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General hospitals have commonly involved a wide range of medical specialists in the care of unselected medical emergency admis- sions. In 1999, the Royal Liverpool University Hospital, a 915-bed hospital with a busy emer- gency service, changed its system of care for medical emergencies to allow early placement of admitted patients under the care of the most appropriate specialist team, with interim care provided by specialist acute physicians on an acute medicine unit - a system we have termed 'specialty triage'. Here we describe a retrospec- tive study in which all 133,509 emergency med- ical admissions from February 1995 to January 2003 were analysed by time-series analysis with correction for the underlying downward trend from 1995 to 2003. This showed that the imple- mentation of specialty triage in May 1999 was associated with a subsequent additional reduc- tion in the mortality of the under-65 age group by 0.64% (95% CI 0.11 to 1.17%; P=0.021) from the 2.4% mortality rate prior to specialty triage, equivalent to approximately 51 fewer deaths per year. No significant effect was seen for those over 65 or all age groups together when corrected for the underlying trend. Length of stay and readmission rates showed a consistent down- ward trend that was not significantly affected by specialty triage. The data suggest that appro- priate specialist management improves outcomes for medical emergencies, particularly amongst younger patients.

This paper outlines design philosophies and methods for healthcare knowledge systems. Clinical priorities for knowledge are discussed in terms of temporal and individual needs. Book centred organisation of healthcare knowledge, which has proven effective in clinical practice, is proposed as the basis of virtual libraries available at the point of care for target groups of healthcare professionals.

WAX Active-Library (Cambridge Centre for Clinical Informatics) is a knowledge management system that seeks to support doctors' decision making through the provision of electronic books containing a wide range of clinical knowledge and locally based information. WAX has been piloted in several regions in the United Kingdom and formally evaluated in 17 GP surgeries based in Cambridgeshire. The evaluation has provided evidence that WAX Active-Library significantly improves GPs' access to relevant information sources and by increasing appropriate patient management and referrals this might also lead to an improvement in clinical outcomes.

Introduction: Asthma is no longer thought of as a single disease, but rather a collection of varying symptoms expressing different disease patterns. One of the ongoing challenges is understanding the underlying pathophysiological mechanisms that may be responsible for the varying responses to treatment.Areas Covered: This review provides an overview of our current understanding of the asthma phenotype concept in childhood and describes key findings from both conventional and data-driven methods.Expert Commentary: With the vast amounts of data generated from cohorts, there is hope that we can elucidate distinct pathophysiological mechanisms, or endotypes. In return, this would lead to better patient stratification and disease management, thereby providing true personalised medicine.

Current approaches to the management of severe mental illness have four major limitations: 1) symptom reporting is intermittent and subject to problems with reliability; 2) service users report feelings of disengagement from their care planning; 3) late detection of symptoms delay interventions and increase the risk of relapse; and 4) care systems are held back by the costs of unscheduled hospital admissions that could have been avoided with earlier detection and intervention. The ClinTouch system was developed to close the loop between service users and health professionals. ClinTouch is an end-to-end secure platform, providing a validated mobile assessment technology, a web interface to view symptom data and a clinical algorithm to detect risk of relapse. ClinTouch integrates high-resolution, continuous longitudinal symptom data into mental health care services and presents it in a form that is easy to use for targeting care where it is needed. The architecture and methodology can be easily extended to other clinical domains, where the paradigm of targeted clinical interventions, triggered by the early detection of decline, can improve health outcomes.

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.

Applications of simulation modelling in social science domains are varied and increasingly widespread. The effective deployment of simulation models depends on access to diverse datasets, the use of analysis capabilities, the ability to visualize model outcomes and to capture, share and re-use simulations as evidence in research and policy-making. We describe three applications of e-social science that promote social simulation modelling, data management and visualization. An example is outlined in which the three components are brought together in a transport planning context. We discuss opportunities and benefits for the combination of these and other components into an e-infrastructure for social simulation and review recent progress towards the establishment of such an infrastructure.

