
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 1 June 2014 through 4 June 2014
ER -