Adverse Events Detection through Data Mining in Clinical Information Systems. Hans Åhlfeldt Daniel Karlsson Håkan Petersson Sten Walther

Transcription

1 Adverse Events Detection through Data Mining in Clinical Information Systems Hans Åhlfeldt Daniel Karlsson Håkan Petersson Sten Walther Dept Biomedical Engineering Linköping University April 2008

2 OVERVIEW OF RESEARCH AREA There is an increasing interest in patient safety in the delivery of health care. Since the release of The Institute of Medicine report To Err is Human [1], the health care community and the public at large have come to realize that the health care system is not as safe as it should be [2]. In a literature survey of patient safety published by the European Commission (EC) [3] it s estimated that one in ten patients admitted to hospitals are unintentionally harmed. When patients were asked whether they believed they had experienced a medical mistake in treatment, between % reported at least one error [3]. An error in this context is the failure of a planned action to be completed as intended or the use of a wrong plan to achieve an aim. An adverse event results in unintended harm to the patient by an act of commission or omission rather than by the underlying health problem of the patient. Examples of good practice in terms of systematic follow-up do exist in health care, such as the follow-up of complications due to infections after surgery or the follow-up of medication errors, but the scope is now extended to systematic identification of common patterns in safety-relevant events beyond merely reporting nosocomial infections or adverse drug events. In the report Patient Safety [2], a list of recommendations is put forward for the improvement of patient safety. Recommendations address the general need for improved information systems at the point of care, including immediate access to complete patient information and decision support tools, and capture of information on adverse events as well as near misses. The lack of comprehensive standards for representation of clinical data is addressed as one major hindrance. The Patients and Citizens Task Force of the European Health Telematics Asociation (ETHEL) has in a position paper [4] put forward a list of requirements for improving patient safety in Europe, including the importance of actual implementation of standards, the establishment of systematic incident reporting, and alert broadcast. In the recent EC report on impact of information and communication technology (ICT) on patient safety and risk management [3], a review of research challenges is given together with a survey on risk management in practice in Europe. An outstanding research challenge identified is the use of routine electronic health records (EHR) data for monitoring and alerting incidents and adverse events. Considerable effort has been invested over the years by the standardisation community of CEN TC251 1 in advancing the formalism of the EHR, specifically addressed in EN13606, a CEN standard for EHR architecture. A specific contribution of EN13606 is the two-level modelling approach, represented by a reference model (RM) holding the general attributes of a health record, and an archetype model (AM), allowing detailed and domain-specific clinical information to be defined, stored and communicated. Another important component would be a common reference terminology (or ontology). Sweden has as a response to this challenge joined the International Health Terminology Standards Development Organisation (IHTSDO) 2 by which the SNOMED CT terminology system is further developed. The framework depicted above could be further expanded into a three-level model as put forward by Rector [5], comprised of an process-oriented information model of clinical documentation (such as CEN TC251 EN 13606), a reference terminology (such as SNOMED CT) and a knowledge representation model, being able to perform reasoning over patient data. Many research efforts are focusing on the feasibility of adverse events detection to increase the quality of care [6]. The potential of scanning the health record for detecting and reducing adverse events has recently been demonstrated in a Swedish study from the Danderyd Hospital [7]. Of 395 patient records manually analysed, some kind of adverse event was identified in every sixth record. When compared to the official systems for reporting of adverse events in Sweden (e.g. Lex Maria and HSAN), only four of these events were found to be reported. The Danderyd study,

