SlideShare a Scribd company logo
1 of 1
Download to read offline

I. BACKGROUND
Automation of pump control by closed-loop systems has
long-recognized advantages. The attention of the clinician may
be directed to other matters, while desired conditions are
automatically maintained. Such systems been explored for
over 20 years [1, 2]. In some cases they have demonstrated
performance superior to expert clinicians [3]. Potential hazards
are also recognized and must be addressed in model systems.
A critical enabling technology for such closed-loop
systems is open nonproprietary standard interfaces for
acquisition and transmission of data in a device network
facilitating monitoring and control, allowing monitors,
sensors, pumps, and controllers to be integrated. Efforts at
standard device interfaces over the past 30 years [4, 5], have,
however, borne little fruit. Only a few proprietary or
customized systems have been developed. Enabling
nonproprietary device interactions has been a pipedream. Yet,
this is essential to enable research, development, and adoption
of systems of interconnected devices.
II. PURPOSE
The main goal of this work is to develop minimalist open
standard interfaces to facilitate research and development.
Clinically-appropriate standardization would require much
more sophistication. Standardization enables interaction of
components with otherwise incompatible proprietary
communications. Open software in the device interfaces and
protocols permits logging of communication at every
interface level to fully understand all interactions of closed-
loop systems. Applications include capture of clinical events
and interventions, and implementation of clinical controller
systems. Work in progress includes development of closed-
loop physiological animal testing and computer-in-the-
middle model systems.
III. METHODS
We have adopted hardware and an architecture similar to the
integrated clinical environment work by MDPnP [5]. Each
medical device is connected to a dongle that translates the
specific proprietary device protocol into a standardized one.
The dongles then connect to a local network allowing them to
interact with applications on a computer that handle device
and data management and processing algorithms. Unlike the
* Research in part by Bucknell-Geisinger Research Initiative Phase IV
Grant No. GPE094
P. Asare, A. Acharya, Y. Huang, D. Karki, W. Kyaw, C. Mahoney are
with Bucknell University, Lewisburg, PA 17837 USA
MDPnP work, Python is used for software development to
allow rapid-prototyping and ease of adoption and
modification. In addition, the Robot Operating System (ROS)
[6] is our middleware because of its proven effectiveness for
developing production-grade distributed systems and Python
integration.
IV. RESULTS
We will demonstrate a system that: (a) interacts with a patient
monitor (Philips IntelliVue MP50) and sends commands to an
unmodified commercial pump (CME America BodyGuard
121 Dual-Channel Infusion Pump); (b) detects whether a
device is connected to the system; (c) logs system
performance, device data, and monitored data (simulated
patient data and infusion actions); (d) configures and controls
the system through a graphical user interface; (e) runs a
simple illustrative closed-loop control scenario.
V. CONCLUSION
Our progress suggests that an easier-to-use experimentation
platform for closed-loop control is feasible.
ACKNOWLEDGMENT
The authors thank CME America, LLC for providing us
with the infusion pump.
REFERENCES
[1] D. Ramakrishna, K. Behbehani, K. Klein, J. Mokhtar, W.W. von
Maltzahn, R.C. Eberhart, M.Dollar, “In vivo evaluation of a closed loop
monitoring strategy for induced paralysis.” J Clin Monit Comput. 1998
Aug;14 (6):393–402.
[2] G. C. Kramer, M. P. Kinsky, D. S. Prough, J. Salinas, J. L. Sondeen, M.
L. Hazel-Scerbo, C. E. Mitchell. “Closed-loop control of fluid therapy
for treatment of hypovolemia.” J Trauma. 2008 Apr;64(4 Suppl):S333-
41.
[3] J. Rinehart, M. Lilot, C. Lee, A. Joosten, T. Huynh, C. Canales, D.
Imagawa, A. Demirjian, M. Cannesson, “Closed-loop assisted versus
manual goal-directed fluid therapy during high-risk abdominal surgery:
a case–control study with propensity matching.” Crit Care [Internet].
2015 19(1).
[4] R. J. Kennelly, R. M. Gardner, “Perspectives on development of IEEE
1073: the Medical Information Bus (MIB) standard.” Int J Clin Monit
Comput. 1997 Aug;14(3):143-9.
[5] Medical Device "Plug-and-Play" Interoperability Program.
http://mdpnp.org
[6] Robot Operating System (ROS). http://www.ros.org/.
(P. Asare is corresponding author: phone: 570-577-2344; fax: 570-577-1449;
e-mail: philip.asare@bucknell.edu).
S. M. Poler, J. R. La Valley, R. Tevis are with Geisinger Medical Center,
Geisinger Health System, Danville, PA 17822 USA.
Demo of Platform for Enabling Research and Development of
Closed-Loop Control of Infusion in the Operating Room*
Philip Asare, Member, IEEE, Adit Acharya, Yuxuan Huang, Dikendra Karki, Win Kyaw, Caitlin
Mahoney, S. Mark Poler, Senior Member, IEEE Jean R. La Valley, Rick Tevis

