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Background: Understanding the distribution and determinants of disease in animal populations must be underpinned by knowledge of animal demographics. For companion animals, these data have been difficult to collect because of the distributed nature of the companion animal veterinary industry. Here we describe key demographic features of a large veterinary-visiting pet population in Great Britain as recorded in electronic health records, and explore the association between a range of animal's characteristics and socioeconomic factors., Results: Electronic health records were captured by the Small Animal Veterinary Surveillance Network (SAVSNET), from 143 practices (329 sites) in Great Britain. Mixed logistic regression models were used to assess the association between socioeconomic factors and species and breed ownership, and preventative health care interventions. Dogs made up 64.8% of the veterinary-visiting population, with cats, rabbits and other species making up 30.3, 2.0 and 1.6% respectively. Compared to cats, dogs and rabbits were more likely to be purebred and younger. Neutering was more common in cats (77.0%) compared to dogs (57.1%) and rabbits (45.8%). The insurance and microchipping relative frequency was highest in dogs (27.9 and 53.1%, respectively). Dogs in the veterinary-visiting population belonging to owners living in least-deprived areas of Great Britain were more likely to be purebred, neutered, insured and microchipped. The same association was found for cats in England and for certain parameters in Wales and Scotland., Conclusions: The differences we observed within these populations are likely to impact on the clinical diseases observed within individual veterinary practices that care for them. Based on this descriptive study, there is an indication that the population structures of companion animals co-vary with human and environmental factors such as the predicted socioeconomic level linked to the owner's address. This 'co-demographic' information suggests that further studies of the relationship between human demographics and pet ownership are warranted.

Introduction: The construction of reliable, reusable clinical code sets is essential when re-using Electronic Health Record (EHR) data for research. Yet code set definitions are rarely transparent and their sharing is almost non-existent. There is a lack of methodological standards for the management (construction, sharing, revision and reuse) of clinical code sets which needs to be addressed to ensure the reliability and credibility of studies which use code sets., Objective: To review methodological literature on the management of sets of clinical codes used in research on clinical databases and to provide a list of best practice recommendations for future studies and software tools., Methods: We performed an exhaustive search for methodological papers about clinical code set engineering for re-using EHR data in research. This was supplemented with papers identified by snowball sampling. In addition, a list of e-phenotyping systems was constructed by merging references from several systematic reviews on this topic, and the processes adopted by those systems for code set management was reviewed., Results: Thirty methodological papers were reviewed. Common approaches included: creating an initial list of synonyms for the condition of interest (n=20); making use of the hierarchical nature of coding terminologies during searching (n=23); reviewing sets with clinician input (n=20); and reusing and updating an existing code set (n=20). Several open source software tools (n=3) were discovered., Discussion: There is a need for software tools that enable users to easily and quickly create, revise, extend, review and share code sets and we provide a list of recommendations for their design and implementation., Conclusion: Research re-using EHR data could be improved through the further development, more widespread use and routine reporting of the methods by which clinical codes were selected., (Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.)

Despite the increasing availability of online patient portals that provide access to electronic health records, little is known about their adoption by patients. We systematically reviewed the literature to investigate adoption of patient portals across studies. We searched MEDLINE and Scopus to identify relevant papers. We included 40 studies: 24 were controlled experiments, with prospective data collection in an actively recruited population; 16 were real-world experiments, with adoption being evaluated retrospectively after system deployment in clinical practice. Our meta-analysis showed an overall mean adoption rate of 52% (95% Confidence Interval [CI], 42 to 62%). Rates differed markedly between study types: controlled experiments yielded a mean adoption rate of 71% (95% CI 64 to 79%), compared to 23% (95% CI, 13 to 33%) in real-world experiments. This difference was confirmed in a meta-regression analysis of the influence of study characteristics on adoption rates. Our findings suggest that adoption rates reported in controlled studies do not reflect those in everyday clinical practice. Until we understand how to effectively increase adoption, patient portals are unlikely to consistently lead to improvements in care processes and health outcomes.

Background: Chronic kidney disease (CKD) is a major and increasing constituent of disease burdens worldwide. Early identification of patients at increased risk of developing CKD can guide interventions to slow disease progression, initiate timely referral to appropriate kidney care services, and support targeting of care resources. Risk prediction models can extend laboratory-based CKD screening to earlier stages of disease; however, to date, only a few of them have been externally validated or directly compared outside development populations. Our objective was to validate published CKD prediction models applicable in primary care., Methods: We synthesised two recent systematic reviews of CKD risk prediction models and externally validated selected models for a 5-year horizon of disease onset. We used linked, anonymised, structured (coded) primary and secondary care data from patients resident in Salford (population ~234 k), UK. All adult patients with at least one record in 2009 were followed-up until the end of 2014, death, or CKD onset (n = 178,399). CKD onset was defined as repeated impaired eGFR measures over a period of at least 3 months, or physician diagnosis of CKD Stage 3-5. For each model, we assessed discrimination, calibration, and decision curve analysis., Results: Seven relevant CKD risk prediction models were identified. Five models also had an associated simplified scoring system. All models discriminated well between patients developing CKD or not, with c-statistics around 0.90. Most of the models were poorly calibrated to our population, substantially over-predicting risk. The two models that did not require recalibration were also the ones that had the best performance in the decision curve analysis., Conclusions: Included CKD prediction models showed good discriminative ability but over-predicted the actual 5-year CKD risk in English primary care patients. QKidney, the only UK-developed model, outperformed the others. Clinical prediction models should be (re)calibrated for their intended uses.

