August 13, :35 WSPC/INSTRUCTION FILE gidatacentre. Multi-Datacenter Network of Knowledge Extraction and Dissemination for IBD Clinics

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1 International Journal of Software Engineering and Knowledge Engineering c World Scientific Publishing Company Multi-Datacenter Network of Knowledge Extraction and Dissemination for IBD Clinics Kamran Sartipi Department of Electrical, Computer and Software Engineering University of Ontario Institute of Technology Oshawa, ON, L1H 7K4, Canada Kamran.Sartipi@uoit.ca David Armstrong Division of Gastroenterology, McMaster University Medical Centre Hamilton, Ontario, L8S 4K1, Canada armstro@mcmaster.ca david.html Mohamed Abouzahra and Joseph Tan DeGroote School of Business, McMaster University Hamilton, Ontario, L8S 4K1, Canada abouzamm@mcmaster.ca Dr. Armstrong: Huge efforts have been invested in adoption, but translation into care is a challenge, especially for chronic diseases. Characteristics of the IBD: it is an archetypal chronic disease; affects young; it is complex and costly; life long; multiple guidelines but limited adoption; disease manifestation vary immensely between patients so uniform application of guidelines may not be appropriate. Why IBD is an important problem and why solution to IBD management is relevant to much of medicine? How much clinical background is required here? That is, to what extent do we need to describe the problem? For IBD or for medicine, generally? The healthcare community has invested huge efforts in adoption of ITC solutions in different medical speciality areas to take advantage of facilities such as speed, accuracy, automation, integration, and information management that are provided by electronic health systems. Such facilities are provided through a set of complementary techniques and technologies. The applications of knowledge engineering (data mining and AI solutions) in ehealth provides effective clinical decision support systems (CDSS) to assist the clinicians perform their tasks more effectively. The new advances in computing, networking, and storage vitalization have offered the utilization of cloud-based infrastructures with low cost, minimal maintenance, and flexible functionality to the ehealth community. The availability of a variety of standards allows to communicate operations (web services), data (HL7 v3), concepts (SNOMED), and knowledge (PMML) for seamless integration of centres for advanced medical studies and dissemination of their findings within their speciality communities. Mobile communication and processing power of the smart phones and tablet computers highly facilitates collaborative decision 1

2 making among clinicians and researchers. Such facilities can also be used by other medical centres with the same speciality to access expertise, knowledge and results that are not locally available to them. However, the translation of such services into effective and usable care process is still a big challenge which requires close collaboration and understanding among the healthcare professionals and technical experts to harmonize the existing technology and services with the delicate attributes of different healthcare environments and workflow processes. Such a harmonization allows a smooth and effective delivery of care from the care-providers to patients. In particular, providing effective ehealth services to care-providers to manage patients with the chronic diseases is very challenging. As an example, the IBD is an archetypal chronic disease with attributes such as: affecting a wide range of ages; can persist for life time; it is complex and costly; and disease manifestation vary immensely between patients. Therefore, while a variety of guidelines exist their adoption is limited and uniform application of guidelines may not be appropriate. Such characteristics of IBD as a chronic disease make it a very suitable speciality area and the provided IBD management solutions can be reused for other chronic diseases. More specifically, in this research we investigate the design, development and evaluation of a multi-centre network of ambulatory care IBD clinics that will benefit from the IBD Data Analysis Centre (IBD-DAC) developed in this project. Each centre will be deployed as a separate private cloud infrastructure for collecting, analyzing and communicating data and knowledge about IBD patients, and can communicate the results through standard information communication protocols. Such a network allows effective and seamless integration of the centres to communicate the generated clinical knowledge and guidelines with other centres of the network. The evaluation of the developed centre in terms of usability aspects include: i) demonstrating the feasibility of data acquisition from an ambulatory care specialist IBD clinic OSCAR EHR; ii) demonstrating the feasibility of querying this database to determine disease activity for specific groups of patients according to standard criteria (e.g. CDAI index and HBI index); iii) predicting the likelihood of disease are in specific patients or groups of patients; iv) transporting the generated clinical knowledge to other centres; and v) measuring the effectiveness of the proposed clinical decision support system to assist GI specialists in a collaborative decision making. The proposed research takes advantage of leading-edge and standard-based technologies such as: HL7 RIM, data and service interoperability, knowledge discovery, decision support techniques, as well as a suite of software tools for developing web-enabled prototypes and reliable final systems. This pilot project is based on the collaboration between complementary groups of experts in health sciences, software engineering, and information systems from two universities: McMaster University and the University of Ontario Institute of Technology. 1. Introduction Dr. Armstrong: VISION: Flexible linkage of multiple EHRs to allow extraction of relevant comparable data from all EHRs to provide knowledgebase that can be interrogated to answer clinical questions on disease management for the benefit of individual patients. Electronic health records (EHR) are being used increasingly in ambulatory care environments and will, ultimately, become a mainstay of clinical care. In addition to providing important administrative benefits, EHRs provide a means to acquire and store clinical data that will support clinical care whilst facilitating clinical research, practice audit and quality improvement in clinical practice. However, currently there is a multitude of different EHRs which vary with respect to their structure, functionality and data entry requirements; as a result of this and different design decisions, the data acquired and stored by these EHRs will 1

