Imperial College London

Dr Thomas Woodcock

Faculty of MedicineSchool of Public Health

Improvement Science Fellow
 
 
 
//

Contact

 

+44 (0)20 3315 8875thomas.woodcock99

 
 
//

Location

 

H.4.21Chelsea and Westminster HospitalChelsea and Westminster Campus

//

Summary

 

Publications

Publication Type
Year
to

36 results found

Adeleke Y, Matthew D, Porter B, Woodcock T, Yap J, Hashmy S, Mathew A, Grant R, Kaba A, Unger-Graeber B, Khan S, Bell D, Cowie MRet al., 2019, Improving the quality of care for patients with or at risk of atrial fibrillation: an improvement initiative in UK general practices, Open Heart, Vol: 6, ISSN: 2053-3624

Objective Atrial fibrillation (AF) is a growing problem internationally and a recognised cause of cardiovascular morbidity and mortality. The London borough of Hounslow has a lower than expected prevalence of AF, suggesting poor detection and associated undertreatment. To improve AF diagnosis and management, a quality improvement (QI) initiative was set up in 48 general practices in Hounslow. We aimed to study whether there was evidence of a change in AF diagnosis and management in Hounslow following implementation of interventions in this QI initiative.Methods Using the general practice information system (SystmOne), data were retrospectively collected for 415 626 patients, who were actively registered at a Hounslow practice between 1 January 2011 and 31 August 2018. Process, outcome and balancing measures were analysed using statistical process control and interrupted time series regression methods. The baseline period was from 1 January 2011 to 30 September 2014 and the intervention period was from 1 October 2014 to 31 August 2018.Results When comparing the baseline to the intervention period, (1) the rate of new AF diagnoses increased by 27% (relative risk 1.27; 95% CI 1.05 to 1.52; p<0.01); (2) ECG tests done for patients aged 60 and above increased; (3) CHA2DS2-VASc and HAS-BLED risk assessments within 30 days of AF diagnosis increased from 1.7% to 19% and 0.2% to 8.1%, respectively; (4) among those at higher risk of stroke, anticoagulation prescription within 30 days of AF diagnosis increased from 31% to 63% while prescription of antiplatelet monotherapy within the same time period decreased from 17% to 7.1%; and (5) average CHA2DS2-VASc and HAS-BLED risk scores did not change.Conclusion Implementation of interventions in the Hounslow QI initiative coincided with improved AF diagnosis and management. Areas with perceived underdetection of AF should consider similar interventions and methodology.

Journal article

Woodcock T, Liberati E, Dixon-Woods M, A mixed-methods study of challenges experienced by clinical teams in measuring improvement, BMJ Quality and Safety, ISSN: 2044-5415

Objective: Measurement is an indispensable element of most quality improvement (QI) projects, but it is undertaken to variable standards. We aimed to characterise challenges faced by clinical teams in undertaking measurement in the context of a safety QI programme that encouraged local selection of measures. Methods: Drawing on an independent evaluation of a multi-site improvement programme (Safer Clinical Systems), we combined a qualitative study of participating teams’ experiences and perceptions of measurement with expert review of measurement plans and analysis of data collected for the programme. Multidisciplinary teams of frontline clinicians at nine UK NHS sites took part across the two phases of the programme between 2011 and 2016. Results: Developing and implementing a measurement plan against which to assess their improvement goals was an arduous task for participating sites. The operational definitions of the measures that they selected were often imprecise or missed important details. Some measures used by the teams were not logically linked to the improvement actions they implemented. Regardless of the specific type of data used (routinely collected or selected ex-novo), the burdensome nature of data collection was underestimated. Problems also emerged in identifying and using suitable analytical approaches. Conclusion: Measurement is a highly technical task requiring a degree of expertise. Simply leveraging individual clinicians’ motivation is unlikely to defeat the persistent difficulties experienced by clinical teams when attempting to measure their improvement efforts. We suggest that more structural initiatives and broader capability-building programmes should be pursued by the professional community. Improving access to, and ability to use repositories of validated measures, and increasing transparency in reporting measurement attempts, is likely to be helpful.

