From Safety to the Risk Management Cycle

Journal of Medical Safety 2009 (2) p.**- p.** May, 2009

From Safety to the Risk Management Cycle

Dirk Proske University of Natural Resources and Applied Life Sciences, Vienna, Institute of Mountain

Risk Engineering, Vienna, Austria

Abstract

The paper describes the development from the term “safety”, towards numerical expressions of it as

“risks” and finally the emerging concept of “quality-of-life”; in order to prove the fulfilment of the

requirement of “safety”. The paper starts with a definition of “safety”, but discusses also roughly the

term “optimal safety” and its limitations. The term “safety” is then transferred into the parameter of

“risk”. Since the earliest numerical expressions of “risk” the concept has experienced significant

development and many different risk metrics have been introduced subsequently. The evolution of

risk parameters and the consideration of optimal safety have finally yielded to the development and

application of “quality-of-life” parameters. It is worthwhile to note that this development has been

observed in many different scientific fields such as economy, engineering, social sciences and

medicine. However, the application of “quality-of-life metrics” yields to the same problem as using

the term “safety”. For a start it is extremely difficult to express “safety” in formal numerical

expressions. This is an unsatisfying situation since only formal decisions are testable. In contrast to

the common numerical time-invariant proofs of “safety” or the ultimate goal of “optimal safety”, the

new concept of “risk cycles” considers a never-ending development and change of conditions and

actions and impressively illustrates the limitation of common safety concepts. Therefore in the last

years the terms “risk management” and “risk cycle” have become widely applied.

1 Introduction All human activities have to fulfil the requirement of safety. Such requirements can be found in the earliest law collections such as the one by Hammurabi (Mann 1991). Therefore safety concepts were introduced historically in many technical fields; the first application of metrical global safety factors can be traced back to Philo von Byzantium around 300 B.C (Shigley & Mischke 2001). Since this time many different numerical safety concepts have been developed

using different metrics, such as probability measures or risk measures. An overview is given in Proske (2008). However this development of substitutes has yielded to a loss of the original goal, the provision of “safety”. 2 Concept of safety The term “safety” S is often defined as a situation with a lower risk R compared to an acceptable risk:

existing permitted existing permitted

R R SR R S

≤ →> → (1)


or as a situation “without any danger impending”. Other definitions describe safety as “peace of mind” (Proske 2008). The former definition which uses the term risk is already based on a substitution; therefore the latter term using “peace of mind” is a better definition. The author considers “safety” to be the result of an evaluation process of a certain situation done by every system that is able to perform a decision making process, such as animals, humans, societies or computers. Whereas computers entirely use formal numerical representation, humans and societies may use informal measures and include cognitive errors. Whether it is true or not that humans and societies can visualise more than just system borders in formal models is an important issue but will not be discussed here (Proske 2008, Gigerenzer 2004). However since all decisions are finally done by humans, their “safety” is understood as a feeling. Furthermore the decision-making process deals with the question, whether resources should be spent to decrease hazards and danger to an acceptable level. In

other terms “safety” is a feeling, which describes that no further resources have to be spent to decrease any threats. If one considers the term “no further resources have to be spent” as a degree of freedom of resources, one can define “safety” as a region of a function which includes the degree of freedom of resources. Furthermore one can assume, that the degree of freedom is related to some degree of distress and relaxation. Whereas in safe conditions relaxation occurs, in dangerous situations a high degree of distress is clearly reached. The possible shape of the function between degree of relaxation which ranges from “danger” to “peace of mind” and the value of the function as degree of freedom of resources is shown in Fig. 1. It is assumed here, that the relationship is non-linear with at least one region of over-proportional growth of the relative freedom of resources. In Fig. 1 this region of maximum growth (point of inflection) is defined as the starting point of the safety region:

{ }| ( ) 0S x f x′′= = (2)

Fig. 1. Assumed function shape between degree of relaxation and relative freedom of

resources

The degree of relaxation (DoR) can be evaluated based on a function considering a number of influence variables a, b, c,…:


( , , , ...)DoR f a b c d= (3)

