A MODEL TO OPTIMIZE THE USE OF IMAGING EQUIPMENT

AND HUMAN SKILLS SCATTERED IN VERY LARGE

GEOGRAPHICAL AREAS

Daniel Ferreira Polónia, Carlos M. A. Costa and José Luís Oliveira

DETI/IEETA, Universidade de Aveiro

Campus Universitário de Santiago, 3810-193 Aveiro, Portugal

Keywords: Health Information Systems, Radiology Information Systems, Teleradiology, Ubiquitous Systems, CSCW.

Abstract: Recent studies have shown that the good geographical coverage of Imagiologic Information Systems and

equipment such as Picture Archiving and Communication Systems (PACS) is not matched by similar

coverage levels of radiologists, especially in rural and academic health institutions. In this paper, we address

this problem proposing a solution that is twofold, with the first one being process based, through the

optimization of work assignment within pools of human resources according to Service Providers

availability, and the second part being technology based, through the interconnection of all the health

institutions PACS equipment and radiologists geographically dispersed. After describing the high level

solution, we present some of the results of the implementation of this concept and some of the technical

challenges still to overcome. Finally, the conclusion chapter presents the impact of the system in the

involved institutions.

1 INTRODUCTION

During the last 10 years, the use of digital medical

imaging systems increased tremendously inside

healthcare institutions (Reiner, Siegel et al. 2003),

representing today one the most valuable tools

supporting the medical decision process and

treatment procedures.

Initially, the benefits of digital technology were

confined to the equipment machines and to the

relatively few examples of image data migration into

a centralized shared archive, for posterior utilization.

The medical imaging digitalization and the

implementation of PACS, Picture Archiving and

Communication Systems, enabled practitioner’s

satisfaction through improved, faster and ubiquitous

access to image data (Costa, Oliveira et al. 2005).

Moreover, it reduced the costs associated to the

storage and management of image data and it

increased the intra and inter-institutional data

portability (Costa, Silva et al. 2004). In fact, one the

most important benefits of digital medical imaging is

to allow widespread sharing and remote access to

medical data outside institutions. This type of

system presents an opportunity to improve

cooperative work among groups, taking place within

or across healthcare institutions. With PACS a new

saying emerged: “Any Image, Anywhere and at Any

Time”.

2 MATERIALS AND METHODS

The most important contribute to the exchange of

structured medical image was the establishment of

DICOM, Digital Imaging and Communications in

Medicine, standard in 1992. Medical images in

electronic format opened doors to numerous post-

processing techniques that allow extraction of more

and better information from the acquired data.

However, modelling and quantitative imaging

analysis tools are especially expensive regarding

software packages and computational power

requirements, thus becoming a very scarce goods,

that, although are available 24 hours a day, are only

used for a few hours (Reiner, Salkever et al. 2005).

Most statistical data and productivity studies

show us that medical image data can be generated in

practically any healthcare institution, even with

limited human or financial resources (Reiner, Siegel

et al. 2002). However, the expensive computational

tools and the human skills are usually concentrated

in a reduced number of specialized medical centres,

and there is no direct relationship between the

208

Ferreira Polónia D., M. A. Costa C. and Luís Oliveira J. (2007).

A MODEL TO OPTIMIZE THE USE OF IMAGING EQUIPMENT AND HUMAN SKILLS SCATTERED IN VERY LARGE GEOGRAPHICAL AREAS.

In Proceedings of the Ninth International Conference on Enter prise Information Systems - SAIC, pages 208-212

DOI: 10.5220/0002394902080212

 SciTePress

existence of digital imaging equipment and the

existence of sophisticated software and highly

skilled technicians and examiners (Reiner, Siegel et

al. 2005).

Our research question was centred in the

following motto: Is there a way to optimize the even

distribution of equipment across the country and the

uneven distribution of software analysis packages

and skilled examiners using technology and

processes for trans-institutional use of resources

from the equipment and human point of view?

