ObjectivesThe relationship between patient feedback in the General Practice Patient Survey (GPPS) and Care Quality Commission (CQC) inspections of practices was investigated to understand whether ...there is an association between patient views and regulator ratings of quality. The specific aims were to understand whether patients’ self-reported experiences of primary care can predict CQC inspection ratings of GP practices by: (i) Measuring the association between GPPS results and CQC inspection ratings of GP practices; (ii) Building a predictive model of GP practice quality ratings that use GPPS results; and (iii) Evaluating the predictive model for risk stratification.DesignRetrospective analysis of routinely collected data using decision tree modelling.SettingPrimary care: GP practices in England.Primary and secondary outcome measuresGPPS scores and GP practice CQC inspection ratings during 2018.ResultsMost GP practices (72%, 974/1350) were rated as ‘Good’ overall by CQC. Simply assuming that all practices will be rated as ‘Good’ results in a correct prediction 72% of the time, and it was not possible to improve on this overall level of predictive accuracy using decision tree modelling (correct in 73% of cases). However, a set of GPPS questions were found to have value in identifying practices at elevated risk of a poor inspection rating.ConclusionsAlthough there were some associations between GPPS data and CQC inspection ratings, there were limitations to the use of GPPS data for predictive analysis. This is a likely result of the majority of CQC inspections of GPs resulting in a ‘Good’ or ‘Outstanding’ rating. However, some GPPS questions were found to have value in identifying practices at higher risk of an ‘Inadequate’ or ‘Requires Improvement’ rating, and this may be valuable for surveillance purposes. For example, the CQC could use key questions from the survey to target inspection planning.
ObjectivesPatients diagnosed with pancreatic cancer have the poorest survival prognosis of any cancer. This survey aimed to describe their experiences of care and supportive care needs to inform ...future service provision.DesignCross-sectional questionnaire survey of patients with pancreatic cancer in the UK.SettingIndividuals at any stage along the care pathway were recruited via five National Health Service sites in the UK, and online, from January to June 2018.Participants274 individuals completed the questionnaire (78% (215) were completed online). Approximately half of participants were diagnosed within the last year (133/274). Of 212 providing gender details, 82 were male and 130 were female. Ninety per cent (192/213) described themselves as White British.Primary outcome measuresExperiences of communication and information; involvement in treatment decisions; supportive care needs.ResultsCommunication with, and care received from, clinical staff were generally reported positively. However, 29% (75/260) of respondents did not receive enough information at diagnosis, and 10% (25/253) felt they were not involved in decisions about their treatment, but would have liked to be. Supportive care needs were greatest in psychological and physical/daily living domains. 49% (108/221) of respondents reported one or more moderate/high unmet needs within the last month, of which the most commonly reported were: dealing with uncertainty about the future; fears about the cancer spreading; not being able to do things they used to; concerns about those close to them; lack of energy; anxiety; feelings of sadness and feeling down/depressed. Experiences were poorer, and unmet supportive care needs greater, in patients with unresectable disease.ConclusionsPatients with pancreatic cancer have unmet information and support needs across the cancer trajectory. Psychological and physical support appears to be the biggest gap in care. Needs should be assessed and supportive care interventions implemented from the point of diagnosis, and monitored regularly to help patients live as good a quality of life as possible.
ObjectiveWe seek to address gaps in knowledge and agreement around optimal frailty assessment in the acute medical care setting. Frailty is a common term describing older persons who are at increased ...risk of developing multimorbidity, disability, institutionalisation and death. Consensus has not been reached on the practical implementation of this concept to assess clinically and manage older persons in the acute care setting.DesignModified Delphi, via electronic questionnaire. Questions included ranking items that best recognise frailty, optimal timing, location and contextual elements of a successful tool. Intraclass correlation coefficients for overall levels of agreement, with consensus and stability tested by 2-way ANOVA with absolute agreement and Fisher's exact test.ParticipantsA panel of national experts (academics, front-line clinicians and specialist charities) were invited to electronic correspondence.ResultsVariables reflecting accumulated deficit and high resource usage were perceived by participants as the most useful indicators of frailty in the acute care setting. The Acute Medical Unit and Care of the older Persons Ward were perceived as optimum settings for frailty assessment. ‘Clinically meaningful and relevant’, ‘simple (easy to use)’ and ‘accessible by multidisciplinary team’ were perceived as characteristics of a successful frailty assessment tool in the acute care setting. No agreement was reached on optimal timing, number of variables and organisational structures.ConclusionsThis study is a first step in developing consensus for a clinically relevant frailty assessment model for the acute care setting, providing content validation and illuminating contextual requirements. Testing on clinical data sets is a research priority.
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.
We derive the maxima and minima for the average moving range in data without inherent ordering, and show how to calculate this for any data set. We permute a real world data set and calculate control limits based on these extrema.
In the real world data set, permuting the order of the data affected an absolute difference of 109 percent in the width of the control limits.
