Faculty Bibliography

BACKGROUND: Serum phosphate is a known risk factor for cardiovascular events and mortality in people with chronic kidney disease (CKD), however data on the association of these outcomes with serum phosphate in the general population are scarce. We investigate this relationship in people with and without CKD in a large community-based population. METHODS: Three groups from an adult cohort of the Quality Improvement in Chronic Kidney Disease (QICKD) cluster randomised trial (ISRCTN56023731) were followed over a period of 2.5 years: people with normal renal function (N = 24,184), people with CKD stages 1-2 (N = 20,356), and people with CKD stages 3-5 (N = 13,292). We used a multilevel logistic regression model to determine the association between serum phosphate, in these groups, and a composite outcome of all-cause mortality, cardiovascular events, and advanced coronary artery disease. We adjusted for known cardiovascular risk factors. FINDINGS: Higher phosphate levels were found to correlate with increased cardiovascular risk. In people with normal renal function and CKD stages 1-2, Phosphate levels between 1.25 and 1.50 mmol/l were associated with increased cardiovascular events; odds ratio (OR) 1.36 (95% CI 1.06-1.74; p = 0.016) in people with normal renal function and OR 1.40 (95% CI 1.09-1.81; p = 0.010) in people with CKD stages 1-2. Hypophosphatemia (<0.75 mmol/l) was associated with fewer cardiovascular events in people with normal renal function; OR 0.59 (95% CI 0.36-0.97; p = 0.049). In people with CKD stages 3-5, hyperphosphatemia (>1.50 mmol/l) was associated with increased cardiovascular risk; OR 2.34 (95% CI 1.64-3.32; p<0.001). Other phosphate ranges were not found to have a significant impact on cardiovascular events in people with CKD stages 3-5. CONCLUSIONS: Serum phosphate is associated with cardiovascular events in people with and without CKD. Further research is required to determine the mechanisms underlying these associations.

BACKGROUND: Most chronic diseases are managed in primary and ambulatory care. The chronic care model (CCM) suggests a wide range of community, technological, team and patient factors contribute to effective chronic disease management. Ontologies have the capability to enable formalised linkage of heterogeneous data sources as might be found across the elements of the CCM. OBJECTIVE: To describe the evidence base for using ontologies and other semantic integration methods to support chronic disease management. METHOD: We reviewed the evidence-base for the use of ontologies and other semantic integration methods within and across the elements of the CCM. We report them using a realist review describing the context in which the mechanism was applied, and any outcome measures. RESULTS: Most evidence was descriptive with an almost complete absence of empirical research and important gaps in the evidence-base. We found some use of ontologies and semantic integration methods for community support of the medical home and for care in the community. Ubiquitous information technology (IT) and other IT tools were deployed to support self-management support, use of shared registries, health behavioural models and knowledge discovery tools to improve delivery system design. Data quality issues restricted the use of clinical data; however there was an increased use of interoperable data and health system integration. CONCLUSIONS: Ontologies and semantic integration methods are emergent with limited evidence-base for their implementation. However, they have the potential to integrate the disparate community wide data sources to provide the information necessary for effective chronic disease management.

Computerised Medical Record (CMR) data are widely used for secondary purposes such as service evaluation and epidemiological research. Data are increasingly aggregated from different medical facilities with various CMR vendors over time. It is increasingly difficult to manage the large quantity of data. Experiential learning in diabetes and chronic kidney disease (CKD) suggests simplistic processing can lead to errors. To maximise analytical ability for the Quality Improvement in CKD (QICKD) trial, we developed an agile data management process. By removing the need to import and process data in a relational database, we reduced processing and analysis time. We demonstrated usage of our new agile method to rapidly develop complex queries to identify how blood pressure varied between patients included or excluded from Quality and Outcomes Frameworks (QOF) pay-for-performance (P4P) targets in UK primary care. We describe a novel specification language that allows clinicians to focus on identifying variables to extract useful information from CMRs. Data for research questions were available in &lt;1hour instead of longer times previously required through use of an SQL database.

OBJECTIVES: To model the frequency of 'last minute' cancellations of planned elective procedures in the English NHS with respect to the patient and provider factors that led to these cancellations. METHODS: A dataset of 5,288,604 elective patients spell in the English NHS from January 1st, 2007 to December 31st, 2007 was extracted from the Hospital Episode Statistics. A binary dependent variable indicating whether or not a patient had a Health Resource Group coded as S22--'Planned elective procedure not carried out'--was modeled using a probit regression estimated via maximum likelihood including patient, case and hospital level covariates. RESULTS: Longer waiting times and being admitted on a Monday were associated with a greater rate of cancelled procedures. Male patients, patients from lower socio-economic groups and older patients had higher rates of cancelled procedures. There was significant variation in cancellation rates between hospitals; Foundation Trusts and private facilities had the lowest cancellation rates. CONCLUSIONS: Further research is needed on why Foundation Trusts exhibit lower cancellation rates. Hospitals with relatively high cancellation rates should be encouraged to tackle this problem. Further evidence is needed on whether hospitals are more likely to cancel operations where the procedure tariff is lower than the S22 tariff as this creates a perverse incentive to cancel. Understanding the underlying causes of why male, older and patients from lower socio-economic groups are more likely to have their operations cancelled is important to inform the appropriate policy response. This research suggests that interventions designed to reduce cancellation rates should be targeted to high-cancellation groups.