2.1 “As is” Scenario

As shown previously, most studies show us that

there is a good distribution of hospitals and

radiological equipment across the country in which

it is not matched by the distribution of resources

(physicians and radiologists) nor by the productivity

of the equipment (number of exams made by that

equipment).

In the Portuguese case, the National Health

Service shows, in its statistical data (Saúde 2004),

that most of its secondary care institutions possess

PACS equipment and a radiology technician.

However, if we measure the productivity ratio of

each of this equipment we reach the conclusion that

most of the equipment is under utilized, either by

lack of appropriate human and technical resources to

perform a quick follow up on the patient situation,

and either by lack of external clients that acquire

services to the institution, thus maximizing

equipment productivity.

Most of the institutions are properly equipped

with PACS software acquisition modules, including

digital image modalities interfaces, also known as

“dicomizers”, that run over appropriate internal

institutional LAN infrastructures and that are

connected among themselves through a private

governmental network infrastructure, the RIS,

acronym for Rede de Informação da Saúde, (Saúde

2006) that interconnects to the Internet. Also, staff

that operates this equipment is considerably skilled

in managing and operating its basic features, through

multiple periodic training actions made by

equipment vendors.

However, these institutions have difficulties in

recruiting and/or economically supporting specialists

(full time or partially) like, for instance, a physician

radiologist. So, these hospitals engage external

services to third party service providers, in the form

of institutions or singular persons, to perform

detailed examinations and provide feedback to the

institution physician.

These entities have remote access to the

institution PACS and the communication is

established in a “peer-to-peer” architecture,

supported by a VPN channel that connects to the

institution LAN.

The system requires a high speed internet

connection (or private dialup link) and a specific

client PACS application installed on remote PCs.

Many times, these external specialists are physically

distant more than 300 kilometres away.

2.2 Proposed “to be” Model

First of all, the proposed model assumed that all

equipment that joins the system uses the technical

norm DICOM v3 as support format for archiving

and transmitting medical imaging among the

different partners (DICOM).

In our model, we have a twofold approach to

optimize the use of computational resources and

human skills scattered in very large geographical

areas. The first is a process-based approach where,

from analysing the human resources and software

infra-structure available, a process optimization

method is developed. The second approach is a

technical one where the required technological

infrastructure is described, demonstrating the

information workflow and the way it is possible to

optimize the existing systems.

2.2.1 Process Based Approach

From the process side (including economic, human

and computational perspectives), first of all, it is be

necessary to make an inventory of all the interested

parties in participating in the process, either as

Client Institution (the one that performs the

Service Requests and feeds the system with data to

be serviced), either as Service Provider (the one

that gathers the data to be serviced, downloads it,

performs the requested service and uploads it again

to the Client Institution).

The Client Institution is identified according to

the type of institution ,i.e., clinical practice, hospital,

etc.; the Users allowed to access, the type of

Modalities that are attached to the PACS system

and a Track Record of the requested services,

including the number of providers requested for each

service and their experience level. Each Client

Institution has the ability to, optionally and

according to the status of the user which performs

the request, includes the specific type of expertise

required by the provider and if any specific type of

equipment is necessary, thus maximizing the

existing equipment and computational power

available by the pool of Service Providers. Also as

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209

an option, a priority level for the Service Request

can be attached, thus defining minimum and

maximum response time.

According to the complexity level required for

each Service Request, a given number of tokens is

automatically assigned by the Client Institution

Interface Engine (Figure 1), based on the number

of examiners, experience level, expertise required,

equipment required and priority level. These tokens

determine the “virtual value” to be paid by the Client

Institution to the Service Provider for the service

requested (Prinz 1999).

The Service Provider is identified according to

the type of institution that it belongs to (free lancer,

clinical practice, hospital, etc.), the experience level

that possesses, the type of expertise that it has and

the equipment available. It also has attached

information concerning service level agreements

such as minimum and maximum response time to a

request that it must contractually meet.

