KON3: a Clinical Decision Support System, in oncology environment, based on knowledge management

Michele Ceccarellia, Antonio Donatiellob, Dante Vitaleb

a University Of Sannio, RCOST (Research Centre On Software Technology), Benevento, 82100 Italy

b Unlimited Software S.r.l., Napoli, 80143 Italy

e-mail: ceccarelli@unisannio.it, a.donatiello@u-s.it, dv@unlimitedsoftware.it

Abstract

The application of scientific methodology to clinical practice is typically realized through recommendations, policies and protocols represented as Clinical Practice Guidelines (CPG). CPGs have the purpose to help the clinicians in their choices and to improve the patient care process.

The representation of Guidelines and their introduction in medical information system can lead to efficient Clinical Decision Support Systems (CDSS), however this poses several interesting challenges as it involves problems of knowledge representation, inference, workflow definition, access to unstructured databases of medical records and others.

In this paper we analyze the approaches and methods in computer-based CPG realization, and then we illustrate the choices (tools, architecture and clinical domain) to realize KON3 System.

KON3 purpose is to achieve a CDSS based on guideline and semantic information representation, in oncology environment.

Keywords: CDSS (Clinical Decision Support System), Ontology, OWL (Ontology Web Language), Protégé, SWRL (Semantic Web Rule Language) JESS (Java Expert System Shell), HER (Health Electronic Record), KON3 (Knowledge ON ONcology through ONtology), GLIF (Guideline Interchange Format)

1. Introduction The importance of clinical practice guidelines (CPG) has been widely recognized and clinicians are increasingly consulting such guidelines for decision support during patient encounters [1,2]. However, the main obstacle to the wide adoption of shared guidelines consists into the fact that most CPG are typically available as test (pdf files, web pages, charts, diagrams, etc…). Therefore, clinicians must consult the appropriate guideline and then determine how the guideline recommendations apply

to the patient at hand. In order to reducing the variability of care and by reducing omission of recommended best treatment practices the use of CPG should be encouraged and their use should be as simple and automated as possible [2,3]. Several studies have recently emerged in literature proposing decision support systems that use the guidelines as the knowledge base and the clinical information system as the source of instances for the inference engine [4] and several computer-based clinical decision support systems (CDSSs) embodying specific guidelines have been developed to facilitate timely decision support for clinicians. In order to be integrated into the clinical workflow, the guideline based approach should be depend on the specific patient and pathology at the hand. This implies that the CDSS should be an integral part of the clinical information system and the inference engine must be linked with all available clinical records of the patient [5]. Indeed, the wide-spread distribution and use of computable CPG content can be improved if the research community focuses on lack of standards for representing medical knowledge, and on the prohibitive complexity and expense required to adapt encoded guideline content across the heterogeneity of data structures, semantics, and medical vocabularies in use in the nation’s health care information systems. The main objective of the KON3, a joint effort among companies, university and regional government agencies is the development of technologies for a sharable knowledge based on CPG at a reasonable cost and effort, and in a form that can be integrated gracefully and supportively into the clinician’s workflow via functions of the local clinical information system. The main features of KON3 is the adoption an Ontology based on representation of the guidelines in addition to a registry, based healthcare information infrastructure, which is based on standard being implemented at a regional and national level in Italy. The paper is organized as follows: the next section reports some of the principal approaches proposed in literature to try to standardize and share

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3.3 KON3 rules in DCIS domain

As stated in the previous paragraph, it’s necessary, now, to develop a set of rules in order to create guideline, both for actions and decisions. The domain is DCIS.

In DCIS, a possible set of actions is the following:

Fig. 9 - An action lists in DCIS Work-Up phase

In figure 9 is shown an action lists in DCIS Work-Up

phase. If there’s a patient, who was diagnosed a DCIS, then in Work-Up phase is necessary to make:

• Medical history and physical exam; • Diagnostic mammogram; • Pathology review; • Measure hormone receptor of tumor;

After Work-Up action, it’s necessary to take decision

among the following options: • Complete surgical excision; • Patient prefers mastectomy; • DCIS in two or more separate areas of the breast;

A possible SWRL rule is the following:

PatientData(?patient)   hasDiagnosed(?patient, ?cancer)  DCIS(?cancer)  isInStage(?cancer, Stage0)

→ makeWorkUp(?cancer, H&P)   makeWorkUp(?cancer, Diagnostic Mammogram)   makeWorkUp(?cancer, Measure hormone receptor of tumor)   makeWorkUp(?cancer, Pathology review) alternativeDecisionWorkUp(?cancer, Complete surgical excision)   alternativeDecisionWorkUp(?cancer, Patient prefer mastectomy)    alternativeDecisionWorkUp(?cancer, DCIS in two or more separate area of the breast)

In SWRL rule, it’s possible to notice that if exist a Patient (PatientData (?patient)), who was diagnosed a DCIS cancer, then it’s necessary to make some actions (through makeWorkUp relationship) and take some decisions among those illustrated in alternativeDecisionWorkUp relationship.

In DCIS, a possible list of recommendations is the

following: A. The NCCN recommends a pathology review

(another pathologist to look at the biopsy sample) to be certain that you have DCIS and not an invasive cancer or other condition;

B. If DCIS is present in only one area and no cancer is found at the edges of the first surgical excision, the surgical options are either a total mastectomy or a lumpectomy;

C. If a lumpectomy is chosen, then radiation therapy to the whole breast with a boost to the site of the tumor may or may not be done depending on several factors, such as woman’s age, other health problems, certain characteristics of the tumor, and the woman’s preference;

D. Mastectomy is recommended if the margins of the excision contain cancer and, even with repeat surgery, the DCIS cannot be completely removed;

E. If the mammogram, physical examination or biopsy results show that two or more separate areas of the breast contain DCIS, mastectomy is recommended;

F. After lumpectomy, a mammogram is suggested to ensure that the entire tumor has been removed;

PatientData(?patient) hasDiagnosed(?patient, ?cancer) DCIS(?cancer) isInStage(?cancer, Stage0) StageGrouping(Stage0) takeDecisionWorkUp(?cancer, ?decision) margin(?cancer, "positivo") numeroRipetizioniChirurgia(?cancer, ?numRip) swrlb:greaterThan(?numRip, 2) → recommendedDecisionPrimaryTreatment(?cancer, Total Mastectomy)

In this rule is mapped D recommendation.

4. Conclusions

In this paper, we have shown the main approaches to realization, computer based, of a CDSS. Our approach is based on development of an ontology for patient data, guideline and oncology taxonomy. The ontology includes a set of rules to build both specifies guideline and decision support systems, in order to get recommendations, and to help clinicians in their choices. 5. References

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