Journal article

Sakonidou S, Andrzejewska I, Kotzamanis S, Carnegie W, Nakubulwa M, Woodcock T, Modi N, Bell D, Gale Cet al., 2019, Better use of data to improve parent satisfaction (BUDS): protocol for a prospective before-and-after pilot study employing mixed methods to improve parent experience of neonatal care, BMJ Paediatrics Open, Vol: 3, ISSN: 2399-9772

Introduction Having a baby that requires neonatal care is stressful and traumatic. Parents often report dissatisfaction with communication of clinical information. In the UK neonatal care data are recorded daily using electronic patient record systems (EPR), from which deidentified data form the National Neonatal Research Database (NNRD). We aim to evaluate the impact of sharing neonatal EPR data with parents, on parent-reported satisfaction, parent–staff interactions, staff workload and data completeness.Methods A prospective, before-and-after, mixed-method study. Participants are parents of inpatient babies (maximum 90) and staff in a tertiary neonatal intensive care unit, London, UK. The intervention was developed by former neonatal parents, neonatologists and neonatal nurses: a communication tool for parents comprising individualised, written, daily infant updates for parents, derived from EPR data. The intervention will be provided to parents over 6 weeks. Plan-Do-Study-Act cycles will inform the tool’s iterative development and improvement. The tool’s impact will be measured using a validated parent survey, staff survey, data completeness measures and interviews.Analysis Primary outcome: parent satisfaction ‘with communication of clinical information and involvement in care’. Secondary outcomes: parent–staff interactions, staff workload, data completeness. Baseline survey data will be obtained from clinical service evaluation preceding the intervention. Baseline data completeness will be derived from the NNRD. During the intervention, surveys will be administered biweekly and data completeness assessed daily. We will analyse outcomes using run charts and partially paired statistical tests. Parent and staff interviews will explore information exchange and the communication tool’s impact.Discussion This study will evaluate the impact of a parent co-designed intervention on communication with parents in neonatal care and

Journal article

McNicholas C, Lennox L, Woodcock T, Bell D, Reed Jet al., 2019, Evolving quality improvement support strategies to improve Plan-Do-Study-Act cycle fidelity: a retrospective mixed methods study, BMJ Quality and Safety, Vol: 28, Pages: 356-365, ISSN: 2044-5415

BackgroundThough widely recommended as an effective approach to quality improvement (QI), the Plan-Do-Study-Act (PDSA) cycle method can be challenging to use and low fidelity of published accounts of the method has been reported. There is little evidence of the fidelity of PDSA cycles used by frontline teams, nor how to support and improve the method’s use. Data collected from 39 frontline improvement teams provided an opportunity to retrospectively investigate PDSA cycle use and how strategies were modified to help improve this overtime.MethodsThe fidelity of 421 PDSA cycles was reviewed using a pre-defined framework, and statistical analysis examined whether fidelity changed over three annual rounds of projects. The experiences of project teams and QI support staff were investigated through document analysis and interviews.ResultsAlthough modest, statistically significant improvements in PDSA fidelity occurred, however, overall fidelity remained low. Challenges to achieving greater fidelity reflected problems with understanding the PDSA methodology, intention to use, and application in practice. These problems were exacerbated by assumptions made in the original QI training and support strategies: that PDSA was easy to understand; that teams would be motivated and willing to use PDSA; and that PDSA is easy to apply. QI strategies that evolved to overcome these challenges included project selection process, redesign of training, increased hands-on support and investment in training QI support staff.ConclusionsThis study identifies support strategies that may help improve PDSA cycle fidelity. It provides an approach to assess minimum standards of fidelity which can be replicated elsewhere. The findings suggest achieving high PDSA fidelity requires a gradual and negotiated process to explore different perspectives and encourage new ways of working.

