Preventing Hospital Acquired Infections through a Workflow-based
Cyber-physical System
Maria Iuliana Bocicor
1,2
, Arthur-Jozsef Molnar
1,2
and Cristian Taslitchi
3
1
SC Info World SRL, Bucharest, Romania
2
Faculty of Mathematics and Computer Science, Babes-Bolyai University, Cluj-Napoca, Romania
3
Faculty of Automatic Control and Computers, University Politehnica of Bucharest, Bucharest, Romania
Keywords:
Hospital Acquired Infection, Nosocomial Infection, Outbreak, Clinical Workflow Monitoring, Cyber-physical
System.
Abstract:
Hospital acquired infections (HAI) are infections acquired within the hospital from healthcare workers, pa-
tients or from the environment, but which have no connection to the initial reason for the patient’s hospital
admission. HAI are a serious world-wide problem, leading to an increase in mortality rates, duration of hos-
pitalisation as well as significant economic burden on hospitals. Although clear preventive guidelines exist,
studies show that compliance to them is frequently poor. This paper details the software perspective for an
innovative, business process software based cyber-physical system that will be implemented as part of a Eu-
ropean Union-funded research project. The system is composed of a network of sensors mounted in different
sites around the hospital, a series of wearables used by the healthcare workers and a server side workflow en-
gine. For better understanding, we describe the system through the lens of a single, simple clinical workflow
that is responsible for a significant portion of all hospital infections. The goal is that when completed, the
system will be configurable in the sense of facilitating the creation and automated monitoring of those clinical
workflows that when combined, account for over 90% of hospital infections.
1 INTRODUCTION
Hospital acquired infections (HAI) or nosocomial in-
fections are defined as infections ”acquired in hospi-
tal by a patient who was admitted for a reason other
than that infection. An infection occurring in a pa-
tient in a hospital or other healthcare facility in whom
the infection was not present or incubating at the time
of admission. This includes infections acquired in the
hospital but appearing after discharge, and also occu-
pational infections among staff of the facility” (World
Health Organization, 2002). Thus, in addition to de-
creasing the quality of life and increasing mortality
rates, the duration of hospitalisation as well as the
costs of medical visits for patients, HAI represent a
direct occupational risk for healthcare workers, which
significantly affects costs and has the potential of cre-
ating personnel deficits in case of an outbreak.
Existing research shows that HAI are prevalent
across the globe, regardless of geographical, politi-
cal, social or economic factors (World Health Orga-
nization, 2002), (World Health Organization, 2010).
Even more compelling is the fact that while the so-
phistication of medical care is constantly increas-
ing, reported HAI rates have not seen meaningful
decrease (Tikhomirov, 1987), (Coello et al., 1993),
(World Health Organization, 2010), (European Cen-
tre for Disease Prevention and Control, 2015).
Studies undertaken between 1995 and 2008 in
several developed countries have revealed infection
rates between 5.1% to 11.6% (World Health Organi-
zation, 2010); data from lesser developed countries
is in many cases limited and deemed of low quality.
According to the European Centre for Disease Pre-
vention and Control, approximately 4.2 million HAI
occurred in 2013 alone in European long-term care fa-
cilities, the crude prevalence of residents with at least
one HAI being 3.4%; this translates to more than 100
thousand patients on any given day (European Cen-
tre for Disease Prevention and Control, 2015). The
total cases of HAI in Europe amount to 16 million
extra hospitalisation days, 37.000 attributable deaths
and an economic burden of e7 billion in direct costs
(World Health Organization, 2010). A study con-
ducted in the United Kingdom (Plowman, 2000) con-
cluded that patients who developed HAI stayed in
Bocicor, M., Molnar, A-J. and Taslitchi, C.
Preventing Hospital Acquired Infections through a Workflow-based Cyber-physical System.
In Proceedings of the 11th International Conference on Evaluation of Novel Software Approaches to Software Engineering (ENASE 2016), pages 63-68
ISBN: 978-989-758-189-2
Copyright
c
2016 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
63
hospital 2.5 times longer and the hospital costs (for
nursing care, hospital overheads, capital charges, and
management) tripled. In the USA, in 2002 alone, 1.7
million patients were affected by HAI and the annual
economic impact was approximately US$6.5 billion
in 2004. Besides all these resource expenses, what
is even worse is that HAI are responsible for an in-
crease in mortality rates, as these infections lead to
death in 2.7% of cases (World Health Organization,
2009). A more recent study (Magill and et al., 2014)
conducted in 183 US hospitals revealed that 4% of the
patients had one or more HAI, which lead to an esti-
mated number of 648.000 patients with HAI in acute
care hospitals in the US, in 2011. According to a re-
cent study (M.L. et al., 2015), the prevalence of HAI
in Southeast Asia between 2000 and 2012 was 9%,
the excess length of stay in hospitals of infected pa-
tients varied between 5 and 21 days and the attributed
mortality was estimated between 7% and 46%. In
Canada, more than 220.000 HAI result in 8000 deaths
a year, making infections the fourth leading cause of
death in the country, with $129 million in extra costs
incurred in 2010 (Government of Newfoundland and
Labrador. Department of Health and Community Ser-
vices, 2013), (Canadian Union of Public Employees,
2014). In Australia, there is an estimated number of
200.000 cases of HAI per year, resulting in 2 mil-
lion hospitalization days (Australian Commission on
Safety and Quality in Health Care, 2008).
