Semantically Inspired Electronic Healthcare Records

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1 Semantically Inspired Electronic Healthcare Records Kamran Farooq 1, Amir Hussain 1, Stephen Leslie 2, Chris Eckl 3, Calum MacRae 4, and Warner Slack 5 1 Department of Computing Science and Mathematics, University of Stirling, FK9 4LA, UK 2 Cardiac Unit, Raigmore Hospital, Inverness, IV2 3UJ,UK 3 Sitekit Labs, Isle of Skye, Inverness, IV51 9HL, UK 4 Brigham and Women's Hospital, Cardiovascular Division, Boston MA 02115, US 5 Beth Israel Deaconess Medical Center, Harvard Medical School, Boston MA 02446, US {kfa,ahu}@cs.stir.ac.uk, stephen.leslie@nhs.net, chris.eckl@sitekit.net, cmacrae@partners.org, wslack@bidmc.harvard.edu Abstract. The adoption of Electronic Healthcare Records (EHRs) holds the key for the success of next generation intelligent healthcare systems to improve the quality of healthcare and patient safety by facilitating the exchange of critical patient s episodic information among different stakeholders. The primary and secondary care healthcare systems store the episodic information for future reuse and for auditing purposes. The conventional healthcare information management systems for primary and secondary care are expected to be able to communicate and exchange complex medical knowledge (often expressed in numerous languages in different parts of the world) in an efficient and unequivocal way. For the purpose of this research, we present a novel technique to transform conventional patients data into OWL-based Electronic Healthcare Records (EHRs) which addresses the issues of interoperability, flexibility, and scalability through the utilization of ontology inspired framework. Using ontologies is a cost effective and pragmatic solution to implementing a shift from simple patient interviewing systems to more intelligent systems in the primary and secondary care. The Patient Semantic Profile specifically developed for generating EHRs has been validated using a sample of real patients data acquired from the Raigmore Hospital s RACPC (Rapid Access Chest Pain Clinic). Keywords: Electronic Healthcare Records, OWL-based EHRs, Ontology driven cardiovascular decision support framework. 1 Introduction Electronic Healthcare Records are widely renowned for providing good clinical indicators to the clinicians [1] for effective clinical decision making for disease management and in order for these systems to be fully effective, the healthcare provider must only see the relevant information needed to make a specific recommendation or diagnosis. As an example, a heart risk score may be sufficient for H. Zhang et al. (Eds.): BICS 2012, LNAI 7366, pp , Springer-Verlag Berlin Heidelberg 2012

2 Semantically Inspired Electronic Healthcare Records 43 the clinician to come to a conclusion about prescribing a specific drug for a patient without the need for him to know the exact values/parameters used by the cardiac risk calculators to calculate patient s risk scores, etc. Electronic Healthcare Records have not been rolled out at National Level despite heavy spending by the UK healthcare authorities, the core underlying issue which these healthcare information management systems are facing today is their failure to adapt to complex clinical requirements and processes and lack of general astuteness [2] which is expected of these systems. The underlying mechanics are hard-wired and based on rigid architectures which make maintenance and upgrade operations quite difficult. In the presence of a powerful ontology based systems, it is a shame that we have not yet fully exploited the offerings of clinical Ontologies like SNOMEDCT and GALEN capable of providing appealing standardization solutions to the healthcare providers. SNOMEDCT has become a new gold clinical documentation standard for the modern healthcare systems because of extensive in depth clinical repositories with powerful search capabilities developed using ontology based techniques. Ontologies offer flexible, scalable, adaptive solutions for clinical systems. Using this approach we hope to transform the conventional health care into the next generation by using a pragmatic approach to develop next generation healthcare systems. In light of literature review and after evaluating the success case studies of SNOMED CT [3], we have started the development of an ontology driven decision support framework in the cardiovascular domain [4]. Using legacy patients data acquired from the consultant cardiologist at the Raigmore Hospital in the UK, electronic healthcare records have been created in a semantically inspired OWL format which is the documentation standard for the proposed ontology driven cardiovascular decision support framework [4]. This helped us transform textual data held in distributed databases into semantic partitions using web ontology language. This transformed data has been used as an input by the DL Reasonser engine (Pellet) to perform risk assessment and classification of patients using their medical histories and domain specific decision support Ontologies. The rest of the paper is organized as follows: Section 2 reviews the state of the art in healthcare information management systems specifically from Electronic Healthcare Records perspective and section 3 explains our methodology for the development of novel ontology driven technique for the development of Electronic Healthcare Records. Section 4 presents preliminary results which include ontology testing and validation results using real patients' data acquired from the specialized chest pain clinic (RACPC) at the Raigmore Hospital and finally some concluding remarks are given in section 5. 2 Background The fundamental goals of the modern healthcare information management systems are to promote interoperability by providing mechanisms for seamless information exchange between different healthcare organizations, healthcare trusts etc. The second

