MOVING FROM REMOTE PATIENT MONITORS TO

CLOUD-BASED PERSONAL HEALTH

INFORMATION SYSTEMS

A Way to Practicing Patient-centered Chronic Care Model

Juha Puustjärvi

and Leena Puustjärvi

Department of Computer Science, University of Helsinki, P.O. Box 68, Helsinki, Finland

The Pharmacy of Kaivopuisto, Neitsytpolku 10, Helsinki, Finland

Keywords: Remote patient monitoring, Personal health systems, Healthcare models, Cloud computing, Semantic Web,

Ontologies, OWL, RDF.

Abstract: Recent advances in remote patient monitoring allow patient to transmit vital health data from their home to

physicians’ offices and receive health coaching from their healthcare providers based on the clinical data

they have sent. Unfortunately such a remote monitoring technology only provides the connection between

patients and healthcare providers and thus does not support new emerging healthcare models such as

patient-centered care, pharmaceutical care or chronic care models. These healthcare models need

technology solutions that (i) support the co-operation within patient’s healthcare team (i.e., connect patients,

patient’s family members and healthcare professionals), (ii) provide a platform for sharing patient’s

healthcare data among the healthcare team, and (iii) provide a mechanism for disseminating relevant

educational material for the patient and the healthcare team. In this paper, we describe our work on

designing a personal health information system, which supports patient remote monitoring and the new

emerging healthcare models as well. The key idea is to develop the system by integrating relevant e-health

tools through a shared ontology and to exploit the flexibility of cloud computing in its implementation. In

developing the ontology we have used semantic web technologies such as OWL and RDF.

1 INTRODUCTION

The introduction of new emerging healthcare

models, such as patient-centered care,

pharmaceutical care, and chronic care model, are

changing how people think about health and of

patients themselves.

Patient-centered care (Bauman et al., 2003;

Gillespie et al., 2004; Little et al., 2001) emphasizes

the coordination and integration of care, and the use

of appropriate information, communication, and

education technologies in connecting patients,

caregivers, physicians, nurses, and others into a

healthcare team where health system supports and

encourages cooperation among team members. It is

based on the assumption that physicians, patients

and their families have the ability to obtain and

understand health information and services, and

make appropriate health decisions (Michie et al.,

2003). This in turn requires that health information

should be presented according to individuals

understanding and abilities (Stewart, 2004).

Pharmaceutical care emphasizes the movement

of pharmacy practice away from its original role on

drug supply towards a more inclusive focus on

patient care (Wiedenmayer et al., 2006; Mil et al.,

2004; Hepler and Strand, 1990). It emphasizes the

responsible provision of drug therapy for the

purpose of achieving definite outcomes that improve

patient’s quality of life (WHO, 1997; Hepler, 2004).

Chronic care model (Fiandt, 2011; Boult et al,

2008) emphasizes patients’ long-term healthcare

needs as a counterweight to the attention typically

paid to acute short-term, and emergency care. In this

sense, the traditional care models are not appropriate

as the patients with chronic illness do not receive

enough information about their condition, and they

are not supported in caring themselves after they

leave the doctor’s office or hospital.

Patient remote monitoring and home telehealth

technologies provide a variety of tools for patients to

Puustjärvi J. and Puustjärvi L..

MOVING FROM REMOTE PATIENT MONITORS TO CLOUD-BASED PERSONAL HEALTH INFORMATION SYSTEMS - A Way to Practicing

Patient-centered Chronic Care Model.

DOI: 10.5220/0003736500370045

In Proceedings of the International Conference on Health Informatics (HEALTHINF-2012), pages 37-45

ISBN: 978-989-8425-88-1

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

take an active role in the management of their

chronic diseases. Especially, the ability to monitor

and interact with patient from a distance by

exploiting electronic devices to record and send the

measurements of patients’ vital signs to a caregiver

has been a key technology in fostering patients’

ability to receive care at home. Earlier the only

reliable means of controlling such measurements has

been for a medical professional to take them

directly, or for a patient to be constantly monitored

in hospital, which would normally only happen once

the patient has become seriously ill.