We introduce an information score for longitudinal healthcare record data, specifically in the monitoring of chronic conditions. The score is designed to capture the value of different observation patterns in terms of shaping and testing clinical epidemiological hypotheses. The score is first developed for the simple case where equally spaced observations are most informative, then extended to a more context-specific version where the optimal density of observations can be elicited. It can be interpreted as a measure of the average quantity of information provided by each observation in an individual's time course, where information is lost whenever the observation density deviates from a defined optimal density. We illustrate the score on routine healthcare records from the population of Salford, UK - focusing on repeat testing of liver function in people with common long-term conditions. We demonstrate validity of the score in terms of concordance between score levels and clinically meaningful patterns of repeat testing.

The issues of confidentiality and privacy have become increasingly important as Grid technology is being adopted in public sectors such as healthcare. This paper discusses the importance of protecting the confidentiality and privacy of patient health/medical records, and the challenges exhibited in enforcing this protection in a Grid environment. It proposes a novel algorithm to allow traceable/linkable identity privacy in dealing with de-identified medical records. Using the algorithm, de-identified health records associated to the same patient but generated by different healthcare providers are given different pseudonyms. However, these pseudonymised records of the same patient can still be linked by a trusted entity such as the NHS trust or HealthGrid manager. The paper has also recommended a security architecture that integrates the proposed algorithm with other data security measures needed to achieve the desired security and privacy in the HealthGrid context.

© 2014 Prosperi et al.; licensee BioMed Central Ltd.Background: Several single nucleotide polymorphisms (SNPs) at different loci have been associated with breast cancer susceptibility, accounting for around 10% of the familial component. Recent studies have found direct associations between specific SNPs and breast cancer in BRCA1/2 mutation carriers. Our aim was to determine whether validated susceptibility SNP scores improve the predictive ability of risk models in comparison/conjunction to other clinical/demographic information.Methods. Female BRCA1/2 carriers were identified from the Manchester genetic database, and included in the study regardless of breast cancer status or age. DNA was extracted from blood samples provided by these women and used for gene and SNP profiling. Estimates of survival were examined with Kaplan-Meier curves. Multivariable Cox proportional hazards models were fit in the separate BRCA datasets and in menopausal stages screening different combinations of clinical/demographic/genetic variables. Nonlinear random survival forests were also fit to identify relevant interactions. Models were compared using Harrell's concordance index (1 - c-index).Results: 548 female BRCA1 mutation carriers and 523 BRCA2 carriers were identified from the database. Median Kaplan-Meier estimate of survival was 46.0 years (44.9-48.1) for BRCA1 carriers and 48.9 (47.3-50.4) for BRCA2. By fitting Cox models and random survival forests, including both a genetic SNP score and clinical/demographic variables, average 1 - c-index values were 0.221 (st.dev. 0.019) for BRCA1 carriers and 0.215 (st.dev. 0.018) for BRCA2 carriers.Conclusions: Random survival forests did not yield higher performance compared to Cox proportional hazards. We found improvement in prediction performance when coupling the genetic SNP score with clinical/demographic markers, which warrants further investigation.

Policy simulation models (PSMs) have been used extensively, both to shape health policies before real-world implementation and to evaluate post-implementation impact. However, an accepted quality assessment framework for simulation models is lacking. This systematic review aimed to develop a novel quality assessment framework for tobacco control PSMs. We searched five databases to identify peer-reviewed tobacco control PSMs that projected tobacco-use and tobacco-related outcomes from smoking policy scenarios. We extracted information on modelling inputs, structure and outputs. Using our proposed quality assessment framework, we scored models on nine dimensions: representativeness of population, policy effectiveness evidence, simulated smoking histories, simulated smoking-related diseases, exposure-outcome lag time, transparency, sensitivity analysis, validation and equity. We then compared the model score with the number of cited papers using each model. The results were narratively presented. After screening 5046 candidate papers; 145 papers were included and categorised into 25 PSMs. After scoring the models using our proposed framework, we observed that seven models were given seven and more points. The higher-scored models were generally those with a higher number of publications. While all included models had been subject to sensitivity analysis, other best practices were often lacking. Nine models did not explicitly consider smoking-related diseases. Smoking histories were commonly collapsed into crude smoking status categories rather than reflecting smoking intensity or quitting history. Furthermore, only four models estimated policy equity impact. Our systematic review revealed a variety of modelling techniques used in tobacco control. Our novel quality assessment framework offers a potential quality measure for tobacco control policy simulation models. It may guide health decision modellers and inform health policymaking. Key messages All nine framework dimensions were observed in two tobacco control PSM, suggesting the framework relevance and feasibility. However, few models achieved high scores in all dimensions. This novel quality assessment framework aims to support the development and sharing of good modelling practice and thus promote better health policy decision-making.

