ORGANIC COMPUTING FOR HEALTH CARE SYSTEMS

Possible Beneﬁts and Challenges

Florian Nafz, Frank Ortmeier, Hella Seebach and Wolfgang Reif

Lehrstuhl f¨ur Softwaretechnik und Programmiersprachen, Unversit¨at Augsburg

Universit¨atsstr. 14, 86135 Universit¨at Augsburg, Germany

Keywords:

Organic Computing, multi-agent systems, health-care applications, security.

Abstract:

Todays health care institutions will undergo major changes in the next two decades. The reason for this is the

change of ageing structure in many industrialized countries. In Germany statistics indicate that the costs for

health care systems will at least double per person while the number of contributing, working citizens will

signiﬁcantly lower. At the same time average life expectation will rise above 80 years. To cope up with this

development adaptations to organization and process of health care are necessary. Typically tasks in stationary

health care can be divided in two groups: task which incorporate direct interaction with the patient (care tasks)

and tasks which focus on logistics and organization (background tasks). In health care it is not desirable and

feasible to reduce efforts in care tasks. So costs and efforts must be reduced within the second group of tasks.

This is possible if new paradigms – both in organization and underlying software architecture – are applied.

One such paradigm is organic computing. Organic computing aims at systems, which are self-organizing,

self-adapting to new challenges and self-optimize during runtime. Such systems can take away a lot of organi-

zatorial work form the staff and thus allow for more and better care without rising budgets. The paper outlines

the idea of organic computing as well as opportunities and challenges for applying it in the health care context.

1 INTRODUCTION

It is a widely accepted fact, that cost pressure will

make changes in todays health care institutions nec-

essary. Reasons for this development are the chang-

ing ageing structure and the rising life expectation of

many industrial countries’ population. In 2005 there

were in average 31.7 persons older than 65 years for

every 100 persons in working age. This percentage

will rise to 54.2 persons in 2030 and to 70.9 persons

in the year 2050 (Statistisches Bundesamt, 2006). At

the same time the average per year cost for health care

for persons older than 65 years is four times the aver-

age cost for younger people (Statistisches Bundesamt,

2007). These two facts together show that costs will

dramatically rise while the income will stay almost

constant.

It is not feasible to simply cut health care beneﬁts

to reduce costs. Therefore it is necessary to provide

health care more cost efﬁcient. Humans play – and

will always play – the most important role in caring

for others. So it is not possible and wanted to “re-

move” human staff from the caring process. To keep

the health care systems affordable and survivable it

will be of uttermost importance to support the staff in

all tasks, which are not directly related to the patients

as much as possible.

This can only be done by providing intelligent in-

frastructure and background systems, which support

the medical staff autonomously whenever possible.

A paradigm for construction of such systems is Or-

ganic Computing (VDE/ITG/GI, 2003). It allows for

constructing ﬂexible systems, which dynamically (re-

)conﬁgure and autonomously self-adapt to changing

tasks and requirements.

In this paper a brief overview on Organic Com-

puting is given in Sect. 2. Sect. 3 shows chances and

possibilities of applying Organic Computing in health

care on a little example. Open questions for research

and challenges are discussed in Sect. 4. Sect. 5 con-

cludes this position paper.

2 ORGANIC COMPUTING

TECHNIQUES

The complexity of modern systems has grown im-

mensely in the last years. In particular the trend to

replace hardware solutions with software components

has made the software become (a) more and more im-

286

Nafz F., Ortmeier F., Seebach H. and Reif W. (2008).

ORGANIC COMPUTING FOR HEALTH CARE SYSTEMS - Possible Beneﬁts and Challenges.

In Proceedings of the First International Conference on Health Informatics, pages 286-290

 SciTePress

portant for the functionality and (b) more and more

complex to develop. A good example is a modern

(digi-)cam. Five years ago most cameras where using

analogous media to record pictures. Extra function-

alities like (visual) noise suppression or image sta-

bilization where implemented by using speciﬁc me-

dia or by adding extra hardware components. Today

such functions are usually implemented in software

and can relatively easy be transfered from one cam-

era to another. This led to very complex software and

this process is now reaching a saturation level. To

further enhance the capabilities of both – the system

in general and the controlling software in particular –

new approaches are necessary to deal with the grow-

ing complexity. One such approach is Organic Com-

puting (M¨uller-Schloer, 2004).