Figure 1: PACS based Service Request Pool.

Providing a use case, we have the following:

Once the exam is performed in an Image

Modality of the Client Institution (Figure 1), it is

placed in the Institution PACS and the Client

Professional, examines it, determining that it needs

a service to be provided by a number of specialists

in the field, with a certain priority level and,

eventually, requiring the use of specific equipment.

The Service Request is then placed in Client

Institution Interface Engine where its clinical

information is anonymized. It is also attached some

information concerning clinical data, inserted as a

DICOM structured report file (Noumeir 2003).

The Service Request is then sent through the

Internet (or a Virtual Private Network) to a Medical

Imaging Network that has attached to it a Service

Request Pool, where the exam is placed.

The Service Request Pool Web Portal (Figure

1) shows a personalized view to each Service

Provider, where the Service Request that is most

likely to fit its profile comes on top, followed by a

series of all the other it can execute according to its

profile.

Once a Service Provider selects a Service

Request, it counts down the number of reviewers for

that Service Request. If the number reaches zero, it

will disappear from the “pending” list in the web

portal. A Service Provider can only select

simultaneously up to a predetermined number of

service requests, thus preventing a Service Provider

from gathering the most interesting (or profitable)

Service Requests.

Another feature included is to increase the

number of tokens associated with a Service Request

at a given rate as time passes by, thus guaranteeing

that, at a given point, all exams are sufficiently

attractive to receive a response from the Service

Provider.

After performing the service requested, and

according to a Service Level Agreement to be

negotiated the moment the Service Provider joins the

system, based on the number of tokens associated

and the time that it took to perform the service, a

number of credits will be assigned to its “virtual

purse”, coming out from the Client Institution

“virtual purse”.

Once the service is concluded, data not only is

returned to the Client Institution Interface Engine

to be “de-anonymized” and sent the requiring

clinician, but it can also be kept, under its

anonymized form, in the Service Request Pool

Database so that an optional random Quality

Control review (Treitl, Wirth et al. 2005) can be

made a posteriori by a panel of reviewers that can,

based on these controls, change the Service Provider

experience level and/or type of expertise.

The same system that filters the data in the

Service Request Pool for Quality Control can also

have other utilizations like, for instance, keep data to

be used in epidemiological and scientific studies

(Krug, Bottge et al. 2003) where researchers can

gather anonymised data to be inserted into clinical

and/or research studies and where Service Providers

can reference previous cases to fundament their

decisions.

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2.2.2 Technology based Approach

The technological approach, consists in

implementing an information gatherer in every

institution that adheres to the system, which

interconnects the institution PACS, that by itself

interconnects the existing modalities, (Figure 2 –

step 1). Thus it is able, through the Client Institution

Interface Engine, to receive the data from the

institution PACS in DICOM format, anonymize the

patient identification data in the DICOM headers,

label them with an internal ID number, ID request,

as well as service priority and service code intended

from the structure.

It should be noticed that the ability of radiology

technicians to operate modality equipment, to use

the PACS system and to perform basic imaging

adjustment such as brightness or contrast adjustment

is not jeopardized by the introduction of this engine.

Since PACS systems have the ability to

configure external DICOM servers to export

imaging procedures, in this case it is added a new

export unit to the local PACS system designated

“PACS POOL”. In order to use this functionality,

the radiology technician only has to select the

intended information and “Send to PACS POOL”

(Figure 2 – step 2), just like when a DICOM image

is sent to another server or institution nowadays.

Whenever it is necessary to add extra clinical

data to support the intended service code (patient

history, lab analysis, etc.), this information can be

given as a DICOM file of the type Structured

Report. In this DICOM file it can also be included

the specification of the intended service,

represented, for instance, as an ICD10 or as

ontology. The specification can also be placed in a

“DICOM Private Tag” especially inserted.