The most common target sites of HAI are the uri-
nary and respiratory tracts and areas involved in in-
vasive procedures or catheter insertion areas. While
the methodology for prevention exists, it is often ig-
nored due to lack of time, unavailability of appropri-
ate equipment or because of inadequate staff training.
Research shows that the most important transmission
route for HAI are members of staff coming into con-
tact with patients or contaminated equipment with-
out following proper hygiene procedures (Hammer,
2013).
During the twentieth century, several specific
measures were taken to prevent the occurrence and
spread of infections, which have been translated into
a series of instructions for controlling the vectors that
propagate infection as well as to properly manage out-
breaks and epidemics. Originally, these instructions
were established as guidelines for healthcare workers,
later they were also transformed into prevention rules
included in the documentation concerning workplace
safety, and in recent years they were incorporated into
various software or cyber-physical solutions to moni-
tor and ensure compliance. An unquestionable benefit
and substantial improvement in this respect has been
brought by the Internet of Things (IoT) technologies,
which are currently employed in healthcare and many
other areas of life. Since the use of various IoT moni-
toring systems, significant improvement has been ob-
served regarding compliance to hygiene regulations
and prevention standards, as well as decreases in the
rates of infections.
In this paper we present an IoT-based cyber-
physical system that targets HAI prevention, on which
development has started under funding from the Eu-
ropean Union. Integrating a network of sensors
that monitor clinical workflows and ambient condi-
tions with monitoring software, the system will pro-
vide real-time information and alerts. The system
will monitor general processes known to affect HAI
spread such as cleaning and equipment maintenance,
together with clinical processes at risk for HAI such
as catheter insertion, postoperative care or mechanical
ventilation. In this paper, we will illustrate the sys-
tem using a clinical workflow that is often involved in
HAI transmission, together with a motivating exam-
ple that details a software perspective regarding how
the system ensures compliance to established preven-
tive guidelines.
2 RELATED WORK
Several automated solutions have been implemented
to avert and reduce HAI and their impact. The un-
derlying idea used by several of these systems is con-
tinuous monitoring of healthcare workers’ hand hy-
giene and real-time alert generation in case of non-
compliance with established guidelines. The final
purpose is to modify human behaviour towards bet-
ter hand hygiene compliance. This is usually accom-
plished using wearable devices worn by healthcare
workers, which interact with sensors placed in key
hospital locations in order to record personnel activi-
ties and hand hygiene events. When a hand hygiene
event is omitted or performed, the device provides
visual, auditory or haptic notification. The Intelli-
gentM (Ryan, 2013) and Hyginex (Hyginex, 2015)
systems use bracelet-like devices that are equipped
with motion sensors to ensure that hand sanitation is
correctly performed. Biovigil technology (BIOVIGIL
Healthcare Systems, Inc., 2015) and MedSense (Gen-
eral Sensing, 2014) are designed for the same pur-
pose, only in these cases bracelets are replaced with
badges worn by the healthcare workers. The Biovigil
device uses chemical sensors to detect whether hand
hygiene is in accordance with established standards.
The systems can be configured to remind clinicians
to disinfect their hands before entering patient rooms,
or before procedures such as intravenous infusions or
ENASE 2016 - 11th International Conference on Evaluation of Novel Software Approaches to Software Engineering
64
catheter insertion. Furthermore, these systems record
hygiene events, centralise them and enable analysis,
visualisation and report generation. Unlike the solu-
tions presented so far, the SwipeSense (Swipe Sense,
2015) system employs small devices that are alcohol-
based and easy to use gel dispensers which can be
worn by medical personnel. This exempts healthcare
workers from interrupting their activities in order to
walk to a sink or a disinfectant dispenser (Simonette,
2013).
A different type of system that is of assistance in
the fight against infection is Protocol Watch (Philips,
2015), a decision support system used to improve
compliance with the ”Surviving Sepsis Campaign”
international guidelines (Surviving Sepsis Campaign,
2012) for the prevention and management of sepsis.
Protocol Watch regularly checks certain medical pa-
rameters of the patients (Philips, 2015), its main goal
being to reduce the time period between the debut of
sepsis (the moment when it is first detected) and the
beginning of treatment. If the system detects that cer-
tain conditions that may cause sepsis are met, it alerts
the medical staff and indicates which tests, observa-
tions and interventions must be performed, according
to prevention and treatment protocols.