The Organic Computing Paradigm. Or-

ganic Computing (M¨uller-Schloer et al., 2004;

VDE/ITG/GI, 2003) is an extension to Ubiquitous

and Autonomic Computing (Kephart and Chess,

2003). The core goal of Organic Computing is,

that future (computing) systems should be able

to dynamically adapt to changes in requirements,

to automatically detect and neutralize component

failures and to continuously optimize themselves for

better performance. Such capabilities are called self-

adaptation, self-healing and self-optimization. The

hope is, that systems with self-x capabilities are by

far superior to standard systems in terms of possible

functionalities, availability, reliability and effort for

maintenance. At the same time self-organization

mechanisms allow for easier construction. As not

every single scenario has to be anticipated at design

time. New scenarios and requirements will be

detected by the system during runtime and self-

adaptation mechanisms will reconﬁgure the system to

this new challenges. So the systems behave from an

external point of view very much like living beings.

They monitor their environment, reﬂect upon changes

and adapt to new situations. Therefore such systems

are called “Organic Computing” systems.

It is often useful and possible to cope mecha-

nisms and techniques from biology/sociology for the

design of self-X algorithms and systems – so called

bio-inspired algorithms. One example is a (self-

)protection algorithm which function like the human

immune system (Pietzowski et al., 2006). But there

also exists a variety of other algorithms, which are

based on more traditional approaches.

Technically, implementations of Organic Comput-

ing systems often contain one part which is respon-

sible for delivering the wanted/intended functionality

and one part which constantly monitors the environ-

ment and – if necessary – controls/changes the func-

tional part. There already exists a broad variety of

methods (Seebach et al., 2007; Richter et al., 2006),

middlewares (Trumler et al., 2004; Trumler, 2006)

and analysis tools (G¨udemannet al., 2007; G¨udemann

et al., 2006) for design, analysis and construction of

such systems.

Application Domains. Organic Computing is typ-

ically applied to software-intensive embedded sys-

tems. Example domains are large networks of sen-

sors, trafﬁc control systems or production automa-

tion. One example could be a vision of the next

generation of production cells. Assume a produc-

tion cell which should process workpieces followinga

given speciﬁcation. The functional part of this system

may be a set of robots which perform certain tasks

– e.g. drilling holes, inserting screws and tightening

screws – on workpieces and a set of transport units,

which transport workpieces from station to station. If

new types of workpiece are to be processed, then the

robots must be reprogrammed (or these new work-

pieces must have already been anticipated during sys-

tem design). If a single robot/component of the sys-

tem fails, then production will often come to a stand

still.

If in contrast this system is designed in an or-

ganic way, then each robot will be enhanced with

a supervising (software) component. This software

not only monitors the supervised robot, but also de-

cides/plans what tasks the robot will perform an from

where workpieces are taken resp. where workpieces

are to be placed next. If new workpieces – with a new

speciﬁcation about how they are to be processed – are

given to the production cell, then the supervisors try

together to ﬁnd a reconﬁguration to achieve the new

goal.

Assume for example a cell that consists of three

robots, which are all capable of drilling holes, in-

serting screws and tightening them and a set of au-

tonomous carts for transportation, then an (initial)

conﬁguration could state that one robot only drills,

one only inserts screw and one only tightens the

screws. The workpieces will be transported in badges

from robot 1 to robot 2 to robot 3. The supervisors

constantly monitor the functionality of the robots and

the type of workpieces which are to be processed. If

now for example the drill of robot 1 breaks, then the

supervisors will search for a new conﬁguration to sub-

sume production. One solution is to let robot 2 do the

drilling, assign robot 1 to inserting screws and change

the transportation routes accordingly. This is called

self-healing. If a a new type of workpiece is to be pro-

cessed; e.g. workpieces now only need holes (and no

screws at all). Then the supervisorwill recognizes this

and reconﬁgure all robots to drill (self-adaptation)

The technical basis for such effects is (i) the ca-

pability of the supervisors to reﬂect their controlled

subsystems capabilities, (ii) to interact/communicate

with each other and to (iii) use a common language

about goals and capabilities. In the last years there

have been big advances in building Organic Comput-

ing systems. There exists now a number of working

middlewares, organic architectures and intelligent re-

conﬁguration algorithms. They have also been suc-

cessfully applied to various technical scenarios. But

there exist hardly any application to systems in which

privacy and individual user-trust play an important

role.