More Related Content

Similar to Published Research Paper

Data supporting precision oncology fda wakibbe
Data supporting precision oncology fda wakibbeData supporting precision oncology fda wakibbe
Data supporting precision oncology fda wakibbeWarren Kibbe
 
A Microservice Architecture for the Design of Computer-Interpretable Guidelin...
A Microservice Architecture for the Design of Computer-Interpretable Guidelin...A Microservice Architecture for the Design of Computer-Interpretable Guidelin...
A Microservice Architecture for the Design of Computer-Interpretable Guidelin...Martin Chapman
 
Hospital Management System
Hospital Management SystemHospital Management System
Hospital Management Systemidowume
 
blockhain in telihealth doctore apoinment system
blockhain in telihealth doctore apoinment systemblockhain in telihealth doctore apoinment system
blockhain in telihealth doctore apoinment systemRajesh Rajesh.Bca.McA9
 
Virtual Clinical Trials-06-12-2023.pdf
Virtual Clinical Trials-06-12-2023.pdfVirtual Clinical Trials-06-12-2023.pdf
Virtual Clinical Trials-06-12-2023.pdfprocth2
 
A fuzzy inference system for assessment of the severity of the peptic ulcers
A fuzzy inference system for assessment of the severity of the peptic ulcersA fuzzy inference system for assessment of the severity of the peptic ulcers
A fuzzy inference system for assessment of the severity of the peptic ulcerscsandit
 
A fuzzy inference system for assessment of the severity of the peptic ulcers
A fuzzy inference system for assessment of the severity of the peptic ulcersA fuzzy inference system for assessment of the severity of the peptic ulcers
A fuzzy inference system for assessment of the severity of the peptic ulcerscsandit
 
Moderator’s Report on Workshop on Wearable Biosensors in Clinical Trials
Moderator’s Report on Workshop on Wearable Biosensors in Clinical TrialsModerator’s Report on Workshop on Wearable Biosensors in Clinical Trials
Moderator’s Report on Workshop on Wearable Biosensors in Clinical TrialsMarie Mc Carthy
 
Big data, big knowledge big data for personalized healthcare
Big data, big knowledge big data for personalized healthcareBig data, big knowledge big data for personalized healthcare
Big data, big knowledge big data for personalized healthcareredpel dot com
 
Low Complexity System Designs for Medical Cyber Physical Human Systems
Low Complexity System Designs for Medical Cyber Physical Human SystemsLow Complexity System Designs for Medical Cyber Physical Human Systems
Low Complexity System Designs for Medical Cyber Physical Human SystemsMDPnP_UIUC
 
computers in clinical development
 computers in clinical development computers in clinical development
computers in clinical developmentSUJITHA MARY
 
Care expert assistant for Medicare system using Machine learning
Care expert assistant for Medicare system using Machine learningCare expert assistant for Medicare system using Machine learning
Care expert assistant for Medicare system using Machine learningIRJET Journal
 
iHT² Health IT Summit Seattle 2013 - Josephine Briggs, MD, National Center fo...
iHT² Health IT Summit Seattle 2013 - Josephine Briggs, MD, National Center fo...iHT² Health IT Summit Seattle 2013 - Josephine Briggs, MD, National Center fo...
iHT² Health IT Summit Seattle 2013 - Josephine Briggs, MD, National Center fo...Health IT Conference – iHT2
 
“Detection of Diseases using Machine Learning”
“Detection of Diseases using Machine Learning”“Detection of Diseases using Machine Learning”
“Detection of Diseases using Machine Learning”IRJET Journal
 
A New Real Time Clinical Decision Support System Using Machine Learning for C...
A New Real Time Clinical Decision Support System Using Machine Learning for C...A New Real Time Clinical Decision Support System Using Machine Learning for C...
A New Real Time Clinical Decision Support System Using Machine Learning for C...IRJET Journal
 
ICU Patient Deterioration Prediction : A Data-Mining Approach
ICU Patient Deterioration Prediction : A Data-Mining ApproachICU Patient Deterioration Prediction : A Data-Mining Approach
ICU Patient Deterioration Prediction : A Data-Mining Approachcsandit
 

Similar to Published Research Paper (20)

Data supporting precision oncology fda wakibbe
Data supporting precision oncology fda wakibbeData supporting precision oncology fda wakibbe
Data supporting precision oncology fda wakibbe
 
A Microservice Architecture for the Design of Computer-Interpretable Guidelin...
A Microservice Architecture for the Design of Computer-Interpretable Guidelin...A Microservice Architecture for the Design of Computer-Interpretable Guidelin...
A Microservice Architecture for the Design of Computer-Interpretable Guidelin...
 
