A Two–Tiered Model of Medical Guideline

Vojtěch SVÁTEK†, Jana ZVÁROVÁ‡, Radim JIROUŠEK†
† Laboratory of Intelligent Systems, Faculty of Informatics and Statistics, 
University of Economics, Prague, W.Churchill Sq.4, 13067 Praha 3, Czech Republic 
e-mail: {radim, svatek}@vse.cz
‡ European Centre for Medical Informatics, Statistics and Epidemiology of Charles University and Academy of Sciences
Pod Vodárenskou věží 2, 182 07 Praha 8, Czech Republic
e-mail:  zvarova@euromise.cz

Abstract. One of the most important issues in designing a formal model for representing medical guidelines is the trade–off between modularity and compactness. We briefly analyse this problem, review some existing approaches, and suggest a two–tiered model that relies on a secondary structure superposed on the set of fine–grained knowledge modules. We hypothesise that such model is particularly suitable for tasks related to long–term patient management with respect to high–level, loosely structured guidelines. The model is currently being operationalised and tested in the hypertension treatment domain.

1. Introduction

Medical guidelines (MG) are a standard means for dissemination of medical knowledge and for putting through healthcare standards. They are applied in various areas of medicine, and recognised (although sometimes criticised) by the medical community. In addition to the textual form of MGs, attention is currently paid to the possibility of their formalisation and subsequent computational processing. Most critical requirements on the guideline representation language follow from the computational task for which the model is to be used, and from the class of MG, which corresponds to the characteristics of the underlying healthcare problem. Among the most common computational tasks, let us mention (cf. [12]):
1.	MG–based support of the physician in the course of decision–making (including the initial eligibility decision);
2.	posterior comparison of the actions undertaken by a physician and reflected in the electronic patient record (EPR) with the MG;
3.	critique and suggestion of revision of the MG based on analysis of  multiple EPRs;
4.	comparison of several MGs from the same domain, with respect to their impact on a target population (and thus, indirectly on the EPRs);
5.	refinement of a general MG into a more detailed workplan. 
In terms of input/output, the first four tasks rely upon the MG, the EPR, and usually some more–or–less generic structures usable for abstraction (such as hierarchies of medical concepts, or definitions of temporal concepts). Conversely, the last task, denoted as „refinement“, requires specific knowledge which is external to the guidelines, such as descriptions of local clinical practice (see e.g. [5]) and available resources (see e.g. [3]).
The identifiable classes of MGs (let alone the subject area) have to do with the
•	level of generality both in terms of the position of the underlying medical concept (usually disease) in the medical taxonomy, and in terms of statistical prevalence in the population;
•	level of prescriptivity (vs. descriptivity);
•	time aspect, i.e. overall duration of MG application on a particular patient, as well as degree of granularity (size of the smallest time unit distinguished);
•	type of patient (such as in–patient, out–patient or homecare patient);
•	most frequent actions and processes (e.g. physical, instrumental and laboratory investigations, invasive therapeutic procedures, drug treatment...). 
Obviously, these features are not independent of each other. Guidelines for out–patient management are typically oriented on long–term treatment of diseases with relatively high prevalence, and most frequent actions are related to drug administration. Due to the coarse granularity of time considered, the events related to individual patient visits are only loosely interconnected; parts of medical knowledge are specified only descriptively and vaguely. All this favours a modular view of the whole MG. Guidelines (or, clinical protocols) for clinical healthcare, on the other hand, involve instantaneous, tightly coupled decisions and actions (including invasive procedures), and are more precisely and prescriptively specified. This favours a compact view of the MG as of a graph structure (plan). It has been recognized recently (e.g. in [12]) that a single, monolithic, guideline model is incapable of fitting such distinct types of MG. In this paper, we suggest a guideline model that is tuned to
•	tasks not requiring significant extra–MG expertise;
•	classes of MG related to long–term out–patient management.
After a brief review of the state–of–the–art in guideline modelling, we will informally sketch our new model, with stress on suitable compromise between modularity and compactness.

