A tool for routine monitoring and feedback of morbidities following paediatric cardiac surgery

Short-term survival after paediatric cardiac surgery has improved significantly over the past 20 years and increasing attention is being given to measuring and reducing incidence of morbidities following surgery. How to best use routinely collected data to share morbidity information constitutes a challenge for clinical teams interested in analysing their outcomes for quality improvement. We aimed to develop a tool facilitating this process in the context of monitoring morbidities following paediatric cardiac surgery, as part of a prospective multi-centre research study in the United Kingdom. We developed a prototype software tool to analyse and present data about morbidities associated with cardiac surgery in children. We used an iterative process, involving engagement with potential users, tool design and implementation, and feedback collection. Graphical data displays were based on the use of icons and graphs designed in collaboration with clinicians. Our tool enables automatic creation of graphical summaries, displayed as a Microsoft PowerPoint presentation, from a spreadsheet containing patient-level data about specified cardiac surgery morbidities. Data summaries include numbers/percentages of cases with morbidities reported, co-occurrences of different morbidities, and time series of each complication over a time window. Our work was characterised by a very high level of interaction with potential users of the tool, enabling us to promptly account for feedback and suggestions from clinicians and data managers. The United Kingdom centres involved in the project received the tool positively, and several expressed their interest in using it as part of their routine practice

Assessing the number of users who are excluded by domestic heating controls

This is the pre-print version of the Article. This Article is also referred to as: "Assessing the 'Design Exclusion' of Heating Controls at a Low-Cost, Low-Carbon Housing Development". - Copyright @ 2011 Taylor & FrancisSpace heating accounts for almost 60% of the energy delivered to housing which in turn accounts for nearly 27% of the total UK's carbon emissions. This study was conducted to investigate the influence of heating control design on the degree of ‘user exclusion’. This was calculated using the Design Exclusion Calculator, developed by the Engineering Design Centre at the University of Cambridge. To elucidate the capability requirements of the system, a detailed hierarchical task analysis was produced, due to the complexity of the overall task. The Exclusion Calculation found that the current design placed excessive demands upon the capabilities of at least 9.5% of the UK population over 16 years old, particularly in terms of ‘vision’, ‘thinking’ and ‘dexterity’ requirements. This increased to 20.7% for users over 60 years old. The method does not account for the level of numeracy and literacy and so the true exclusion may be higher. Usability testing was conducted to help validate the results which indicated that 66% of users at a low-carbon housing development could not programme their controls as desired. Therefore, more detailed analysis of the cognitive demands placed upon the users is required to understand where problems within the programming process occur. Further research focusing on this cognitive interaction will work towards a solution that may allow users to behave easily in a more sustainable manner

Combining qualitative and quantitative operational research methods to inform quality improvement in pathways that span multiple settings

BACKGROUND: Improving integration and continuity of care across sectors within resource constraints is a priority in many health systems. Qualitative operational research methods of problem structuring have been used to address quality improvement in services involving multiple sectors but not in combination with quantitative operational research methods that enable targeting of interventions according to patient risk. We aimed to combine these methods to augment and inform an improvement initiative concerning infants with congenital heart disease (CHD) whose complex care pathway spans multiple sectors. METHODS: Soft systems methodology was used to consider systematically changes to services from the perspectives of community, primary, secondary and tertiary care professionals and a patient group, incorporating relevant evidence. Classification and regression tree (CART) analysis of national audit datasets was conducted along with data visualisation designed to inform service improvement within the context of limited resources. RESULTS: A 'Rich Picture' was developed capturing the main features of services for infants with CHD pertinent to service improvement. This was used, along with a graphical summary of the CART analysis, to guide discussions about targeting interventions at specific patient risk groups. Agreement was reached across representatives of relevant health professions and patients on a coherent set of targeted recommendations for quality improvement. These fed into national decisions about service provision and commissioning. CONCLUSIONS: When tackling complex problems in service provision across multiple settings, it is important to acknowledge and work with multiple perspectives systematically and to consider targeting service improvements in response to confined resources. Our research demonstrates that applying a combination of qualitative and quantitative operational research methods is one approach to doing so that warrants further consideration