Journal article

Mummery V, Rogers E, Padmanaban V, Matthew D, Woodcock T, Bloch Set al., 2019, Transcutaneous CO2 measurement is not a reliable alternative to arterial blood gas sampling in the acute medical setting, European Respiratory Journal, Vol: 53, ISSN: 0903-1936

Journal article

Mummery V, Rodgers E, Padmanaban V, Matthews D, Woodcock T, Bloch Set al., 2018, TRANSCUTANEOUS CO2 MEASUREMENT IS NOT A RELIABLE ALTERNATIVE TO ARTERIAL BLOOD GAS SAMPLING IN THE ACUTE MEDICAL SETTING, Winter Meeting of the British-Thoracic-Society, Publisher: BMJ PUBLISHING GROUP, Pages: A228-A228, ISSN: 0040-6376

Conference paper

Mummery V, Adeleke Y, Lin F, Matthew D, Woodcock T, Ritchie L, McNicholas C, Bloch Set al., 2018, NON-INVASIVE VENTILATION: MAKING CHANGES AND DEMONSTRATING IMPROVEMENT USING QUALITY IMPROVEMENT METHODOLOGY, Winter Meeting of the British-Thoracic-Society, Publisher: BMJ PUBLISHING GROUP, Pages: A205-A205, ISSN: 0040-6376

Conference paper

Issen L, Woodcock T, McNicholas C, Lennox L, Reed JEet al., 2018, Criteria for evaluating programme theory diagrams in quality improvement initiatives: a structured method for appraisal, International Journal for Quality in Health Care, Vol: 30, Pages: 508-513, ISSN: 1353-4505

Background: Despite criticisms that many Quality Improvement (QI) initiatives fail due to incomplete programme theory, there is no defined way to evaluate how programme theory has been articulated. The objective of this research was to develop, and assess the usability and reliability of, scoring criteria to evaluate programme theory diagrams. Methods: Criteria development was informed by published literature and QI experts. Inter-rater reliability was tested between two evaluators. 63 programme theory diagrams (42 driver diagrams and 21 action effect diagrams) were reviewed to establish whether the criteria could support comparative analysis of different approaches to constructing diagrams.Results: Components of the scoring criteria include: assessment of overall aim, logical overview, clarity of components, cause/effect relationships, evidence, and measurement. Independent reviewers had 78% inter-rater reliability. Scoring enabled direct comparison of different approaches to developing programme theory; Action-Effect diagrams were found to have had a statistically significant but moderate improvement in programme theory quality over Driver Diagrams; no significant differences were observed based on the setting in which Driver Diagrams were developed.Conclusions: The scoring criteria summarise the necessary components of programme theory that are thought to contribute to successful QI projects. The viability of the scoring criteria for practical application was demonstrated. Future uses include assessment of individual programme theory diagrams, and comparison of different approaches (e.g. methodological, teaching or other QI support) to produce programme theory. The criteria can be used as a tool to guide the production of better programme theory diagrams, and also highlights where additional support for QI teams could be needed.

Journal article

Etchells E, Woodcock T, 2017, Value of small sample sizes in rapid-cycle quality improvement projects (Part II): Assessing Fidelity of Implementation for Improvement Interventions, BMJ Quality and Safety, Vol: 27, Pages: 61-65, ISSN: 2044-5415

Journal article

Reed JE, Stillman N, Lennox L, Barber S, Woodcock Tet al., 2017, Applying the concept of 'hard core' and 'soft periphery' of interventions to share learning from quality improvement efforts, ISQua 34th International Conference, Publisher: Oxford University Press (OUP), Pages: 47-48, ISSN: 1353-4505

Conference paper

Poots A, Reed J, Woodcock T, Bell D, Goldmann Det al., 2017, How to attribute causality in quality improvement: lessons from epidemiology, BMJ Quality & Safety, Vol: 26, Pages: 933-937, ISSN: 2044-5423

Quality improvement and implementation (QI&I) initiatives face critical challenges in an era of evidence-based, value-driven patient care. Whether front-line staff, large organisations or government bodies design and run QI&I, there is increasing need to demonstrate impact to justify investment of time and resources in implementing and scaling up an intervention.Decisions about sustaining, scaling up and spreading an initiative can be informed by evidence of causation and the estimated attributable effect of an intervention on observed outcomes. Achieving this in healthcare can be challenging, where interventions often are multimodal and applied in complex systems.1 Where there is weak evidence of causation, credibility in the effectiveness of the intervention is reduced with a resultant reduced desire to replicate. The greater confidence of a causal relationship between QI&I interventions and observed results, the greater our confidence that improvement will result when the intervention occurs in different settings.Guidance exists for design, conduct, evaluation and reporting of QI&I initiatives;2–4; the Standards for QUality Improvement Reporting Excellence (SQUIRE) and the Standards for Reporting Implementation Studies (STARI) guidelines were developed specifically for reporting QI&I initiatives.5 6 However, much of this guidance is targeted at larger formal evaluations, and may require levels of resource or expertise not available to all QI&I initiatives. This paper proposes QI&I initiatives, regardless of scope and resources, can be enhanced by applying epidemiological principles, adapted from those promulgated by Austin Bradford Hill.7