3 differ very significantly. These problems constitute an important barrier to standardization of clinical care and the application of evidence-based principles to clinical practice at individual centres; the problems are compounded when one considers the need to aggregate and analyse data from multiple sites for multicentre network projects that are, currently, being supported by provincial ministries of health and the Canadian Institute for Health Research (CIHR) in their calls for proposals for national networks to evaluate the development and implementation of clinical practice guidelines (CPGs). One potential solution to the problem of interoperability and connectivity might be to mandate a standard EHR or a standard data entry process at all sites involved in a network; this would, however, be very difficult and, probably, very expensive to implement. Furthermore, it would be very restrictive and would hamper the developments needed to adapt to changes in our understanding of diseases and their management. An alternative solution, to be explored in this proposal, is to develop a mechanism that would allow the extraction of comparable data from different EHRs and their organization and storage in a standardized, structured format for subsequent, aggregate analysis and exploratory data mining. Based on the HL-7 v3 specifications for the Reference Information Model (RIM) and SNOMED clinical terminology standards, it is possible to develop a disease-specific data collection centre that will collate clinical and demographic EHR information such that it can be evaluated in the context of published, evidence-based guidelines. This will allow: (1) evaluation of current clinical practice with respect to current CPGs; (2) evaluation of clinical data to identify predictors of clinical outcomes; (3) development of clinical decision support systems (CDSS); (4) identification of practice gaps that need to be met by new prospective research studies or amended CPGs; and (5) identification of regional and national variations in the epidemiology and management of specific diseases. The Division of Gastroenterology & the Farncombe Family Digestive Health Research Institute at McMaster University has implemented an ambulatory care clinic for patients with inflammatory bowel disease (IBD -Crohns disease and ulcerative colitis), funded by an innovation grant from the Hamilton Academic Health Services Organisation (HAHSO) for the period July 2009 to June This novel specialty clinic incorporates a multidisciplinary clinic, with multiple health care specialties, as well as routine IBD care and is founded on the implementation of OSCAR (an open source EHR), in collaboration with Dr. David Chan. To date, over 1,000 patients have been enrolled in the clinical study, which has a roster of 6 gastroenterology specialists with the prospect of including another gastroenterologists and their patients. This clinic has also established links with other gastroenterologists in the region (Local Health Integration Network LHIN), with the prospect that other specialists will join the group and contribute to the database. The aim of this pilot project is to develop the data structure for the McMaster IBD Clinic that can be scaled up and applied to multicentre collaborations to support a network of ambulatory care IBD clinics that will contribute to and benefit from an IBD Data Analysis Centre (IBD-DAC). Specifically, the projects initial aims will be: To demonstrate the feasibility of data acquisition from an ambulatory care specialist IBD clinic EHR. 2