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.

Multimorbidity is common among older people and presents a major challenge to health systems worldwide. Metrics of multimorbidity are, however, crude: focusing on measuring comorbid conditions at single time-points rather than reflecting the longitudinal and additive nature of chronic conditions. In this paper, we explore longitudinal comorbidity metrics and their value in predicting mortality.\ud Using linked primary and secondary care data, we conducted a retrospective cohort study on adults in Salford, UK from 2005 to 2014 (n = 287,459). We measured multimorbidity with the Charlson Comorbidity Index (CCI) and quantified its changes in various time windows. We used survival models to assess the relationship between CCI changes and mortality, controlling for gender, age, baseline CCI, and time-dependent CCI. Goodness-of-fit was assessed with the Akaike Information Criterion and discrimination with the c-statistic.\ud Overall, 15.9% patients experienced a change in CCI after 10 years, with a mortality rate of 19.8%. The model that included gender and time-dependent age, CCI, and CCI change across consecutive time windows had the best fit to the data but equivalent discrimination to the other time-dependent models. The absolute CCI score gave a constant hazard ratio (HR) of around 1.3 per unit increase, while CCI change afforded greater prognostic impact, particularly when it occurred in shorter time windows (maximum HR value for the 3-month time window, with 1.63 and 95% confidence interval 1.59–1.66).\ud Change over time in comorbidity is an important but overlooked predictor of mortality, which should be considered in research and care quality management.

Background Clinical prediction models (CPMs) are increasingly deployed to support healthcare decisions but they are derived inconsistently, in part due to limited data. An emerging alternative is to aggregate existing CPMs developed for similar settings and outcomes. This simulation study aimed to investigate the impact of between-population-heterogeneity and sample size on aggregating existing CPMs in a defined population, compared with developing a model de novo. Methods Simulations were designed to mimic a scenario in which multiple CPMs for a binary outcome had been derived in distinct, heterogeneous populations, with potentially different predictors available in each. We then generated a new ‘local’ population and compared the performance of CPMs developed for this population by aggregation, using stacked regression, principal component analysis or partial least squares, with redevelopment from scratch using backwards selection and penalised regression. Results While redevelopment approaches resulted in models that were miscalibrated for local datasets of less than 500 observations, model aggregation methods were well calibrated across all simulation scenarios. When the size of local data was less than 1000 observations and between-population-heterogeneity was small, aggregating existing CPMs gave better discrimination and had the lowest mean square error in the predicted risks compared with deriving a new model. Conversely, given greater than 1000 observations and significant between-population-heterogeneity, then redevelopment outperformed the aggregation approaches. In all other scenarios, both aggregation and de novo derivation resulted in similar predictive performance. Conclusion This study demonstrates a pragmatic approach to contextualising CPMs to defined populations. When aiming to develop models in defined populations, modellers should consider existing CPMs, with aggregation approaches being a suitable modelling strategy particularly with sparse data on the local population. Electronic supplementary material The online version of this article (doi:10.1186/s12874-016-0277-1) contains supplementary material, which is available to authorized users.

