Nada Nadhrah
email: n.nadhrah@pgr.reading.ac.uk

Vaughan Michell
email: v.a.michell@henley.reading.ac.uk

Workaround Motivation Model (WAMM): An Adaptation of Theory of Interpersonal Behaviour

Keywords: Workaround, Behaviour, Motivation, Healthcare, Patient safety, Consequences, Norm

The study of workarounds (WA) has increased in importance due to their impact on patient safety and efficiency. However, there are no adequate theories to explain the motivation to create and use a workaround in a healthcare sitting. Although theories of technology acceptance help to understand the reasons to accept or reject technology, they fail to explain drivers for alternatives. Also workarounds involve creators and performers that have different motivations. Models such as Theory of Planned Behaviour (TPB) or Theory of Reasoned Action (TRA) can help to explain the role of workaround users, but lack explanation of workaround creators' dynamics. Our aim is to develop a theoretical foundation to explain workaround motivation behaviour models with norms that relate to sanctions to provide an integrated Workaround Motivation Model; WAMM. The development of WAMM model is explained in this paper based on workaround cases as part of further research to establish the model.

Introduction

Healthcare professionals are continually exposed to new Information Systems (IS) that affects their daily working activities. Many healthcare workers fully use these systems; however, many choose to perform an alternative process or activity manually or via an alternative system. Some have retained old processes, and others have added additional or alternative activities to do their work. Deviations from the formal system or process, i.e. alternative activities are typically addressed in the literature as workarounds (WA) [START_REF] Halbesleben | Work-arounds in health care settings: literature review and research agenda[END_REF], [START_REF] Kobayashi | Work coordination, workflow, and workarounds in a medical context[END_REF], or sometimes deviations from dysfunctional systems [START_REF] Morath | To do no harm[END_REF], [START_REF] Zhou | CPOE workarounds, boundary objects, and assemblages[END_REF], [START_REF] Reason | Safety in the operating theatre -Part 2: Human error and organisational failure[END_REF]. We can define WA as "an alternative work process created by individuals or groups to achieve a benefit over the use of the existing processes" [START_REF] Nadhrah | Measuring physicians WA-Implications on Work Processes and Patient Safety[END_REF].

Many WA involve a single individual alternative and continuous process or action with a single actor or driver [START_REF] Nadhrah | A Normative Method to Analyse Workarounds in a Healthcare Environment: their Motivations, Consequences, and Constraints[END_REF], Our research identified a classification of four other types. A process WA involves more than one actor working together on a sequence of different activities. A compound WA is a variant of the process WA where more than one continuous process is involved with separate performers. A consequential WA exists where the results of a WA drive a further and separate WA process, often used to convert the WA outputs back to a form to feed into the formal process e.g. converting manual documents to automated form. These different types of WA have different motivation impacts referred to later. Many workarounds involve a single actor. However as workarounds are in general unique alternatives to a standard formal process or system they usually have a designer or workaround creator that defines the workaround [START_REF] Nadhrah | Workarounds: Risk or Benevolence for Patient Safety?: IGI Global[END_REF]. This creator is also usually the driver of the workaround. The performers in a workaround if separate from the creator can have a different behaviour in the way they are motivated to execute it. This has implications for the motivation to work around for example we may expect workaround creators motivations to execute the workaround to be higher than performers. These motivation implications will be discussed later. This paper seeks to develop a testable model of the factors affecting motivation to WA. Motivation is 'the reason or reasons one has for acting or behaving in a particular way' [START_REF] Weiner | Human motivation: Metaphors, theories, and research[END_REF]. A number of authors have investigated the motivation reasons to perform WA. We classify these into action motives (blocks) and goal motives for WAs [START_REF] Nadhrah | Workarounds: Risk or Benevolence for Patient Safety?: IGI Global[END_REF] Many authors see positive motivations to WA as a result of a block in normal expected work activities [START_REF] Halbesleben | Work-arounds in health care settings: literature review and research agenda[END_REF]. A block is a system disruption preventing the worker from completing a task or action as desired [START_REF] Halbesleben | Work-arounds in health care settings: literature review and research agenda[END_REF]. For example in order to save time, clinicians try to avoid blocks in the system and have an action motive to work around the block. Block motivated WAs range from the complex design of processes and systems, poor system usability, inadequate user training, inflexible clinical guidelines [START_REF] Halbesleben | Work-arounds in health care settings: literature review and research agenda[END_REF], [START_REF] Vogelsmeier | Technology implementation and workarounds in the nursing home[END_REF] and slow and time consuming processes [START_REF] Ash | Factors and forces affecting EHR system adoption: report of a 2004 ACMI discussion[END_REF]. Some users are motivated to create a WA to avoid safety features such as systems produced alarms. These are seen as inconvenient and time consuming. Overriding a system alarm that was designed intentionally to improve patient safety might be considered as a negative WA [START_REF] Vogelsmeier | Technology implementation and workarounds in the nursing home[END_REF], or deviation and should be eliminated. However, many WAs have a positive safety benefit, For example WAs may be motivated by a physician's superior local and timely event knowledge that improves the safety of the patient [START_REF] Niazkhani | Evaluating the medication process in the context of CPOE use: the significance of working around the system[END_REF]. A second motivating group is where there is a personal or professional benefit goal or improvement that could better be achieved by abandoning the formal process or system for a WA [START_REF] Nadhrah | Workarounds: Risk or Benevolence for Patient Safety?: IGI Global[END_REF]. Pernejad cites the example of a new system reducing communication between doctors and nurses ruining their original informal communication and forcing them to WA the system to restore the communications [START_REF] Pirnejad | Impact of a computerized physician order entry system on nurse-physician collaboration in the medication process[END_REF]. Overworked clinicians may simply wish to reduce their workload and stress and hence choose to miss out actions or perform alternative actions to those prescribed formally [START_REF] Nadhrah | A Normative Method to Analyse Workarounds in a Healthcare Environment: their Motivations, Consequences, and Constraints[END_REF], [START_REF] Nadhrah | Measuring physicians WA-Implications on Work Processes and Patient Safety[END_REF]. This often involves reversion to simpler previous habits or norms of behaviour established socially or in a team.

Motivation Models

Our aim is to identify which factors motivate people to the WA i.e. a general WA model i.e. most relevant factors that drive WA. There are two typestechnology acceptance model and general behavioural models.

2.1

Technology Acceptance models

Technology Acceptance Model (TAM). In TAM Davis [START_REF] Davis | Perceived usefulness, perceived ease of use, and user acceptance of information technology[END_REF] suggests the attitude of a potential user of a system has towards using it depends on the how useful they see it (perceived usefulness) and how easy it is to use the system (Perceived ease of use). This results in an attitude and behaviour to use or reject the system. However, we note that a workaround decisions is about accepting or rejecting only a very small part of a system/process and choosing an alternative ad-hoc system/process. We have seen earlier that WA's are driven by blocks to work or goal motives resulting in improvements/changes to the activity. If an automated system provides a block, effort needs to be increased to use it. We would expect ease of use to be considered low e.g. in the case of lack or training, system complexity, slow systems etc. [START_REF] Nadhrah | Workarounds: Risk or Benevolence for Patient Safety?: IGI Global[END_REF] compared with an alternative system. A system is ideally designed for ease of use e.g. by providing a record log, automatic communication and dissemination of data etc. However, if the automated system is perceived to be less useful personally to an individual they may favour an alternative WA.

