Derek Corrigan

Translational Medicine and Patient Safety in Europe: TRANSFoRm—Architecture for the Learning Health System in Europe

The Learning Health System (LHS) describes linking routine healthcare systems directly with both research translation and knowledge translation as an extension of the evidence-based medicine paradigm, taking advantage of the ubiquitous use of electronic health record (EHR) systems. TRANSFoRm is an EU FP7 project that seeks to develop an infrastructure for the LHS in European primary care. Methods. The project is based on three clinical use cases, a genotype-phenotype study in diabetes, a randomised controlled trial with gastroesophageal reflux disease, and a diagnostic decision support system for chest pain, abdominal pain, and shortness of breath. Results. Four models were developed (clinical research, clinical data, provenance, and diagnosis) that form the basis of the projects approach to interoperability. These models are maintained as ontologies with binding of terms to define precise data elements. CDISC ODM and SDM standards are extended using an archetype approach to enable a two-level model of individual data elements, representing both research content and clinical content. Separate configurations of the TRANSFoRm tools serve each use case. Conclusions. The project has been successful in using ontologies and archetypes to develop a highly flexible solution to the problem of heterogeneity of data sources presented by the LHS.

Diagnostic accuracy of GPs when using an early-intervention decision support system: a high-fidelity simulation

Background Observational and experimental studies of the diagnostic task have demonstrated the importance of the first hypotheses that come to mind for accurate diagnosis. A prototype decision support system (DSS) designed to support GPs’ first impressions has been integrated with a commercial electronic health record (EHR) system. Aim To evaluate the prototype DSS in a high-fidelity simulation. Design and setting Within-participant design: 34 GPs consulted with six standardised patients (actors) using their usual EHR. On a different day, GPs used the EHR with the integrated DSS to consult with six other patients, matched for difficulty and counterbalanced. Method Entering the reason for encounter triggered the DSS, which provided a patient-specific list of potential diagnoses, and supported coding of symptoms during the consultation. At each consultation, GPs recorded their diagnosis and management. At the end, they completed a usability questionnaire. The actors completed a satisfaction questionnaire after each consultation. Results There was an 8–9% absolute improvement in diagnostic accuracy when the DSS was used. This improvement was significant (odds ratio [OR] 1.41, 95% confidence interval [CI] = 1.13 to 1.77, P<0.01). There was no associated increase of investigations ordered or consultation length. GPs coded significantly more data when using the DSS (mean 12.35 with the DSS versus 1.64 without), and were generally satisfied with its usability. Patient satisfaction ratings were the same for consultations with and without the DSS. Conclusion The DSS prototype was successfully employed in simulated consultations of high fidelity, with no measurable influences on patient satisfaction. The substantially increased data coding can operate as motivation for future DSS adoption.

Evidence-based rules from family practice to inform family practice; the learning healthcare system case study on urinary tract infections.

BackgroundAnalysis of encounter data relevant to the diagnostic process sourced from routine electronic medical record (EMR) databases represents a classic example of the concept of a learning healthcare system (LHS). By collecting International Classification of Primary Care (ICPC) coded EMR data as part of the Transition Project from Dutch and Maltese databases (using the EMR TransHIS), data mining algorithms can empirically quantify the relationships of all presenting reasons for encounter (RfEs) and recorded diagnostic outcomes. We have specifically looked at new episodes of care (EoC) for two urinary system infections: simple urinary tract infection (UTI, ICPC code: U71) and pyelonephritis (ICPC code: U70).MethodsParticipating family doctors (FDs) recorded details of all their patient contacts in an EoC structure using the ICPC, including RfEs presented by the patient, and the FDs’ diagnostic labels. The relationships between RfEs and episode titles were studied using probabilistic and data mining methods as part of the TRANSFoRm project.ResultsThe Dutch data indicated that the presence of RfE’s “Cystitis/Urinary Tract Infection”, “Dysuria”, “Fear of UTI”, “Urinary frequency/urgency”, “Haematuria”, “Urine symptom/complaint, other” are all strong, reliable, predictors for the diagnosis “Cystitis/Urinary Tract Infection” . The Maltese data indicated that the presence of RfE’s “Dysuria”, “Urinary frequency/urgency”, “Haematuria” are all strong, reliable, predictors for the diagnosis “Cystitis/Urinary Tract Infection”.The Dutch data indicated that the presence of RfE’s “Flank/axilla symptom/complaint”, “Dysuria”, “Fever”, “Cystitis/Urinary Tract Infection”, “Abdominal pain/cramps general” are all strong, reliable, predictors for the diagnosis “Pyelonephritis” . The Maltese data set did not present any clinically and statistically significant predictors for pyelonephritis.ConclusionsWe describe clinically and statistically significant diagnostic associations observed between UTIs and pyelonephritis presenting as a new problem in family practice, and all associated RfEs, and demonstrate that the significant diagnostic cues obtained are consistent with the literature. We conclude that it is possible to generate clinically meaningful diagnostic evidence from electronic sources of patient data.