Background Patient outcomes in specialist burns units have been used as a metric of care needs and quality. Besides patient factors there are service factors that might influence Length of Stay (LOS) and mortality, e.g. pressure on beds. Although the bed needs of UK hospitals have dropped significantly over the past three decades, with changes in policies and practices, recent reports suggest that hospitals have 90% bed occupancy for 48 weeks of the year. In the UK, the specialist burn injury service is organised so that patients are assessed on arrival at hospital, and those needing admission are found a nearby bed in a suitable unit through the National Burn Bed Bureau. The aim of this study was to investigate the effect on outcomes of service pressures due to shortages of beds. Methods We took an extract of the anonymised patient data from the specialised burn injury database, iBID, and created a new database based on matching that data with bed availability data provided by the national Burn Bed Bureau. Cox proportional hazard modelling was used for analysis to investigate if there is an impact of bed occupancy (a proxy measure of workload) on LOS. Results Cox proportional hazard modelling indicated that half of the services in England and Wales are less likely to discharge a patient if the bed availability is high. Two of the services have abnormally high bed availability and LOS, therefore a model without these two services indicates a general reluctance to discharge patients when beds are available. Conclusions It is possible that the effect we observed is a result of gaming as service providers are paid by the number of admissions. In addition, providers many not all give the same level of accuracy of bed availability information to the NBBB: some may under report availability, for example at times of high pressure on staff. Furthermore, burn services may not empty beds to avoid being filled up by work from other specialties, thus making them unable to admit a burn when referred.

Background The health sciences literature incorporates a relatively large subset of epidemiological studies that focus on population-level findings, including various determinants, outcomes and correlations. Extracting structured information about those characteristics would be useful for more complete understanding of diseases and for meta-analyses and systematic reviews. Results We present an information extraction approach that enables users to identify key characteristics of epidemiological studies from MEDLINE abstracts. It extracts six types of epidemiological characteristic: design of the study, population that has been studied, exposure, outcome, covariates and effect size. We have developed a generic rule-based approach that has been designed according to semantic patterns observed in text, and tested it in the domain of obesity. Identified exposure, outcome and covariate concepts are clustered into health-related groups of interest. On a manually annotated test corpus of 60 epidemiological abstracts, the system achieved precision, recall and F-score between 79-100%, 80-100% and 82-96% respectively. We report the results of applying the method to a large scale epidemiological corpus related to obesity. Conclusions The experiments suggest that the proposed approach could identify key epidemiological characteristics associated with a complex clinical problem from related abstracts. When integrated over the literature, the extracted data can be used to provide a more complete picture of epidemiological efforts, and thus support understanding via meta-analysis and systematic reviews.

Background There is increasing recognition that asthma and eczema are heterogeneous diseases. We investigated the predictive ability of a spectrum of machine learning methods to disambiguate clinical sub-groups of asthma, wheeze and eczema, using a large heterogeneous set of attributes in an unselected population. The aim was to identify to what extent such heterogeneous information can be combined to reveal specific clinical manifestations. Methods The study population comprised a cross-sectional sample of adults, and included representatives of the general population enriched by subjects with asthma. Linear and non-linear machine learning methods, from logistic regression to random forests, were fit on a large attribute set including demographic, clinical and laboratory features, genetic profiles and environmental exposures. Outcome of interest were asthma, wheeze and eczema encoded by different operational definitions. Model validation was performed via bootstrapping. Results The study population included 554 adults, 42% male, 38% previous or current smokers. Proportion of asthma, wheeze, and eczema diagnoses was 16.7%, 12.3%, and 21.7%, respectively. Models were fit on 223 non-genetic variables plus 215 single nucleotide polymorphisms. In general, non-linear models achieved higher sensitivity and specificity than other methods, especially for asthma and wheeze, less for eczema, with areas under receiver operating characteristic curve of 84%, 76% and 64%, respectively. Our findings confirm that allergen sensitisation and lung function characterise asthma better in combination than separately. The predictive ability of genetic markers alone is limited. For eczema, new predictors such as bio-impedance were discovered. Conclusions More usefully-complex modelling is the key to a better understanding of disease mechanisms and personalised healthcare: further advances are likely with the incorporation of more factors/attributes and longitudinal measures.