4 To demonstrate the feasibility of querying this HL7-compliant, RIM-based database with the goal of: Determining disease activity for specific groups of patients, according to standard criteria (e.g. CDAI Crohns Disease Activity Index, HBI Harvey- Bradshaw Index, Mayo Clinic Score for ulcerative colitis), for validation purposes. Predicting the likelihood of disease are in specific patients or groups of patients Predicting the likelihood of disease complications (stricture, surgery) in specific patients or groups of patients. Development and initial validation of a single centre IBD-DAC will form the basis for a data structure and collection centre that could be used to acquire and analyse data from multiple EHRs at different sites (local, regional and national). Methods Using the current implementation of OSCAR for the McMaster IBD Clinic, clinical data on all IBD patients will be collected for all visits to the clinic. Many of these data will be collected by the physician, advanced practice nurse or dietitian in the course of a routine clinical visit, using the OSCAR EHR; these data will include: (i) age, gender, medication history, allergies and previous diseases; (ii) signs, symptoms, and vitals variables such as blood pressure, weight and temperature, color; and (iii) laboratory investigation and diagnostic imaging results downloaded from the regional (LHIN) clinical data repository (Clinical-Connect, MedSeek). Other data, currently being collected on patient quality of life and satisfaction for the HAHSO project, will also become available through OS- CAR, for correlation with the clinical data. Data acquired through OSCAR and through the IBD Clinic will be categorized and mapped, using SNOMED clinical terminology, to the IBD-DAC. Data extraction procedures will be developed, in collaboration with Dr. David Chan and his team to ensure long-term applicability to future single and multicentre implementations of OSCAR and data integration programs. Initial validations of the IBD-DAC will be performed using simple clinical queries regarding disease activity or adherence to basic CPGs (e.g. frequency of bone densitometry measurements, adherence to laboratory monitoring programs for patients receiving immunosuppressants). A variety of data query and analysis programs will then be developed, in collaboration with the clinicians, with respect to specific evidence-based CPGs. In the next step, the IBD-DAC system will analyse the patients context-information and suggests a list of relevant and lightweight data mining operations to be applied. Patient Context is defined as a set of information about the patient in the form of a tuple of attributes, such as: < age, symptoms, complaints, painlevel, medication, chronic, occurrencef requency > that are used by the system to recognize the circumstances of a patient to perform a specific task. Each mining operation will compare individual patients contexts with a large number of contexts in the centre for specific CPG queries, with the aim of providing detailed ad- 3

5 ditional information about the patients condition to assist the health care professionals in categorizing the patient for specific treatment. At this time, the IBD-DAC database will be updated with the patients new contexts. Implications for the future The pilot project for a single-centre, IBD-DAC will provide a mechanism for collecting, analyzing and disseminating data and knowledge for IBD patients. The center will be expandable to allow for the addition of other IBD clinics, such that each clinic, or participation of other physicians (GIs), or clinical groups providing care for IBD patients, will maintain the integrity and privacy of their own data whilst enabling continued, productive communication and collaboration with other groups. The major characteristics of the proposed, multicentre IBD-DAC are that it should be standards-based whilst allowing interoperability with other centres and, potentially, other EHRs. It should form the basis for exploratory, hypothesis-generating studies, clinical research studies, quality improvement programs and continuing professional development across multiple sites and it should provide a template for the integration of clinical care, using EHRs, into clinical research and education that will be relevant to other disease areas (chronic diseases) in gastroenterology (e.g. celiac disease, Barretts esophagus, functional bowel disease, hepatitis, obesity) and in other specialties. Proposed process flow We will follow the process flow shown in Figure Related Work In this section different approaches that are related to this research are discussed. EHR Currently used open source EHR systems in terms of the degree of adoption and their comparison with the OSCAR EHR will be discussed. Data mining The application of different data mining techniques (association, clustering, aggregation, classification, prediction) in identifying the patterns and trends in clinical data. Collaborative decision making Techniques that allows for collaborating decision making among a group of speciality (e.g., GI specialists) or among an ad hoc team within the hospital to perform patient treatment (e.g., surgery). Intelligent decision making Different approaches that use data mining, agents or AI techniques to enable physicians to make knowledgeable decisions based on prior knowledge that is captured in a knowledge- 4