Identifying improvements to complex pathways: evidence synthesis and stakeholder engagement in infant congenital heart disease

OBJECTIVES: Many infants die in the year following discharge from hospital after surgical or catheter intervention for congenital heart disease (3–5% of discharged infants). There is considerable variability in the provision of care and support in this period, and some families experience barriers to care. We aimed to identify ways to improve discharge and postdischarge care for this patient group. DESIGN: A systematic evidence synthesis aligned with a process of eliciting the perspectives of families and professionals from community, primary, secondary and tertiary care. SETTING: UK. RESULTS: A set of evidence-informed recommendations for improving the discharge and postdischarge care of infants following intervention for congenital heart disease was produced. These address known challenges with current care processes and, recognising current resource constraints, are targeted at patient groups based on the number of patients affected and the level and nature of their risk of adverse 1-year outcome. The recommendations include: structured discharge documentation, discharging certain high-risk patients via their local hospital, enhanced surveillance for patients with certain (high-risk) cardiac diagnoses and an early warning tool for parents and community health professionals. CONCLUSIONS: Our recommendations set out a comprehensive, system-wide approach for improving discharge and postdischarge services. This approach could be used to address challenges in delivering care for other patient populations that can fall through gaps between sectors and organisations

Death and Emergency Readmission of Infants Discharged After Interventions for Congenital Heart Disease: A National Study of 7643 Infants to Inform Service Improvement.

Improvements in hospital-based care have reduced early mortality in congenital heart disease. Later adverse outcomes may be reducible by focusing on care at or after discharge. We aimed to identify risk factors for such events within 1 year of discharge after intervention in infancy and, separately, to identify subgroups that might benefit from different forms of intervention.Cardiac procedures performed in infants between 2005 and 2010 in England and Wales from the UK National Congenital Heart Disease Audit were linked to intensive care records. Among 7976 infants, 333 (4.2%) died before discharge. Of 7643 infants discharged alive, 246 (3.2%) died outside the hospital or after an unplanned readmission to intensive care (risk factors were age, weight-for-age, cardiac procedure, cardiac diagnosis, congenital anomaly, preprocedural clinical deterioration, prematurity, ethnicity, and duration of initial admission; c-statistic 0.78 [0.75-0.82]). Of the 7643, 514 (6.7%) died outside the hospital or had an unplanned intensive care readmission (same risk factors but with neurodevelopmental condition and acquired cardiac diagnosis and without preprocedural deterioration; c-statistic 0.78 [0.75-0.80]). Classification and regression tree analysis were used to identify 6 subgroups stratified by the level (3-24%) and nature of risk for death outside the hospital or unplanned intensive care readmission based on neurodevelopmental condition, cardiac diagnosis, congenital anomaly, and duration of initial admission. An additional 115 patients died after planned intensive care admission (typically following elective surgery).Adverse outcomes in the year after discharge are of similar magnitude to in-hospital mortality, warrant service improvements, and are not confined to diagnostic groups currently targeted with enhanced monitoring

Improving Risk Adjustment for Mortality After Pediatric Cardiac Surgery: The UK PRAiS2 Model