Journal article

Reed JE, Davey N, Woodcock T, 2016, The foundations of quality improvement science, Future Hospital Journal, Vol: 3, Pages: 199-202, ISSN: 2055-3331

As an alternative to ‘Big Bang’ initiatives, Plan-Do-Study-Act (PDSA) cycles are an increasingly popular approach to conduct tests of change to support quality improvement in healthcare. Using PDSA can help clinicians deliver improvements in patient care through a structured experimental approach to learning and tests of change. Its facilitation of individual, team and organisational learning makes this an essential tool for the future hospital. This paper provides an example of the benefits of using PDSA in practice to test and develop a change idea to ensure it is fit for purpose. As with any new skill or competency, learning to use PDSA cycles takes time and practice and is necessary to ensure that the method is being used to its full effect. This paper explores some of the challenges encountered by clinicians in learning to use PDSA cycles well, and provides advice on how they can be overcome to help practitioners to get more out of using the method.

Journal article

Marvin V, Kuo S, Poots A, Woodcock T, Vaughan L, Bell Det al., 2016, Applying Quality Improvement methods to address gaps in medicines reconciliation at transfers of care from an acute UK hospital, BMJ Open, Vol: 6, ISSN: 2044-6055

Objectives: Reliable reconciliation of medicines at admission and dischargefrom hospital is key to reducing unintentional prescribing discrepancies attransitions of health care. We introduced a team approach to the reconciliationprocess at an acute hospital with the aim of improving the provision ofinformation and documentation of reliable medication lists to enable clear,timely communications on discharge.Setting: An acute 400 bedded teaching hospital in London UK.Participants: The effects of change were measured in a simple randomsample of ten adult patients a week on the Acute Admissions Unit over 18months.Interventions: Quality Improvement methods were used throughout.Interventions included education and training of staff involved at ward leveland in the pharmacy department, introduction of medication documentationtemplates for electronic prescribing and for communicating information onmedicines in discharge summaries co-designed with patient representatives.Results: Statistical Process Control analysis showed reliable documentation(complete, verified and intentional changes clarified) of current medication on49.2% of patients’ discharge summaries. This appears to have improved (to85.2%) according to a post-study audit the year after the project end.Pharmacist involvement in discharge reconciliation significantly increased,and improvements in the numbers of medicines prescribed in error or omittedfrom the discharge prescription are demonstrated. Variation in weeklymeasures is seen throughout but particularly at periods of changeover of newdoctors and introduction of new systems.Conclusion: New processes led to a sustained increase in reconciledmedications and thereby an improvement in the number of patientsdischarged from hospital with unintentional discrepancies (errors oromissions) on their discharge prescription.The initiatives were pharmacist-led but involved close working and sharedunderstanding about roles and responsibilities between doctors, nurses

Journal article

Dunn S, Jones M, Woodcock T, Cullen F, Bell D, Reed Jet al., 2016, Consistent services throughout the week for acute medical care., Journal of the Royal College of Physicians of Edinburgh, Vol: 46, Pages: 77-80, ISSN: 1478-2715

Journal article

Blair M, Watson M, klaber R, woodcock Tet al., 2016, G311 How exactly does integrated paediatric care work? A theoretical research framework, Archives of Disease in Childhood, Vol: 101, Pages: A178-A179, ISSN: 1468-2044