6 base. Fig. 1. Proposed process flow for the project. 3. Background 3.1. Infrastructure These include Canada Health Infoway Infostructure, German HTI (Health Information Technology Infrastructure) and other similar infrastructures Standards These include standards for: i) representing health and medical information domain (RIM and its derivatives D-MIM, R-MIM, HMD); ii) messaging and communication (HL7 v2, v3, FHIR, PMML); and iii) terminology systems (SNOMED, UMLS) Technologies These include those technologies that are frequently used in the health domain such as: semantics analysis, natural language analysis, Ontology, guideline languages and processing (GLIF3), business process engines, mobile technology (IOS, Android). 5

7 4. Approach Fig. 2. Proposed framework for IBD patients data centre. Figure 2 illustrates the proposed framework for IBD patient data centre. The proposed architecture consists of four layers as follows: 4.1. Application layer The application layer consists of the following services: Customizable user-interface and workflow designer. This layer provides a context-driven mechanism for tool-guided information collection from patients using the processed information and knowledge extracted from the IBD datacenter and OSCAR EHR. The patient context is a tuple of attributes such as: < symptoms, complaints, pain level, medication, chronic, f requency, age >. Smart mobile device. This layer provides lightweight information acquisition and customized processing facilities for collecting data. Decision support system. This layer uses clinical guidelines and/or organizational workflows in order to assist the physician or administration to make more 6

8 knowledgeable decisions by generating reminders, alerts, warnings. Knowledge Engineering. This layer extracts patterns and trends from the IBD datacenter for GI specialists and disseminate the knowledge to other IBD centers based on a pull or push mechanism Clinical interoperability layer Each data center is autonomous in terms of the adopted EHR, data schema and communication protocols, and can be implemented using private or community cloud infrastructure. However, the integration among the centers is based on standards, such as SOA (SOAP or RESTfull), HL7 v3, SNOMED CT, and PMML Physical layer Wireless body area sensor network provides new types of primitive information/data from the patients body and can be implemented through wireless ad-hoc network for data acquisition from sensors. Such a network should use mechanisms and protocols for highly secure data communication and storage of information in order to provide the security and privacy required for patient data. There are commercial solutions (e.g., FitBit wireless sensors) which can be effectively used in ehealth projects. These sensors consists of lowpower, low-cost, and highly miniaturized wireless sensor devices and associated system integration technology Evaluation layer This layer provides human oriented evaluations of the process, application, and information aspects. Such evaluations could be based on measuring: (i) improvement of the quality of care for IBD patients; (ii) usability of the developed applications for user (physicians, nurses, administration, other staff) satisfaction; (iii) resource allocation and waiting time management; and (iv) guideline adoption by the primary care physicians. 5. Case Studies The case study (for this paper) will be a simple proof-of-concept implementation of the proposed framework in Figure 2. This includes: (i) IBD data extraction from OSCAR (using Integrator) and storing it into IBD data centre; (ii) performing association rule mining to generate the mined-knowledge base ; and (iii) using the association rules in the knowledge base to assist the GI specialist in decision making. We have published a paper in this regard which provides an example on how to do the data mining and decision support part: Scenario-Oriented Information Extraction from Electronic Health Records. A. Yousefi, N. Mastouri and K. Sartipi. IEEE International Symposium on Computer-Based Medical Sys- 7

9 tems (CBMS 2009), pages 1-5. August , Albuquerque, New Mexico, USA. 6. Discussion Add the Discussion about the proposed approach here. 7. Conclusion Add your Conclusion here. bliographystyleabbrv 8