Templates as a method for implementing data provenance in decision support systems

Decision support systems are used as a method of promoting consistent guideline-based diagnosis supporting clinical reasoning at point of care. However, despite the availability of numerous commercial products, the wider acceptance of these systems has been hampered by concerns about diagnostic performance and a perceived lack of transparency in the process of generating clinical recommendations. This resonates with the Learning Health System paradigm that promotes data-driven medicine relying on routine data capture and transformation, which also stresses the need for trust in an evidence-based system. Data provenance is a way of automatically capturing the trace of a research task and its resulting data, thereby facilitating trust and the principles of reproducible research. While computational domains have started to embrace this technology through provenance-enabled execution middlewares, traditionally non-computational disciplines, such as medical research, that do not rely on a single software platform, are still struggling with its adoption. In order to address these issues, we introduce provenance templates - abstract provenance fragments representing meaningful domain actions. Templates can be used to generate a model-driven service interface for domain software tools to routinely capture the provenance of their data and tasks. This paper specifies the requirements for a Decision Support tool based on the Learning Health System, introduces the theoretical model for provenance templates and demonstrates the resulting architecture. Our methods were tested and validated on the provenance infrastructure for a Diagnostic Decision Support System that was developed as part of the EU FP7 TRANSFoRm project.

Development of a complex intervention to promote appropriate prescribing and medication intensification in poorly controlled type 2 diabetes mellitus in Irish general practice

BackgroundPoorly controlled type 2 diabetes mellitus (T2DM) can be seen as failure to meet recommended targets for management of key risk factors including glycaemic control, blood pressure and lipids. Poor control of risk factors is associated with significant morbidity, mortality and healthcare costs. Failure to intensify medications for patients with poor control of T2DM when indicated is called clinical inertia and is one contributory factor to poor control of T2DM. We aimed to develop a theory and evidence-based complex intervention to improve appropriate prescribing and medication intensification in poorly controlled T2DM in Irish general practice.MethodsThe first stage of the Medical Research Council Framework for developing and evaluating complex interventions was utilised. To identify current evidence, we performed a systematic review to examine the effectiveness of interventions targeting patients with poorly controlled T2DM in community settings. The Behaviour Change Wheel theoretical approach was used to identify suitable intervention functions. Workshops, simulation, collaborations with academic partners and observation of physicians were utilised to operationalise the intervention functions and design the elements of the complex intervention.ResultsOur systematic review highlighted that professional-based interventions, potentially through clinical decision support systems, could address poorly controlled T2DM. Appropriate intensification of anti-glycaemic and cardiovascular medications, by general practitioners (GPs), for adults with poorly controlled T2DM was identified as the key behaviour to address clinical inertia. Psychological capability was the key driver of the behaviour, which needed to change, suggesting five key intervention functions (education, training, enablement, environmental restructuring and incentivisation) and nine key behaviour change techniques, which were operationalised into a complex intervention. The intervention has three components: (a) a training program/academic detailing of target GPs, (b) a remote finder tool to help GPs identify patients with poor control of T2DM in their practice and (c) A web-based clinical decision support system.ConclusionsThis paper describes a multifaceted process including an exploration of current evidence and a thorough theoretical understanding of the predictors of the behaviour resulting in the design of a complex intervention to promote the implementation of evidence-based guidelines, through appropriate prescribing and medication intensification in poorly controlled T2DM.

