Ebook: Patient Safety Informatics

Patient safety has become an important theme of the research agenda in the course of the last decade, both throughout Europe and worldwide. Improving patient safety and the quality of healthcare poses many challenges, and information technology (IT) has always been seen as having the potential to support the measures necessary to address these. But the risk of adverse events is unfortunately rising alongside the increasing sophistication and maturity of the health IT systems incorporated into the hospital environment. One major source of such errors is related to medication, i.e. adverse drug events (ADEs), which can incur considerable extra healthcare costs, as well as posing a risk to the safety of patients.

From a research perspective, different approaches have been introduced to eliminate ADEs, such as reporting systems, records and chart reviews, detection methods (with varying degrees of automation), etc. The major concerns raised by all of these approaches and methods are related to reliability and quality of results, reproducibility or generalisation of the conclusions drawn, appropriate identification of the contributing factors and interpretation of the outcomes, and knowledge management.

From a practical perspective, the transferability and use of such tools in clinical practice is a major challenge. Aspects related explicitly to the healthcare environment have to be taken carefully into account, such as organisational and procedural parameters, contextualisation issues, human factors and usability features, to name but a few. The adoption of these tools into real clinical settings is only possible by means of a holistic, validated and qualitative approach.

Following the success of the first workshop, organised in the context of the EU-funded Patient Safety through Intelligent Procedures in medication (PSIP) project and held in Belgirate, Italy, in September 2009, this second workshop presents current, novel methods and applications that have achieved concrete results and that are relevant to the domain of patient safety as a whole. Reading the papers of this book, which review the state-of-the-art, it is evident that significant progress has been made in the field, but that even greater challenges must still be faced if a successful transfer of research ideas and outcomes into clinical practice is to be accomplished. It is the diversity of these challenges and the complexity of the domain which indicate and justify the necessity to introduce a new direction in healthcare IT devoted to patient safety per se. Hence the title of this book: “Patient Safety Informatics”.

To this end, the contributions in this book include: (a) designing IT systems for patient safety, coping in particular with information contextualisation, human factor engineering, the design aspects of clinical decision support systems, and e-prescription frameworks; (b) methods and technologies for developing patient safety systems, devoted to medical information extraction via semantic mining techniques, multiterminology systems linked with semantic interoperability, knowledge representation techniques and standardisation aspects, etc.; (c) novel applications of patient safety informatics, such as an ADE retrospective analysis framework, the exploitation of decision support services for ADE prevention via a variety of systems (i.e. a commercial electronic health record, a commercial computerised physician order entry system, and an autonomous web-based platform), a standardised patient summary framework, a terminology mapping framework applicable in several domains, etc., and (d) validation and impact assessment studies for patient safety informatics outcomes, analysing in particular patient empowerment solutions, clinical decision support systems and knowledge bases targeting ADE prevention, an ADE retrospective analysis system, medical information extraction from discharge letters, etc.

In addition, this workshop is an opportunity for experts active in the field, including the contributors to this book, to meet and confront ideas and experiences arising from many different perspectives, i.e. research, clinical practice and healthcare IT industry oriented, as well as from several EU projects funded to contribute to patient safety as a whole.

We would like to express our gratitude to Prof. David Bates, Prof. Jos Aarts and Dr. Beth Lilja for their participation and their keynote speeches; to Mr. Michele Carenini, Prof. Peter Elkin, Dr. Zoi Kolitsi, Prof. Andre Kushniruk and Prof. Gianluca Trifirò for accepting the invitation to participate in the Workshop; to all the participants and the authors of the Workshop; to the Scientific Committee and the reviewers who helped in the preparation of qualitative contributions in the Workshop and, last but not least, to the European Commission which, by funding European projects in the domain of patient safety, have made the organisation of this workshop possible, as well as the editing and publication of this book.

The book cover is inspired by the paper entitled “Implementation of a Taxonomy Aiming to Support the Design of a Contextualised Clinical Decision Support System” by Stéphanie Bernonville, Romaric Marcilly, Radja Messai, Nicolas Leroy, Emma Przewozny, Nathalie Souf and Marie-Catherine Beuscart-Zéphir which is published in this book (pp. 74–83). The picture denotes the design approach proposed by the authors for developing contextualised clinical decision support systems (CDSS) for medication safety.