3 ORGANIC COMPUTING IN

HEALTH CARE

How can Organic Computing help for health care?

What are the challenges, which have to be met to

allow for using Organic Computing in health care?

These question can be best answered, if an example

scenario is taken into account.

A possible example scenario may be support and

comfort functionality in future rehabilitation centers.

This scenario is only one selected example of a broad

class of background support systems both inside and

outside the domain of health care.

An Example. The scenario assumes, that during the

next decade patients as well as medical staff and doc-

tors will be equipped with mobile devices, which can

form ad-hoc-networks. The topology of these net-

works will be continuously changing as the device

are carried around by their possessors. Stationary de-

vices like ergo-meters or central services will also be

equipped with wireless communication and can also

participate in the network.

Typically patients in a rehabilitation clinic arrive

with a ﬁxed treatment concept. This concept states

a number of treatments (e.g. physio, massage, etc.)

which have to be applied to the patient in certain

intervals (e.g. twice a week). Periodically – often

every two weeks – these concepts may be changed

or updated according to the physical state of the pa-

tient. Using the treatment concept and the capacities

of the staff and installations as input data, a central

administration service creates a treatment plan for the

patients. This plan schedules treatments to certain

A more detailed description of this example may be

found in (G¨udemann et al., 2006).

time slots and staff/installations. Similarly work plans

for the staff are created. This process is relatively

complex and requires intensive planning. It is usu-

ally done on a weekly basis. The plans are typically

printed and handed out to patients and staff.

This process is performing adequately while there

are no unpredicted events. In reality there typically

occur a lot of disturbances – like patients coming to

late to their treatments, illness of staff or malfunction

of some equipment. The consequence is that the plans

must be adapted. This is often not possible or only if

the disturbance can be foreseen (like illness of staff).

As a consequence in most cases treatments will ei-

ther be canceled or at least postponed for several days.

The root of this problem lies in the static nature of this

process and the central planning.

Beneﬁts of Organic Computing in Health Care.

Better results can be achieved if an Organic Comput-

ing systems is implemented for this task. In an or-

ganic approach, the treatment plan will not be calcu-

lated a priori by a central administration. It will rather

evolve by interaction of all agents with each others.

This allows for dynamic reconﬁguration and continu-

ous adaptation for optimal solutions. The agents can

much better locally decide if a schedule for treatments

is possible, impossible or borderline. Agents are in

this context the mobile devices of all relavant paritci-

pants. An automatic replanning algorithm could for

example ﬁrst collect localization information of the

patient and the next treatment‘s position. It will then

consider the physical state of patients or delays of the

treatment staff (for example because of meetings or

emergencies). Together this information will often al-

low for dynamic re-organization of treatment sched-

ules. The consequence is that many disturbances can

be compensated by a little reorganization. The big

beneﬁt is, that this can be done “on-the-ﬂy” without

the need of a central coordination.

An example: assume patient A will not make it

to his next treatment in time, while patient B, who is

scheduled right after A, is already close to the treat-

ment. It is then possible to inform A, B and the

staff and to dynamically switch the patients‘ treat-

ment. Note, that this simple scenario could also be

possible without a computer aided planing system.

But it is clear that the same method can also be used

to make more complex re-schedulings, which may in-

volve multiple changes. This is better than traditional

approaches, because ﬁrstly re-planning starts as soon

as a disturbance is detected and secondly it is not nec-

essary for all agents to physically meet at one place

but rather share and exchange information through

the ad-hoc network. Similar algorithms can compen-

sate for delays of staff, broken equipment and many

other disturbances. Furthermore the system could be

used to automatically integrate new patients into the

system by calculating treatment schedules for them.

As a result of later automatic optimization some re-

scheduling of existing schedules could make sense to

improve the overall quality of the planning system.

All the properties described above are typical

properties of Organic Computing systems. In Organic

Computing these effects are called self-organizing

(e.g. autonomously integrating new patients and cal-

culating schedules), self-optimizing (e.g. minimizing

waiting times), self-healing (e.g. compensating for

broken equipment or illness of staff) and self-adapting

(e.g. adapting to changing treatment requirements

and health situation of a patient). So its only a log-

ical step to apply techniques, tools and middlewares

of Organic Computing to the domain of health care.