An ontological treatment of clinical prediction rules implementing the Alvarado score.

A lack of acceptance has hindered the widespread adoption and implementation of clinical prediction rules (CPRs). The use of clinical decision support systems (CDSSs) has been advocated as one way of facilitating a broader dissemination and validation of CPRs. This requires computable models of clinical evidence based on open standards rather than closed proprietary content. The on-going TRANSFoRm project has developed ontological models of CPRs suitable for providing CPR based decision support. This paper presents a description of the design and implementation of the ontology model for CPRs that has been proposed. The conceptual validity of the ontology is discussed using the example of a specific CPR in the form of the Alvarado Score for acute appendicitis. We demonstrate how the model is used to query the structure of this particular rule, providing a computable representation suitable for CPRs in general.

A methodology for mining clinical data: experiences from TRANSFoRm project.

Data mining of electronic health records (eHRs) allows us to identify patterns of patient data that characterize diseases and their progress and learn best practices for treatment and diagnosis. Clinical Prediction Rules (CPRs) are a form of clinical evidence that quantifies the contribution of different clinical data to a particular clinical outcome and help clinicians to decide the diagnosis, prognosis or therapeutic conduct for any given patient. The TRANSFoRm diagnostic support system (DSS) is based on the construction of an ontological repository of CPRs for diagnosis prediction in which clinical evidence is expressed using a unified vocabulary. This paper explains the proposed methodology for constructing this CPR repository, addressing algorithms and quality measures for filtering relevant rules. Some preliminary application results are also presented.

A Multi-step Maturity Model for the Implementation of Electronic and Computable Diagnostic Clinical Prediction Rules (eCPRs)

Introduction: The use of Clinical Prediction Rules (CPRs) has been advocated as one way of implementing actionable evidence-based rules in clinical practice. The current highly manual nature of deriving CPRs makes them difficult to use and maintain. Addressing the known limitations of CPRs requires implementing more flexible and dynamic models of CPR development. We describe the application of Information and Communication Technology (ICT) to provide a platform for the derivation and dissemination of CPRs derived through analysis and continual learning from electronic patient data. Model Components: We propose a multistep maturity model for constructing electronic and computable CPRs (eCPRs). The model has six levels – from the lowest level of CPR maturity (literaturebased CPRs) to a fully electronic and computable service-oriented model of CPRs that are sensitive to specific demographic patient populations. We describe examples of implementations of the core model components – focusing on CPR representation, interoperability, electronic dissemination, CPR learning, and user interface requirements. Conclusion: The traditional focus on derivation and narrow validation of CPRs has severely limited their wider acceptance. The evolution and maturity model described here outlines a progression toward eCPRs consistent with the vision of a learning health system (LHS) – using central repositories of CPR knowledge, accessible open standards, and generalizable models to avoid repetition of previous work. This is useful for developing more ambitious strategies to address limitations of the traditional CPR development life cycle. The model described here is a starting point for promoting discussion about what a more dynamic CPR development process should look like.

Requirements and validation of a prototype learning health system for clinical diagnosis

Diagnostic error is a major threat to patient safety in the context of family practice. The patient safety implications are severe for both patient and clinician. Traditional approaches to diagnostic decision support have lacked broad acceptance for a number of well‐documented reasons: poor integration with electronic health records and clinician workflow, static evidence that lacks transparency and trust, and use of proprietary technical standards hindering wider interoperability. The learning health system (LHS) provides a suitable infrastructure for development of a new breed of learning decision support tools. These tools exploit the potential for appropriate use of the growing volumes of aggregated sources of electronic health records.

Classification Method for Differential Diagnosis Based on the Course of Episode of Care

The main goal of the paper is to propose a classification method for differential diagnosis in primary care domain. Commonly, the final diagnosis for the episode of care is related with the initial reason for encounter (RfE). However, many distinct diagnoses can follow from a single RfE and they need to be distinguished. The new method exploits the data about whole episodes of care quantified by individual patients’ encounters and it extracts episode features from electronic health record to learn the classifier. The experimental studies carried out on two primary care dataset from Malta and the Netherlands for three distinct diagnostic groups revealed the validity of the proposed approach.