Background Many areas in the UK are experimenting with different models of care delivery to improve integration of services and experiences of children young people and their carers. One such initiative “Connecting Care for Children” (CC4C) is based on three key components:- specialist outreach to a number of GP “hubs”, open access for advice and referrals and public and patient engagement. Robust evaluation of such health system change is desirable but often complex to conceptualise and achieve.Aim To develop an agreed conceptual framework to facilitate measurement of the quality of health system delivery in a defined population and to support research on proposed mediating factors.Methods A number of methods were used including stakeholder mapping, experiential “word cloud” capture, and “Action Effect Diagram” (AED) development.1 Engagement of staff, patients and young people at a number of collaborative events over a two year period. A joint workshop with academics from a number of institutions helped to refine specific measures and identify gaps in current knowledge. Over 100 individuals have been involved in drawing up the final model.Results Word cloud highlighted clinical and organisational issues (See Figure 1). There was considerable consistency across populations. An AED was developed over a series of iterations which elucidated the possible theoretical mechanisms for cause and effect of the three key components of the CC4C model. This was subsequently redrawn in a standardised logic model format to aid understanding (Figure 2). We have highlighted those elements which we believe are common to all such developments in integrated care and those which are for local determination and adaptation. Potential metrics for each of these segments are highlighted in Table 1.Conclusions We found a high degree of agreement for a conceptual framework which explains how integrated care processes might be mediated. Local aca

Journal article

Soong JTY, Poots AJ, Scott S, Donald K, Woodcock T, Lovett D, Bell Det al., 2015, Quantifying the prevalence of frailty in English hospitals, BMJ Open, Vol: 5, ISSN: 2044-6055

Objectives Population ageing has been associated with an increase in comorbid chronic disease, functional dependence, disability and associated higher health care costs. Frailty Syndromes have been proposed as a way to define this group within older persons. We explore whether frailty syndromes are a reliable methodology to quantify clinically significant frailty within hospital settings, and measure trends and geospatial variation using English secondary care data set Hospital Episode Statistics (HES).Setting National English Secondary Care Administrative Data HES.Participants All 50 540 141 patient spells for patients over 65 years admitted to acute provider hospitals in England (January 2005—March 2013) within HES.Primary and secondary outcome measures We explore the prevalence of Frailty Syndromes as coded by International Statistical Classification of Diseases, Injuries and Causes of Death (ICD-10) over time, and their geographic distribution across England. We examine national trends for admission spells, inpatient mortality and 30-day readmission.Results A rising trend of admission spells was noted from January 2005 to March 2013(daily average admissions for month rising from over 2000 to over 4000). The overall prevalence of coded frailty is increasing (64 559 spells in January 2005 to 150 085 spells by Jan 2013). The majority of patients had a single frailty syndrome coded (10.2% vs total burden of 13.9%). Cognitive impairment and falls (including significant fracture) are the most common frailty syndromes coded within HES. Geographic variation in frailty burden was in keeping with known distribution of prevalence of the English elderly population and location of National Health Service (NHS) acute provider sites. Overtime, in-hospital mortality has decreased (>65 years) whereas readmission rates have increased (esp.>85 years).Conclusions This study provides a novel methodology to reliably quantify clinically significant frailty. Applications in

Journal article

Portela MC, Pronovost PJ, Woodcock T, Carter P, Dixon-Woods Met al., 2015, How to study improvement interventions: a brief overview of possible study types, POSTGRADUATE MEDICAL JOURNAL, Vol: 91, Pages: 343-354, ISSN: 0032-5473

Journal article

Green SA, Honeybourne E, Chalkley S, Poots A, Woodcock T, Price G, Bell D, Green Jet al., 2015, A retrospective observational analysis to identify patient and treatment-related predictors of outcomes in a community mental health programme, BMJ Open, ISSN: 2044-6055

Journal article

Portela MC, Pronovost PJ, Woodcock T, Carter P, Dixon-Woods Met al., 2015, How to study improvement interventions: a brief overview of possible study types, BMJ Quality and Safety, Vol: 24, Pages: 325-336, ISSN: 2044-5415