4 DOMAIN SPECIFIC

CHALLENGES

What are the speciﬁc challenges to implementing an

Organic Computing system in health care? The sad

point is: traditional Organic Computing techniques

are not directly applicable to this domain. The prob-

lem is, that most Organic Computing approaches

make two assumptions of the systems and its com-

ponents agents. The ﬁrst assumption is, that all par-

ticipants/agents share a common goal. The second

one is, that there are no “malicious” parties involved.

This means, all agents work for common interests and

no agent tries to gain only beneﬁts for himself while

causing major drawbacks for all others. This arises a

number of challenge, which must be mastered for suc-

cessfully integrating Organic Computing in the do-

main of health care:

1) Individual Goals

Whenever agents of an Organic Computing system

are personalized to individual persons, they in general

won‘t share a common goal. In the example described

above, agents owned by patients, medical staff and

administration will have different goals. Patients are

interested in treatment schedules, which are compat-

ible with their leisure times, medical employees are

interested in compact working times and the clinic‘s

administration is interested in maximum proﬁts i.e.

no canceled treatments. These conﬂicting goals have

to be balanced and weighted in a meaningful manner.

It must also be assured, that security mechanisms pro-

hibit individual persons from getting beneﬁts on the

cost of drawbacks for multiple others. So it will be

a challenge for the next generation of Organic Com-

puting algorithms to be able to cope with individual

goals and requirements.

2) Sensitive Data

Personal data is sensitive. Medical data is even more

sensitive. Therefore privacy and security is a prime

requirement for every system, which handles data in

a medical care scenario. On the other hand, the Or-

ganic Computing paradigm strongly relies on inter-

action and exchange of data between system com-

ponents/agents. New and optimal conﬁgurations are

computed at runtime jointly by all agents. This is only

possible, because of information exchange and reﬂec-

tion of the exchanged data. For health care it will be

a challenge to adapt Organic Computing algorithms

such that only for reconﬁguration and planning nec-

essary data is exchanged and that sensitive data can

be kept private wherever and whenever possible.

3) Behavioral Guarantees

Health care is a highly critical domain. Whenever a

system is allowed to autonomously make decisions,

which have effects on the medical treatment of a pa-

tient, it must be assured that these decisions may

never pose a thread to the health of the patient. This

problem can be solved by applying formal methods

for analyzing the systems. They allow for rigorously

proving, that the system will always fulﬁll some be-

havioral guarantees. For Organic Computing such

analysis is much more difﬁcult, because it can often

not be anticipated at design time in which environ-

ments/scenarios the system will eventually be asked

to (self-)adapt to. Therefore new analysis methods

must be developed, which allow for giving behavioral

guarantees for Organic Computing systems.

4) User Trust

This is possibly the most important challenge. Or-

ganic Computing systems can achieve a lot of bene-

ﬁts from a global point of view. The beneﬁts result

from their ability to self-organize and self-adapt. Im-

provements become typically visible on system wide

performance metrics. For local agents, (re-) conﬁgu-

ration is not necessarily beneﬁcial or even traceable.

This will also affect the user, who own the agents. If

users are often confronted with decisions/planningsof

the system, which they can not understand (and from

which they don‘t beneﬁt), then they will likely not ac-

cept the system. This must not happen. So it will be a

challenge to provide algorithms, which provide users

with enough information to understand and accept the

decision of the system (without violating privacy con-

straints).

5 CONCLUSIONS

Organic Computing is a very promising new method

for construction of modern software systems. It has

provento be an superior architecture in many domains

like trafﬁc control, sensor networks or production au-

tomation. Organic Computing is also very promising

for a variety of user-intensive scenarios, where indi-

vidual users own individual agents. The purpose of

these agents can be to assist, support and/or guide

their users. One scenario in health care is a plan-

ning/scheduling system in a rehabilitation clinic. An

organic system can perform in this context much bet-

ter than traditional approaches. On the other hand

constraints, which arise in this domain – like con-

current/individual goals of users, privacy, behavioral

guarantees and user-trust – require new OrganicCom-

puting algorithms and techniques. Developing such

algorithms and methods can have a signiﬁcant im-

pact on many domains and open new opportunities

and functionalities.

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