A different approach for the prevention of infec-
tion is taken by Xenex (Xenex, 2015): the Xenex
”Germ-Zapping Robot” can disinfect a room by us-
ing pulses of high-intensity, high-energy ultraviolet
light. The robot must be taken inside the room to
be disinfected and in most cases, the deactivation of
pathogens takes place in five minutes. After disinfec-
tion, the room will remain at a low microbial load, un-
til it is recontaminated by a patient, healthcare worker
or through the ventilation system.
Another relevant issue regards the problem of
identifying control policies and optimal treatment in
infection outbreaks, as introduced in (Curtis et al.,
2013). The authors propose a comprehensive ap-
proach using electronic health records to build health-
care worker contact networks with the objective of
putting into place efficient vaccination policies in case
of outbreaks. Relevant software systems developed to
also improve treatment policies in case of outbreaks
and epidemics are RL6: Infection (RL Solutions,
2015) - a software solution developed to assist hos-
pitals in the processes of controlling and monitoring
infections and outbreaks, and Accreditrack (Excelion
Technology Inc., 2013) - a software system designed
to ensure compliance with hand hygiene guidelines
and to verify nosocomial infection management pro-
cesses.
3 MOTIVATING EXAMPLE -
HAND HYGIENE
Research shows that most cases of hospital infection
are tightly connected to certain clinical workflows
(World Health Organization, 2002). Thus, we pro-
pose a cyber-physical system which monitors work-
flows considered relevant in HAI propagation. Al-
though there are other ICT automated solutions that
target the prevention of HAI, there are no other sys-
tems that use the clinical workflow monitoring based
approach, to our knowledge. In order to successfully
prevent infection and outbreaks, the system must fo-
cus on the typical sites where these infections usu-
ally occur (urinary and respiratory tracts, sites of
surgery or invasive procedures (World Health Orga-
nization, 2002)), as well as track workflows that are
not infection-site specific, but which have a signifi-
cant contribution to HAI rates, such as hand hygiene
and transmission from the environment.
A hardware-centric motivating example of the
proposed system was presented in (Shhedi et al.,
2015). The authors of (Shhedi et al., 2015) focus
on the hardware components of the system, namely
types and location of sensors used to monitor clini-
cal activities and the wearable technology employed
by the medical staff for identification, monitoring and
Figure 1: A typical hospital room. Figure adapted from
(Shhedi et al., 2015).
Preventing Hospital Acquired Infections through a Workflow-based Cyber-physical System
65
alerting. The purpose of our paper is to illustrate the
software-side of the proposed cyber-physical system
using a motivating example based on one of the most
relevant, and often occurring clinical workflows.
On the software side, the system will model and
encode clinical workflows using the Business Process
Modelling and Notation (BPMN) (Object Manage-
ment Group, 2015) standard and it will be compati-
ble with leading medical informatics standards such
as HL7 V3 (Health Level Seven International, 2016),
thus allowing seamless interconnection to hospital in-
frastructure via HL7-compliant Hospital Information
Systems (HIS). In addition, it will integrate a network
of hardware sensors, able to identify ongoing clini-
cal processes, provide location information and track
the use of hospital equipment and materials. Health-
care workers will use wearable devices that continu-
ously monitor their location and activity. The central
hub of the system will be a server side engine able to
load and execute BPMN-based workflows. Integra-
tion with the HIS enables the retrieval of key informa-
tion regarding patients, such as admissions, transfers
and discharges as well as records of past of planned
invasive interventions, which can be used to deter-
mine the level of risk. Hence, infections and outbreak
management could be improved so that in case of sus-
picion, the locations of previous admissions, as well
patients of members of staff considered at risk can be
contacted.
The modelled clinical workflows will be executed
using a BPMN engine. When the workflow leads to
a state that is a risk of HAI, the engine will gener-
ate alerts that are received directly by the involved
healthcare workers. In this paper we illustrate the in-
terplay between the hardware and software compo-
nents of the proposed system using a motivating ex-
ample based on hand hygiene, which remains one of
the most common pathways of HAI transmission. We
must note that the provided example is only used to
portray the workflow-based system, and that the final
implementation will allow the creation and monitor-
ing of a variety of clinical workflows, that will be ex-
ecuted by the BPMN engine whenever necessary.
We illustrate our motivating example using a typ-
ical scenario: a healthcare worker enters a room with
two beds, and interacts with both inpatients before ex-
iting. Both common as well as highly relevant to hand
hygiene, our example is illustrated in Figure 2, using
simplified BPMN-like notation to reduce the number
of decision points and emphasize readability.