3 44 K. Farooq et al. most essential objective of these systems is to provide the ability to ascertain the uniformity of data from disparate sources/repositories. The third most important deliverable expected of these systems is to provide good quality clinical data (patient s data, lab tests etc) to ensure the measurement of completeness, accurateness and correctness [5]. 2.1 Diversity in Healthcare Information Management Systems Healthcare information management systems are quite diverse and their underlined communication and documentation standards are somewhat varied, utilizing different communication and documentation standards as. In conventional healthcare systems interoperability is a major issue which makes it difficult for the communication of data between heterogeneous systems. The Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) is an ontological resource specifically developed some thirty years ago with a view to standardize healthcare systems [49]. SNOMED CT and UMLS are clinical thesauruses, aiming to resolve documentation standardization issues in clinical systems. These are large scale medical taxonomies which have been adopted in modern clinical systems showing significant good results in the targeted clinical systems. In [6] it shows that the clinicians using healthcare systems equipped with SNOMED outperformed clinicians using conventional systems without SNOMED CT capabilities. The focus of this discussion is on the documentation standardization which is of vital importance for this case study. As a result of a literature review and analysis of healthcare management systems [7], [8] and inspired by the success stories of SNOMED CT (taxonomy driven approach), we have utilized ontology driven approach to model patient s medical history in OWL from heterogeneous data sources in legacy clinical systems. We used a reverse engineering approach to generate the patient semantic profile ontology using the patient s data provided by RACPC (Rapid access chest pain clinic) nurses at the Raigmore Hospital in Inverness. The Patient Semantic Profile is one of the key components of the proposed decision support framework [8]. The Clinical decision support framework relies on Electronic Healthcare Records to carry out the dynamic logic procedures and key decision support operations using domain specific ontologies. 2.2 Benefits of Ontologies Driven Systems Ontology driven decision support systems have been used extensively in the clinical assessment of chronic diseases. They are well-known for their flexible architectures, easy to reuse knowledge modelling structures and inexpensive maintenance operations. Ontology engineering is a popular branch of artificial intelligence which is used in the simulation of complex computing systems, clinical decision support systems, emotions sensing, sentic computing, opinion mining using language corpuses and semantic web applications [9-11]. The study conducted in [8,12], showed exceptional results in the risk assessment and disease management of breast cancer patients which was deployed as a commercial clinical system. They utilized