TAM tells us why we might not use the automated system, but it does not tell us why we might choose to use a specific WA, manual or automated. TAM is a technology acceptance model and many WA's include both manual and other technology alternatives [START_REF] Nadhrah | A Normative Method to Analyse Workarounds in a Healthcare Environment: their Motivations, Consequences, and Constraints[END_REF]. We also note that the professional and personal motives for perusing a WA [START_REF] Nadhrah | Workarounds: Risk or Benevolence for Patient Safety?: IGI Global[END_REF] are not covered by TAM. A much deeper understanding of the behaviour is required. Unified Theory of Acceptance and Use of Technology (UTAUT). The Unified Theory of Acceptance and Use of Technology (UTAUT) proposed by [START_REF] Venkatesh | User acceptance of information technology: Toward a unified view[END_REF] extended TAM to include four new constructs. (1) Performance Expectancy is a broader version of usefulness. (2) Effort Expectancy, a broader version of ease of use identifies the extent of effort involved. (3) Social Influence measures of how much a user is influenced to use the system by people who are socially important to them [START_REF] Fishbein | Belief, attitude, intention and behaviour: An introduction to theory and research[END_REF]. Social influence relates to subjective norms of behaviour that influence individual decisions making [START_REF] Ajzen | From intentions to actions: A theory of planned behavior[END_REF]. Our research suggests social and indeed power influence is important to the workaround decisions. In previous work we identified two types of actors in the WA process, the creator and the performer [START_REF] Nadhrah | A Normative Method to Analyse Workarounds in a Healthcare Environment: their Motivations, Consequences, and Constraints[END_REF]. Often WA are created and performed by the same person, but in process and compound WA's [START_REF] Nadhrah | Workarounds: Risk or Benevolence for Patient Safety?: IGI Global[END_REF] a separate and often senior creator such as clinical consultants, senior surgeons etc. is present. Creators often allocate the WA to more junior actors to perform. The performer is the individual executes the WA process in order to achieve the task goal. They often have no decision in creating the WA and no choice to participate in it. Hence the social norms for a senior surgeon WA creator are likely to be very different from the junior doctor and different again for the nurse or porter that executes WA and need to be included in the model. ( 4) Facilitating conditions relates directly to whether the user behaviour is realistic and feasible [START_REF] Gagnon | An adaptation of the theory of interpersonal behaviour to the study of telemedicine adoption by physicians[END_REF], [START_REF] Bergeron | Determinants of EIS use: Testing a behavioral model[END_REF], [START_REF] Venkatesh | User acceptance of information technology: Toward a unified view[END_REF], [START_REF] Ajzen | The theory of planned behavior[END_REF]. For example resources, user, experience knowledge and training. Facilitating conditions relate to many of the drivers of workarounds e.g. the availability of resources, which is known to cause technology blocks [START_REF] Nadhrah | A Normative Method to Analyse Workarounds in a Healthcare Environment: their Motivations, Consequences, and Constraints[END_REF] or lack of training and experience [START_REF] Nadhrah | A Normative Method to Analyse Workarounds in a Healthcare Environment: their Motivations, Consequences, and Constraints[END_REF], [START_REF] Ferneley | Resist, comply or workaround? An examination of different facets of user engagement with information systems[END_REF]. UTAUT assumes a user can accept or reject technology on a voluntary basis. Very often system use is mandatory and hence some WA's allow for a manual output to be converted back to system inputs, but the motivation for the WA remains. Some senior staff may mandate the WA must be done by virtue of their influence. Social influences, i.e. subjective norms for WA need to include the norms and norm relationships between WA creator and driver and those of the performer. Whilst TAM and UTAUT have limitations in their focus on technology they provide a useful basis for WA behaviour to build on. We now turn to behavioural models to help build the model.

Behavioural models

We discuss 3 key motivational behaviour models. WA's as deviations are a decisions between the formal obligation to execute a formal process and the option to execute an informal process. We need to understand the use of a system and the extent of freedom to do something different in studying WA. Behavioural models are needed in this context to capture the formal and informal social rules for behaviour and sanctions that influence the selection WA over the formal process and their consequences.

The theory of reasoned action (TRA) and Theory of Planned Behaviour (TPB). The theory of Planned Behaviour (TPB) is driven from TRA and assumes three determinants to behaviour: : attitude, subjective norm, and perceived behavioural controls [START_REF] Ajzen | The theory of planned behavior[END_REF]. Theory of Planned Behaviour (TPB) is driven from TRA assumes independent determinants of; intention. Attitude and subjective norm, similar to TRA, TAM, UTAUT, but perceived behavioural control (PBC) is an added [START_REF] Fishbein | Belief, attitude, intention and behavior: An introduction to theory and research[END_REF]. Perceived behavioural control relates to the level of influence a user has on the behaviour and their ability to change it-in our case the workaround.

Theory of Interpersonal Behaviour (TIB).

Triandis' model considers direct variables useful for workaround behaviour: Intention (I), Habit (H), and Facilitating Conditions (FC). Intention and facilitating conditions were discussed earlier. Habit (H) is "situation-behaviour sequences that are or have become automatic, so that they occur without self-instruction" [START_REF] Bergeron | Determinants of EIS use: Testing a behavioral model[END_REF]. Habit is an important factor in WA motivation as many users prefer to use what is familiar to them often in place of the formal system. WA agents often revert back to old habits e.g. manual paper and pen vs automated systems as it is perceived to be easier and less effort [START_REF] Varpio | Working off the record: Physicians' and nurses' transformations of electronic patient record-based patient information[END_REF]. Alternatively they may use newer habits e.g. their iPad in preference to a desktop application [START_REF]Workflow concerns and workarounds of readers in an urban safety net teleretinal screening study[END_REF] or their iPhone as a simpler communication tool in a WA [START_REF] Nadhrah | Measuring physicians WA-Implications on Work Processes and Patient Safety[END_REF]. Indirect variables (via intention) are: Affect (AF), Perceived Consequences (PC), and Social Factors (SF) [START_REF] Bergeron | Determinants of EIS use: Testing a behavioral model[END_REF]. Affect, relates to the individual's emotional feelings of pleasure, displeasure, toward a given behaviour [START_REF]Values, attitudes, and interpersonal behavior[END_REF]. Perceived behavioural consequences relate to the perception of the overall impact/benefit of the action behaviour which is the second determinant of intention [START_REF] Gagnon | An adaptation of the theory of interpersonal behaviour to the study of telemedicine adoption by physicians[END_REF]. The perception of workaround consequences again relates to the type of actor (creator, performer) discussed earlier. For example, a physician might avoid using a cumbersome system he dislikes by writing a discharge summary manually as a WA and perceive consequential benefits of time and effort saved [START_REF] Nadhrah | A Normative Method to Analyse Workarounds in a Healthcare Environment: their Motivations, Consequences, and Constraints[END_REF]. However, it may be a less happy and enjoyable task for the WA performer who is forced to comply with the WA and convert the text to electronic form. Perceived social norms unlike subjective norms in (TPB) or social factors adopted in UTAUT discussed. It includes two normative dimensions; normative and role beliefs [START_REF] Gagnon | An adaptation of the theory of interpersonal behaviour to the study of telemedicine adoption by physicians[END_REF]. Normative beliefs consist of the internalisation by an individual of referent people or groups' opinion about executing the behaviour [START_REF] Gagnon | An adaptation of the theory of interpersonal behaviour to the study of telemedicine adoption by physicians[END_REF], whereas role beliefs refer to the rules of the role or behaviour formally expected of the individual [START_REF] Gagnon | An adaptation of the theory of interpersonal behaviour to the study of telemedicine adoption by physicians[END_REF]. An additional perceived social norm added to a TIB application is personal normative (PN) belief [START_REF] Francis | Report of the Mid Staffordshire NHS Foundation Trust Public Inquiry: Executive Summary[END_REF] representing the individual's personal rules of behaviour regarding whether they should perform the behaviour, i.e. do my personal rules suggest I should perform the workaround [START_REF] Gagnon | An adaptation of the theory of interpersonal behaviour to the study of telemedicine adoption by physicians[END_REF]. Based on the above we use TIB as the base model for WA motivation. Our previous work has identified that WA's are often discouraged and sanctions can exist. Therefore our model needs to include the impact of sanctions on motivation to the workaround. Also our research has identified the importance of the role of professional rules or norms. For example a workaround was justified if it met the professional clinical norms better than the original system [START_REF] Nadhrah | Workarounds: Risk or Benevolence for Patient Safety?: IGI Global[END_REF]. The next section discusses these 2 additional factors.

Extension of Consequences and Norms Workaround Factors

Perceived Behavioural Consequences. Triandis' factor Perceived consequences (PC) refers to the probability that a given consequence or expected benefit will follow from performing the behaviour. The value of the consequence is the "impact attached to the consequence" [START_REF] Bergeron | Determinants of EIS use: Testing a behavioral model[END_REF]. The higher the expected value of the act, the more likely the person will intend to perform it [START_REF] Bergeron | Determinants of EIS use: Testing a behavioral model[END_REF]. This is an important factor for WA's as a WA is perceived as a 'deviation in medicine with potential risk and safety consequences.

Hence the risk must be balanced by a superior benefit. Triandis' model does not differentiate of specify specific consideration and focuses on consequences related to the individual. For example analysis of Gagnon's paper Gagnon, et al. [START_REF] Gagnon | An adaptation of the theory of interpersonal behaviour to the study of telemedicine adoption by physicians[END_REF] using TIB suggests the questions related to con sequences refer to increased or reduced process time, knowledge and workload and impact on roles. There are two separate dimensions of consequence/value related to the person, as an individual impact, and related to the process and professional medical benefit [START_REF] Nadhrah | Workarounds: Risk or Benevolence for Patient Safety?: IGI Global[END_REF]. As a workaround is seen as a deviation it can have very high consequences for the individual that need to be considered separately from the benefits regarding process goal. Creating a WA may primarily save physician's time (personal goal) but it might also breach patient confidentiality policy (organisational goal) [START_REF] Nadhrah | Measuring physicians WA-Implications on Work Processes and Patient Safety[END_REF]. This implies a process impact in terms of patient safety and error. A clinician's professional responsibilities ensure the consequential value is not limited to individual's feelings such as satisfied, distressed, and unhappy, but related to a process/professional dimension e.g. may save time and may have a negative e.g. safety impact on the patient. The need to focus on the patient and professional/organisational goals, has been highlighted in the Stafford enquiry in the UK [START_REF] Francis | Report of the Mid Staffordshire NHS Foundation Trust Public Inquiry: Executive Summary[END_REF]. Unlike most behaviour models that focus on industry the health service is very person focused and safety is critical so we need to include this component.