BACKGROUND: Partial Risk Adjustment in Surgery (PRAiS), a risk model for 30-day mortality after children's heart surgery, has been used by the UK National Congenital Heart Disease Audit to report expected risk-adjusted survival since 2013. This study aimed to improve the model by incorporating additional comorbidity and diagnostic information. METHODS: The model development dataset was all procedures performed between 2009 and 2014 in all UK and Ireland congenital cardiac centers. The outcome measure was death within each 30-day surgical episode. Model development followed an iterative process of clinical discussion and development and assessment of models using logistic regression under 25 × 5 cross-validation. Performance was measured using Akaike information criterion, the area under the receiver-operating characteristic curve (AUC), and calibration. The final model was assessed in an external 2014 to 2015 validation dataset. RESULTS: The development dataset comprised 21,838 30-day surgical episodes, with 539 deaths (mortality, 2.5%). The validation dataset comprised 4,207 episodes, with 97 deaths (mortality, 2.3%). The updated risk model included 15 procedural, 11 diagnostic, and 4 comorbidity groupings, and nonlinear functions of age and weight. Performance under cross-validation was: median AUC of 0.83 (range, 0.82 to 0.83), median calibration slope and intercept of 0.92 (range, 0.64 to 1.25) and -0.23 (range, -1.08 to 0.85) respectively. In the validation dataset, the AUC was 0.86 (95% confidence interval [CI], 0.82 to 0.89), and the calibration slope and intercept were 1.01 (95% CI, 0.83 to 1.18) and 0.11 (95% CI, -0.45 to 0.67), respectively, showing excellent performance. CONCLUSIONS: A more sophisticated PRAiS2 risk model for UK use was developed with additional comorbidity and diagnostic information, alongside age and weight as nonlinear variables

Exploring communication between parents and clinical teams following children’s heart surgery: a survey in the UK

Objective: To explore communication between clinicians and families of children undergoing heart surgery. Design: This study was part of a larger study to select, define and measure the incidence of postoperative complications in children undergoing heart surgery. Parents of children recruited to a substudy between October 2015 and December 2017 were asked to complete a questionnaire about communication during their child’s inpatient stay. We explored all responses and then disaggregated by the following patient characteristics: presence of a complication, length of stay, hospital site, ethnicity and child’s age. This was a descriptive study only. Setting: Four UK specialist hospitals. Results: We recruited 585 children to the substudy with 385 responses (response rate 66%). 81% of parents reported that new members of staff always introduced themselves (18% sometimes, 1% no). Almost all parents said they were encouraged to be involved in decisionmaking, but often only to some extent (59% ‘yes, definitely’; 37% ‘to some extent’). Almost two-thirds of parents said they were told different things by different people which left them feeling confused (10% ‘a lot’; 53% ‘sometimes’). Two-thirds (66%) reported that staff were definitely aware of their child’s medical history (31% ‘to some extent’). 90% said the operation was definitely explained to them (9% ‘to some extent’) and 79% that they were definitely told what to do if they were worried after discharge (17% ‘to some extent’). Parents of children with a complication tended to give less positive responses for involvement in decision-making, consistent communication and staff awareness of their child’s medical history. Parents whose children had longer stays in hospital tended to report lower levels of consistent communication and involvement in decision-making. Conclusions: Our results emphasise the need for consistent communication with families, particularly where complications arise or for children who have longer stays in the hospital

Evaluating an innovative approach to the diagnostic processes for chronic eye disease: a feasibility study

The aim of this study was to develop a framework that would support the evaluation of new ways of diagnosing and monitoring chronic eye disease being planned and implemented by a large NHS hospital. The study involved interviews with a range of health care professionals within the Trust, observation of glaucoma outpatient clinics and related meetings, analysis of routinely collected data, and planning an economic analysis to evaluate the cost and cost-effectiveness of the new service. The information used to inform this study was collected between February 2013 and June 2014. The framework highlights three areas that should be taken into account when evaluating innovation: (1) organisational context, (2) operational impact, and (3) cost and cost effectiveness relative to existing services. In relation to organisational context, those evaluating innovation should seek to understand how different professional groups are involved in, and affected by, the implementation of change and aim to identify the underlying social and organisational factors that may inhibit or support the implementation of innovation. Evaluation should also aim to capture patients’ perceptions of existing services and proposed changes to services and how changes to the delivery of services may affect interactions between patients and clinical staff. From an operational perspective, quantitative analysis should aim to provide estimates of the level of improvement required to meet the challenges presented by anticipated increases in the burden of disease and the likely impact of the suggested changes on patient access metrics. To undertake an economic analysis of the new service, researchers should consider the main cost components of the new and existing services, how to collect resource use and unit cost data for each of these cost components, and a range of potential outcome measures