Improvement (defined broadly as purposive efforts to secure positive change) has become an increasingly important activity and field of inquiry within healthcare. This article offers an overview of possible methods for the study of improvement interventions. The choice of available designs is wide, but debates continue about how far improvement efforts can be simultaneously practical (aimed at producing change) and scientific (aimed at producing new knowledge), and whether the distinction between the practical and the scientific is a real and useful one. Quality improvement projects tend to be applied and, in some senses, self-evaluating. They are not necessarily directed at generating new knowledge, but reports of such projects if well conducted and cautious in their inferences may be of considerable value. They can be distinguished heuristically from research studies, which are motivated by and set out explicitly to test a hypothesis, or otherwise generate new knowledge, and from formal evaluations of improvement projects. We discuss variants of trial designs, quasi-experimental designs, systematic reviews, programme evaluations, process evaluations, qualitative studies, and economic evaluations. We note that designs that are better suited to the evaluation of clearly defined and static interventions may be adopted without giving sufficient attention to the challenges associated with the dynamic nature of improvement interventions and their interactions with contextual factors. Reconciling pragmatism and research rigour is highly desirable in the study of improvement. Trade-offs need to be made wisely, taking into account the objectives involved and inferences to be made.

Journal article

Reid E, King A, Mathieson A, Woodcock T, Watkin SWet al., 2015, Identifying reasons for delays in acute hospitals using the Day-of-Care Survey method, CLINICAL MEDICINE, Vol: 15, Pages: 117-120, ISSN: 1470-2118

Journal article

Issen LA, Reed JE, McNicholas C, Woodcock T, Bell Det al., 2014, Designing quality improvement initiatives: the action effect method, a structured approach to identifying and articulating programme theory, BMJ Quality & Safety, ISSN: 2044-5423

Background The identification and articulation of programme theory can support effective design, execution and evaluation of quality improvement (QI) initiatives. Programme theory includes an agreed aim, potential interventions to achieve this aim, anticipated cause/effect relationships between the interventions and the aim and measures to monitor improvement. This paper outlines the approach used in a research and improvement programme to support QI initiatives in identifying and articulating programme theory: the action effect method.Background to method development Building on a previously used QI method, the driver diagram, the action effect method was developed using co-design and iteration over four annual rounds of improvement initiatives. This resulted in a specification of the elements required to fully articulate the programme theory of a QI initiative.The action effect method The action effect method is a systematic and structured process to identify and articulate a QI initiative's programme theory. The method connects potential interventions and implementation activities with an overall improvement aim through a diagrammatic representation of hypothesised and evidenced cause/effect relationships. Measure concepts, in terms of service delivery and patient and system outcomes, are identified to support evaluation.Discussion and conclusions The action effect method provides a framework to guide the execution and evaluation of a QI initiative, a focal point for other QI methods and a communication tool to engage stakeholders. A clear definition of what constitutes a well-articulated programme theory is provided to guide the use of the method and assessment of the fidelity of its application.

Journal article

Patterson CM, Woodcock T, Mollan IA, Nicol ED, McLoughlin DCet al., 2014, United Kingdom Military Aeromedical Evacuation in the Post-9/11 Era, Aviation, Space, and Environmental Medicine, Vol: 85, Pages: 1005-1012, ISSN: 0095-6562

Journal article

Curcin V, Woodcock T, Poots A, Majeed A, Bell Det al., 2014, Model-driven approach to data collection and reporting for quality improvement, Journal of Biomedical Informatics, Vol: 52, Pages: 151-162, ISSN: 1532-0480

Continuous data collection and analysis have been shown essential to achieving improvement in healthcare. However, the data required for local improvement initiatives are often not readily available from hospital Electronic Health Record (EHR) systems or not routinely collected. Furthermore, improvement teams are often restricted in time and funding thus requiring inexpensive and rapid tools to support their work. Hence, the informatics challenge in healthcare local improvement initiatives consists of providing a mechanism for rapid modelling of the local domain by non-informatics experts, including performance metric definitions, and grounded in established improvement techniques. We investigate the feasibility of a model-driven software approach to address this challenge, whereby an improvement data model designed by a team is used to automatically generate required electronic data collection instruments and reporting tools. To that goal, we have designed a generic Improvement Data Model (IDM) to capture the data items and quality measures relevant to the project, and constructed Web Improvement Support in Healthcare (WISH), a prototype tool that takes user-generated IDM models and creates a data schema, data collection web interfaces, and a set of live reports, based on Statistical Process Control (SPC) for use by improvement teams. The software has been successfully used in over 50 improvement projects, with more than 700 users. We present in detail the experiences of one of those initiatives, Chronic Obstructive Pulmonary Disease project in Northwest London hospitals. The specific challenges of improvement in healthcare are analysed and the benefits and limitations of the approach are discussed.