According to established hand hygiene guidelines
(World Health Organization, 2016), upon entering a
patient room, workers must perform hand disinfec-
tion. If this procedure is skipped or performed in-
adequately (e.g. without disinfectant, shorter wash-
ing time than recommended), the system will gener-
ate an alert to warn the clinician about the detected
non-compliance. After each patient contact, and be-
fore leaving the room, hand disinfection should again
be correctly performed. All these events and alert re-
ports will be persisted to enable later analyses, such as
identification of an outbreak’s patient zero and route
of transmission.
Figure 1, which was adapted from (Shhedi et al.,
2015) illustrates a typical patient room, with two
beds, a sink and a bathroom. As soon as the healthcare
worker enters it, the Radio-Frequency Identification
(RFID) tag and motion sensor combination detect this
and identify them. The system records and interprets
the data received from the sensors and the BPMN en-
gine starts a new instance of the relevant workflows,
including the one for hand-hygiene. According to the
hand hygiene workflow, the clinician should perform
the hand disinfection procedure before going near a
patient and before leaving their surrounding. In our
example, this is achieved using the sink or the bath-
room sink and the disinfectant dispenser. All of them
have inexpensive sensors together with RFID tags and
Bluetooth Low Energy transceivers; their role is to
provide input to the workflow engine. The workflow
engine records received data into the persistent repos-
itory. Furthermore, by running the workflow, the soft-
ware engine ascertains that hand hygiene guidelines
were observed. In our example, the healthcare worker
performs hand disinfection before contact with the
first patient, which is recorded by the sensors inte-
grated with the sink and the disinfectant dispenser.
However, the worker can move to the second patient
directly, as shown in Figure 2. In this case, the system
records their proximity to the second bed via RFID;
if a disinfection event that is compliant with guide-
lines was not recorded before the contact, the system
interprets this as non-compliance, and emits an alert
that is recorded and received by the healthcare worker
through their wearable device. Once they become
compliant by undergoing hand disinfection, they can
resume contact with the patient. Information that is
persisted is planned to be reused at later dates and in
the context of more than one workflow, including at
least all the workflows active during that time. As an
example, the room entry and exit events from Figure
2 can be used for finding the source of an outbreak, or
tracking its propagation. In our example, the current
instance of the hand hygiene workflow ends once the
healthcare worker exists the patient room.
ENASE 2016 - 11th International Conference on Evaluation of Novel Software Approaches to Software Engineering
66
Figure 2: Example of hand-hygiene relevant clinical workflow (simplified for readability).
4 CONCLUSIONS
This paper is centred on technology-driven preventive
measures for a serious public health issue, namely
hospital acquired infections. While most transmis-
sion routes are well understood and standard guide-
lines exist to curb them, available research shows that
in practice, they are either not applied, or not ap-
plied thoroughly. As such, we propose an innova-
tive technological solution for preventing HAI and
outbreaks. Our proposed approach will employ a
standards-compliant, workflow based, cyber-physical
system able to monitor and enforce compliance with
clinical workflows that are associated with over 90%
of HAI instances, in accordance with current guide-
lines and best practices.
We have illustrated an overall picture of the sys-
tem using a software workflow perspective through
a motivating example based on hand-hygiene guide-
lines. However, our aim is to enable the creation,
configuration and execution of location-specific clin-
ical workflows using BPMN-like notation. As ex-
isting research shows that infections within the uri-
nary tract, respiratory tract, surgical sites, or skin and
soft tissue (World Health Organization, 2002) account
for over 75% of the total number, we aim to also
evaluate the system using workflows related to those
sites. Another category is represented by workflows
that are not infection-site specific, but which repre-
sent transmission vectors for numerous types of infec-
tions: hand hygiene (such as presented in this paper)
and transmission from the environment.
Another future functionality, that currently sits be-
yond the scope of our research is a graphically inter-
active, configurable component that allows the man-
agement of the monitored workflows, by taking into
account the specific infrastructure of the deployed
sensors and of the unit of care. Furthermore, addi-
tional components that can be added to the system in-
clude advanced analysis tools for the gathered data
that allow building risk maps and contact networks
enabling new ways of pinpointing elusive reasons of
hospital infection that occur even when best practices
are adhered to.
In addition to the software-based functionalities,
another issue that must be addressed is related to the
legal aspects regarding the deployment of such a sys-
tem, more precisely those regarding data protection.
Considering that the system involves tracking of pa-
tients and healthcare workers alike, the software side
of such systems must remain compliant with regula-
tions regarding the protection of highly-sensitive per-
sonal data.
ACKNOWLEDGEMENT
This work was supported by a grant of the Romanian
National Authority for Scientific Research and Inno-
vation, CCCDI UEFISCDI, project number 9831
1
.
1
https://www.eurostars-eureka.eu/project/id/9831
Preventing Hospital Acquired Infections through a Workflow-based Cyber-physical System
67
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