A new distributed model of health care management is being introduced in England. Family practitioners have new responsibilities for the management of health care budgets and commissioning of services. There are national datasets available about health care providers and the geographical areas they serve. These data could be better used to assist the family practitioner turned health service commissioners. Unfortunately these data are not in a form that is readily usable by these fledgling family commissioning groups. We therefore Web enabled all the national hospital dermatology treatment data in England combining it with locality data to provide a smart commissioning tool for local communities. We used open-source software including the Ruby on Rails Web framework and MySQL. The system has a Web front-end, which uses hypertext markup language cascading style sheets (HTML/CSS) and JavaScript to deliver and present data provided by the database. A combination of advanced caching and schema structures allows for faster data retrieval on every execution. The system provides an intuitive environment for data analysis and processing across a large health system dataset. Web-enablement has enabled data about in patients, day cases and outpatients to be readily grouped, viewed, and linked to other data. The combination of web-enablement, consistent data collection from all providers; readily available locality data; and a registration based primary system enables the creation of data, which can be used to commission dermatology services in small areas. Standardized datasets collected across large health enterprises when web enabled can readily benchmark local services and inform commissioning decisions.

BACKGROUND: Innovators have piloted improvements in communication, changed patterns of practice and patient empowerment from online access to electronic health records (EHR). International studies of online services, such as prescription ordering, online appointment booking and secure communications with primary care, show good uptake of email consultations, accessing test results and booking appointments; when technologies and business process are in place. Online access and transactional services are due to be rolled out across England by 2015; this review seeks to explore the impact of online access to health records and other online services on the quality and safety of primary health care. OBJECTIVE: To assess the factors that may affect the provision of online patient access to their EHR and transactional services, and the impact of such access on the quality and safety of health care. METHOD: Two reviewers independently searched 11 international databases during the period 1999-2012. A range of papers including descriptive studies using qualitative or quantitative methods, hypothesis-testing studies and systematic reviews were included. A detailed eligibility criterion will be used to shape study inclusion. A team of experts will review these papers for eligibility, extract data using a customised extraction form and use the Grading of Recommendations Assessment, Development and Evaluation (GRADE) instrument to determine the quality of the evidence and the strengths of any recommendation. Data will then be descriptively summarised and thematically synthesised. Where feasible, we will perform a quantitative meta-analysis. Prospero (International Prospective Register of Systematic Reviews) registration number: crd42012003091.

Hospitals need to understand patient flows in an increasingly competitive health economy. New initiatives like Patient Choice and the Darzi Review further increase this demand. Essential to understanding patient flows are demographic and geographic profiles of health care service providers, known as 'catchment areas' and 'catchment populations'. This information helps Primary Care Trusts (PCTs) to review how their populations are accessing services, measure inequalities and commission services; likewise it assists Secondary Care Providers (SCPs) to measure and assess potential gains in market share, redesign services, evaluate admission thresholds and plan financial budgets. Unlike PCTs, SCPs do not operate within fixed geographic boundaries. Traditionally, SCPs have used administrative boundaries or arbitrary drive times to model catchment areas. Neither approach satisfactorily represents current patient flows. Furthermore, these techniques are time-consuming and can be challenging for healthcare managers to exploit. This paper presents three different approaches to define catchment areas, each more detailed than the previous method. The first approach 'First Past the Post' defines catchment areas by allocating a dominant SCP to each Census Output Area (OA). The SCP with the highest proportion of activity within each OA is considered the dominant SCP. The second approach 'Proportional Flow' allocates activity proportionally to each OA. This approach allows for cross-boundary flows to be captured in a catchment area. The third and final approach uses a gravity model to define a catchment area, which incorporates drive or travel time into the analysis. Comparing approaches helps healthcare providers to understand whether using more traditional and simplistic approaches to define catchment areas and populations achieves the same or similar results as complex mathematical modelling. This paper has demonstrated, using a case study of Manchester, that when estimating the catchment area of a planned new hospital, the extra level of detail provided by the gravity model may prove necessary. However, in virtually all other applications, the Proportional Flow method produced the optimal model for catchment populations in Manchester, based on several criteria: it produced the smallest RMS error; it addressed cross-boundary flows; the data used to create the catchment was readily available to SCPs; and it was simpler to reproduce than the gravity model method. Further work is needed to address how the Proportional Flow method can be used to reflect service redesign and handle OAs with zero or low activity. A next step should be the rolling out of the method across England and looking at further drill downs of data such as catchment by Healthcare Resource Group (HRG) rather than specialty level.