Once the service request is inserted in the POOL

(Figure 2– step 3), i.e.: the medical images are

stored to the “DICOM Storage” and the “Database”

is updated with a new request in “pending” status,

the Service Request Pool Web Portal informs all

suitable Service Providers that there is a pending

request for that specific type of clinical service.

Once a Service Provider shows interest in analysing

that request, it passes from the state of “pending” to

the state of “lock” and it is automatically sent to the

service provider PACS system (Figure 2– step 4).

The Service Provider then processes the request

according to the request made earlier (Figure 2 –

steps 5 and 6) and sends back to the Service Request

Pool the complete process (Figure 2 – step 7). The

service results (images and reports) are, once again,

supported and transferred using DICOM standard

(Storage Syntax and Communications Protocol).

In the Service Request Pool, the Service Request

is passed from “lock” to “complete” in the “Web

Portal” and the results are sent back to the requesting

Client institution (Figure 2– step 8) i.e.: to the

Interface Engine of the Client institution.

Once arrived here, the patient identification data

is reset to all DICOM headers and the information

sent to the “Local PACS” (Figure 2– step 9) where

the results and the requested service are available for

the clinician that requested the service (Figure 2–

step 10).

3 RESULTS

With the conceptual model previously proposed, we

point to a solution to several problems that now

impairs the optimal provision of radiological

services, such as the imbalance in the distribution of

equipment and specialists over wide geographical

areas, reducing the response time and establishing a

virtual market for the provision of radiological

services.

This virtual market has a set of features such as

the ability to set up an Engine that acts as a broker

that guarantees functionalities such as:

• Double blind review of the exam if desired (the

Client Institution may not know the identity of

the Service Provider and the Service Provider

may not know the identity of the Client

Institution and the Patient that it is reviewing,

with the broker being able to reverse this

situation at any time),

• Quality of service in terms of time and

expertise (every request gets serviced through

an embedded priority management system),

• Review of the quality of service provided

(every service request processed can, randomly,

go to a database where an expert committee

samples them in order to grade the Service

Providers),

• Build up of an anonymized studies database for

epidemiological and scientific reference

analysis and fundament decisions,

• Set up a virtual market where only the most

appropriate service request are presented to the

Service Providers, performing a previous

Figure 2: A PACS based management of resources.

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filtering of all the requests not suitable, and

listing them according a list of preferences set

by the Service Provider, and

• Perform administrative and financial

management of the system, guaranteeing

payment of the Service Providers by the Clients

through a token-based system that prices the

service according to its complexity, response

time and expertise of the service provider

required.

4 DISCUSSION

The implementation of such a network will be of

utmost interest for well implemented nationwide

networks of PACS infra-structures, usually owned

by National Health Service and/or by large insurance

companies in order to optimize turn-around times of

detailed diagnosis by external specialists and will

also be of interest for insurance companies that have

insurance policies options that enable the extension

of the quality of service provided to the speed of

response time, quality of the service provider and/or

the number of service provisioners.

The use of ontologies in the processing the

request in (Figure 2 – steps 5 and 6) will enable the

possibility of extending the network at a pan

European and/or at a worldwide level (admitting that

there are no ethical and legal problems with the

mutual recognition of service providers), in

extraordinary cases such as rare malformations or in

cases that require a specific expertise and/or

software only available outside the network.

From the purely economical point of view, and

abstracting the legal and ethical problems associated,

a global market could be established, thus

transforming a purely local market onto global

network of service providers, thus making available

a set of expertise and software anywhere and at any

time.

5 CONCLUSION

This entire model is designed so that there is no need

in changing the existing institutional PACS structure

of the entities involved, with no need for additional

hardware or applicational software and with a quick

learning curve for the clinical staff involved.

Furthermore, the underlying economic rationale

allows an optimization in the use of resources,

creating mutual benefits for the parties involved in

the service provisioning.

ACKNOWLEDGEMENTS

The present work has been funded by the European

Commission (FP6, IST thematic area) through the

INFOBIOMED NoE (IST-507585).

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