These influence parameters need to be chosen. As already mentioned, the term “safety” considers subjective effects, such as trust, control or benefit. Wojtecki & Peter (2000) have tried to introduce some numerical equivalents for such conditions and state that trust may shift the individual acceptable risk by a factor of 2,000. That means, if one convinces people through dialogue that a house is safe, a much higher risk (no resources are spent) will be accepted, whereas with only a few negative words trust can be destroyed and further resources for protection are spent. Many additional factors, such as voluntariness, benefit, control, age and experience can be considered (see Proske 2008 or Covello et al. 2001). The multi-variability indicates that the mathematical formulation of such a degree of distress and relaxation is complicated and often the most important factors are identified by surveys. At this point we should also look at “optimal safety”. This term is widely used to assess the efficiency of certain protection measures. Mostly the Pareto criteria or the Kaldor-Hicks

compensation tests are used for the efficiency assessment (Pliefke & Peil 2007). However such measures are strongly based on formal numerical expression and it is doubtful that such theories can be directly related to the issue of safety. Furthermore, in this instance “optimal safety” is defined as a condition, which yields to a maximum performance of humans, not a maximum utility. Such maximum performance can be described in relation to different degrees of stress and relaxation by the Yerkes-Dodson-curve and is shown in Fig. 2. It can be shown in the same diagram format as Fig. 1 (Proske 2008). Finally one may argue that maximum human performance and maximum utility are the same. Indeed, the major differences are the time horizons which cause completely different results (Münch 2005, Proske 2008). The question remains, if “optimal safety” based on numerical expressions is indeed related to “optimal safety” assumed by individual humans and human societies.

Fig. 2. Function between degree of relaxation and relative freedom of resources

Fig. 2 indicates that humans do not reach a maximum performance under extreme safe conditions or high degrees of freedom of resources. Instead, humans tend to return to slightly unsafe regions leading to benefits not illustrated in the figure. Therefore a time-invariant optimal safety can not be computed. This fits very well to the statement by Arrow et al. (1996), that

no risk based decisions are taken by humans; only risk informed ones. However in engineering sciences, subjective elements are usually neglected and only formal numerical expressions are used, mainly to fulfil liability requirements. Here mainly risk parameters are used as numerical expressions of safety. Some risk parameters will be introduced in the next section.


3. Risk Parameters 3.1 Mortalities

The risk parameter of mortality corresponds with the classical definition of risk, as it can be found in many references:

R P C= ⋅ (4)

Here, risk R is defined as a product of the probability P of occurrence of an incident with some negative consequences and the extent of

the consequences C. Different units are possible for the two components. This risk measure is probably the oldest risk parameter (Proske 2008).

Fig. 3. Example mortalities for various situations/actions (Proske 2008)

As an example Fig. 3 lists some of the frequencies of death for people in various actions. In Proske (2008), a collection of over 125 values for such mortalities in various situations or actions can be found. Other adaptations of mortality models using distances, areas, volumes, toxicity measures and weights are also possible and in use. When comparing such mortalities, one has to consider the basic population and the date of data gathering. However it seems to be that the highest risks are risks of social failure, since in general the infant mortality in Mali could be significantly reduced and War should not be a common occurrence in human societies. Unfortunately the parameter does not give evidence of how frequent a person was exposed to a certain action over a time period. For example people are exposed much longer to building

failure then to airplane failure over the course of a year (Fig. 3). In order to improve the quality of the risk measure, a calibration of time is necessary. This risk measure is named “fatal accident rate” which usually relates to the number of fatalities over a standard time of 108 hours. Example values can be found in Proske (2008). 3.2 Family of the F-N-diagrams Mortalities and fatal accident rates do not consider the extent of specific singular accidents. The figures will be the same for an accident with one fatality which occurs one thousand times, and an accident with one thousand fatalities which occurs only once. Experience has shown that people differentiate strongly between these two cases in regards to the subjective judgment. Such subjective risk aversion can be shown in F-N-