Hospital Management System
Hospital Management SystemHospital Management System
Hospital Management System
 
Informatics
Informatics Informatics
Informatics
 
blockhain in telihealth doctore apoinment system
blockhain in telihealth doctore apoinment systemblockhain in telihealth doctore apoinment system
blockhain in telihealth doctore apoinment system
 
Virtual Clinical Trials-06-12-2023.pdf
Virtual Clinical Trials-06-12-2023.pdfVirtual Clinical Trials-06-12-2023.pdf
Virtual Clinical Trials-06-12-2023.pdf
 
A fuzzy inference system for assessment of the severity of the peptic ulcers
A fuzzy inference system for assessment of the severity of the peptic ulcersA fuzzy inference system for assessment of the severity of the peptic ulcers
A fuzzy inference system for assessment of the severity of the peptic ulcers
 
A fuzzy inference system for assessment of the severity of the peptic ulcers
A fuzzy inference system for assessment of the severity of the peptic ulcersA fuzzy inference system for assessment of the severity of the peptic ulcers
A fuzzy inference system for assessment of the severity of the peptic ulcers
 
I017546373
I017546373I017546373
I017546373
 
2016 iHT2 San Diego Health IT Summit
2016 iHT2 San Diego Health IT Summit2016 iHT2 San Diego Health IT Summit
2016 iHT2 San Diego Health IT Summit
 
Moderator’s Report on Workshop on Wearable Biosensors in Clinical Trials
Moderator’s Report on Workshop on Wearable Biosensors in Clinical TrialsModerator’s Report on Workshop on Wearable Biosensors in Clinical Trials
Moderator’s Report on Workshop on Wearable Biosensors in Clinical Trials
 
Big data, big knowledge big data for personalized healthcare
Big data, big knowledge big data for personalized healthcareBig data, big knowledge big data for personalized healthcare
Big data, big knowledge big data for personalized healthcare
 
Poster
PosterPoster
Poster
 
Low Complexity System Designs for Medical Cyber Physical Human Systems
Low Complexity System Designs for Medical Cyber Physical Human SystemsLow Complexity System Designs for Medical Cyber Physical Human Systems
Low Complexity System Designs for Medical Cyber Physical Human Systems
 
computers in clinical development
 computers in clinical development computers in clinical development
computers in clinical development
 
Care expert assistant for Medicare system using Machine learning
Care expert assistant for Medicare system using Machine learningCare expert assistant for Medicare system using Machine learning
Care expert assistant for Medicare system using Machine learning
 
iHT² Health IT Summit Seattle 2013 - Josephine Briggs, MD, National Center fo...
iHT² Health IT Summit Seattle 2013 - Josephine Briggs, MD, National Center fo...iHT² Health IT Summit Seattle 2013 - Josephine Briggs, MD, National Center fo...
iHT² Health IT Summit Seattle 2013 - Josephine Briggs, MD, National Center fo...
 
“Detection of Diseases using Machine Learning”
“Detection of Diseases using Machine Learning”“Detection of Diseases using Machine Learning”
“Detection of Diseases using Machine Learning”
 
A New Real Time Clinical Decision Support System Using Machine Learning for C...
A New Real Time Clinical Decision Support System Using Machine Learning for C...A New Real Time Clinical Decision Support System Using Machine Learning for C...
A New Real Time Clinical Decision Support System Using Machine Learning for C...
 