The new technologies and principle of practicing

medicine holds significant promise of improving on

major health care delivery problems. However, there

are many functions in the patient-centered care,

pharmaceutical care and chronic care models that the

home telehealth devices and the e-heath tools such

as personal health records do not support.

For example, patients that are out of hospital or

who are left hospital often have concerns about their

medicines, and so there is strong demand for

extending the functionalities of home telehealth

devices by the functions of pharmaceutical care.

Neither the current e-heath tools support the

coordination of the care, nor the social connections

among the members of patient’s healthcare team.

They also fail in providing comprehensive access to

patient’s health data and in promoting patient’s

medical education.

Inspired by the (semantic) web technologies and

the flexibility of cloud computing, we have studied

their suitability for supporting the emerging

healthcare models. Our studies have indicated that

Personal Health Information Systems (PHIS) should

support the functionalities of many traditional e-

health tools such as remote patient monitors,

personal health records, health-oriented blogs, and

health-oriented information servers. It is also turned

out that by gathering these functionalities into one

system we can achieve synergy, i.e., achieve

functionalities that would not be obtainable by any

of the e-health tools independently.

In gathering the functionalities we have adapted

the ideas of knowledge centric organizations to

PHIS, i.e., we have revolved the e-health tools

around a health oriented knowledge base. So, all the

e-heath tools share patient’s health data. Further by

exploiting the characteristics provided by cloud

computing we can easily ensure the interoperation of

patient’s healthcare team: accessing the PHIS

requires only internet connection. Instead of the

prevailing systems provided by healthcare

organizations do not provide appropriate technology

for co-operation as their use is devoted to

organization’s healthcare personnel only.

The rest of the paper is organized as follows.

First, in Section 2, we motivate our work by

considering the recent advances in patient remote

monitoring. Then, in Section 3, we present the

requirements of PHIS that we have derived from

emerging healthcare models, and then, in Section 4,

we analyze the suitability of cloud computing for

satisfying these requirements. In Section 5, we

present the architecture of the knowledge oriented

PHIS and the PHIS-ontology that is shared by the e-

health tools. In Section 6 we describe how the PHIS-

ontology can be exploited in promoting patient’s

medical education and in delivering relevant

information within patient’s healthcare team. In

Section 7, we illustrate how XSLT-transformations

is used in transforming XML-coded medical data in

the format that is compliant with the PHIS-ontology.

Finally Section 8 concludes the paper by discussing

the challenges of our solutions as well as our future

research.

2 REMOTE PATIENT

MONITORING

Telemedicine is the use of medical information

exchanged from one site to another via

communications to improve a patient's health

(Angaran, 1999; Kontaxakis et al., 1990).

Telemedicine is viewed as a cost-effective

alternative to the more traditional face-to-face way

of providing medical care (Kontaxakis, 2006).

Telemedicine can be broken into three main

categories: store-and-forward, interactive services

and remote patient monitoring.

• Store-and-forward telemedicine involves

acquiring medical data and then transmitting

this data to the system that is accessible to

patient’s physician. So it does not require the

presence of patient and physician at the same

time.

• Interactive services provide real-time

interactions between patient and physician. It

includes phone conversations, online

communication and home visits.

• Remote monitoring enables medical

professionals to monitor a patient remotely

using various technological devices. Remote

monitoring is above all used for managing

chronic diseases such as heart disease, diabetes

and asthma.

HEALTHINF 2012 - International Conference on Health Informatics

Nowadays remote patient monitoring

technologies are becoming a more sophisticated,

integrated, and systematic approach to healthcare

that can be personalized to each patient’s medical

needs. In particular, Personal Health Systems

(PHSs) go beyond the simple remote patient

monitoring systems in that they enable the

communication between patients and healthcare

professionals and provide clinicians with access to

current patient data. They also provide interactive

tools for personalized care management including

vital sign collection, patient reminders and

communication tools such as video conferencing

capabilities, allowing remotely located health care

professionals to interview, observe and educate the

patient, as well as assist in the use of the peripherals

or other medical devices. Some devices also have

the ability to show video, which can be used for

educating the patient.