Vassilis Koutkias, Julie Niès, Sanne Jensen, Nicos Maglaveras and Régis Beuscart

(editors)

May 2011

Adverse Drug Events (ADEs) are injuries due to medication management rather than the underlying condition of the patient. They endanger the patients and most of them could be avoided and prevented. The detection of ADEs usually relies on spontaneous reporting or medical chart reviews. The first objective of the PSIP Project is to automatically detect cases of ADEs by means of Data Mining, and to provide these cases to healthcare professionals. The second objective is to prevent ADEs by means of contextualised Clinical Decision Support Systems (Cx-CDSS) connected with Computerised Physician Order Entry (CPOE) or Electronic Health Record (EHR) systems. The detection of ADEs has been made possible through a set of rules able to identify relevant cases is a set of 92,000 medical cases. The results of this detection are provided through “ADE Scorecards”. Contextualized Decision Support Systems have been developed by using the same set of rules and implemented in different software environments. The initial objectives of the PSIP project have been reached. The evaluation of the clinical impact has to be completed.

Implementing electronic prescribing in health care has been a slow process. Health authorities are now requiring mandatory electronic prescribing because of patient safety concerns. Electronic prescribing is not yet a mature technology, and may therefore pose a risk if especially organizational conditions are not taken into account. The paper offers some thoughts on the future of electronic prescribing in practice. It is especially important to extend electronic prescribing to the continuum of care in order avoid that medication safety falls in the cracks of fragmented health care organizations.

Numerous studies have confirmed that the patient safety challenge remains tangible. Innovative use of healthcare IT (Information Technology) could play a part in the solution, if the costs of development and implementation are weighed against the major potential savings by improving quality and safety. It is suggested through the “Safe Seven”-checklist, that the design of supporting eHealth solutions lends principles from the patient safety and physical design domains.

The EU-ADR project aims to exploit different European electronic healthcare records (EHR) databases for drug safety signal detection. In this paper we report the preliminary results concerning the comparison of signal detection between EU-ADR network and two spontaneous reporting databases, the Food and Drug Administration and World Health Organization databases. EU-ADR data sources consist of eight databases in four countries (Denmark, Italy, Netherlands, and United Kingdom) that are virtually linked through distributed data network. A custom-built software (Jerboa©) elaborates harmonized input data that are produced locally and generates aggregated data which are then stored in a central repository. Those data are subsequently analyzed through different statistics (i.e. Longitudinal Gamma Poisson Shrinker). As potential signals, all the drugs that are associated to six events of interest (bullous eruptions - BE, acute renal failure - ARF, acute myocardial infarction - AMI, anaphylactic shock - AS, rhabdomyolysis - RHABD, and upper gastrointestinal bleeding - UGIB) have been detected via different data mining techniques in the two systems. Subsequently a comparison concerning the number of drugs that could be investigated and the potential signals detected for each event in the spontaneous reporting systems (SRSs) and EU-ADR network was made. SRSs could explore, as potential signals, a larger number of drugs for the six events, in comparison to EU-ADR (range: 630-3,393 vs. 87-856), particularly for those events commonly thought to be potentially drug-induced (i.e. BE: 3,393 vs. 228). The highest proportion of signals detected in SRSs was found for BE, ARF and AS, while for ARF, and UGIB in EU-ADR. In conclusion, it seems that EU-ADR longitudinal database network may complement traditional spontaneous reporting system for signal detection, especially for those adverse events that are frequent in general population and are not commonly thought to be drug-induced. The methodology for signal detection in EU-ADR is still under development and testing phase.

We have previously studied system failures involved in medication errors using a limited number of root cause analyses as source. The aim of this study was to describe a larger number of medication errors with respect to harm, involved medicines and involved system problems – thus providing information for the development of IT-based decision support. We evaluated 3,520 medication error reports derived from 12 months of consecutive reporting from 13 hospitals in the Capital Region of Denmark. We found 0.65% errors with serious harm and 16% with moderate harm. A small number of medicines were involved in the majority of the errors. The problems in the medication error process were heterogeneous. Some were related to specific medicines and others were related to the computerized order entry system. Accordingly decision support targeted at specific medicines and improved IT systems are part of the continuing work to reduce the frequency of medication errors.