Journal article

Poots A, Green SA, Honeybourne E, Green J, Woodcock T, Barnes R, Bell Det al., 2014, Improving mental health outcomes: achieving equity through quality improvement, International Journal for Quality in Health Care, Vol: 46

Objective To investigate equity of patient outcomes in a psychological therapy service, following increased access achieved by a quality improvement (QI) initiative.Design Retrospective service evaluation of health outcomes; data analysed by ANOVA, chi-squared and Statistical Process Control.Setting A psychological therapy service in Westminster, London, UK.Participants People living in the Borough of Westminster, London, attending the service (from either healthcare professional or self-referral) between February 2009 and May 2012.Intervention(s) Social marketing interventions were used to increase referrals, including the promotion of the service through local media and through existing social networks.Main Outcome Measure(s) (i) Severity of depression on entry using Patient Health Questionnaire-9 (PHQ9). (ii) Changes to severity of depression following treatment (ΔPHQ9). (iii) Changes in attainment of a meaningful improvement in condition assessed by a key performance indicator.Results Patients from areas of high deprivation entered the service with more severe depression (M = 15.47, SD = 6.75), compared with patients from areas of low (M = 13.20, SD = 6.75) and medium (M = 14.44, SD = 6.64) deprivation. Patients in low, medium and high deprivation areas attained similar changes in depression score (ΔPHQ9: M = −6.60, SD = 6.41). Similar proportions of patients achieved the key performance indicator across initiative phase and deprivation categories.Conclusions QI methods improved access to mental health services; this paper finds no evidence for differences in clinical outcomes in patients, regardless of level of deprivation, interpreted as no evidence of inequity in the service with respect to this outcome.

Journal article

Doyle C, Howe C, Woodcock T, Myron R, Phekoo K, McNicholas C, Saffer J, Bell Det al., 2013, Making change last: applying the NHS institute for innovation and improvement sustainability model to healthcare improvement., Implementation Science, Vol: 8, ISSN: 1748-5908

The implementation of evidence-based treatments to deliver high-quality care is essential to meet the healthcare demands of aging populations. However, the sustainable application of recommended practice is difficult to achieve and variable outcomes well recognised. The NHS Institute for Innovation and Improvement Sustainability Model (SM) was designed to help healthcare teams recognise determinants of sustainability and take action to embed new practice in routine care. This article describes a formative evaluation of the application of the SM by the National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care for Northwest London (CLAHRC NWL). Data from project teams' responses to the SM and formal reviews was used to assess acceptability of the SM and the extent to which it prompted teams to take action. Projects were classified as 'engaged,' 'partially engaged' and 'non-engaged.' Quarterly survey feedback data was used to explore reasons for variation in engagement. Score patterns were compared against formal review data and a 'diversity of opinion' measure was derived to assess response variance over time. Of the 19 teams, six were categorized as 'engaged,' six 'partially engaged,' and seven as 'non-engaged.' Twelve teams found the model acceptable to some extent. Diversity of opinion reduced over time. A minority of teams used the SM consistently to take action to promote sustainability but for the majority SM use was sporadic. Feedback from some team members indicates difficulty in understanding and applying the model and negative views regarding its usefulness. The SM is an important attempt to enable teams to systematically consider determinants of sustainability, provide timely data to assess progress, and prompt action to create conditions for sustained practice. Tools such as these need to be tested in healthcare settings to assess strengths and weaknesses and findings disseminated to aid development. This study

Journal article

Woodcock T, Poots AJ, Bell D, 2013, The impact of changing the 4 h emergency access standard on patient waiting times in emergency departments in England, Emergency Medicine Journal

Journal article

Poots AJ, Woodcock T, 2012, Statistical process control for data without inherent order, Bmc Medical Informatics and Decision Making, Vol: 12