From technology point of view personal health

systems consist of a hub and wireless peripheral

devices that collects physiologic data. Typical

peripheral devices include blood pressure cuffs,

pulse oximeters, weight scales blood glucose meter.

The data gathered from peripheral devices are

transmitted by the hub to a clinical database for later

analysis (Figure 1).

Ahubdeviceat

patient’shome

Healthcareprovider

(physician,nurse,pharmacist).

Clinical

information

database

Peripheraldevices

Figure 1: Remote monitoring through a clinical

information database.

3 INFORMATION

FLOWS IN PHIS

The technology that supports both patient centered

healthcare and pharmaceutical care of the patients

with chronic conditions have to coordinate the flows

of information that are coming from a variety of

sources. These information flows include:

• Vital sign information from peripheral devices

(that collect physiologic data) to PHIS.

• Health regimen information between healthcare

providers (physicians, nurses and pharmacists).

• Information between patient and healthcare

providers.

• Healthcare information between healthcare

providers and patient’s family members.

• Relevant educational health information from

healthcare providers to patient.

Supporting these information flows is much

more challenging as the simple vital sign

information flow that characterized an earlier

generation of remote patient monitoring. In

particular the traditional remote monitoring model

(illustrated in Figure 1) supports only partially these

requirements as the usage of the clinical information

system is isolated from third parties such as from

patient’s family members.

Apart from the co-operation support, the

member’s of patient’s healthcare team should have a

seamless access to patient’s health data, which is

usually stored in electronic health record (EHR)

(EHR, 2011) or personal health records (PHR)

(Raisinghani and Young, 2008). The former is

managed by medical authorities while the latter

managed by the patient and all that are authorized by

the patient are allowed to access it (Puustjärvi and

Puustjärvi, 2011). Hence patient’s PHR, which in

our architecture is a component of the PHIS, has a

central role to support emerging healthcare models.

4 CLOUD-BASED PHIS

Cloud computing is a technology that uses the

Internet and central remote servers to maintain data

and applications (Chappel, 2011). It is an evaluation

of the widespread adoption of virtualization, service

oriented architecture and utility computing

(Wikipedia, 2011). The name cloud computing was

originally inspired by the cloud symbol that's often

used to represent the internet in diagrams.

Cloud computing allows consumers and

businesses to use applications without installation,

and they can access their personal files at any

computer with internet access. This technology

allows for more efficient computing by centralizing

storage, memory, processing and bandwidth.

Further, unlike traditional hosting it provides the

following useful characteristics:

• The resources of the cloud can be used on

demand, typically by the minutes.

MOVING FROM REMOTE PATIENT MONITORS TO CLOUD-BASED PERSONAL HEALTH INFORMATION

SYSTEMS - A Way to Practicing Patient-centered Chronic Care Model

• The used resources are easily scalable in the

sense that users can have as much or as little of

a service as they want at any given time.

• The resources are fully managed by the

provider. The consumer does not need any

complex resource, only a personal computer

with internet access.

Software as a service (SaaS), is a type of cloud

computing. In this service model, a service provider

licenses an application to customers either as a

service on demand, through a subscription, in a

"pay-as-you-go" model, or at no charge (Khajeh-

Hosseini, 2011). The SaaS model to application

delivery is part of the utility computing model where

all of the technology is in the "cloud" accessed over

the internet as a service.

There are various architectural ways for

implementing the SaaS model including the

followings (Chappel, 2011):

• Each customer has a customized version of the

hosted application that runs as its own instance

on the host's servers.

• Many customers use separate instances of the

same application code.

• A single program instance serves all customers.