The overall value or benefit to the clinician WA user will be determined by their feelings about both personal and professional/organisational value and benefit resulting from the WA. We divide consequences construct into two types: personal value and organisational goal or process impact value. For personal value we measure the individual's expected value in creating/ performing WA e.g. 'for me, creating/ performing WA in my practice would give me the greatest personal benefit in terms of personal WA benefits e.g. save me time, reduce stress, increase my knowledge, increased control be relaxing or satisfying and dissatisfying. So that will cover the positive and negative dimension of WA perceived personal value. The process impact value can be addressed from two benefit perspectives the impact on the process and impact on the individual client or patient. For example, a typical question to test this might be 'creating/ performing WA in my practice would provide an overall benefit to professional/organisational goals of'(1) reduce time; (2) allow to update my knowledge; for individual perspective; (3) reduce patients risk; and (4) reduce patient waiting times; (5) improve team work. Professional norm (PN). Our research has highlighted that an important motivator of a WA is the perceived professional rules and cultural training which influence clinicians to choose a workaround as it better meets these professional norms than the formal system. For example, there is an error in alternative drug system in pharmacy department: alternative drug is usually drop down list appears to the user when there is certain medicine not available in the stock. Although it has been updated and the alternative medicine is available, yet there is a system error message papers to physicians preventing them from completing the request. They have to call the pharmacist and indicate the problem, so the pharmacist due to their professional norm they will perform the workaround and repeat the request to order alternative drug. Professional norms are rule based 'components of the professional behaviour 'socialized through education and training, maintained by professional colleges, and have a profound impact on professional work' [START_REF] Kirchhoff | Norm Systems in Professional Work[END_REF]. We propose two aspects to test this relationship. (a) Questions relating to improvements or reduction in professional standards related to the WA users profession. (b) Questions relating to client or patient benefits.

WAMM an integrated framework of workaround motivation

A model of WA motivation must include the key elements of technical (section 2.1above) and behavioural (section 2.2 above) models in addition to perceived consequences and norm modification constructs. This section details the reasoning in developing the Workaround Motivational Model (WAMM). Each of the factors selected will need to be tested in relation to the intention to create or use a WA. As mentioned earlier unlike normal motivation models the WA stakeholder role e.g. as a creator of WA, user and level of use in WA must be recorded as this is expected to reflect the answers to many of the factor questions.

As mentioned, the theoretical foundation in the model is TIB which involves variables: intention, affect, social factors (normative beliefs and personal beliefs), perceived behavioural consequences, habits, and facilitating conditions. The following hypotheses will need to be tested (1) Affect is a predictor of healthcare professionals' intention to create or perform WA; (2) Perceived consequences are predictors of healthcare professionals' intention to create or perform WA; (3) Perceived social norms are predictors of healthcare professionals' intention to create or perform WA; (4) Personal normative (PN) belief is predictors of healthcare professionals' intention to create or perform WA; (5) Facilitating conditions are predictors of healthcare professionals' intention to create or perform WA; and (6) Habit are predictors of healthcare professionals' intention to create or perform WA (Fig. 1).

Fig. 1: Workaround Motivational Model (WAMM)

There are four constructs added: perceived behavioural controls (discussed in TPB in section 2.2). Perceived ease of use (discussed in TAM section 2.1), perceived behavioural process value consequences (discussed in section 2.2), and professional norm (discussed in section 2.3). The following hypotheses will be tested as part of the integrated model: [START_REF] Nadhrah | A Normative Method to Analyse Workarounds in a Healthcare Environment: their Motivations, Consequences, and Constraints[END_REF] perceived behavioural controls is a predictor of healthcare professionals' intention to create or perform WA; (8) Perceived ease of use is a predictor of healthcare professionals' intention to create or perform WA; (9) perceived behavioural process value consequences is a predictor of healthcare professionals' intention to create or perform WA; and (10) Perceived professional norm is a predictor of healthcare professionals' intention to create or perform WA. Hypothesis 7 and 8 are driven from TPB and TAM respectively. Hypothesis 9 and 10 are going to be developed because they are new to the model and needed to be tested.

Case Examples from practice

At this point the model questionnaires have been developed and we are in the process of gathering data from questionnaires for analysis of the factors and relationships. Timescales preclude reporting at this stage and hence we demonstrate the use of the model by showing how the variables relate to our previous case studies. The use of the WAMM model can be illustrated by a case example from our research. Many WA's are created by the same individual that performs WA and hence use of WAMM would be expected to be straightforward. However, with more complex WA's such as process and compound WA's there is a creator and separate performers. The WA motivating factors with respect to a creator of a WA are expected to be different to the actors recruited in by WA creator to perform the process. We would expect and need to test the possible positive correlations in this activity. For this reason we choose a compound WA case to show how we might expect different results for the WAMM model The Discharge summary (DS) report process is a compound WA case study that has been identified in one of the hospitals [START_REF] Nadhrah | Measuring physicians WA-Implications on Work Processes and Patient Safety[END_REF]. The DS is an electronic report that must be written and authenticated within 24 hours from the decision to be discharged by the consultant [START_REF] Nadhrah | Measuring physicians WA-Implications on Work Processes and Patient Safety[END_REF]. Table1 below summarises the activities performed in the DS case vs the positive or negative influence of the motivation factors and how we suggest they may vary in relation to the actor types. There are two types of actors identified: creator and performer. Creator in that case is the physician who initiated and controls the WA process by insisting on writing a discharge report manually rather than via the system. This initiated a chain of activities and involvement by other actors in the process to convert the handwritten report back to the electronic forma in the system to release DS report. From our example case the main motivational factor is likely to be the physician's habit (the case suggested a key motivator for the WA was his habit of writing, not using eth system. The facilitating condition, the factors that make WA easy are likely to be access to and control of the staff needed to convert his manual script back into the format for the system, otherwise this negates the WA benefit. The perceived behavioural controls would be expected to be high motivating factor for the physician as he is responsible and controls the patient's discharge. In terms of affect, we would expect the physician to feel very positive that he does not need to type into the system, but can simply scribble manually at high speed. We would also expect a positive correlation for the personal value impact as the physician has reduced his personal stress in using the system. These points were mentioned by different physicians in reasoning their decision to write in paper sheet rather than the system. We would expect lower or negative correlation in terms of other factors. For example professional norms generally would insist on data entry. Process impact value is likely to be negative as more resources in terms of a nurse and porter are required for the WA and reduce the process benefits. The performer actors were nurse, medical secretary, and porter. As performers of WA, we would expect different correlations as the users are lower down the power spectrum of the hospital and the nature of some of their work roles e.g. a porter, suggest different motivations from say health professional. We would expect significant motivational variables to be professional norms, and process impact value. Professional norm here was highlighted in a relation to their responsibility to get the job done and the feeling that they must do the WA as it is has become part of their job. The perceived behavioural consequences include personal and process value. We would expect a reasonable positive correlation with process value impact as many of the performers mentioned being motivated to get the job done. Also, perceived social norm were emphasized in the interviews by performers i.e. that 'doctors have asked me to do this WA'. The social norm here is highlighted because doctors must be followed according to the performers in that case. The assumption here is to what extent doctors will be followed and what is the consideration for the WA impact on the patient and the rest of the team working in the hospital whom will affected by those WA behaviours. Space precludes detailing the remaining variables.

Summary and conclusion

This paper aimed to develop a theoretical foundation to explain WA motivation among healthcare professional. We explained the structure and different types of WA from the literature and our own research that had a bearing on motivational factors. We reviewed literature related to technology acceptance models and behavioural models used in healthcare context and showed how these models could be adapted to the WA motivation behaviours in healthcare. We identified additional variables to include as: perceived behavioural controls, perceived process value impact as pas part of the perceived consequences construct, and professional norm. We also identified the variation in motivational criteria that depends on the creator vs performer role in WA's. The creator role is an important factor that for obvious reasons is not included in most system and behaviour motivation applications and hence is important to identify. This study explained how these factors were integrated into the Workaround Motivational Model (WAMM). We applied WAMM factors to a compound WA example from our research to explain the type of results we might expect. These will be determined from the results of the questionnaires for creators and performers of workaround types that are currently being gathered ready for analysis. Future work is focused on completing and distributing the questionnaires, in testing heir relation to WA behaviour in healthcare context. We are particularly interested in the differences for compound WA processes and simple single actor workarounds.

