Exception Management in Healthcare Processes

2017 ◽  
pp. 195-216
Author(s):  
Mor Peleg ◽  
Giuseppe Pozzi
Keyword(s):  
Healthcare Processes

scholarly journals Health informatics and EHR to support clinical research in the COVID-19 pandemic: an overview

2021 ◽  
Author(s):  
Arianna Dagliati ◽  
Alberto Malovini ◽  
Valentina Tibollo ◽  
Riccardo Bellazzi
Keyword(s):  
Health Informatics ◽  
Data Privacy ◽  
Lessons Learned ◽  
Data Governance ◽  
Data Interoperability ◽  
Data Stewardship ◽  
Data Infrastructures ◽  
Healthcare Processes ◽  
Day By Day ◽  
Open Issues

Abstract The coronavirus disease 2019 (COVID-19) pandemic has clearly shown that major challenges and threats for humankind need to be addressed with global answers and shared decisions. Data and their analytics are crucial components of such decision-making activities. Rather interestingly, one of the most difficult aspects is reusing and sharing of accurate and detailed clinical data collected by Electronic Health Records (EHR), even if these data have a paramount importance. EHR data, in fact, are not only essential for supporting day-by-day activities, but also they can leverage research and support critical decisions about effectiveness of drugs and therapeutic strategies. In this paper, we will concentrate our attention on collaborative data infrastructures to support COVID-19 research and on the open issues of data sharing and data governance that COVID-19 had made emerge. Data interoperability, healthcare processes modelling and representation, shared procedures to deal with different data privacy regulations, and data stewardship and governance are seen as the most important aspects to boost collaborative research. Lessons learned from COVID-19 pandemic can be a strong element to improve international research and our future capability of dealing with fast developing emergencies and needs, which are likely to be more frequent in the future in our connected and intertwined world.

Modelling Healthcare Processes Using Role Activity Diagramming

2008 ◽  
Vol 28 (2) ◽  
pp. 147-155 ◽  
Author(s):  
F. Abu Rub ◽  
M. Odeh ◽  
I. Beeson ◽  
D. Pheby ◽  
B. Codling
Keyword(s):  
Healthcare Processes ◽  
Role Activity

Models and Architectures for the Enactment of Healthcare Processes

2017 ◽  
pp. 3-33
Author(s):  
Carlo Combi ◽  
Barbara Oliboni ◽  
Giuseppe Pozzi ◽  
Francesca Zerbato
Keyword(s):  
Healthcare Processes

Artificial Intelligence in Healthcare

2020 ◽  
Author(s):  
Ivana Ognjanovic
Keyword(s):  
Artificial Intelligence ◽  
Business Processes ◽  
Technology Development ◽  
Rapid Development ◽  
Modern Technology ◽  
Quality Monitoring ◽  
Intelligent Support ◽  
Treatment Plans ◽  
Healthcare Processes ◽  
Personalised Treatment

Modern technology development created significant innovations in delivery of healthcare. Artificial intelligence as “a branch of computer science dealing with the simulation of intelligent behaviour in computers” when applied in health care resulted in intelligent support to decision-making, optimised business processes, increased quality, monitoring and delivering of personalised treatment plans and many other applications. Even though the benefits are clear and numerous, there are still open issues in creating automation of healthcare processes, ensuring data protection and integrity, reduction of medical waste etc. However, due to rapid development of AI techniques, more advances and improvements are still expected.

The USE IT Model Case Studies

2011 ◽  
pp. 177-191 ◽  
Author(s):  
Margreet B. Michel-Verkerke ◽  
Roel W. Schuring ◽  
Ton A.M. Spil
Keyword(s):  
Multiple Sclerosis ◽  
Case Studies ◽  
General Practitioners ◽  
Mobile Device ◽  
Theoretical Background ◽  
Model Case ◽  
Stroke Service ◽  
Ex Post ◽  
Ex Ante ◽  
Healthcare Processes

In the previous two chapters, the determinants and theoretical background of the USE IT model is discussed. In this chapter, the application of the USE IT model in three cases are described to show the value and benefits of the USE IT model in practice. The USE IT model has four determinants: resistance, relevance, requirements, and resources. It can be used ex ante and ex post. The USE IT model is applied ex ante to find relevance and appropriate choices to overcome resistance for an ICT support of the multiple sclerosis (MS) healthcare chain and the rheumatism care guide, and as well ex ante as ex post in a local stroke service to measure the feasibility of a mobile device for general practitioners. The USE IT model proved to be very helpful not only in revealing the most urgent and relevant problems but also in discovering the crucial obstacles and prerequisites for implementing a solution to these problems. By that, the USE IT model served as a strong tool to decide whether healthcare processes should be supported by ICT and, if so, what processes should be used and how.

Surgery Operations Modeling and Scheduling in Healthcare Systems

2020 ◽  
pp. 352-366
Author(s):  
Fatah Chetouane ◽  
Eman Ibraheem
Keyword(s):  
Linear Model ◽  
Resource Sharing ◽  
Completion Time ◽  
Health Care Systems ◽  
Utilization Rate ◽  
Operations Scheduling ◽  
Care Systems ◽  
Operation Duration ◽  
High Utilization ◽  
Healthcare Processes

Surgery operations scheduling is a complex task due to operation duration uncertainties and resource sharing and availabilities in healthcare processes. In current health care systems it is important to minimize staff idle time and maintain a high utilization rate for surgery facilities. In the present study a nonlinear mathematical model for surgery scheduling is described, and an approximated linear model is deduced based on a set of assumptions. The linear model is solved using heuristic approach. The objective is to maximize the utilization of operating rooms and the surgery staff. Computational results show that our model improved the surgery schedule and the resources utilization. Our model also showed the potential of adding cases to the schedule due to minimizing the completion time of the schedule.

Using Simulation to Design and Improve an Outpatient Procedure Center

2011 ◽  
pp. 216-228 ◽  
Author(s):  
Todd R. Huschka ◽  
Thomas R. Rohleder ◽  
Brian T. Denton
Keyword(s):  
Patient Flow ◽  
Discrete Event ◽  
Pain Clinic ◽  
Outpatient Procedure ◽  
Event Simulation ◽  
Simulation Process ◽  
Recovery Times ◽  
Patient Waiting Time ◽  
Healthcare Processes ◽  
Improve Patient

Discrete-event simulation (DES) is an effective tool to for analyzing and improving healthcare processes. In this chapter we discuss the use of simulation to improve patient flow at an outpatient procedure center (OPC) at Mayo Clinic. The OPC addressed is the Pain Clinic, which was faced with high patient volumes in a new, untested facility. Simulation was particularly useful due to the uncertain patient procedure and recovery times. We discuss the simulation process and show how it helped reduce patient waiting time while ensuring the clinic could meet its target patient volumes.

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