The majority of questions that arise in the practice of medicine relate to drug information. Additionally, adverse reactions account for as many as 98,00o deaths per year in the United States. Adverse drug reactions account for a significant portion of those errors. Many authors believe that clinical decision support associated with computerized physician order entry has the potential to decrease this adverse drug event rate. This decision support requires knowledge to drive the process. One important and rich source of drug knowledge is the DailyMed product labels. In this project we used computationally extracted SNOMED CT^TM codified data associated with each section of each product label as input to a rules engine that created computable assertional knowledge in the form of semantic triples. These are expressed in the form of “Drug” HasIndication “SNOMED CT^TM code”. The information density of drug labels is deep, broad and quite substantial. By providing a computable form of this information content from drug labels we make these important axioms (facts) more accessible to computer programs designed to support improved care.

The effective evaluation of the usability of health information systems is currently a major challenge. It is essential that the applications we develop are not only usable, but that they are also shown to be safe and do not inadvertently introduce errors. Furthermore, to provide appropriate feedback to designers of systems new methods for evaluation are needed as applications become more complex and distributed. To ensure system usability and safety a variety of methods have emerged from the area of usability engineering that have been adapted to healthcare. The authors have applied and adapted methods of usability engineering, working with hospitals and other healthcare organizations for designing and evaluating a range of health information systems over a number of years. We describe a methodological framework for considering some of these advances and show how a range of usability evaluations can be used to evaluate both the usability and safety of healthcare information systems both in artificial mocked up and real clinical settings using in-situ testing approaches. We conclude with a discussion of recent trends in the area of usability engineering in healthcare that have potential for improving the safety of healthcare information systems.

Risk Management in healthcare is a particularly challenging task. From a health system perspective a systemic and person centered approach is needed. From an ICT perspective, continuity of care and sharing information for clinical purposes, research and care improvement can be supported though interoperable systems and services and concurrent ability of proper interpretation of this knowledge by different users. Research provides solutions to specific patient safety challenges. Supporting the dynamics of change will furthermore necessitate strategies to shorten the innovation cycle from research to implementation, deployment, adoption and routine use. Transferring research results to deployable solutions requires in addition a high degree of co-ordination at EU level, with strong links to the national competent organisations and stakeholder communities. The breadth and complexity of the issues that need to be addressed require that an appropriate, EU Collaborative Governance is set up.

The paper presents results from a design research project of a user interface (UI) for a Computerised Clinical Decision Support System (CDSS). The ambition has been to design Human-Computer Interaction (HCI) that can minimise medication errors. Through an iterative design process a digital prototype for prescription of medicine has been developed. This paper presents results from the formative evaluation of the prototype conducted in a simulation laboratory with ten participating physicians. Data from the simulation is analysed by use of theory on how users perceive information. The conclusion is a model, which sum up four principles of interaction for design of CDSS. The four principles for design of user interfaces for CDSS are summarised as four A's: All in one, At a glance, At hand and Attention. The model emphasises integration of all four interaction principles in the design of user interfaces for CDSS, i.e. the model is an integrated model which we suggest as a guide for interaction design when working with preventing medication errors.

Clinical Decision Support Systems (CDSS) are recently implemented in hospital settings to improve the reliability of drug ordering. However, such systems have limited effects due to their tendency to overalert. To healthcare professionals consider alerts, it is necessary to adapt the CDSS to their activity. Thus, it is necessary to consider contextualisation aspects in the system design. In this article, we propose a taxonomy integrating contextualisation elements issued from an activity analysis to guide the design of a contextualised CDSS. This taxonomy has been developed within the framework of the European project PSIP (Patient Safety through Intelligent Procedures in medication) aiming to make easier the identification and the prevention of Adverse Drug Events.