Table 1 :

 1 DS workaround case study WAMM measure of expected positive/negative influence AF=Affect, PBC=Perceivied Behavioural Controls, H=Habit, FC=Facilitating Conditions, EU= Ease of Use, PNB= Perceived Normative Belifes, RNB= Role Normative Beleifs, PFN=Perceived Professional Norm, PV= Personal Value, PIV= Process Impact Value.

	Actor Type	Activity	AF PBC	H	FC	EU	Social norms PNB RNB	PFN	PV PIV
	Physician Creat or	Writing DS on a piece of Paper.	+ + + + +	+	+	-	-+
	Nurse	Perfo rmer	Nurse has to photocopy three copies.	-----	+	+	+ -+
	Medical secretary	Perfo rmer	MS has to type the information from paper into the computer.	-----	+	+	+ -+
	Porter	Perfo rmer	Porter has to transfer one copy to MS	-----	+	+	+ -+


John Krogstie
email: krogstie@idi.ntnu.no

Alexander Nossum

A Semiotic Approach for Guiding the Visualizing of Time and Space in Enterprise Models

Keywords: Quality of models, Spatial enterprise models, Maps, Semiotics

Even if geographical aspects such as location are included already in the Zachman framework (as the where-perspective), it is not common to have detailed geographical aspects included in enterprise models. Cartography is the science of visualizing geographical information in maps. Traditionally the field has not included conceptual relationships that you find in enterprise models. Both cartography and enterprise modelling have developed guidelines for obtaining high quality visualizations. SEQUAL is a quality framework developed for understanding quality of models and modelling languages based on semiotic theory. In cartography such frameworks are not common. An adaptation of SEQUAL in the context of cartographic maps called MAPQUAL has been presented earlier. Differences between quality of maps and quality of conceptual models, pointing to guidelines for combined representations have been performed, and we try in this paper to investigate the utility of these guidelines in a simple trial. The result of the trial is presented, indicating that it is possible to represent conceptual, temporal, and spatial aspects in the same models in many ways, but that the choice of main perspective should depend on participant appropriateness.

Introduction

A conceptual model is traditionally defined as a description of the phenomena in a domain at some level of abstraction, which is expressed in a semi-formal or formal visual language. An enterprise model can be regarded as a kind of conceptual model.

The field has spawn from information systems development and computer science with methodologies like Data Flow Diagram (DFD), Entity Relationship diagrams (ER) and more recently Unified Modeling Language (UML), Business Process Model and Notation (BPMN) and Archimate. The languages used for conceptual modelling largely contain nodes and links between nodes, and containment relationships. In enterprise modelling a number of perspectives to modelling are distinguished. For instance the Zachman Framework [START_REF] Zachman | A framework for information systems architecture[END_REF] describes 6 perspectives; What (material) it is made of, How (process) it works and Where (location) the components are, relative to one another, Who is involved, When are tasks done relative to each other and Why. In conceptual and enterprise modelling, we traditionally deal with what (data modelling), how (process modelling), who (organizational and actor modelling), when (behavioural and temporal modelling), and why (goal-oriented modelling). On the other hand the location aspect (where) is seldom dealt with in detail, although as we see e.g. in the development of BIM [START_REF] Succar | Building information modelling framework: a research and delivery foundation for industry stakeholders[END_REF] of buildings that topological aspects get more and more important to represent. Cartography on the other hand, focuses on aspects of location through maps. Maps at first sight appear to be very different from conceptual models. However, many similarities among these representations can be found.

The ultimate goal of the work is to develop an understanding of quality of enterprise models when also including geographical/topological constructs. To get to this we have earlier developed a framework for understanding and assessing quality of maps (MAPQUAL [START_REF] Nossum | Integrated Quality of Models and Quality of Maps[END_REF]), based on the SEQUAL-framework [START_REF] Krogstie | Model-based Development and Evolution of Information Systems[END_REF] for quality of models and modelling languages. Differences between SEQUAL and MAPQUAL are used to assess how combined geographical and conceptual models should be developed to achieve high quality models, and the aim of this paper is to report in experiences with modelling notations that takes into account both conceptual, topological and temporal issues.

In section 2, we present background on quality of maps and conceptual models. Section 3 describes the case and section 4 present a simple trial with two alternative visualizations of situations in the case-domain. In section 5 we sum up the experience and describe further work on an integrated approach.

Background and Related Work

A map is a kind of model. An underlying assumption has been that cartographic maps represent, primarily, geographic concepts. Some research has been put into applying cartographic visualization techniques as described by [START_REF] Bertin | Semiology of Graphics: Diagrams, Networks, Maps[END_REF] on general non-geographic information [START_REF] Van Der Aalst | TomTom for Business Process Management Proceedings CAiSE[END_REF][START_REF] Moody | Evaluating the Visual Syntax of UML: Improving the Cognitive Effectiveness of the UML Family of Diagrams[END_REF][START_REF] Skupin | Spatialisation Methods: A Cartographic Research Agenda for Non-geographic Information Visualization[END_REF] and the opposite, applying general information visualization techniques on geographic information. However, little work has looked on combining conceptual models with cartographic maps. Earlier work on quality of models and quality of maps described in MAPQUAL [START_REF] Nossum | Integrated Quality of Models and Quality of Maps[END_REF] has illustrated that the main semiotic levels related to quality of models as described in SEQUAL applies also to maps. There are also distinct differences, which is a challenge when you want to visualize both geographical and conceptual aspects.

The differentiation between language and model (map) are usually not discussed in cartography. There exists no tradition of defining the syntax for languages for making maps, although standardizations towards both symbol sets and rules for applying them exist. MAPQUAL recognize this and aims at investigating how existing cartographic research can be structured following the SEQUAL structure inspired by semiotics.

Generally the visualization in maps can be said to comprise three graphic primitives; point, line and area and relations between these (Points being within an area, line crossing an area or being the border of an area etc). This is inherently different from meta-meta models in conceptual modelling which usually comprise of only nodes and links between nodes, in addition to visual containment.

The main aspects of the basic MAPQUAL [START_REF] Nossum | Integrated Quality of Models and Quality of Maps[END_REF], extended to take into account additional aspects of indoor maps [START_REF] Nossum | Developing a Framework for Describing and Comparing Indoor Maps[END_REF] is described here.

Comparing Quality of Maps and Quality of Models

We here discuss the different levels of quality according to the semiotic ladder first described in the work of Stamper [START_REF] Falkenberg | A Framework of information system concepts -The FRISCO Report[END_REF].



Physical quality: The basic quality goal is that the model exists physically and is available to the relevant actors. Cartography is traditionally more geared towards making tangible representation of maps (i.e. printed maps) -although this is shifting towards more intangible representations for instance in a software environment (i.e. web mapping tools). SEQUAL focus much on guidelines for a modelling environment and different functionalities that it should provide. It should be noted that these guidelines are adapted to an information systems context, however, the guidelines should hold true for a cartographic environment as well. Also aspects of security (privacy) and currency as discussed in [START_REF] Nossum | Developing a Framework for Describing and Comparing Indoor Maps[END_REF] is relevant for both types of representations.  Empirical quality deals with comprehension when a visual model M is read by different social actors. MAPQUAL is significantly different from SEQUAL in this area. This is mainly due to the differences in meta-meta model discussed above. Colours are heavily used in cartography to separate different concepts from each other. In conceptual modelling the use of colours has been avoided to a large degree. Gopalakrishnan et al. [START_REF] Gopalakrishnan | Capturing Location in Process Models: Comparing Small Adaptations of Mainstream Notation[END_REF] suggest using colours more in conceptual models. The inherent topological attributes of cartographic concepts often restricts the freedom of layout modifications, such as choosing where a concept should be placed on a map. Guidelines for increasing empirical quality of conceptual models base themselves, mostly, on the freedom of layout, supported by guidelines for graph aesthetics. These guidelines are thus not directly applicable to a map. In cartography one could see the aesthetics and geographical attributes as orthogonal dimensions. Conceptual modelling and cartography share the background for the guidelines for empirical quality.  Syntactical quality: Is the model according to the syntax of the language. In cartography there is a lack of definition of formal languages in designing maps.  Semantic and perceived semantic quality is the relation between the domain, map/model and social actor knowledge and interpretation. Thus, this facet is assumed to be generally applicable for cartography as well as for conceptual modelling. In cartography the quality of the data, in terms of measure errors, is quite common to use as a semantic quality measure.



Pragmatic quality relates to that human interpreters understand the model. MAPQUAL does not include an extensive investigation in the research of human interpretation of maps in cartography, but recognise that there are many similarities between this and SEQUAL' focus on human interpretation of models. MAPQUAL and SEQUAL are thus more or less equal with respect to pragmatic quality, although with increasing focus on interactivity of digital maps [START_REF] Nossum | Developing a Framework for Describing and Comparing Indoor Maps[END_REF].



Social quality: Do different stakeholders agree on their interpretation of the model. MAPQUAL base the discussion of social quality of cartographic maps solely on the discussion of social quality in SEQUAL.