4 Semantically Inspired Electronic Healthcare Records 45 the semantic web approach to model the clinical practice guidelines which were encoded in the clinical decision support system for generating patient specific recommendations. The construction of knowledge base through an ontology inspired approach provides the key benefit of problem independence. This knowledge base can be extended and reused in a variety of different problems and therefore will have multiple mapping among knowledge base and decision models. The knowledge base updates the decision models without any costly software engineering work and maintenance operations are cost effective across decision models and within the knowledge base. Ontology inspired approach helps in knowledge structuring which also facilitates system developers and domain experts to acquire knowledge, reuse and ensuring knowledge consistency within the knowledge base. 3 Methodology 3.1 Electronic Healthcare Records We have utilized ontology driven approach to model patient s medical history in OWL from heterogeneous data sources in legacy clinical systems. We have implemented a novel reverse engineering approach to generate a patient semantic profile ontology using the patient s data provided by RACPC (Rapid access chest pain clinic) nurses at Raigmore Hospital in Inverness. The Patient Medical Profile is one of the key components of the proposed decision support framework [4]. The clinical decision support framework relies on the data encapsulated in the form of Electronic Healthcare Records to perform the decision support operations using domain specific ontologies and dynamic logic procedures facilitated by the Protégé ontology development editor. 3.2 Reverse Engineering Methodology Reverse engineering is a popular method in software engineering discipline which is the process of analyzing the system components and their relationships and replicate same data representation in the targeted domain in another structure or at a higher level of abstraction. The other concept used in this case study is forward engineering which allowed us to make a transition from high level abstractions to the physical implementation of the system. The Re-engineering patterns describe the techniques/patterns which are applied to the data in order to achieve transformation from non ontological resources to the ontological definitions. These patterns contain the conditions and requirements which are requirements/ guidelines of the targeted system. The patient s data used for this case study came from a relational database hosted in the Raigmore Hospital. We used the ontology reverse engineering approach to transform legacy data to the patient semantic profile. We mapped the clinical processes and created clinical workflows through the development of a domain specific ontology encapsulating hierarchical classes-subclasses relationships.

5 46 K. Farooq et al. We modelled clinical data of RACPC patients using Patient Semantic profile ontology. This allowed us to create patient medical histories in OWL which is the agreed data exchange format selected for the ontology driven clinical decision support system framework as represented in Fig 1. The CDSS can use this semantic profile along with decision support ontologies for risk assessment; lab tests recommendation and prescribing activities. These EHRs have been used for the generation of electronic doctor notes. EHRs will also be hosted on Microsoft Health vault for exchanging patients critical information among different healthcare providers in the US. Fig. 1. High level view of the ontology driven cardiovascular decision support framework 3.3 Ontology Development The high level design of the classes was carried out with a view to incorporate the stakeholders/participants which take part during the course of GP referral to RACPC clinics followed by Cardiologist consultation should they need to be consulted regarding abnormal ECG or failure to do an exercise tolerance test. The clinical workflow which has used for the development is as follows: The patient goes into GP practice with chest pain symptoms; they get referred to specialized chest Pain clinics. The nurses in these clinics take patients through a series of assessment sessions to mitigate the risk of heart attack by assessing the seriousness of the chest pain. If the presentation suggests during the course of action that the chest pain patient has suffered from is not cardiac related then the patient gets discharged from these clinics after been given advice by these specialized nurses. Patients with suspected angina who are not able to carry out an exercise tolerance test or suffered pain during ETT are most likely to get coronary angiography as part of preventative measure. The ontology design incorporated different stages during the referral process through relationships among parent-subclasses. The information about patient s chest

6 Semantically Inspired Electronic Healthcare Records 47 pain type, their past family history, previous cardiovascular history and personal demographic information is also modelled through domain-specific classes within the ontology design. 3.4 Object Properties and Data Properties The object properties are defined in order to establish relationship between individual classes as represented in Fig 2. The properties are also referred as Roles or relations in UML terms. The purpose of the data properties is to be able to define the relationship between individual class and the XML schema data type. Object properties establish a relationship between specific classes in order to encapsulate and model the desired behaviour which is set as a clinical use case. The modelling of chest pain patients who suffered from suspected angina has been achieved via the object property has_chest_pain_type which binds the specific pain type with the patient using Patient" and Chest_Pain_Type Classes. The XML schema data type comes from the data properties which describes pain _type as an enumerated type showing the values as typical, atypical or nontypical. Has_diagnosis_done object type describes the relationship between Patient and General Medical Practitioner, Cardiologist and RACPC classes. This relationship encapsulates and models the behaviour of diagnosis done at each stage by the clinicians involved in the referral process. Fig. 2. Data Properties, Object properties and ontology consistency checking