In the case of PHISs the required computation is

rather small compared to traditional business

applications and thus the last mentioned architecture

is appropriate for the implementation of the PHIS,

i.e., a single PHIS serves all patients. However,

patient specific data can only be accessed by the

patient and those that are authorized by the patient.

The SaaS-based PHIS and its users are presented in

Figure 2.

PHIS‐server

PatientathomePatientawayfromhome

Healthcareprovider

sician

nurse



harmacist

)

Patient’sfamilymembers

Thecloud

Aserverin

thecloud

Aserverin

thecloud

Aserverin

thecloud

Peripheraldevices

Figure 2: The users of the cloud-based PHIS.

We next itemize some clarifying aspects of the

figure:

• The cloud takes the advantages of SOA

(Service Oriented Architecture) in the

interoperation of the services, e.g., in importing

patient’s health data the PHIS-server

interoperates with the servers of other

healthcare organizations including hospitals,

physicians’ offices and health centers.

• As the figure illustrates the peripheral devices

that the patient has at home are connected to

patient’s PC, and so the vital signs collected by

the devices are transmitted via the PC to the

cloud, i.e., to the PHIS.

• The patient accesses his or her health data

stored in PHIS through the browser. As the

patient needs nothing but an internet access, the

patient can easily connect to the PHIS at home,

as well as being away from home.

• Healthcare providers and patient’s family

members that are authorized by the patient can

access patient’s health data as well as

communicate through their browsers.

Next, we consider the internal structure of the PHIS-

server.

5 PHIS-ONTOLOGY

The architecture of the PHIS and its connections in

the cloud are presented in Figure 3. As the figure

illustrates patient and the members of his or her

healthcare team access the PHIS-server through the

personalized health portal. It is a site on WWW that

provides personalized capabilities for its users and

links to other relevant servers.

In designing the PHIS we have followed the

idea of knowledge oriented organizations (Daconta

et al., 2003), where the key idea is to revolve all

applications around a shared ontology (stored in a

knowledge base), which we call PHIS-ontology. It is

developed by integrating the ontologies of the e-

health tools supported by the PHIS. For now we

have integrated the ontologies of the Blog manager,

Information therapy (Ix) manager, Remote manager,

and PHR manager. Such an internal architecture of

the PHIS is presented in Figure 4.

HEALTHINF 2012 - International Conference on Health Informatics

XSLT-

transformationPHIS

Gym

Health data

from patient’s

peripheral

devices

Non-healthcare organizations

Hospital

Information

system

Physician

office

system

Healthcare organizations

CDA-

documents

RDF-coded

documents

Patient

Patient’s

authorized users

Healthcare

providers

Browser

BrowserBrowser

…

Personalized health portal

PHIS- server

Links to

other

servers

Figure 3: The component of the PHIS-server and its

external connections.

PHIS‐ontology

Blog

manager

PHR

manager

.. .

PersonalHealthPortal

PHIS

RDF‐ codeddata

Remotepatient

controller

RDF‐ codeddocuments

Figure 4: The e-heath tools accessing the PHIS-ontology.

Figure 5 illustrates the idea of the knowledge

base and the case where PHIS-ontology is developed

by integrating the Blog-ontology, Ix-ontology, PHR-

ontology and RM-ontology (Remote Monitoring

ontology). In the figure ellipses represent OWL´s

classes, rectangles represent OWL’s data properties

and the lines between ellipses represent OWL’s

object properties. Accordingly class A is shared by

all the four ontologies.

e1d1

Knowledgebase

PHR‐ ontology

Blog‐ ontology

RM‐ ontology

Ix‐ ontology

Figure 5: PHIS-ontology.

In order to illustrate shared classes, A could be

class Disease, B class Patient, and C class

Informal_entity. Further assume that object property

A-B is suffer_from, object property A-E is deals,

data property b1 is patient_name, and data property

e1 is a url. In such as setting we could specify by

RDF (Resource Description Framework) that John

Smith suffers from diabetes and the educational

material dealing diabetes is stored in a specific url.