Deontic quality: Do the model help to achieve the overall goal of modelling? Emphasise is put on how maps can support the achievement of goals of the map.

Looking on language quality we have found the following:

 Domain appropriateness: Due to the lack of discussion and formal separation of domain and language in cartography, MAPQUAL is similar to SEQUAL with respect to domain appropriateness (is it possible to represent the domain using the language).  Participant appropriateness: Cartography has a tradition of exploiting the "natural" or cognitive knowledge of participants to a large extent. E.g. the use of colour for type of areas in a map reflects the colour in the real world. In conceptual modelling the tradition of creating a new language and thus disseminate this knowledge in a tailored way is more common.  Modeller (cartographer) appropriateness: Similar to participant appropriateness, MAPQUAL and SEQUAL are similar with respect to modeller appropriateness.



Comprehensibility appropriateness: Comprehensibility is divided into two areas; conceptual basis and external representation. Conceptual basis comprise the discussion on which concepts that are included in the language. SEQUAL provides several concrete guidelines for the conceptual basis. These guidelines have validity in cartography as well as for conceptual modelling. External representation focus on how the notation of the language is formed, i.e. the graphical aspects of the language. In this area there are significant differences between MAPQUAL and SEQUAL. Cartography has a strong tradition of investigating graphic design principles based on so-called visual variables [START_REF] Bertin | Semiology of Graphics: Diagrams, Networks, Maps[END_REF].

Traditionally maps have a larger focus on the use of colours and the use of texture as a visual technique. SEQUAL encourage being able to support a free approach to composition of symbols. Such free composition of symbols cannot be a general guideline in cartography since the geographical reality often is constraining this freedom.  Tool appropriateness: Tool appropriateness is traditionally not considered in cartography. Thus MAPQUAL adopts SEQUAL on the discussion of tool appropriateness, although with less focus on executional semantics.  Organisational appropriateness: Here MAPQUAL focus more on a cartographic context and the current standardisation efforts in this area.

Thus, two interrelated facets have large differences: comprehensibility appropriateness for language quality and empirical quality for map/model quality.

When investigating and comparing MAPQUAL and SEQUAL, we devise some important guidelines when using both conceptual and geographical aspects:

1. Clearly discriminate between geographical oriented lines and conceptual lines.

2. Clearly differentiate between nodes (concept) which are often depicted by a geometric shape, and geographic areas (by texture or colour for instance)

3. Indicate topological information by positioning of conceptual nodes according to the topology when possible. 4. Position concepts according to their temporal nearness. 5. Use visual variables where appropriate, especially the use of colour and shading for differentiation is necessary for integrated models. 6. Design the visualization based on the participants' cognitive metaphor of the most important information attribute. For instance, temporal attributes tend to be lean towards a sequential metaphor. Spatial attributes, like nearness, tend to lean towards a distance metaphor (i.e. closer is nearer).

Quality of Integrated Conceptual and Topological Models

In the investigation of developing quality guidelines for integrated visualizations that exhibit both conceptual and geographic information, a case study has been performed. The case study is in the health-care domain, which is an area where space support is found to be very important [START_REF] Gulliver | Changing Building User Attitude and Organisational Policy towards Sustainable Resource use in healthcare[END_REF] and which lends itself well to these kinds of visualizations. Research suggests that providing awareness of the hospital environment is one mean to lower the complexity of the decision-making. Both a focus towards the spatial dimension (i.e. location), but also the conceptual dimension (i.e. state, relationship etc.) is needed [START_REF] Bardram | Mobility Work: The Spatial Dimension of Collaboration at a Hospital[END_REF][START_REF] Bardram | AwareMedia -A Shared Interactive Display Supporting Social, Temporal, and Spatial Awareness in Surgery[END_REF]. The spatial dimension in indoor environments is commonly visualized either directly in a floor-plan (i.e. an indoor map) [START_REF] Mccarty | ActiveMap: A Visualization Tool for Location Awareness to Support Informal Interactions[END_REF] or as an attribute in a diagram-like fashion [START_REF] Bardram | AwareMedia -A Shared Interactive Display Supporting Social, Temporal, and Spatial Awareness in Surgery[END_REF]. Both approaches aim at visualizing the spatial dimension as well as the conceptual dimension including relationships, states and similar. However, both approaches focus the visualization towards their respective field (i.e. floor map on the spatial dimension, diagrams on the conceptual dimension) without successfully obtaining a good communication of both dimensions at the same time. This section will describe the background for the case and the perspective chosen for the trial, as well as a concrete situation that instantiates the information space under investigation.

Self-coordination of Hospital Staff

The primary focus for the case is the self-coordination of hospital-staff. A typical work situation for hospital staff includes various tasks that can either be scheduled or occur spontaneously. The tasks may require different equipment, several other staff members may be involved in a task, either throughout the whole duration of the task or only briefly, and often involving patients. Additionally, the staff member may have special interests in specific patients. This can be modelled by concepts in the information space, with relationships among them. Moreover, all the concepts have a spatial relationship of some sort. Concepts also have several non-physical properties, such as state, importance, staff type and so on.

The spatial dimension of the information is regarded as highly important and relevant for collaboration and coordination. The absolute location of concepts is not necessarily what best satisfies the actors' information needs. Deduced information, based on the absolute location of concepts may prove to be more suitable, especially towards the topology of concepts. Topology aspects may be spatial or temporal. Spatial topology is commonly exhibited in cartography for instance in subway maps, where less emphasis is put on distance than in topographical maps. Temporal aspects can be deduced by, for instance, the travelling distance divided by the travelling speed. In an indoor environment, this is very useful as the distances may be large, but conceptual entities can travel fast, for instance by elevators across floors. Additional obstacles that slow actors down are also present, such as wardrobes, bathrooms, disinfection areas, etc, where actors are held up temporarily.

Concrete Case Relative to Trial

Since the conceptual information model can attain fairly high complexity, it is important to constrain the case-study to a moderately complex sub-domain. The casestudy revolves around one specific actor as the intended main user. The user has certain interests, or relevant information associated with him/her, this information is a subset of the total information available. The goal for the trial was to provide a visual communication method which satisfies this users specific information needs. However, the method developed and investigated should also be able to be adapted to satisfy similar scenarios with other user types.

The scenario focuses on a typical working day for an anaesthetist. An anaesthetist's tasks are typically distributed throughout the hospital and each task has a small time span. Often the anaesthetist is working in parallel on two or more tasks, providing a need to self-coordinate in an effective matter, often requiring the knowledge of spatial attributes.

Development of the visual representations could be performed in several different ways, each with different benefits. For the initial investigation, a paper-prototype inspired approach was taken. Paper-prototypes supports for fairly rapid development, allowing for several ideas to be manifested and iteratively developed. The development of paper-prototypes ended with primarily two different kinds of visual representation methods, spatial Gantt charts (see example in Fig. 2) and Spatial Circles (see example in Figure 1). Both take into account the guidelines in the end of section 2, but Spatial Gantt chart have a higher emphasis on the temporal dimension. Due to the previous experiences of the subject (with Gantt-charts), we expected that the Spatial Gantt chart would perform best. A full description of the notation and snapshots used in the trial is found in [START_REF] Nossum | Visual Representation of Integrated Information[END_REF].

Trial

The design of the trial was inspired by experiments conducted by [START_REF] Opdahl | Experimental comparison of attack trees and misuse cases for security threat identification[END_REF] on differences between two conceptual modelling techniques. It should, however, be noted that the trial in this project was never intended to be as comprehensive as Opdahl and Sindre [START_REF] Nossum | Integrated Quality of Models and Quality of Maps[END_REF], especially with respect to the number of participants. Since it was not meaningful to use proxies (e.g. students) in the case, the number of available subjects was also too small to use statistical methods.

The trial is designed with strong inspiration from both the Latin-Square experimental design method, work by [START_REF] Gemino | Evaluating modeling techniques based on models of learning[END_REF] and the Technology Acceptance Model (TAM) [START_REF] Davis | Perceived usefulness, perceived ease of use, and user acceptance of information technology[END_REF]. The main intention of the trial is to investigate the properties of comprehension of the different types of visual representations and their acceptance.

In the trial the participants were given an introduction to the area, and then presented with a tutorial of the visualization to be presented. Then the visualization representing the scenario snapshot was presented. Instructions were given to answer a questionnaire -the time frame of this was 5 minutes. This process was performed iteratively for each of the snapshots in the scenario, but by changing the order of visualization types presented i.e. changing the order in which the different visualizations were presented, and having different visualizations representing similar information for the two different trials to leverage the potential learning factor for the visualization types. Additionally it is desirable to be able to compare the two different visualization types against the same information without repeating the presentation of the same scenario snapshots to the same participant. One potential shortcoming of this design is the differences in the participants -if the number of participants is small the impact of this will increase and can lead to wrong conclusions if not taken into account.