We prove quantitatively that XmR chart analysis is problematic for data without an inherent ordering, and using real-world data, demonstrate the problem this causes for calculating control limits. The resulting ambiguity in the analysis renders it unacceptable as an approach to making decisions based on data without inherent order.
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.
Hospital board members are asked to consider large amounts of quality and safety data with a duty to act on signals of poor performance. However, in order to do so it is necessary to distinguish ...signals from noise (chance). This article investigates whether data in English National Health Service (NHS) acute care hospital board papers are presented in a way that helps board members consider the role of chance in their decisions.
Thirty English NHS trusts were selected at random and their board papers retrieved. Charts depicting quality and safety were identified. Categorical discriminations were then performed to document the methods used to present quality and safety data in board papers, with particular attention given to whether and how the charts depicted the role of chance, that is, by including control lines or error bars.
Thirty board papers, containing a total of 1488 charts, were sampled. Only 88 (6%) of these charts depicted the role of chance, and only 17 of the 30 board papers included any charts depicting the role of chance. Of the 88 charts that attempted to represent the role of chance, 16 included error bars and 72 included control lines. Only 6 (8%) of the 72 control charts indicated where the control lines had been set (eg, 2 vs 3 SDs).
Hospital board members are expected to consider large amounts of information. Control charts can help board members distinguish signals from noise, but often boards are not using them. We discuss demand-side and supply-side barriers that could be overcome to increase use of control charts in healthcare.
Display omitted
•Addressing the challenge of the second translational gap is key to improving healthcare processes.•Data-driven methodologies improve likelihood of success.•We propose the Improvement ...Data Model (IDM) for data collection and reporting for local improvement.•WISH, a prototype software tool based on IDM is used by over 600 users in 50+ improvement projects.
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 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.
ObjectivesReliable reconciliation of medicines at admission and discharge from hospital is key to reducing unintentional prescribing discrepancies at transitions of healthcare. We introduced a team ...approach to the reconciliation process at an acute hospital with the aim of improving the provision of information and documentation of reliable medication lists to enable clear, timely communications on discharge.SettingAn acute 400-bedded teaching hospital in London, UK.ParticipantsThe effects of change were measured in a simple random sample of 10 adult patients a week on the acute admissions unit over 18 months.InterventionsQuality improvement methods were used throughout. Interventions included education and training of staff involved at ward level and in the pharmacy department, introduction of medication documentation templates for electronic prescribing and for communicating information on medicines in discharge summaries co-designed with patient representatives.ResultsStatistical process control analysis showed reliable documentation (complete, verified and intentional changes clarified) of current medication on 49.2% of patients' discharge summaries. This appears to have improved (to 85.2%) according to a poststudy audit the year after the project end. Pharmacist involvement in discharge reconciliation increased significantly, and improvements in the numbers of medicines prescribed in error, or omitted from the discharge prescription, are demonstrated. Variation in weekly measures is seen throughout but particularly at periods of changeover of new doctors and introduction of new systems.ConclusionsNew processes led to a sustained increase in reconciled medications and, thereby, an improvement in the number of patients discharged from hospital with unintentional discrepancies (errors or omissions) on their discharge prescription. The initiatives were pharmacist-led but involved close working and shared understanding about roles and responsibilities between doctors, nurses, therapists, patients and their carers.
Studies in North America and Europe indicate that the prevalence of blood-borne viruses (BBVs) is elevated in individuals with severe mental illness; there are no comparable data for the UK. We ...offered routine testing for HIV, and hepatitis B and C in an inner-London in-patient psychiatric unit as a service improvement. Of the patients approached 83% had mental capacity to provide informed consent for testing and 66% of patients offered testing accepted. Although it was not our objective to establish the prevalence of BBVs, 18% of patients had serological evidence of a current or previous BBV infection. We found that offering routine testing in an in-patient psychiatric setting is both practical and acceptable to patients.
Identifying older people with clinical frailty, reliably and at scale, is a research priority. We measured frailty in older people using a novel methodology coding frailty syndromes on routinely ...collected administrative data, developed on a national English secondary care population, and explored its performance of predicting inpatient mortality and long length of stay at a single acute hospital.
We included patient spells from Secondary User Service (SUS) data for those ≥65 years with attendance to the emergency department or admission to West Middlesex University Hospital between 01 July 2016 to 01 July 2017. We created eight groups of frailty syndromes using diagnostic coding groups. We used descriptive statistics and logistic regression to explore performance of diagnostic coding groups for the above outcomes.
We included 17,199 patient episodes in the analysis. There was at least one frailty syndrome present in 7,004 (40.7%) patient episodes. The resultant model had moderate discrimination for inpatient mortality (area under the receiver operating characteristic curve (AUC) 0.74; 95% confidence interval (CI) 0.72–0.76) and upper quartile length of stay (AUC 0.731; 95% CI 0.722–0.741). There was good negative predictive value for inpatient mortality (98.1%).
Coded frailty syndromes significantly predict outcomes. Model diagnostics suggest the model could be used for screening of elderly patients to optimise their care.
PDF