7 48 K. Farooq et al. 3.5 Ontology Evaluation and Testing In order to test the developed ontology, several test patients were introduced as part of the training. The patient semantic profile ontology was used to generate their electronic healthcare records. The information shown in Fig 3 is a formal representation of the information initially collected through the test data. After performing the consistency checking, Pellet dynamic logic reasoner was used to check the classification results. 3.6 Ontology Testing Results The patient semantic profile component generated the clinical histories using legacy patients data. Many items of information which are clinically useful to the clinicians (GPs, Nurses, and Cardiologists) are being held using Boolean-type clauses. The critical medical conditions are modelled using Has Presence and Has Absence clauses, this sort of clinical information is very useful for clinicians in the primary and secondary care and without spending too much time they can get a snapshot of the patient s medical history and diseases/conditions which require urgent attention from referral perspective. The purpose of this clinical history is to lend a helping hand to clinicians during clinical decision making and to flag potential clinical issues which need urgent attention or further examination by the clinical experts. 3.7 Important Historical information In cardiology clinic the key information which is critical to the clinical decision making is the episodic information pertinent to a heart attack or a heart abnormality of any kind in the past. This critical information is of huge importance in pre-operative risk assessment before any surgical operation is scheduled for the patient. This information is modeled through an ontology using Has absence and Has Presence data types Qualitative Information The Qualitative information is presented in the Figure 3 by example 4. It shows that the patient is suffering from chest pain which is Typical chest pain. What clinically defines typical pain can be asserted in the chest pain risk assessment ontology developed as part of the chest pain risk assessment ontology Cardinal Information In web ontology language, one of its best features to specify cardinal information using cardinal restrictions by expressing it in number and ranges. There are two types of cardinal restrictions you can apply in OWL; they are referred as Temporal and Quantity units. Patient s age is modelled as 75 years in item 4 which personify temporal unit. One of the best selling features of OWL is the functionality it providers to define unit

8 Semantically Inspired Electronic Healthcare Records 49 classes and storage information in a single unit which can be updated in a cost effective way when new clinical guidelines are provided by the healthcare authorities Range Information The range information modelled in item 5 make clinicians aware that the patient in question was diagnosed with coronary angiography treatment 7 months ago. Fig. 3. Electronic Healthcare Records in the form of Patient Semantic Profile 4 Preliminary Results 4.1 Maintenance and Evaluation The implemented ontology was tested and validated to verify whether it was fit for purpose and exhibits the clinical behaviour envisaged through the high level design by the domain experts. The implemented ontology was then evaluated on the basis of its clarity, possibilities of reuse, consistency, readiness to present held logic/information in a clear and unequivocal way. One of the important traits and key benefits of ontology driven solutions is its capability to provide knowledge designers and non expert users ease of reuse and cost effective maintenance. 4.2 Consistency Checking Using Pellet (OWL- DL) Reasoner Protege-OWL supplies semantic web developers an intelligent validation facility which determines the sub-language of the ontology being edited. One of the important features offered through the Pellet Reasonser is its ability to be able to check classsubclass associations to ensure consistency. This additional capability will allow

9 50 K. Farooq et al. ontology designers to perform validation check on all of the classes included in the ontology and also to work out the inferred ontology class hierarchy [13]. 4.3 Ontology Consistency Checking The dynamic logic Reasoner (Pellet) was used for the consistency checking on all of the classes of Patient Semantic Profile ontology. During consistency checking, it shows the inferred class chest pain type is currently being inferred for consistency checking and Pellet performs a hierarchy check on the parent classes associated with this class under test. The purpose of the consistency checking is to ensure that the envisaged design has been implemented without any syntactical or programming errors during the course of the development of this ontology. This consistency check enabled us to do further validation testing using individuals in the Protégé development editor in order to insert real patients data containing real clinical findings along with demographics information for the training and testing of the knowledgebase in its entirety using the defined data values and object properties. 4.4 Ontology Testing Using Real Patients Web ontology language (OWL) allows ontology engineers to define individuals and then used them to assert specific properties to test the hierarchical relationship between different classes. These Individuals (Instances of the classes) can also be used in class descriptions, namely in hasvalue restrictions and enumerated classes. In the Patient Semantic Profile ontology, we have defined specific test cases by defining these as individuals representing various patients and their demographics. Electronic Healthcare Records have been generated for the test patients. These test patients were inserted for the testing purposes and also to assert specific clinical conditions which describe their clinical symptoms. Patient s medical history encapsulates their demographics information along with their past and present cardiac and non cardiac related clinical symptoms, location of the current chest pain (left side of the chest) association of the chest pain with breathing, its severity and whether or not their chest pain is spreading. After analyzing the patient s generated medical history generated through the Patient Semantic Profile ontology the semantics are extracted to generate electronic healthcare records. This information is of utmost importance for the clinicians to carry out efficient and accurate risk assessment operations. 5 Conclusions In this paper we discussed the development of a novel technique to generate Electronic Healthcare Records using Patient Semantic Profile component which is one of the key components of the proposed cardiovascular decision support framework. We have also presented an intelligent reverse engineering technique for the transformation of legacy patients data into Patient Semantic Profile using