ICU Patient Deterioration Prediction : A Data-Mining Approach
ICU Patient Deterioration Prediction : A Data-Mining ApproachICU Patient Deterioration Prediction : A Data-Mining Approach
ICU Patient Deterioration Prediction : A Data-Mining Approach
 

Published Research Paper

  • 1.  I. BACKGROUND Automation of pump control by closed-loop systems has long-recognized advantages. The attention of the clinician may be directed to other matters, while desired conditions are automatically maintained. Such systems been explored for over 20 years [1, 2]. In some cases they have demonstrated performance superior to expert clinicians [3]. Potential hazards are also recognized and must be addressed in model systems. A critical enabling technology for such closed-loop systems is open nonproprietary standard interfaces for acquisition and transmission of data in a device network facilitating monitoring and control, allowing monitors, sensors, pumps, and controllers to be integrated. Efforts at standard device interfaces over the past 30 years [4, 5], have, however, borne little fruit. Only a few proprietary or customized systems have been developed. Enabling nonproprietary device interactions has been a pipedream. Yet, this is essential to enable research, development, and adoption of systems of interconnected devices. II. PURPOSE The main goal of this work is to develop minimalist open standard interfaces to facilitate research and development. Clinically-appropriate standardization would require much more sophistication. Standardization enables interaction of components with otherwise incompatible proprietary communications. Open software in the device interfaces and protocols permits logging of communication at every interface level to fully understand all interactions of closed- loop systems. Applications include capture of clinical events and interventions, and implementation of clinical controller systems. Work in progress includes development of closed- loop physiological animal testing and computer-in-the- middle model systems. III. METHODS We have adopted hardware and an architecture similar to the integrated clinical environment work by MDPnP [5]. Each medical device is connected to a dongle that translates the specific proprietary device protocol into a standardized one. The dongles then connect to a local network allowing them to interact with applications on a computer that handle device and data management and processing algorithms. Unlike the * Research in part by Bucknell-Geisinger Research Initiative Phase IV Grant No. GPE094 P. Asare, A. Acharya, Y. Huang, D. Karki, W. Kyaw, C. Mahoney are with Bucknell University, Lewisburg, PA 17837 USA MDPnP work, Python is used for software development to allow rapid-prototyping and ease of adoption and modification. In addition, the Robot Operating System (ROS) [6] is our middleware because of its proven effectiveness for developing production-grade distributed systems and Python integration. IV. RESULTS We will demonstrate a system that: (a) interacts with a patient monitor (Philips IntelliVue MP50) and sends commands to an unmodified commercial pump (CME America BodyGuard 121 Dual-Channel Infusion Pump); (b) detects whether a device is connected to the system; (c) logs system performance, device data, and monitored data (simulated patient data and infusion actions); (d) configures and controls the system through a graphical user interface; (e) runs a simple illustrative closed-loop control scenario. V. CONCLUSION Our progress suggests that an easier-to-use experimentation platform for closed-loop control is feasible. ACKNOWLEDGMENT The authors thank CME America, LLC for providing us with the infusion pump. REFERENCES [1] D. Ramakrishna, K. Behbehani, K. Klein, J. Mokhtar, W.W. von Maltzahn, R.C. Eberhart, M.Dollar, “In vivo evaluation of a closed loop monitoring strategy for induced paralysis.” J Clin Monit Comput. 1998 Aug;14 (6):393–402. [2] G. C. Kramer, M. P. Kinsky, D. S. Prough, J. Salinas, J. L. Sondeen, M. L. Hazel-Scerbo, C. E. Mitchell. “Closed-loop control of fluid therapy for treatment of hypovolemia.” J Trauma. 2008 Apr;64(4 Suppl):S333- 41. [3] J. Rinehart, M. Lilot, C. Lee, A. Joosten, T. Huynh, C. Canales, D. Imagawa, A. Demirjian, M. Cannesson, “Closed-loop assisted versus manual goal-directed fluid therapy during high-risk abdominal surgery: a case–control study with propensity matching.” Crit Care [Internet]. 2015 19(1). [4] R. J. Kennelly, R. M. Gardner, “Perspectives on development of IEEE 1073: the Medical Information Bus (MIB) standard.” Int J Clin Monit Comput. 1997 Aug;14(3):143-9. [5] Medical Device "Plug-and-Play" Interoperability Program. http://mdpnp.org [6] Robot Operating System (ROS). http://www.ros.org/. (P. Asare is corresponding author: phone: 570-577-2344; fax: 570-577-1449; e-mail: philip.asare@bucknell.edu). S. M. Poler, J. R. La Valley, R. Tevis are with Geisinger Medical Center, Geisinger Health System, Danville, PA 17822 USA. Demo of Platform for Enabling Research and Development of Closed-Loop Control of Infusion in the Operating Room* Philip Asare, Member, IEEE, Adit Acharya, Yuxuan Huang, Dikendra Karki, Win Kyaw, Caitlin Mahoney, S. Mark Poler, Senior Member, IEEE Jean R. La Valley, Rick Tevis