Medication related Computerized Decision Support System (CDSS) are known to have a positive impact on Adverse Drug Events (ADE) prevention but they face acceptance problems due to over alerting and usability issues. We present here a Human factors approach to the design of these Clinical Decision Support (CDS) functions and to their integration into different Electronic Health Record (EHR) / Computerized Physicians Order Entry (CPOE) systems, so that the resulting CDSS corresponds to the users needs and fits clinical workflows and cognitive processes. We used ethnographic observations completed with semi-structured interviews to analyse existing work situations and work processes. These were then described in detail using the SHEL (Software, Hardware, Environment & Liveware) formalism, which enables a structured description of the work system and provides an appropriate classification of human errors potentially leading to ADEs. We then propose a Unified Modelling Language (UML) model supporting the characterization by the CDSS of the drug monitoring and clinical context of patients at risk of ADE. This model combines the status of the lab test orders on the one hand with the validity and normality of the lab results on the other hand. This makes the system able to catch the context of the monitoring of the drugs through their corresponding lab tests and lab results (e.g. kalemia for potassium) and also part of the context of the clinical status of the patient (actual lab values, but also diseases and other pathologies that are identified as potential causes of the ADE e.g. renal insufficiency and potassium). We show that making the system able to catch the monitoring and clinical contexts opens interesting opportunities for the design of the CDS information content and display mode. Implementing this model would allow the CDSS to take into account the actions already engaged by the healthcare team and to adapt the information delivered to the monitoring and clinical context, thus making the CDSS a partner to the clinicians, nurses and pharmacists.

This paper presents an analysis of hospitals' organization and Hospital Information Systems' features which can contribute in contextualization of Clinical Decision Support Systems (CDSS) for Adverse Drug Event (ADE) prevention. We identified four categories of contextualization: ENVIRONMENT, TASKS, USERS and TEMPORAL ASPECTS. Based on this analysis, we studied the technical possibilities at the architectural level to determine which component(s) of a standalone knowledge platform could technically handle contextualization. The results impact three types of components of this platform: (1) a CDSS providing decision support based on ADE signals mined in large data repositories; (2) a Connectivity Platform providing transformation and routing services (enabling any application to connect to the CDSS); (3) three prototype applications for accessing the decision support services realized within an industrial Computerized Physician Order Entry, an industrial Electronic Health Record and in an independent Web prototype, respectively. In each of the above components we present the dimension(s) of contextualization that has/have been determined to cope with and the design followed in the implementation phase.

E-prescription is amongst the most widespread medical electronic support functions. However, several studies reported acceptance and utilisation rates not as high as expected. This paper performs firstly an analysis of the literature on e-prescription characteristics and functionalities especially with respect to their actual usage. Then a specific field study was conducted in an Internal Medicine ward, to investigate human factor issues associated to the introduction of an e-prescription system. Finally, the findings of the field study are framed within the actual implementation of various electronic support outputs resulting from the European Project “Patient safety through intelligent procedures in medication” (PSIP). The results show the importance of a systemic view when designing, implementing and evaluating medical support systems, as the pre-existing structures and tools largely influence the impact of those systems and their effects.

This paper presents methods for shallow Information Extraction (IE) from the free text zones of hospital Patient Records (PRs) in Bulgarian language in the Patient Safety through Intelligent Procedures in medication (PSIP) project. We extract automatically information about drug names, dosage, modes and frequency and assign the corresponding ATC code to each medication event. Using various modules for rule-based text analysis, our IE components in PSIP perform a significant amount of symbolic computations. We try to address negative statements, elliptical constructions, typical conjunctive phrases, and simple inferences concerning temporal constraints and finally aim at the assignment of the drug ACT code to the extracted medication events, which additionally complicates the extraction algorithm. The prototype of the system was used for experiments with a training corpus containing 1,300 PRs and the evaluation results are obtained using a test corpus containing 6,200 PRs. The extraction accuracy (f-score) for drug names is 98.42% and for dose 93.85%.