The questionnaires are essentially of two different types, one which is to be answered during the presentations of the different visualizations and one to be answered after finishing the presentations. One questionnaire for each of the scenario snapshots was created. The users' comprehension and potential knowledge gain from the visualization are evaluated by investigating the questionnaires. Comprehensibility deals with whether the user understands what the visualization depicts. Knowledge gain deals with whether the user is able to use the information in the visualization and potentially create new knowledge, or take decisions based on the information gained. Typical questions included for these two topics are:



Comprehensibility "Is task 1 far away?, When is task 2 starting?  Knowledge gains "What do you decide to do next?

The post-presentation questionnaire was more comprehensive and aims primarily at investigating the user acceptance of the two different visualization types. Categorization and questions were inspired by the Technology Acceptance Model and [START_REF] Opdahl | Experimental comparison of attack trees and misuse cases for security threat identification[END_REF] with the following categories:

 Perceived usefulness  Perceived ease-of-use  Intention to use
The post-presentation questionnaire used a 5-point Likert scale with the "opposing statements" format of the questions. The answer options indicated to which degree the subject agreed with the statements. In order to avoid repetition and monotony, the questions ordering was randomized.

4.1

Results from the Trial

Two participants were recruited to the trial, both staff at the local hospital. One participant was an anaesthetist and the other a general practitioner. Neither one received any compensation for their participation. Both trials were set up identically with respect to both equipment and introduction of the participants to the trial. Each trial took approximately 30 minutes. This section will present and discuss the results from the trial categorized according to comprehensibility, knowledge gain and technology acceptance briefly (due to the strict page limitations of the conference).

Comprehensibility:

The results of the comprehensibility investigation were calculated as an average over all questions related to comprehensibility, regardless of the information depicted. The questions were not designed to necessarily have a yes/no answer. Coding of the answers given was performed by mapping each answer to correct, semi-correct or wrong. This could potentially introduce bias or errors in the results since it was done by one person only, however due to the limited scope of the trial this was regarded as tolerable. The results indicate a slightly higher proportion of correct answers for the Spatial Circle layout than the Spatial Gantt layout. However the difference is marginal.

Knowledge gain:

The results were calculated by first coding the answers in three different categories; correct, semi-correct and wrong. It is noted, as for results under comprehensibility, that this can introduce biased results. A standard arithmetical average was then calculated for each of the category over the answers related to knowledge gain. A differentiation between the two visualization types was kept, in order to compare their performance. The results indicate that Spatial Circle layout performs slightly better than the Spatial Gantt layout. However, the difference between the two is marginal.

Technology acceptance:

The final and most comprehensive questionnaire revolved around investigating the participants' acceptance of the visualizations. Results from the technology acceptance investigation were produced by calculating an average of the degree of acceptance in the different categories of acceptance for each of the visualizations. Additionally, an average over the three categories was calculated to provide a summary of the acceptance. The degree of acceptance used the 5-point Likert scale which was coded as a range from -2 to +2. The result was that the Spatial Gantt layout appears to be superior to Spatial Circle layout in all categories of acceptance. Spatial Circle layout received negative degree of acceptance in all categories. It is not justifiable to draw a solid conclusion based on these resultshowever the results is regarded as indicative with respect to the comparison between the two visualization types.

The superior acceptance of Spatial Gantt is attributed to the participants' familiarity with the Gantt, or Gantt-like, metaphor. This familiarity was recognized in the design of the visualization types and was thus not unexpected. On the other hand, it is interesting that even if the performance (as for comprehension and knowledge gain) seems relatively equal, the difference in acceptance of the approach appear this clear. Similar results are found in other studies of modelling notations [START_REF] Gopalakrishnan | Capturing Location in Process Models: Comparing Small Adaptations of Mainstream Notation[END_REF].

Conclusion and Future Work

This work is aimed at investigating properties of visual representation of geographical, temporal and conceptual representations. Integrating spatial and nonspatial information is needed in meeting the increased amount of spatial and conceptual information. Information is increasingly including a spatial attribute [START_REF] Maceachren | Research challenges in geovisualization[END_REF] which indicates a need to understand how integrated visual representations works. One way of eliciting this is to merge current understanding of quality in cartography and conceptual modelling and thus create an integrated understanding of quality. In earlier work, the development of MAPQUAL has contributed to a broader understanding of integrated visual representations.

In order to investigate properties of different visual representation techniques a case study was performed. The health-care domain was chosen, with the specific scenario of an anaesthetist work day. Two distinctly different visual representation methods were developed for the scenario following the guidelines for mixed representations. In order to investigate their appropriateness a simple trial was designed and conducted. Due to limited resources the trial was intended to be illustrative rather than provide statistically valid conclusions, largely due to the limited number of participants involved. Further efforts relating to integrated visual representations can benefit by revising and extending the experiment design based on the experience presented in this article.

Through the investigations of visual representations, several issues were discovered. The information was perceived by the participants as through a sequential metaphor, favouring a sequential alignment of the temporal attributes. Thus, the nonsequential layout (i.e. Spatial Circles) was not favoured for the scenario. More generally this is formulated as a guideline which relates to sequentiality of the visual representation relative to the participants cognitive metaphor of the information.

Introducing spatial attributes to a traditionally non-spatial information space, such as schedule information, is new and unfamiliar to the participants. This can introduce issues with the acceptance of the visual representation developed. Moody [START_REF] Moody | Evaluating the Visual Syntax of UML: Improving the Cognitive Effectiveness of the UML Family of Diagrams[END_REF] identifies a similar issue with development of modelling languages.

The investigations did not look on tool support for any of the visual representations. This is intentionally left up to further work. It is strongly encouraged that further efforts delve into the investigation of relevant techniques for tool support. Filtering techniques from conceptual modelling combined with generalization and zooming techniques from cartography and GISc are reckoned to be highly successful and a necessity for the success of integrated visual representations. Additional tool support could introduce the notion of interactive models/maps.

Experience from this work has illustrated that integrating visual representation techniques from cartography and conceptual modelling is feasible and potentially useful. The work reported here has only briefly investigated some of this potential. The close collaboration with domain experts from the application domain proved highly beneficial and led to development which otherwise could not have been possible. Further investigations similar to this one, is encouraged to look into the perceived usefulness for the participants and the effects on collaboration between relevant participants. Additionally, more complex scenarios should be developed and investigated, preferably spatial-intensive scenarios, i.e. where spatial attributes are recognized in advance as important by the participants.


Xiuxiu Chen
email: xiuxiu.chen@pgr.reading.ac.uk

Huiying Gao
email: huiying@bit.edu.cn

Kecheng Liu
email: k.liu@reading.ac.uk

Ying Zhang

Incorporating Semiotics into Fuzzy Logic to Enhance Clinical Decision Support Systems

Keywords: Semiotics, Fuzzy logic theory, CDSSs, Knowledge representation, Knowledge reasoning

In order to enhance the quality of care, healthcare organisations are increasingly resorting to clinical decision support systems (CDSSs), which provide physicians with appropriate health care decisions or recommendations. However, how to explicitly represent the diverse vague medical knowledge and effectively reason in the decision-making process are still problems we are confronted. In this paper, we incorporate semiotics into fuzzy logic to enhance CDSSs with the aim of providing both the abilities of describing medical domain concepts contextually and reasoning with vague knowledge. A semiotically inspired fuzzy CDSSs framework is presented, based on which the vague knowledge representation and reasoning process are demonstrated.

Introduction

Clinical decision support systems (CDSSs)known as the provision of patientspecific assessments or recommendations to support the clinical decision making [START_REF] Hunt | Effects of computer-based clinical decision support systems on physician's performance and patient outcomes: a systematic review[END_REF], have been increasingly applied in various healthcare institutions. Previous studies have shown that CDSSs can improve the clinical practices [START_REF] Kawamoto | Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success[END_REF], reduce serious medication errors and enhance the delivery of healthcare services. However, multiple challenges continue to impede the effective implementation of CDSSs. As vague information exists almost everywhere in the whole clinical decision making process, the interactions between organisational and technical factors should be considered when we design the CDSSs, especially during the representation and interpretation processes of the domain knowledge. Sources of vagueness or imprecision may be qualitative and quantitative. Qualitative vagueness is generated by lacking of precise measurements. For example, some symptoms (patients' facial expressions, feelings and behaviours) are usually described in some subjective terms. Quantitative vagueness is generated by lacking of precision in a measurement even when corresponding precise measurement exists. For example, the value of Body Mass Index (BMI) can be accurately calculated with height and weight. However, not everyone with a high BMI is obese, especially the one who exercises regularly.