Background The XmR chart is a powerful analytical tool in statistical process control (SPC) for detecting special causes of variation in a measure of quality. In this analysis a statistic called the average moving range is used as a measure of dispersion of the data. This approach is correct for data with natural underlying order, such as time series data. There is however conflict in the literature over the appropriateness of the XmR chart to analyse data without an inherent ordering. Results and Discussion We prove quantitatively that XmR chart analysis is problematic for data without an inherent ordering. We derive the maxima and minima for the average moving range in such data, and using real-world data, demonstrate the problem this causes for calculating control limits. Conclusion The XmR chart should only be used for data endowed with an inherent ordering, such as a time series. To detect special causes of variation in data without an inherent ordering we suggest that one of the many well-established approaches to outlier analysis should be adopted. Furthermore we recommend that in all SPC analyses authors should consistently report the type of control chart used, including the measure of variation used in calculating control limits.

Journal article

Balasanthiran A, O'Shea T, Moodambail T, Woodcock T, Poots AJ, Stacey M, Vijayaraghavan Set al., 2012, Type 2 diabetes in children and young adults in East London: an alarmingly high prevalence, Practical Diabetes, Vol: 29, Pages: 193-198

Type 2 diabetes (T2DM) in the young is a growing concern in many countries worldwide. In previous studies, positive associations with obesity, female gender, and family history have been noted. Newham, East London, has one of the highest prevalence of T2DM in the UK as well as one of the youngest populations. Our aim was to establish the prevalence and characteristics of T2DM in young people in Newham, and compare findings with existing data.Forty‐four young people (≤25 years) with T2DM and an equal number of young people with type 1 diabetes were examined. A retrospective analysis of existing patient records utilising diabetes and pathology databases was conducted.The age‐specific prevalence of T2DM in children and young adults within Newham was noted to be the highest in the UK at 0.57/1000 (58 out of 100 300). There was a strong association with obesity and 77% of those with T2DM were found to have a body mass index ≥25kg/m2. Many had features of the metabolic syndrome.This analysis confirms the high prevalence of T2DM with obesity in young people, particularly among minority ethnic groups, and adds to concern among health care providers and commissioners about the need for preventative strategies to tackle this problem.

Journal article

Curcin V, Woodcock T, Reed JE, Bell Det al., 2012, CLAHRC Healthcare Improvement Support System (HISS), Pages: 867-870

This demo presents the main features of the CLAHRC Healthcare Improvement Support System (HISS), a data collection and reporting toolkit which has been designed as a collaboration between the Department of Computing at Imperial College London and NIHR CLAHRC [3] initiative to facilitate measurement for improvement in local multidisciplinary healthcare improvement teams. The HISS software toolkit is supporting a larger methodology to implement research into practice through a series of quality improvement projects and managing the design, introduction, spread and sustainability of those improvements. It allows the project teams to design the desired process model, define quantitative improvement measures, and automatically generate a web application for the team members to enter measurement data at regular (typically weekly) intervals, and monitor their progress in real-time.The demo will showcase some common functions of the system on the example of a real-life improvement project. Copyright © 2012 ACM.

Conference paper

Hopkinson NS, Englebretsen C, Cooley N, Kennie K, Lim M, Woodcock T, Laverty AA, Wilson S, Elkin SL, Caneja C, Falzon C, Burgess H, Bell D, Lai Det al., 2012, Designing and implementing a COPD discharge care bundle., Thorax, Vol: 67, Pages: 90-92

National surveys have revealed significant differences in patient outcomes following admission to hospital with acute exacerbation of COPD which are likely to be due to variations in care. We developed a care bundle, comprising a short list of evidence-based practices to be implemented prior to discharge for all patients admitted with this condition, based on a review of national guidelines and other relevant literature, expert opinion and patient consultation. Implementation was then piloted using action research methodologies with patient input. Actively involving staff was vital to ensure that the changes introduced were understood and the process followed. Implementation of a care bundle has the potential to produce a dramatic improvement in compliance with optimum health care practice.

Journal article

This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.

Request URL: http://wlsprd.imperial.ac.uk:80/respub/WEB-INF/jsp/search-html.jsp Request URI: /respub/WEB-INF/jsp/search-html.jsp Query String: respub-action=search.html&id=00239277&limit=30&person=true