Introduction: The extent of preventable medication-related hospital admissions and medication-related issues in primary care is significant enough to justify developing decision support systems for medication safety surveillance. The prerequisite for such systems is defining a relevant set of medication safety-related indicators and understanding the influence of both patient and general practice characteristics on medication prescribing and monitoring., Objective: The aim of the study was to investigate the feasibility of linked primary and secondary care electronic health record data for surveillance of medication safety, examining not only prescribing but also monitoring, and associations with patient- and general practice-level characteristics., Methods: A cross-sectional study was conducted using linked records of patients served by one hospital and over 50 general practices in Salford, UK. Statistical analysis consisted of mixed-effects logistic models, relating prescribing safety indicators to potential determinants., Results: The overall prevalence (proportion of patients with at least one medication safety hazard) was 5.45 % for prescribing indicators and 7.65 % for monitoring indicators. Older patients and those on multiple medications were at higher risk of prescribing hazards, but at lower risk of missed monitoring. The odds of missed monitoring among all patients were 25 % less for males, 50 % less for patients in practices that provide general practitioner training, and threefold higher in practices serving the most deprived compared with the least deprived areas. Practices with more prescribing hazards did not tend to show more monitoring issues., Conclusions: Systematic collection, collation, and analysis of linked primary and secondary care records produce plausible and useful information about medication safety for a health system. Medication safety surveillance systems should pay close attention to patient age and polypharmacy with respect to both prescribing and monitoring failures; treat prescribing and monitoring as different statistical processes, rather than a combined measure of prescribing safety; and audit the socio-economic equity of missed monitoring.

In Europe, health and medical administrative data is increasingly accumulating on a national level. Looking further than re-use of this data on a national level, sharing health and medical administrative data would enable large-scale analyses and European-level public health projects. There is currently no research infrastructure for this type of sharing. The PHRIMA consortium proposes to realise the Public Health Research Infrastructure for Sharing of health and Medical Administrative data (PHRIMA) which will enable and facilitate the efficient and secure sharing of healthcare data.

Laboratory test results in primary care are flagged as 'abnormal' when they fall outside a population-based Reference Interval (RI), typically generating many alerts with a low specificity. In order to decrease alert frequency while retaining clinical relevance, we developed a method to assess dynamic, patient-tailored RIs based on mixed-effects linear regression models. Potassium test results from primary care were used as proof-of-concept test bed. Clinical relevance was assessed via a survey administered to general practitioners (GPs). Overall, the dynamic, patient-tailored method and the combination of both methods flagged 20% and 36% fewer values as abnormal than the population-based method. Nineteen out of 43 invited GPs (44%) completed the survey. The population-based method yielded a better sensitivity than the patient-tailored and the combined methods (0.51 vs 0.41 and 0.38, respectively) but a lower PPV (0.66 vs 0.67 and 0.76, respectively). We conclude that a combination of population-based and patient-tailored RIs can improve the detection of abnormal laboratory results. We suggest that lab values outside both RIs be flagged with high priority in clinical practice.

Many patients do not receive care consistent with best practice. Health informatics interventions often attempt to address this problem by comparing care provided to patients (e.g., from electronic health record data) to quality standards (e.g., described in clinical guidelines) and feeding this information back to clinicians. Traditionally these interventions are delivered at the patient-level as computerized clinical decision support (CDS) or at the population level as audit and feedback (A&F). Both CDS and A&F can improve care for patients but are variably effective; the challenge is to understand how the efficacy can be maximized. Although CDS and A&F are traditionally considered separate approaches, we argue that the systems share common mechanisms, and efficacy may be improved by cross-fertilizing relevant features and concepts. We draw on the Health Informatics and Implementation Science literature to argue that common mechanisms include functions typically associated with the other system, in addition to other features that may prove fruitful for further research.