Besides, knowledge is usually characterized by a high level of context-dependency [START_REF] Kwiatkowska | Fuzzy logic and semiotic methods in modelling of medical concepts[END_REF] and evolvement [START_REF] Taborsky | Semiosis, information and knowledge. Organisational Semiotics -Evolving a science of information systems[END_REF]; therefore, it cannot be defined independently without their pragmatic meanings. Thus, without appropriately handling with these challenges, the CDSSs might not support the clinical practice effectively. In order to address the aforementioned issues, we draw a refined underpinning and methodology to offer an adaptive framework of CDSSs integrating with more accurate knowledge representation and interpretation mechanism. By integrating the organisational semiotics and fuzzy theory, the proposed framework of CDSSs allows describing medical domain concepts contextually and reasoning with vague knowledge. The reminder of the paper is organized as follows. Section 2 outlines the related theoretical background and main previous researches. The subsequent section provides a detailed illustration of the semiotically inspired fuzzy CDSSs framework; especially the knowledge representation is designed and reasoning process is discussed. Finally, section 4 draws our discussion and conclusions.

Theoretical Background

The proposed hybrid framework integrates two techniques, namely organisational semiotics and fuzzy logic theory, which are shortly outlined in the following subsections.

Organisational Semiotics

Organisational semiotics (OS) is a particularly branch of semiotics related to organisations, business and the IT system. From the OS perspective, IT systems or computer-based systems can be seen as the inside layer of the formal system in the organisational onion as shown in Fig. 1, that means only a small part of the entire organisation. Therefore, in the IT system analysis and design, the related informal and formal aspects of the business should be firstly understood. Based on this view, the effective implementation of CDSSs requires a clear understanding and modelling of the interactions among these three aspects.

INFORMAL IS a sub-culture where meanings are established, Intentions are understood, beliefs are formed and commitments with responsibilities are made, altered and discharged.

FORMAL IS bureaucracy where form and rule replace meaning and intention TECHNICAL IS machine-based system to automate part of the formal system Fig. 1. The organisational onion [START_REF] Stamper | Language and computer in organized behaviour[END_REF][START_REF] Liu | Semiotics in information systems engineering[END_REF] Moreover, the performance of CDSSs largely depends on its underlying knowledge base [START_REF] Purcell | What makes a good clinical decision support system[END_REF]. Stamper shares the view that knowledge is those signs interpreted at the social level of semiotic ladder (composed of physical word, empirics, syntax, semantics, pragmatics and social world), and it is equated with norms and attitudes [START_REF] Stamper | Organisational semiotics: informatics without the computer? Information, Organisation and Technology: Studies in Organisational Semiotics[END_REF]. Therefore, the explicit representation of knowledge should realize the mapping from the informal layer to the formal layer.

Fuzzy Logic Methodology

Fuzzy logic resembles human reasoning in its use of vague information to generate decisions [START_REF] Zadeh | Fuzzy sets[END_REF]. Unlike classical set theory, fuzzy logic provides an alternative way of thinking about the complex system modelling, characterized with the membership of an element instead of the crisp value. The difference between the classical set and fuzzy set is shown in Fig. 2. Definition 1. Fuzzy set A is described by some predefined range X (known as the universe of discourse) and the membership function Because of the inherent vagueness of the medical knowledge, fuzzy logic has been utilized in many medical applications, such as the fuzzy expert support system for coronary artery disease screening using clinical parameters [START_REF] Pal | Fuzzy expert system approach for coronary artery disease screening using clinical parameters[END_REF], determining the caloric intake requirement [START_REF] Nakandala | A novel approach to determining change of caloric intake requirement based on fuzzy logic methodology[END_REF], and monitoring the multiple sclerosis [START_REF] Cox | Fuzzy modelling and genetic algorithms for data mining and exploration[END_REF]. However, fuzzy logic theory has limited ability to elicit the variables/elements and reflect their contextual interpretation. Therefore, incorporating the semiotic approach into fuzzy logic is a candidate solution for conceptual and computational modelling, which allows for the explicit representation of the contextual interpretation [START_REF] Kwiatkowska | Fuzzy logic and semiotic methods in modelling of medical concepts[END_REF] and ensures the effective implementation of CDSSs.

A  , then   ( , ( ) ) | , ( ) [ 0, 1] i A i i A i A x x x X x      x x1 x1 x x2 μ(x)

Semiotically Inspired Fuzzy CDSSs Framework

In this paper, we propose the semiotically inspired fuzzy CDSSs framework, which is shown in the Fig. 3. It is based on the organisational onion, which integrates the informal and formal factors during the IT system design process. Moreover, the use of fuzzy logic enables the quantitative representation with the aim of reducing the ambiguity of knowledge, so that the reasoning as well as the utilization of the vague knowledge can be more effective to support the decision-making.

(

) 1
As shown in the Fig. 3, the proposed CDSSs framework can be described in three levels: informal, formal and technical level.

Informal level

Formal level

Technical level

Knowledge Repository Data Sources ... (1) Informal level: We define system users as agents in this paper, e.g. physicians, nurses, any other healthcare professionals and knowledge engineers. In this paper, we just take two main perspectives (knowledge engineer and physician) to illustrate the mechanism of this model in Fig. 3. The shared beliefs and values on the medical quality improvement and accuracy decision support strategy through the treatment process will be established. In real scenarios, all kinds of agents have to acquire various related data and deliver them to the computer-based system. But the substantial common understanding and explicit representation of these inputs are definitely based on the communications/interactions between the informal level and the formal level. For the physicians, the communication also provides the preparatory work for the following knowledge reasoning and treatment/diagnostics recommendations.

Diagnostics

(2) Formal level: including the problem domain analysis, knowledge elicitation, knowledge representation and knowledge reasoning. We analysis these modules following the activity lines for knowledge engineer and physician, respectively.

As what we have discussed above, a good and effective decision support system cannot be independent on its underlying knowledge base. Therefore, for the knowledge engineers, they will firstly conduct the problem domain analysis, which focuses on the understanding of the organisation from both the informal and formal perspectives and investigating of the potential problem to be dealt with. Subsequently, they will elicit clinical knowledge from multiple sources and represent them with formalized specifications. During this elicitation and representation process, knowledge engineers have to coordinate the formal aspects with many informal aspects in the organisation with the communication module. For example, different physicians may have different preferences on the diagnostic criteria or subjective assessments on the non-verbal expressions; they may also have different beliefs such as low healthcare cost, high quality healthcare services and low healthcare risks; therefore, all of the vagueness should be considered when constructing the knowledge repository. All the elicited knowledge can be classified along two axes: declarative knowledge and procedural knowledge [START_REF] Ryle | The concept of mind[END_REF]. In particular, declarative knowledge describes all kinds of medical domain concepts, their attributes as well as the interrelationships between the concepts and attributes. On the other hand, procedural knowledge specifies a set of prescriptions for actions in relation to certain types of conditions or conclusions to be drawn from the declarative knowledge. In this paper, we introduce the context analysis based on the semiotic triangle model to represent and interpret imprecise knowledge. Specially, declarative knowledge will be interpreted with the semiotic triangle model (section 3.1) and stored in the knowledge facts repository, while procedural knowledge will be demonstrated with fuzzy set theory (section 3.2) and saved in fuzzy rules repository.

For the physicians, their activity line includes the knowledge representation and knowledge reasoning. Knowledge representation mainly addresses the definition of membership according to own understanding and context. In this paper, we adopt the Mamdani-style fuzzy inference procedures [START_REF] Esposito | An ontology-based fuzzy decision support system for multiple sclerosis[END_REF] to implement the fuzzy system, mainly composed of fuzzification of input variables, rule evaluation, aggregation of the rule outputs, and defuzzification.

(3) Technical level: automate the well-defined work procedures of the fuzzy clinical decision support system. It is developed to read signs, re-arrange signs, process and finally display them. Multiple sources of knowledge in this research are also defined in this level: (i) clinical data derived from other current healthcare IT applications including electronic patient record (EPR), clinical information system (CIS), laboratory information system (LIS), picture archiving and communications system (PACS), etc.; (ii) existing clinical pathways and other business processes/procedures in use in hospitals; (iii) social knowledge or shared clinical experiences (recorded and observed) from community members; (iv) existing medical entities (basic statements about the medical concepts) and public medical classification terminologies such as SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms); (v) medical educational resources for practitioners and (vi) academic resources like medical literatures and case studies. Besides, the implementation of knowledge repository including the facts and rules are built according to the informal and formal factors.

Obviously, the computer-based system presupposes a formal system, just as the formal system relies on an informal system. Information flow and interactions among these three levels should be considered in the design of the CDSSs, especially facing the inherent vagueness of the medical knowledge.

Knowledge Fact Representation

In this sub-section, we will further discuss the conceptual modelling in a broader context of knowledge fact representation, mainly focus on the understanding and interpreting of medical knowledge.