A portion of the PHIS-ontology is graphically

presented in Figure 6. In this graphical

representation ellipses represent classes and

subclasses, and rectangles represent data and object

properties.

Patient

Medication

LabTest

BloodPressureTest

ProductProductName

BrandName

StrenghtUnit

Source

ActorIDActorRole

ColesterolTest

Value Unit

Patie ntId

PatientName

SubclassOf

Uses

Performed

ContainsStrenghtValue

Originates

MedicationId

InformationEntity

SubclassOfSubclassOf SubclassOf

Disease

Name

RelatesTo

DiseaseIE

Deals

SubclassOf

ProductIE

Deals

ColesterolTestIE

Deals

BloodPressureTestIE

Deals

Date Source

URL

Author

Predecessor

Deals Subject

Blog BlogName

Includes

BlogItem

Relates

InsertionDate

Associates

URL

Figure 6: A portion of the PHIS-ontology.

A portion of the graphical ontology of Figure 6 is

presented in OWL in Figure 7.

In order to understand the relationship of XML,

OWL and RDF note that XML (Extensible Mark-up

Language) (Harold and Scott, 2002) is just a meta

language for defining markup languages. By a meta

language we refer to a language used to make

statements about statements in another language,

which is called the object language. Accordingly

RDF (RDF, 2011) and OWL (OWL, 2011) are

object languages. Instead, XML says nothing about

the semantics of the used tags. It just provides a

means for structuring documents. Due to the lack of

semantics we do not use XML for representing

PHIS-ontology but instead we use ontology

languages RDF and OWL.

MOVING FROM REMOTE PATIENT MONITORS TO CLOUD-BASED PERSONAL HEALTH INFORMATION

SYSTEMS - A Way to Practicing Patient-centered Chronic Care Model

<rdf:RDF

xmlns:rdf=http://www.w3.org/1999/02/22-rdf-syntax-nsl#

xmln s:rdfs=http://www.w3.org/2000/01/rdf-schema#

xmln s:owl=http://www.w3.org/2002/07/owl#>

<owl:Ontology rd f:about=“”PHA/>

<owl:Class rdf:ID=“Blog/”>

<owl:Class rdf:ID=“BlogItem/”>

<owl:Class rdf:ID=“Patient/”>

<owl:Class rdf:ID=“Medication/”>

<owl:Class rdf:ID=“Source/”>

<owl:Class rdf:ID=“Product/”>

<owl:Class rdf:ID=“LabTest/”>

<owl:Class rdf:ID=“BloodPressureTest”>

<rdfs:subClassOf rdf:resource=“#LabTest”/>

</o wl:Class>

<owl:Class rdf:ID=“ColesterolTest”>

<rdfs:subClassOf rdf:resource=“#LabTest”/>

</o wl:Class>

<owl:ObjectProperty rdf:ID=“Relates”>

<rdfs:range rdf:resource=“#Medication”/>

</owl:ObjectProperty>

<owl:ObjectProperty rdf:ID=“Uses”>

<rdfs:domain rdf:resource=“#Patient”/>

<rdfs:range rdf:resource=“#Medication”/>

</owl:ObjectProperty>

rdf:RDF

Figure 7: A portion of the PHIS-ontology in OWL.

6 PROVIDING INFORMATION

FLOWS AND MEDICAL

EDUCATION

As we have already stated the technology that

supports emerging healthcare models should

coordinate the flows of information within patient’s

healthcare team as well as provide educational

relevant material. In our developed solutions these

functionalities are carried out by the information

stored in the PHIS –ontology. In particular these

functionalities exploit the instances of the classes

BlogItem and InformationEntity.

Each instance of the class BlogItem represents an

entry in patient blog. By its object property

Predecessor the entries are presented in

chronological order with the latest entry listed first,

and by using the object property Deals blog’s entries

can be classified into different subjects. Patient and

patient’s healthcare team are allowed to access and

create new entries for the blog through the

functionalities provided by the Blog manager.