Since the mid-90s, several quality-controlled health gateways were developed. In France, CISMeF is the leading health gateway. It indexes Internet resources from the main institutions, using the MeSH thesaurus and the Dublin Core metadata element set. Since 2005, the CISMeF Information System (IS) includes 24 health terminologies, classifications and thesauri for indexing and information retrieval. This work aims at creating a Health Multi-Terminology Portal (HMTP) and connect it to the CISMeF Terminology Database mainly for searching concepts and terms among all the health controlled vocabularies available in French (or in English and translated in French) and browsing it dynamically. To integrate the terminologies in the CISMeF IS, three steps are necessary: (1) designing a meta-model into which each terminology can be integrated, (2) developing a process to include terminologies into the HMTP, (3) building and integrating existing and new inter-terminology mappings into the HMTP. A total of 24 terminologies are included in the HMTP, with 575,300 concepts, 852,000 synonyms, 222,800 definitions and 1,180,000 relations. Heightteen of these terminologies are not included yet in the UMLS among them, some from the World Health Organization. Since January 2010, HMTP is daily used by CISMeF librarians to index in multi-terminology mode. A health multiterminology portal is a valuable tool helping the indexing and the retrieval of resources from a quality-controlled patient safety gateway. It can also be very useful for teaching or performing audits in terminology management.

Knowledge representation is an important part of knowledge engineering activities that is crucial for enabling knowledge sharing and reuse. In this regard, standardised formalisms and technologies play a significant role. Especially for the medical domain, where knowledge may be tacit, not articulated and highly diverse, the development and adoption of standardised knowledge representations is highly challenging and of outmost importance to achieve knowledge interoperability. To this end, this paper presents a research effort towards the standardised representation of a Knowledge Base (KB) encapsulating rule-based signals and procedures for Adverse Drug Event (ADE) prevention. The KB constitutes an integral part of Clinical Decision Support Systems (CDSSs) to be used at the point of care. The paper highlights the requirements at the domain of discourse with respect to knowledge representation, according to which GELLO (an HL7 and ANSI standard) has been adopted. Results of our prototype implementation are presented along with the advantages and the limitations introduced by the employed approach.

This project was designed to underline any actions relative to medication error prevention and patient safety improvement setting up in North American hospitals which could be implemented in French Parisian hospitals. A literature research and analysis of medication-use process in the North American hospitals and a validation survey of hospital pharmacist managers in the San Diego area was performed to assess main points of hospital medication-use process. Literature analysis, survey analysis of respondents highlighted main differences between the two countries at three levels: nationwide, hospital level and pharmaceutical service level. According to this, proposal development to optimize medication-use process in the French system includes the following topics: implementation of an expanded use of information technology and robotics; increase pharmaceutical human resources allowing expansion of clinical pharmacy activities; focus on high-risk medications and high-risk patient populations; develop a collective sense of responsibility for medication error prevention in hospital settings, involving medical, pharmaceutical and administrative teams. Along with a strong emphasis that should be put on the identified topics to improve the quality and safety of hospital care in France, consideration of patient safety as a priority at a nationwide level needs to be reinforced.

The goal of every effort and actions/interventions in almost all healthcare settings throughout the world's health systems -primary care, inpatient, outpatient encounters, diagnostic and therapeutic interventions, peri-operative settings- is and has been to achieve a well defined outcome (a kind of improvement in health status of the patient under consideration, an observable and significant change(s) in selected set(s) of clinical parameters confirmed by laboratory results and pathology findings, improvements in clinical outcomes). Clinical inefficiencies, in this context, should be addressed very systematically and scientifically. This is achieved through a continuously monitoring approach to adverse drug events based on information repositories and evidence-based rule sets. For monitoring drug-related outcomes and clinical outcomes in general, the concept of DDD (Defined Daily Dose) compliance is explained in this article to eliminate and avoid adverse clinical outcomes.

Although several methods exist for Adverse Drug events (ADE) detection due to past hospitalizations, a tool that could display those ADEs to the physicians does not exist yet. This article presents the ADE Scorecards, a Web tool that enables to screen past hospitalizations extracted from Electronic Health Records (EHR), using a set of ADE detection rules, presently rules discovered by data mining. The tool enables the physicians to (1) get contextualized statistics about the ADEs that happen in their medical department, (2) see the rules that are useful in their department, i.e. the rules that could have enabled to prevent those ADEs and (3) review in detail the ADE cases, through a comprehensive interface displaying the diagnoses, procedures, lab results, administered drugs and anonymized records. The article shows a demonstration of the tool through a use case.