An explicit representation of the medical knowledge is critical for the proper functions of CDSSs. However, the elements of medical knowledge must be defined within a context of their usage for a specific patient, at a specific time, for a specific purpose, by a specific healthcare professional. Although many existing clinical diagnostic criteria and domain terminology (e.g. SNOMED CT, ICD-10) indicate the diagnosis and treatment for diseases, the definition of domain concepts is not contextaware and still requires the communication in the creation of their meanings, especially for the inherent characteristics of medical knowledge such as the vagueness and context-dependency. Therefore, in this paper, the knowledge representation approach based on the peircean semiotic triangle (object, representamen and interpretant) is introduced, as shown in Fig. 4. We define the object as multidimensional variables /concepts including the quantitatively measurable variables and qualitative (subjective) ones, meanwhile, the sign stands for several specific measurements such as multi-parameters on various types of instruments/sensors, laboratory report, CT, observations, questionnaires and psychological scales. Interpretant is the most complex notion in this model and it provides not only semantic meaning but also pragmatic significance. The intepretant is identified by the context in several dimensions: agents (e.g. healthcare professionals, nurses, patients), intentions (e.g. treatment, assessment), preferences (e.g. subjective assessment for the measurements), and temporal information. Therefore, the meanings of any medical elements will be quite different according to their possible interpretations. For the qualitative measurable variables mentioned above, for example, physical activeness, dietary habits, sedentary behaviour and quality of life, they are quite difficult to define, measure, or quantify. Even for some quantitatively measurable variables, e.g. age, the group they belong to might be more meaningful than the actual values for the healthcare professionals. Besides, several cut-off values for existing diagnosis criteria are often arbitrarily established. For example, the value of Body Mass Index (BMI) greater than or equal to 30 indicates obesity; however, somebody whose BMI equals to 29 and be with small muscle mass may means higher fat than a muscular person with BMI = 32. Therefore, fuzzy logic theory is adopted in the interpretation process.

Based on the analysis above, a piece of knowledge fact can be represented by a quintuple: ,( ) ,,( ,,, )

KF C T C U I P H M T 

Where

C is a set of related concepts/variables; () TCis the set of terms for C , U is the universe of discourse;

I , the function of P , H , M and T , means the interpretation process;

P stands for a specific patient; H stands for a specific health professional;

M represents the possible measurements; T means a specific time. Set of the terms will be identified for each concept, such as ´very mild´, ´mild´, ´moderate´, ´severe´, and ´extremely severe´ for the concept of stress. It is also worth mentioning that attributes of the concepts can also be defined when required, e.g. frequency and intensity for the concept of stress. The measurements are mapped into these defined terms according to the interpretation process. With the function I , crisp assessment value for various measurements (especially for the qualitative measurable variables) will be produced as the input data of the fuzzy CDSSs.

Fuzzy Reasoning Process

The medical domain knowledge must be translated into a computational model in order to be identified by the computer-based system. This sub-section will describe the fuzzy reasoning process in four steps, namely fuzzification, rule evaluation, aggregation, defuzzification, as shown in Fig. 5. Fig. 5. Sketch of the computational modelling process (Take three-input process as an example. D means the output concept for final decision, which is also described as a fuzzy set)

Step 1: Fuzzification. With the interpretation process, not only the quantitative variables but also the qualitative ones will be assigned an appropriate crisp value belongs to the universe of discourse. In this step, crisp input values will be converted into corresponding fuzzy terms with appropriate membership degree values. Fig. 6 shows the membership functions for five defined terms of concept "physical activeness". The membership functions will be constructed with the communication between knowledge engineers and healthcare professionals, with the consideration of the context information.

(2)

μ(PA) 1 -40% -20% 0 40% 20% PA IA LA NA VA EA
Fig. 6. Example for the membership functions of physical activeness.

In Fig. 6, the PA axis refers to the universe of the discourse for the physical activeness, whereas the LA is triangular (-40%,-20%, 0). Step 2: Rule evaluation. In this step, multiple fuzzy inputs generated last step can be seen as the antecedents of the fuzzy rules. All the possible values for these antecedents will be calculated according to the defined fuzzy rules. These fuzzy rules are developed by integrating the experiences of healthcare professionals and those of knowledge engineers with the consideration of the problem characteristics. The formal format of fuzzy rule is "If antecedent is…, then consequent is …". Take the type-2 diabetes mellitus (T2DM) prediction as an example. If we adopt BMI, Physical activeness and Stress as three variables, therefore, one typical knowledge rule can be: If BMI is very high and Physical activeness is inactive and Stress is extremely severe, then the risk for the T2DM is very high. Especially, the "Stress" variable here can be qualified with two attributes of this concept, namely the has-intensity and hasfrequency. The reasoning process also applies the fuzzy logic, e.g. If stress hasintensity is extremely high and stress has-frequency is almost-all-the-time, then the stress is extremely severe. Part of the classifications of fuzzy rules repository is shown in Table 1.

Table 1. Classifications of fuzzy rules (based on our previous work [START_REF] Li | Integrated clinical pathway management for medical quality improvement-based on a semiotically inspired systems architecture[END_REF][START_REF] Yang | Knowledge-based clinical pathway for medical quality improvement[END_REF] and background)

Rule repository classification Potential application scenario Clinical diagnosis Preventive reminders

Evaluation of a patient's condition; Evaluation of clinician's suggestions. Health maintenance; Clinical prevention for certain diseases.

Nutrition

Caloric intake requirement; Form healthy dietary habits.

Behaviour recommendation Examination Consultation

Physical activity and calorie consumption. Recommended examinations for certain diseases Assessment for the mental disorders.

Suppose we have predefined n variables, according to membership functions for a group of defined terms, n 2 fuzzy rules in one form can be activated at the same time. The values of consequents for these activated rules can be calculated with Mamdani Operator [START_REF] Nakandala | A novel approach to determining change of caloric intake requirement based on fuzzy logic methodology[END_REF], shown in Eq. 3.



               
Step 3: Aggregation. In this step, all the results of consequents in step 2 will be integrated into a final fuzzy set with the aggregation operator, union (  ). The process can be seen clearly in the Fig. 5 with the comparison of aggregation step and defuzzification step. The shade area in the defuzzifation diagram indicates the final aggregation result.

Step 4: Defuzzification. It is the final step for the computer-based system. In this step, linguistic or crisp value (if required) can be obtained. The most popular technique to convert fuzzy set into a single number is calculating the centre of gravity (COG) of the fuzzy set (shaded area in Fig. 5), using Eq. 4.

a a ( ) d ( ) ( )d b A b A x x x COG A xx     
Where Arepresents the final aggregation fuzzy set, its universe of course is

[ , ] ab.

Discussion and Conclusions

CDSS is increasingly important in supporting the clinical practices including prevention, diagnosis, treatment, evaluation and long-term care. Its performance can be affected by the communication between the business and technical factors in the healthcare organisations, especially because of these characteristics of medical knowledge such as vagueness and context-dependency. In this paper, a semioticallyinspired fuzzy CDSS framework is proposed, while two key points, namely vague knowledge representation and reasoning, are illustrated. The innovation of this study is twofold. Firstly, we emphasize the role of informal factors with the organisational onion in the design phase and operation of CDSS, as the vagueness and imprecision are the intrinsic characteristics of medical knowledge. The contextual information and communication are considered in their definition and interpretation processes. Specifically, we explain the knowledge presentation process with semiosis and construct the knowledge representation model. Secondly, we modify the reasoning process with the semiotic thinking to support CDSS, which is also the formal part of the proposed framework. Although the application of fuzzy logic in healthcare is definitely not new, integrating the fuzzy logic and semiotics in an organisational perspective is necessary for the decision-making process, especially in the definition of membership functions of various linguistic variables and their attributes. Besides, we have applied this approach in a CDSS for the purpose of supporting the diagnosis and treatment of T2DM in a clinical setting in China.

On the other hand, this research faces some potential challenges. The most obvious one would be the computational efficiency. The active knowledge rules will be much more complicated with the increasing of the input variables and the complexity of membership functions. Therefore, future work could involve how to elicit the most significant variables and how to further refine the knowledge rule. Moreover, some

(3) potential application area could be integrating the multiple sensors with the proposed framework with the aim of providing a wider range of support, such as the homecare and mobile hospital for the chronic patients.

Fig. 2 .

 2 Fig.2. Difference between classical set (left) and fuzzy set (e.g. triangular membership function) (right)

Fig. 3 .

 3 Fig.3. Semiotically-inspired fuzzy CDDSs model

Fig. 4 .

 4 Fig.4. Semiotic triangle for knowledge fact representation process (adapted from Peirce's theory)

  is…AND/OR... is ..., THEN...is ...

Acknowledgments. The research is supported by the Beijing Municipal Natural Science Foundation under Grant 9133020 and scholarship program sponsored by China Scholarship Council (CSC). Special thanks to Hongqiao Yang, the Director of the Information Centre of Hospital 309 of People's Liberation Army, China, for providing the research background and valuable guidance.

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