Each instance of the class InformationEntity

represents an educational material. Its data property

url specifies the location of the actual content of the

material, i.e., the instance. As illustrated in Figure 6,

the PHIS-ontology also specifies the relationships of

the class InformationEntity to other relevant classes

such as Medication and Disease. Thus, based on

these relationships relevant educational material can

be automatically delivered to the patient. For

example, we can query the information entities that

deal the diseases that patient John Smith suffers

from. Further, by activating such queries (by the Ix-

manager) when new disease is inserted for a patient,

we can automate information therapy, i.e., prescribe

the right information to right patient at right time.

7 TRANSFORMING XML-CODED

DOCUMENTS

As we have already illustrated in Figure 3, the PHIS-

server does not only import data from patients home

telehealth devices but it also imports data from other

information sources such as a hospital laboratory,

gym and physician office.

If the format of the imported data does not

coincide with the PHIS-ontology, then the stylesheet

engine (Daconta, et al., 2003) is required for

transforming the imported data before its insertion

into the PHIS-ontology. Such a transformation is

illustrated in Figure 8.

XML tree

Transformed

XML/RDF tree

Stylesheet Engine

Transformation

Semantic document

in RDF/XML

Stylesheet

(XSLT document)

CDA –document

In XML

Figure 8: Transforming CDA-documents into semantic

documents.

CDA-documents are typical XML-based

documents, and so they are not compliant with the

PHIS-ontology. By the CDA-documents we refer to

documents that are based on the Clinical Document

Architecture (CDA), which is an ANSI approved

HL7 standard (Dolin et al., 2001). It is proven to be

a valuable and powerful standard for a structured

exchange of persistent clinical documents between

different software systems (Puustjärvi and

Puustjärvi, 2009).

However, in the case of non persistent

documents with CDA we encounter many problems.

The main reason for this is that the semantics of the

CDA-documents is bound to the shared HL7

Reference Information Model (RIM) (Dolin et al.,

2001). Thereby introducing new document types

HEALTHINF 2012 - International Conference on Health Informatics

would require to extending the RIM, which is a long

lasting standardization process. So this approach

contradicts with our requirement of flexibility in

introducing new document types in importing health

information into the PHIS-ontology. Therefore in

importing data from XML-based data sources (e.g.,

from HL7 CDA compliant systems) requires that the

XML-formatted data is first translated (by an XSLT-

based style sheet engine (Harold and Scott, 2002)

into RDF and then inserted into the PHIS-ontology.

In order to illustrate this transformation consider

the CDA document of Figure 9.

<ActorName> Susan Taylor</ActorName>

</Patient>

<ActorID>Pharmacy of Kaivop uisto</ActorID>

<ActorRole>Pharmacy</ActorRole>

</Source>

<Text>One tablet ones a day</Text>

http://www.../medicalinfo/SubstitutionInfo

</ GenericSubst itution Info>

</Descrip tion>

<Produ ctName>Valsartan</ProductName>

http://www.../medicalinfo/Valsartan Info

</ProductInfo>

</Product>

<Un it>m illigram</ Uni t>

</Stren gh t>

</Quantity>

</Medication>

</Medi cations>

</ContinityOfC areRecord>

Figure 9: A CCR document.

Figure 9 represents a CCR file that has a

medication list (element Medications), which is

comprised of one medication (element Medication)

that is source stamped by the Pharmacy of

Kaivopuisto. The CCR file is based on the CCR

standard (CCR, 2011).

The CCR standard as well as the CCD standard

(HL7, 2011) are originally a patient health summary

standards, and later on these standards are

commonly exploited in structuring the data in

personal health records. From technology point of

view these standards represent two different XML

schemas designed to store patient clinical summaries

(Puustjärvi and Puustjärvi 2009). However, both

schemas are identical in their scope in the sense that

they contain the same data elements.