We demonstrate the use of electronic records and repeated measures of risk factors therein, to enable deeper understanding of the relationship between the full longitudinal trajectory of risk factors and outcomes. To illustrate, dynamic mixed effect modelling is used to summarise the level, trend and monitoring intensity of kidney function. The output from this model then forms covariates for a recurrent event Cox proportional hazards model for predicting adverse events (AE). Using data from Salford, UK, our multivariate model finds that steeper declines in kidney function raise the hazard of AE (HR: 1.13, 95% CI (1.05, 1.22)). There is a non-proportional relationship between the hazard of AE and the monitoring intensity of kidney function. Neither of these variables would be present in a classical risk prediction model.. This work illustrates the potential of using the full longitudinal profile of risk factors, rather than just their level. There is an opportunity for deep statistical learning leading to rich clinical insight using longitudinal signals in electronic data.

Given a computerized representation of a care pathway and an electronic record of a patient's clinical journey, with potential omissions, insertions, discontinuities and reordering, we show that we can accurately match the journey to a particular route through the pathway by converting the problem into a string matching one. We discover that normalized string metrics lead to more unique pathway matches than non-normalized string metrics and should therefore be given preference when using these techniques.

Despite widespread use of clinical guidelines, actual care often falls short of ideal standards. Electronic health records (EHR) can be analyzed to provide information on how to improve care, but this is seldom done in sufficient detail to guide specific action. We developed an algorithm to provide practical, actionable information for care quality improvement using blood pressure (BP) management in chronic kidney disease (CKD) as an exemplar. We used UK clinical guidelines and EHR data from 440 patients in Salford (UK) to develop the algorithm. We then applied it to 532,409 individual patient records, identifying 11,097 CKD patients, 3,766 (34%) of which showed room for improvement in their care: either through medication optimization or better BP monitoring. Manual record reviews to evaluate accuracy indicated a positive-predictive value of 90%. Such algorithms could help improve the management of chronic conditions by providing the missing link between clinical audit and decision support.

Medical care data is a valuable resource that can be used for many purposes including managing and planning for future health needs as well as clinical research. However, the heterogeneity and complexity of medical data can be an obstacle in applying data mining techniques. Much of the potential value of this data therefore goes untapped. In this paper we have developed a methodology that reduces the dimensionality of primary care data, in order to make it more amenable to visualisation, mining and clustering. The methodology involves employing a combination of ontology-based semantic similarity and principal component analysis (PCA) to map the data into an appropriate and informative low dimensional space. Throughout the study, we had access to anonymised patient data from primary care in Salford, UK. The results of our application of this methodology show that diagnosis codes in primary care data can be used to map patients into an informative low dimensional space, which in turn provides the opportunity to support further data exploration and medical hypothesis formulation.

Introduction: Analysing variance from care pathways in situations when adverse health outcomes have occurred may identify missed opportunities for healthcare improvement., Methods: We developed a computational model for contrasting observed with expected care in pathway searches of coded electronic health records (EHRs). The model was applied in Salford, UK, looking at blood pressure (BP) control and cardiovascular disease (CVD) events. BP was summarised as the integral of serial measurements., Results: A missed opportunities mapping (MOM) model consisting of a collection of disease Events and pathophysiologic States was used to articulate all CVD scenarios conceived. In 3718 patients suffering CVD events in Salford (2007-2012), 1186 (32%) had suboptimal BP control. This missed opportunity detection rose to 36% using the integral instead of the most recent BP record., Conclusions: MOM provides a useful, computable model for encoding care pathways and searching EHRs to detect variations from expected care. Further research is needed in other disease areas. The indications however, are that this model could be used to embed healthcare quality improvement at both patient and population levels.

Over the last four decades, the UK has made large investments in healthcare information technology. The authors conducted interviews and reviewed published and unpublished documents to describe national-scale clinical information exchange in England, how it was achieved, and the problems experienced that the USA might avoid. Clinical information exchange in the UK was accomplished by establishing a foundation of policy, infrastructure, and systems of care, by creating and acquiring clinical computing applications and with strong use of financial and clinical incentives. Many software and hardware vendors played a part in this effort; they participated in a national framework created by the NHS in which standards for exchange are specified and their applications designed to make clinical information exchange part of normal practice. Great potential exists for cost reduction, increased safety, and greater patient involvement as a result of clinical information exchange.

The discovery of knowledge from raw data is a multistage process, that typical requires collaboration between experts from disparate disciplines, and the application of a range of methods tailored to the research question. The aim of the eLab is to provide a web-based environment for health professionals and researchers to access health datasets, share knowledge and expertise and to apply methods for analysis and visualization of the results. The eLab is built around the core concept of the Research Object as the mechanism for preserving, reusing and disseminating the knowledge discovery process. The possible range of applications of the eLab is vast, and so the consideration of the trade off between specificity and generality is an important one, that is reflected in the requirements. The architecture and implementation of the eLab is described, and we report on the deployment of eLabs for applications in primary care, long-term conditions management, bariatric surgery and public health. [ABSTRACT FROM AUTHOR]