2. State–of–the–art in Guideline Modelling

Since MGs are one of the hot topics in present medical knowledge engineering, many projects and approaches have emerged, which aim at their formalisation and exploitation. 
As a traditional module–oriented approach we can consider shallow situation–action rule formalisation, which has reached maturity in the definition of the Arden syntax for Medical Logic Modules [6]; this symbol–level modularity is, however, connected with undesirable knowledge dispersion and lack of clarity. There have been also interesting attempts to combine situation–action rules with deeper models in a multistrategy system [2]. 
The most prominent among current research streams are oriented toward a compact view of an MG, which is characterized as a plan, using deep knowledge such as domain ontologies, tasks and generic problem–solving methods. The risk of the planning approach is that the individual chunks of knowledge cannot be used per se in case the plan is inapplicable as a whole. The well–known EON [9] project attempts to overcome this problem via understanding MG as skeletal plans, which are further instantiated and revised for each patient’s visit, using additional knowledge. The related Asgaard project [11] aims to discover implicit intentions of the guideline and of the healthcare provider and to compare them at an abstract level. For this, powerful mechanisms for conceptual as well as temporal abstraction have been devised. Among the projects using the „plan paradigm“, Asgaard is probably the one most striving to eliminate the potential rigidity of the MG (as a plan structure) via resorting to a goal–oriented and declarative view. 
Most other projects rather stick to compactness in representing MGs. Within InterMed, another American project, a relatively simple Guideline Interchange Format (GLIF) [4] has been devised, which serves for exchange of formalized MG between specialized, richer formalisms. The guidelines are encoded procedurally as collections of steps arranged into graphs. Among the European projects of the 4th framework programme, we should mention the HC‑REMA (Hospital Care Resource Management) project, and its follow–up PatMan (Patient Workflow Management) project. Their main target is to create a mapping between the vague directives of MGs and more concrete requirements and constraints imposed by day–to–day assignment of resources in a hospital environment; with this respect, a compact formalisation (Petri nets) has been chosen. Other 4th FP projects focusing on medical guidelines are PRESTIGE (Patient Record Supporting Telematics and Guidelines), PROGUIDE (Promoting the Development, Dissemination and Evaluation of Guidelines of Clinical Practice) and PROMPT (Protocols for Medical Procedures and Therapies).

3. Compromising Modularity and Compactness in the Two–Tiered Guideline Model

In the MGT project, we have attempted to build a new guideline formalism, with the aim of achieving symbol–level modularity without completely abandoning the „plan paradigm“.
The meta–model of the formalism consists of two parts:
•	A guideline domain ontology, which describes the most important classes of concepts dealt with in the guideline text, their attributes, and semantic relations among them
•	A guideline representation ontology, which defines how the concepts can be embedded, aggregated and linked so as to make up the overall syntactic structure of a guideline instance. The informal representation ontology is a starting point for a prospective formal language in which guideline knowledge will be input into target applications.
Both parts of the model have been elaborated (as described in more detail in [7]) as structured text, and as diagrams complying with the UML graphical language, a well–established format in software and knowledge engineering. Here, we only sketch the most conspicuous properties.

3.1 Outline of the Guideline Domain Ontology

Prescriptive knowledge in an MG determines „what should be done“ and „under what condition“ it should be done; this corresponds to classes action and decision, respectively. As typical subtypes of action we understand a one–shot therapeutic (or diagnostic) procedure, as well as the start, end or change of an activity. The difference between an action and an activity is (consistently with the recent proposals within the EON model [12]) based on time: while an action (or decision) takes place within a single time point (such as the patient’s visit), an activity has measurable duration. An activity can be ended regularly, as its assigned time expires, or can be interrupted, if its exit condition is evaluated as satisfied. The exit condition can be related to the goal of the treatment (e.g. if there is surprisingly good response to the drug treatment, it can be ended before than expected) or can describe undesirable conditions (such as adverse effects of drugs, which force their administration to be terminated). Descriptive knowledge has mainly the form of goals to be reached by the treatment, and of causal relationships. Each goal is related to a patient state, which, in turn, associates clinical parameters with time specifications. Causal relationships express relations between actions/activities and clinical parameters on the one side and clinical parameters on the other side; they may include information on time delays.