After the XSLT transformation the CCR

document of Figure 9 is in the RDF/XML format

presented in Figure 10. In this format the document

is compliant with PHIS-ontology and can be inserted

into the PHIS-ontology.

<rdf:RDF

xmlns : rdf=h ttp://www.w3. org/1999/02/22-rdf-s yntax-ns#

xmln s : po=htt p://www.l ut.fi/ontologi es/PHIS-ont ology#>

<rdf:Description rdf:about=”AB-12345”>

<rdf:type rdf:resource=“&po;Patient”/ >

<po : PatientName>Susan Taylor</po:PatientName>

<po:Uses rdf:resource=“&po;Med-07092010”/>

</rdf : Description >

<rdf:Description rdf:about =” Med-07092010”>

<rdf:type rdf:resource=“&po;Medication”/>

<po : Stren gh tValue rdf:datatype=

”&xsd;integer”>30</po : StrenghtValue>

</rdf : Description>

<rdf:Description rdf:about=” Valsartan”>

<rdf:type rdf:resource=“&po;Product”/>

<po:Deals rdf:resource=“&info;ValsartanInfo”/>

</rdf : Description>

< rdf:Description rdf:about=” Pharmacy of Kaivopuisto”>

<rdf:type rdf:resource=“&po;Source/>

<p o : ActorRole >P harma cy< /po : Act orR ole>

</rdf : Description>

</rdf:RDF>

Figure 10: Transformed CDA document in RDF/XML

format.

The RDF/XML-formatted document of Figure

10 is comprised of four RDF-descriptions. Further,

the first RDF-description is comprised of three RDF-

statements. The first statement states that the type of

the instance identified by “AB-12345” is Patient in

the PHIS-ontology. The second RDF-statement

states that the name of the instance identified by

“AB-12345” is Susan Taylor.

8 CONCLUSIONS

Monitoring a patient’s vital signs provides an

important source of information to the physician that

treats the patient. Nowadays information and

communication technology provides the possibility

of a new generation of lightweight monitoring

systems which a patient can wear while being at

home or while going about their daily business.

Formerly the only reliable means of such monitoring

has been for a medical professional to take them

directly, or for a patient to be constantly monitored

in hospital.

The new patient remote monitoring technology

MOVING FROM REMOTE PATIENT MONITORS TO CLOUD-BASED PERSONAL HEALTH INFORMATION

SYSTEMS - A Way to Practicing Patient-centered Chronic Care Model

holds significant promise of improving on major

health care delivery problems. However, there are

many functions in the emerging healthcare models

(including patient centered care, pharmaceutical care

and chronic care models) that the modern

monitoring devices and systems do not support as

they only provide the communication between

patient and healthcare provider. Instead the

emerging healthcare models require Information and

Communication Technology (ICT) support for the

co-operation of patient’s healthcare team and the

support in delivering relevant educational material

for the patient and the members of the healthcare

team.

Our studies have shown that the ICT-support of

these requirements requires the integration of

patient’s e-health tools as it significantly simplifies

patients’ interaction with the services, enables the

co-operation within the healthcare team and the

development of new services such as automated

information therapy.

From technology point of view we have

integrated e-heath tools through the shared PHIS-

ontology that is stored in the knowledge base, which

exploits semantic web technologies such as OWL

and RDF. The management of the shared ontology

requires that in importing data the documents that

are not compliant with the ontology have to be

transformed by XSLT transformation into the RDF-

format that is compliant with the ontology, i.e., a

stylesheet has to be defined for each non-compliant

document type.

In our future work we will study the effects of

introducing cloud-based health information systems

on the mind-set of patient and healthcare personnel

as the introduction of these technologies also

changes the daily duties of the patient and many

healthcare employees. Therefore we assume the

most challenging aspect will not be the technology

but rather the changing the mind-set of patient’s

healthcare team.

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MOVING FROM REMOTE PATIENT MONITORS TO CLOUD-BASED PERSONAL HEALTH INFORMATION

SYSTEMS - A Way to Practicing Patient-centered Chronic Care Model