Diagrams. The first of these diagrams were developed by Farmer (1967) for nuclear power radiation. F-N-Diagrams became very famous in the so-called Rasmussen-report in the beginning of the 70`s of the last century (Proske 2008). Since the introduction of the first diagrams, a huge variety of such diagrams has been developed using different units such as time, radiation, energy and so on. A summary of various representations of F-N-Diagrams can be found in Jonkman et al. (2003); Ball & Floyd (2001); and Proske (2008). The proof of “safety” can be done graphically in such diagrams (Ball & Floyd 2001, Proske 2008). 3.3 Lost life years The family of the F-N-Diagrams is excellent for the representation of technical and natural risks, as in the cases of risks where high numbers of casualties are possible. In cases of health risks, the situation is more difficult. Additionally, the age of the person concerned is not considered in F-N-Diagrams or mortalities, but is usually considered during subjective judgement. For example the death of a person of 90 years of age, caused by an illness, will be judged subjectively different, than the death of a young person. The risk parameter of the lost life years (LLE) considers this effect. The parameter is defined as the difference between average life expectation without the analyzed action/situation and the average life expectation including the action/situation. This parameter is widely used in the field of medicine, such as regarding cancer treatment. Cohen (1991) collected lost life days for various diseases and various social circumstances. Further data can be found in Proske (2008).

In addition to the loss of life years, it is possible to consider health reductions during a life time and to calculate the equivalent loss of life time. The terms quality adjusted life years (QALY), disability adjusted life years (DALY) or health years are used equivalently. Fig. 4 clarifies these terms. (Hofstetter & Hammitt 2003, Proske 2008). In developed countries, the share of disability adjusted life years amounts to about 10 % of a life time, and in some of the African countries it is almost 50 % (Proske 2008). Such numbers again indicate a failure of social structures. This situation is demonstrated in detail by Fig. 5, where one can find poverty, poor social status or early school drop out as the highest risk factors. Again these are social failures. Therefore primarily we can state, that the highest risks to humans are social failures. Secondly we need some risk parameters which are able to consider the status of the social systems. 4. Quality-of-life Parameters The building and development of social structures is closely connected with the term quality-of-life. Although the famous social critics of the 19th century did not yet know the term quality-of-life, they already regarded the improvement of the circumstances of living as the main motivational force of human development. One of the first definitions of quality-of-life came from the field of social charity science (Noll 1999). It was the economist Pigou (1920) who coined the term ‘quality-of-life’ at the beginning of the 20th century and who brought the term into academic discussion as a target to reach for social actions and as a measure of individual well-being.

Fig 4. Representation of the concept of lost life years (Hofstetter & Hammitt 2003).


Fig. 5. Lost life days in various situations according to Cohen (1991)

The development and usage of the term quality-of-life, has since then, not only touched economy and social science, but has also been introduced

to many other areas, e.g. medicine. In 1948, the World Health Organization termed `Health` as a condition of absolute physical, mental, and social


well-being (WHO 1948). Over more than the last fifty years, this definition has widened aims and criteria for actions of physicians. Not only somatic aspects of health and illness, but also psychological and social aspects, the patient’s well-being and their capacity to act are part of the physician’s duty. This can all be summarised as quality-of-life. “Health-related quality-of-life means a psychological construct, which describes the physical, psychological, mental, social and functional aspects of the well-being and the function capacity of the patients from their view” (Bullinger 1996). Further definitions of quality-of-life can be found in WHOQOL-Group (1994), Frei (2003) or Proske (2008). To comply with this definition new target metrics (now known as health related quality-of-life measure instruments) have been introduced. The aim of this effort was to make quality-of-life measurable and therefore making treatment efficiency controllable. The application of quality-of-life parameters is nowadays widespread in preventive medical check-ups, in therapy research, quality security and the health economy. Thousands of scientific publications dealing with this issue are now published every year (Bullinger

1996). These scientific strains led to the development of over 1,500 quality-of-life parameters in the field of medicine (Ahrens & Leininger 2003, Frei 2003, Porzsolt & Rist 1997). However the huge number of numerical measures already indicates an emerging problem. According to the respective definition, quality-of-life depends on a number of variables, which are partly hard to seize numerically. To visualise this, table 1 shows input variable numbers, for the numerical estimation of quality-of-life parameters for psychiatric patients, which have been collected tabular. The numbers of input variables for the quality-of-life parameters differ tremendously. Even the assembly of various quality-of-life parameters for identical questions reveals difficulties in the fixing of variables with a functional connection. In some publications, the possibility of a formal metrical description of quality-of-life is categorically excluded (Küchler & Schreiber 1989). Fig. 6 shows the high dimensionality of the problem. However if one agrees with this statement one loses the possibility to evaluate the efficiency of mitigation measures in a formal numerical way.