10 Semantically Inspired Electronic Healthcare Records 51 ontology driven knowledge modelling approach. This intelligent mechanism provides intrinsic meaning to the patient s data and facilitates this information to be utilised by the decision support components included in the proposed ontology driven cardiovascular decision support framework. We also exploited these EHRs for the development of doctor notes and used them for the clinical risk assessment to classify patients into different risk categories using ontology driven cardiovascular decision support framework. We will build on the work we have done so far as a proof of concept and aim towards building this model using ontology auto generation techniques. References 1. Turley, M., et al.: Use Of Electronic Health Records Can Improve The Health Care Industry s Environmental Footprint. Health Affairs 30, 938 (2011) 2. Bouamrane, M.-M., Rector, A., Hurrell, M.: Using Ontologies for an Intelligent Patient Modelling, Adaptation and Management System. In: Meersman, R., Tari, Z. (eds.) OTM LNCS, vol. 5332, pp Springer, Heidelberg (2008) 3. Spackman, K.A., Reynoso, G.: Examining SNOMED from the perspective of formal ontological principles: Some preliminary analysis and observations. In: Proc. KR-MED 2004, Whistler, Canada, pp (2004) 4. Farooq, K., et al.: Ontology-driven cardiovascular decision support system. In: th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), pp (2011) 5. J.T. Case, et al.: Use of SNOMED in HL7 Messaging, pp (2008) 6. Cornet, R., De Keizer, N.: Forty years of SNOMED: a literature review. BMC Medical Informatics and Decision Making 8, S2 (2008) 7. Bouamrane, M.-M., Rector, A.L., Hurrell, M.: Ontology-Driven Adaptive Medical Information Collection System. In: An, A., Matwin, S., Raś, Z.W., Ślęzak, D. (eds.) ISMIS LNCS (LNAI), vol. 4994, pp Springer, Heidelberg (2008) 8. Abidi, S.R., et al.: Ontology-based Modeling of Clinical Practice Guidelines: A Clinical Decision Support System for Breast Cancer Follow-up Interventions at Primary Care Settings Computerization of BC Follow-up CPG Development of Breast Cancer Ontology The BC ontology model. Computer 9. Cambria, E., et al.: Bridging the Gap between Structured and Unstructured Health-Care Data through Semantics and Sentics. Science, 1 14 (2010) 10. Cambria, E., Hussain, A.: Sentic Computing: Techniques, Tools, and Applications. In: SpringerBriefs in Cognitive Computation. Springer, Heidelberg (2012) 11. Cambria, E., et al.: Sentic PROMs: Application of sentic computing to the development of a novel unified framework for measuring health-care quality. Expert Systems with Applications 12. Abidi, S.: Ontology-based knowledge modeling to provide decision support for comorbid diseases. Knowledge Representation for Health-Care, (2011) 13. Horridge, M., et al.: A Practical Guide To Building OWL Ontologies Using The Protégé- OWL Plugin and CO-ODE Tools Edition 1.0, The University Of Manchester, vol. 27 (August 2004)