3 being based on a method for analysing health records developed at Wimmera Hospital in Australia [8, 9], confirm the hypothesis that the ability to automatically analyse routine data from electronic patient records would significantly contribute to the problems of low rate reporting and long-term turnaround often associated with off-line systems for adverse event reporting. Monitoring and early warning of adverse events can be viewed as a problem in a multidimensional space composed of patient-specific data, evidence-based guidelines, and the health care process over time. However, the electronically available patient data is composed of a mix of heterogeneous data elements, of which today only parts are structured and coded with controlled vocabularies (such is the case for diseases, laboratory results and drug prescription). Other parts are mainly narrative textual reports. Thus, scanning and analysis of heterogeneous clinical information, offers the challenge of multimodal data mining. The ability to systematically analyse data from electronic patient records would be a significant progress in the field of patient safety. Data mining as a mean for automatic or semi-automatic analysis of growing volumes of electronically available patient information based on formal description of adverse events together with the integration of prototype system for detection of potentially adverse events, are therefore key components of the research application. ONGOING RESEARCH PROJECTS Ongoing research activities by the research group as presented below are the result of research in the framework of our VR-application ( ), the EU FP6 SemanticMining project 3, the FP7 DebugIT project 4, and our research in the area of data mining as presented in two doctoral dissertations [10, 11]. The research group has during the last years been working on data mining applications in the domains of cardiac surgery [10], and breast cancer [11]. In the cardiology domain, risk factors associated with post-surgery complications [12, 13] were identified by canonical correlation analysis, a method allowing multiple outcome parameters to be associated with multiple potential risk factors [14]. In the oncology applications, emphasis was put on methods for pre-processing of data including handling of outliers, substitution of missing values, and selection of the most important parameters to be included when building predictive models. Canonical correlation analysis was used as a data reduction method, allowing the most important parameters to be selected based on their respective weights (or loadings) when calculating the highest possible correlation between linear combinations of input- and output parameters [15, 16]. Predictive models for recurrence of breast cancer were built with the same accuracy as domain experts [17]. Moreover, a method was developed by which patient cases could be identified and characterized where the treatment of the breast cancer was noncompliant with the recommendation of a clinical guideline [18, 19]. The newly started EC FP7 project, DebugIT, relies heavily on successful application of data mining as its main objective is to identify and reduce patient safety risks in the domain of infectious diseases and the use of antibiotics. The project aims at pooling of heterogeneous, distributed data sets from different partner sites (from Linköping, Geneva, Paris, Freiburg, London), thus calling for methods for merging or mapping of local data sets into a common virtual data repository together with multimodal data mining approaches. The research group from Linköping University (LiU) will contribute to DebugIT with the Swedish Intensive Care Registry (SIR) 5 which is a joint initiative by Swedish Intensive Care Units (ICU) to audit and benchmark intensive care. Apart from contributing with the data material, the LiU group will lead the data mining work package, and contribute to the realization of the virtual data repository, a task where our experiences from the EC FP6 project SemanticMining will be essential [20]

4 The underpinning challenge of SemanticMining, a Network of Excellence coordinated by the LiUgroup, was how to facilitate semantic interoperability. One area of research in SemanticMining concerned the semantically-well defined electronic health record (EHR). Specific contributions relate to visualisation of large scale terminologies and overview of EHRs [21, 22], and software components compliant with the newly released European standard for EHR architecture and its associated specifications put forward by the openehr Foundation 6 [23-25]. A hypothesis in the current application, is that the standardised EHR architecture, comprised of a reference model and an archetype model will provide a suitable framework for development of decision support systems for monitoring and alerting of adverse events linked to clinical documentation. Another contribution from SemanticMining was the construction of a multi-lingual medical dictionary [26-27]. The dictionary was generated by word alignment from a set of parallel medical terminologies in English and Swedish (MeSH, ICD10, KSH97P, ICF, NCSP). The dictionary comprises approximately pairs of entries in Swedish and English, all linked to the underlying terminology systems. The medical dictionary will be one resource in the current application, when transforming textual descriptions of adverse events into logical expressions linked to commonly used terminologies and coding systems in Swedish health care records. Currently, SNOMED CT is being translated into Swedish. This large translation project organised by The National Board of Health and Welfare, is scheduled for three years, and by the end of 2009, a majority of the approximately concepts will be translated into Swedish, (based on a monthly translation of concepts). The medical dictionary could then regularly be extended with entries linked to SNOMED CT with the word alignment approach described above. Insight into structure and content of Swedish health records [28-30] as well as the importance of contextual factors when developing information support systems [31] will also provide important background knowledge for the current application. OBJECTIVES The first objective of the research programs comprise a principle investigation of the feasibility of transforming textual descriptions of adverse events available today into executable logical rules for detection and alerting. A specific objective regarding this transformation is the linkage of textual entities to entries of terminologies and classifications used in routine health care, with special reference to SNOMED CT, a forthcoming large scale terminology system now being introduced in Swedish health care. A second objective is the development and implementation of algorithms for detection and alerting, where major efforts will be paid to the knowledge representation framework. A third objective is the evaluation of the system, since a thorough investigation of performance measures are essential before integrating the alerting system into a clinical environment, which will be the focus of a subsequent application-oriented project. 3 REFERENCES [1] Institute of Medicine, USA, To Err is Human: Building a safer health care system [2] Institute of Medicine, USA, Patient safety: Achieving a new standard for care. [3] European Commission. ehealth for Safety Impact of ICT on Patient Safety and Risk Management, Oct [4] European Commission. ehealth and Patient Safety a Position Paper prepared by the Patient s and Citizens Task Force, ETHEL, September [5] Rector A, Rogers J, Taweel A. Models and inference methods for clinical systems: A principled approach.. MEDINFO 2004: [6] Murff H, Patel V, Hripcsak G, Bates D. Detecting adverse events for patient safety research: a review of current methodologies. J Biomed Inform 2003; 36: [7] Unbeck M et.al. Report on a study at Danderyd Hosital on Adverse Events. To appear in Acta Orthopaedica 2008; 79(3): x-x. [8] Wolff A, Taylor S, McCabe J. Using checklists and reminders in clinical pathways to improve hospital impatient care. Med J Aust 2004; 181:

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