Practice-Centred Awareness Process Model

In this section, we expand on the preceding discussion, and analyse the PCA model as a process in an instance of time rather than just layers in a reference model. Observable data from a user’s operations and work setting constitute the input to the process that provides a view of what is going in the world (i.e. primitive elements of the work environment). See Figure 5.18. The perception layer interacts with practice cues36 in the context management subsystem to cleanse and normalise any attributes associated with the input data and transform into a form that can be used by processes in the PCA model. The observable data we are interested in, in this work, are prompts (information) about the work practices of a clinician as well as their work goals, queries, problem requirements, patient conditions and any logistics (e.g. institutional policies and regional agenda, available tools and services, organisational beliefs and values, and expectations and constraints) that can possibly

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Practice cues are prompts that provide signals as to what sort of behaviours, practices, artefacts, patterns, objects and interactions are to be perceived in a non co-located work setting. They are based on activity and work practice models, work goals and user queries, and can be stored and manipulated in a number of formats, including graphs, Bayesian networks, knowledge models, etc.

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influence clinical decisions. The use of practice cues allows for non co-located perception. In cross-boundary e-health, individuals seeking to gain awareness of a user's work setting do not have visual cues about what is going on in the environment (Bardram and Hansen, 2010; Tadda and Salerno, 2010). As a result, having to rely on the mediation of social artefacts and work practices may lead to cognitive overload. The use of practice cues helps reduce an individual’s cognitive load by ensuring that only relevant work context information is perceived.

Figure 5.18: Practice-centred awareness process model (Arrow lines represent information and process flow)

Conceptualisation and stereotyping are performed statically at design time. At run-time, observable data are perceived and dynamically entered into the perception-conceptualisation- stereotyping cycle. As they are entered, they classified into process-based data and practice-

based data. Process-based include explicit information, working patterns and knowledge that

are largely codified in rules, tools, technologies and processes. Practice-based data are mostly unarticulated knowledge and tacit information and working patterns that are not easily captured or codified (Nonaka and Takeuchi, 1995; Leidner et al., 2006). The use of process- based data in problem-solving is mainly justified by the ontological activity system, whereas the central basis of the use of practice-based data is found in the situated activity system. The stereotyped activity system could provide a basis for process-based data (e.g. organisational guideline) and practice-based data (e.g. organiational values and informal protocols). Broadly

Perception Conceptualisation Stereotyping Com p re h en sion Context models Prior knowledge about

user and their work setting (Stereotypes) Knowledge about domain

of work (e.g. domain ontologies and knowledge models) Observable data from user operations and work environment Cas e -b as ed Rea so n in g wi th Con textu al Kn o w le d ge Con textMo rp h an d Su gge stio n Au gm en ta tio n CO N TE XT MA N A G EME N T – PR A CT ICE C UE S

Information Sources, e.g. collaborating experts Work practice models Work Practice Modelling Agent Work goals and queries Decision Support Agent

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speaking, practice-based data often act as “influencers” (Beyer and Holtzblatt, 1994) to enable or constrain the application of process-based data. A key argument of this work is that process-based data, which include only prescribed procedures for doing work do not often match what happens in the real-world, which are mainly practice-based (see also Chapter 4) and, as a result, DSS design approaches need to incorporate the later. Table 5.2 shows the key differences between process- and practice-based data.

Table 5.2: Process-data vs. practice-based data in decision support (Adapted from Leidner et al., 2006, p. 20)

Element Process-based Data Practice-based Data

Role Handle task execution Mainly act to influence task execution based on

prevailing circumstances of work

Nature of data Formal work specifications, domain

rules and conceptual knowledge

Rigid and generic, i.e. independent of work settings

Informal specifications, common sense knowledge, world views, local norms, organisational values and beliefs, power structures, rituals, stories and myths

Flexible and easily adapts to changes in local work settings, e.g. availability of tools and services

Type of knowledge Explicit knowledge – codified in rules,

tools and processes

Mostly tacit knowledge – unarticulated knowledge not easily captured or codified Context/Model

Type

Mostly ontological context and domain model, and stereotyped context

Mostly situated context and situation model, and often stereotyped context

Means of transmission

Formal controls, procedures, and standard operating procedures with heavy emphasis on information technologies to support knowledge creation, codification, transfer and decision support

Informal social groups that engage in storytelling and improvisation

Affecting factors Factors within internal work processes,

e.g. task methods

External factors, such as economic status, government policies and regional agenda Means of enabling

awareness

Through formal processes Through the extent of influence on work

processes in order to enable or constrain them Means of

mediation

Rules, tools, roles, subjects and objects tools, roles, subjects and objects, community,

history, and social and cultural practices

Paradigm Rationalistic thinking, task structures,

workflow-based technologies

Activity, cultural-historical and social theories

Practice system category

Ontological, stereotyped Situated, stereotyped

Benefits Provides structure to harness generated

ideas and knowledge

Achieves scale in knowledge reuse

Provides an environment to generate and share high value tacit knowledge for decision support Provides spark for fresh ideas and responsiveness to changing environment

Disadvantages Fails to tap into tacit knowledge. May

limit innovation and forces participants

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into fixed patterns of thinking with no structure to implement them.

Role of Information Technology

Heavy investment in IT to connect people with reusable codified knowledge

Moderate investment in IT to facilitate

conversations and transfer of tacit knowledge and “influencers” for more adaptive cross-boundary decision support

During the perception-conceptualisation-stereotyping cycle, the conceptual descriptions are retrieved from a store of domain models, which could be a database system, knowledge about the domain of work. Domain knowledge forms the general backdrop upon which perceived data can be substantiated. The stereotyping component retrieves into the cycle stereotypes about the user and their work setting, e.g. GPS coordinates, local times, weather information, disease demographics, organisational values and beliefs, and regional policies. As soon as no new data are being perceived, the information gathered is fused together in the comprehension component into a knowledge structure that forms a holistic picture of the user’s work setting with a view to addressing user queries and achieving work goals. This picture represents a

work practice instance – a clinical problem or case embedded with work practice information,

i.e. information about how the problem or case is actually solved in a given work practice (see Chapter 6). This is then fed as a new case into the case-based reasoning component of the decision support agent. Newly generated parameters are used, at appropriate times in the cycle (e.g. when there is a significant change in the knowledge structure), to update context and work practice models (as shown by the double arrow lines).

Figure 5.19: Case generation in CaDHealth

Figure 5.19 shows a representation of case generation using the PCA model. A problem description is decomposed into ontological, stereotyped and situated practices, which

Problem Description

Situated Practice Stereotypical Practice

Ontological Practice Expected goal: text;

Domain knowledge: concept1, concept2, …,

conceptn;

Domain rule: rule1, rule2, …,

rulen;

Subtasks: {task1, goal}, …

{taskn, goal};

Social context: text;

Cultural context: text;

Locality: name of a geographical region;. Organisation: name of a clinical organisation; Period: date {day|month|year};

Available tools: (device1,

role}, … {devicen, role}; Available expertise: {staff1,

profile}, …, {staffn, profile};

Case1 … Casen

…

…

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encapsulated the domain, contextual and situational information that describe a work context. For example in CaDHealth, a case model includes the activity being performed, the locality and time of work, the tools available for performing the activity, and a description of the socio-cultural context of work. As a result, a case, in CaDHealth includes features and their specific values that occurred in a particular situation as well as geographical information that help map a case to a point in a spatio-temporal space.