3.2 Outline of the Guideline Representation Ontology

The guideline as a complex object is supposed to contain four types of fine–grained elements, the first three being of descriptive while the last one of prescriptive nature:
•	goals expressing desirable states (of the whole treatment, or within a certain context);
•	causal relationships relating causes to effects;
•	concept definitions defining the criteria for being an instance of a certain concept, and prescribe how the values for variables (clinical parameters or the like) can be computed;
•	scenarios expressing relations between conditions (as patient states and history) and recommendations of steps to be taken (as simple action steps, as well as decision steps). 
In addition, two higher–level structures will be superposed on the descriptive elements: a network of scenarios, which reflects the order of steps (prescriptive aspect) recommended by the guideline, and a network (lattice) of patient states defined by the guideline, which reflects the similarity of states. Since the order of steps is defined only as a secondary structure upon the scenarios, a deviation from the MG does not entail its subsequent inapplicability on the patient – the current state can be matched on all states considered in the model.
In a sense, this two–layer approach is inverse to the EON [9] conception of  „skeletal–plan–refinement“, since it aims at supporting bottom–up guideline interpretation, starting with low–level elements (both prescriptive and descriptive ones, the former being treated analogously as the above mentioned, shallow, Arden rules), and checking their higher–level interrelations (as kind of „plans“) afterward. Our hypothesis, which is yet to be verified by case studies, is that our model will enable to compare certain types of MGs, namely those destined for long–term out–patient management, without recourse to extensively complex languages and computational machinery. Obviously, the lack of the approach is the redundancy due to generation of a large space of patient states from each compact procedural structure.

4 Application in the Hypertension Domain

Hypertension treatment is an example of medical task in which patients are observed for a long period, measurements (in particular, of blood pressure – BP) are repeatedly taken, and activities (most often in the form of drug administration) are carried out.
In our example, let us start with a text from a particular hypertension MG. According to the 1999 WHO/ISH Guidelines for the Management of Hypertension [1], „the primary goal of treatment of patients with high blood pressure is… treatment of all reversible risk factors as well as restoration of blood pressure to normal or optimal levels.“ The representation of the guideline should thus contain the above two goals (treatment of risk factors and lowering of BP), as well as the definitions of the concepts of high BP, reversible risk factors, and normal or optimal BP. Further on, the procedural part of the guideline can be decomposed into a large number of scenarios, which can be exemplified in a semi–structured way e.g. as:
•	Condition: patient is in state „eligible for hypertension treatment“; Recommendation: perform action: start–activity „Lifestyle measures“; perform decision: according to patient’s prognosis, decide whether drug treatment is to be started.
•	Condition: patient is in state „medium risk prognosis“; patient’s history shows that „BP monitoring“ activity is going on for less than 3 months; Recommendation: continue with „BP monitoring“ activity.
Causal relations among BP, various clinical parameters and associated conditions are also present in the guideline text and can be thus included into the representation.
6 Conclusions and Future Work

We have presented a new model of medical guideline and its application in a specific domain (hypertension treatment). Construction of this model was the first step in designing a complex of software tools for guideline–based support of treatment, for evaluation of medical healthcare, and for guideline analysis and critiquing. The elaboration of algorithms for the above tools is the next task to be solved within the MGT project. Parts of the model are currently being operationalised in the OCML conceptual modelling language, see [8]. A library of operational guideline components (in particular, definitions of medical and temporal concepts) is being built [7]. The use of OCML should provide a relatively smooth transition from abstract models to the executable code of prototype software systems. More attention will be also paid to the connection with electronic patient records, represented in the ORCA (Open Record for CAre) multimedia system [10], [13].  We are currently investigating how structured data extracted from the ORCA database can be processed with the operational code in OCML. When this problem is solved, we can proceed to more extensive tests on real medical data. The connection between the textual and formalised versions of a guideline will be maintained by means of semantic mark–up in the text. The possibilities of the XML formalism are examined in this context. In the model itself, certain parts need to be deepened, so that it covers all important concepts and relations. In particular, the area of drug treatment (drug groups, indications, interactions and costs) requires further elaboration. In a longer term, our project should involve processing of uncertain information (by means of probabilistic models) and induction of medical hypotheses from large patient databases.
The work on this paper has been supported by the Copernicus PL973121 (MGT – Medical Guidelines Technology) Project within the 4th Framework Program of the EU, and by the grant no.VS96008 (Laboratory of  Intelligent Systems) of the Czech Ministry of Education.
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