Table 1. Quality-of-life measure instruments for psychiatric patients (Frei 2003) Quality-of-life measure instruments number of parameters

Social Interview Schedule (SIS) 48 Community Adjustment Form (CAF) 140

Satisfaction of Life Domain Scale (SLDS) 15 Oregon Quality-of-life Questionnaire (OQoLQ) 246

Quality-of-life Interview (QoLI) 143 California Well-Being Project Client Interview (CWBPCI) 304

Quality-of-life Questionnaire (QoLQ) 63 Quality-of-life Index for Mental Health (QLI-MH) 113

Quality-of-life in Depression Scale (QLDS) 35 Smith-Kline Beecham Quality-of-life Scale (SBQoL) 28

Quality-of-life Enjoyment and Satisfaction Questionnaire (Q-LES-Q) 93


Fig. 6. Dimension of quality-of-life according to Küchler & Schreiber (1989)

5. Risk Management Cycle The evolution of the risk management process started with the definition of safety, substituting the term safety with the numerical representation called risk. Observing that the highest risks are social risks in different risk metrics; the term risk has been transferred into quality-of-life measures. Such quality-of-life measures may indeed be applied to indicate optimal safety. A nice example, either for the case of structural improvement of bridges or for the application of adjuvant therapy is shown in Proske (2008). However since the numerical expression of quality-of-life is represented by different metrics, which causes problems, how useful are such investigations? On the other hand resources are indeed limited and have to be spent carefully. So what can be done? Parallel to the development and application of quality-of-life parameters, the focus on risk management cycles has also increased in the last decades. This can be seen, for example in a wide range of codes and recommendations which deal with this issue, not only in engineering, but also

for the management of organisations (AS/NZS 2004, COSO 2004, IRGC 2005, Treasury Board of Canada Secretariat 2001, UK Cabinet Office 2002) or natural hazards (Kienholz et al. 2004). The difference between pure risk assessment using either certain numerical risk parameters or quality-of-life parameters is the consideration of a permanent change of conditions and risks. There will be no optimal solution, since the boundary conditions also change and all mathematical models proving an optimal solution simply fail by realising that assumptions and conditions are subject to change (SCM 2007). This constant change is visualised in Fig. 7 as the Risk Management Cycle. The possibility of failure is already considered in its basis. As a conclusion all of the mentioned numerical substitute measures for safety, such as risk measures or quality-of-life measures may be applied, although with caution because their validity is limited to certain conditions. However, such analysis can be repeated regularly and irregularly.


Fig. 9: Integral risk management concept as a cycle according to Kienholz et al. (2004)

6. Conclusion Safety is a general requirement for all human activities. Therefore it has to be decided in advance whether a situation or action will be safe. To prove the condition of safety formal numerical measures have to be introduced. The most common measures to describe safety numerically are risk measures. However the evolution of risk measures towards more realistic ones has shown that quality-of-life measures may perform better then pure risk measures, since they consider a wider range of conditions. However quality-of-life brings us back to the original problem, how can such constructs be represented numerically? At this point one should disregard the idea of a finished safety assessment and consider it as a permanently ongoing process. Although that is not the original idea of risk management – it was merely to widen the evaluative focus from one scientific field to an interdisciplinary approach, risk management yields to the risk management cycle with never-ending development. If one returns to the original term of safety which involves so many subjective elements usually not considered by numerical models, we clearly see that our evaluation process may fail from time to time. Such correctors are needed since our prognosis capacity for biological or social systems is still very limited as current economical developments impressively show (Proske 2008).

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From Safety to the Risk Management Cycle