Cervical ripening at home or in-hospital-prospective cohort study and process evaluation (CHOICE) study: a protocol.

IntroductionThe aim of the cervical ripening at home or in-hospital-prospective cohort study and process evaluation (CHOICE) study is to compare home versus in-hospital cervical ripening to determine whether home cervical ripening is safe (for the primary outcome of neonatal unit (NNU) admission), acceptable to women and cost-effective from the perspective of both women and the National Health Service (NHS).Methods and analysisWe will perform a prospective multicentre observational cohort study with an internal pilot phase. We will obtain data from electronic health records from at least 14 maternity units offering only in-hospital cervical ripening and 12 offering dinoprostone home cervical ripening. We will also conduct a cost-effectiveness analysis and a mixed methods study to evaluate processes and women/partner experiences. Our primary sample size is 8533 women with singleton pregnancies undergoing induction of labour (IOL) at 39+0 weeks' gestation or more. To achieve this and contextualise our findings, we will collect data relating to a cohort of approximately 41 000 women undergoing IOL after 37 weeks. We will use mixed effects logistic regression for the non-inferiority comparison of NNU admission and propensity score matched adjustment to control for treatment indication bias. The economic analysis will be undertaken from the perspective of the NHS and Personal Social Services (PSS) and the pregnant woman. It will include a within-study cost-effectiveness analysis and a lifetime cost-utility analysis to account for any long-term impacts of the cervical ripening strategies. Outcomes will be reported as incremental cost per NNU admission avoided and incremental cost per quality adjusted life year gained.Research ethics approval and disseminationCHOICE has been funded and approved by the National Institute of Healthcare Research Health Technology and Assessment, and the results will be disseminated via publication in peer-reviewed journals.Trial registration numberISRCTN32652461

Quality of medication use in primary care - mapping the problem, working to a solution: a systematic review of the literature

Background: The UK, USA and the World Health Organization have identified improved patient safety in healthcare as a priority. Medication error has been identified as one of the most frequent forms of medical error and is associated with significant medical harm. Errors are the result of the systems that produce them. In industrial settings, a range of systematic techniques have been designed to reduce error and waste. The first stage of these processes is to map out the whole system and its reliability at each stage. However, to date, studies of medication error and solutions have concentrated on individual parts of the whole system. In this paper we wished to conduct a systematic review of the literature, in order to map out the medication system with its associated errors and failures in quality, to assess the strength of the evidence and to use approaches from quality management to identify ways in which the system could be made safer. Methods: We mapped out the medicines management system in primary care in the UK. We conducted a systematic literature review in order to refine our map of the system and to establish the quality of the research and reliability of the system. Results: The map demonstrated that the proportion of errors in the management system for medicines in primary care is very high. Several stages of the process had error rates of 50% or more: repeat prescribing reviews, interface prescribing and communication and patient adherence. When including the efficacy of the medicine in the system, the available evidence suggested that only between 4% and 21% of patients achieved the optimum benefit from their medication. Whilst there were some limitations in the evidence base, including the error rate measurement and the sampling strategies employed, there was sufficient information to indicate the ways in which the system could be improved, using management approaches. The first step to improving the overall quality would be routine monitoring of adherence, clinical effectiveness and hospital admissions. Conclusion: By adopting the whole system approach from a management perspective we have found where failures in quality occur in medication use in primary care in the UK, and where weaknesses occur in the associated evidence base. Quality management approaches have allowed us to develop a coherent change and research agenda in order to tackle these, so far, fairly intractable problems

Public Reporting and Transparency

Provides a short history of efforts to report information on health system performance; identifies policy issues to consider when advancing such efforts; and offers lessons from the experience of public reporting efforts to date

Predicting diabetes-related hospitalizations based on electronic health records

OBJECTIVE: To derive a predictive model to identify patients likely to be hospitalized during the following year due to complications attributed to Type II diabetes. METHODS: A variety of supervised machine learning classification methods were tested and a new method that discovers hidden patient clusters in the positive class (hospitalized) was developed while, at the same time, sparse linear support vector machine classifiers were derived to separate positive samples from the negative ones (non-hospitalized). The convergence of the new method was established and theoretical guarantees were proved on how the classifiers it produces generalize to a test set not seen during training. RESULTS: The methods were tested on a large set of patients from the Boston Medical Center - the largest safety net hospital in New England. It is found that our new joint clustering/classification method achieves an accuracy of 89% (measured in terms of area under the ROC Curve) and yields informative clusters which can help interpret the classification results, thus increasing the trust of physicians to the algorithmic output and providing some guidance towards preventive measures. While it is possible to increase accuracy to 92% with other methods, this comes with increased computational cost and lack of interpretability. The analysis shows that even a modest probability of preventive actions being effective (more than 19%) suffices to generate significant hospital care savings. CONCLUSIONS: Predictive models are proposed that can help avert hospitalizations, improve health outcomes and drastically reduce hospital expenditures. The scope for savings is significant as it has been estimated that in the USA alone, about $5.8 billion are spent each year on diabetes-related hospitalizations that could be prevented.Accepted manuscrip

Complex Care Management Program Overview

This report includes brief updates on various forms of complex care management including: Aetna - Medicare Advantage Embedded Case Management ProgramBrigham and Women's Hospital - Care Management ProgramIndependent Health - Care PartnersIntermountain Healthcare and Oregon Health and Science University - Care Management PlusJohns Hopkins University - Hospital at HomeMount Sinai Medical Center -- New York - Mount Sinai Visiting Doctors Program/ Chelsea-Village House Calls ProgramsPartners in Care Foundation - HomeMeds ProgramPrinceton HealthCare System - Partnerships for PIECEQuality Improvement for Complex Chronic Conditions - CarePartner ProgramSenior Services - Project Enhance/EnhanceWellnessSenior Whole Health - Complex Care Management ProgramSumma Health/Ohio Department of Aging - PASSPORT Medicaid Waiver ProgramSutter Health - Sutter Care Coordination ProgramUniversity of Washington School of Medicine - TEAMcar

How do we evaluate the cost of nosocomial infection? The ECONI protocol: an incidence study with nested case-control evaluating cost and quality of life

Introduction Healthcare-associated or nosocomial infection (HAI) is distressing to patients and costly for the National Health Service (NHS). With increasing pressure to demonstrate cost-effectiveness of interventions to control HAI and notwithstanding the risk from antimicrobial-resistant infections, there is a need to understand the incidence rates of HAI and costs incurred by the health system and for patients themselves. Methods and analysis The Evaluation of Cost of Nosocomial Infection study (ECONI) is an observational incidence survey with record linkage and a nested case-control study that will include postdischarge longitudinal follow-up and qualitative interviews. ECONI will be conducted in one large teaching hospital and one district general hospital in NHS Scotland. The case mix of these hospitals reflects the majority of overnight admissions within Scotland. An incidence survey will record all HAI cases using standard case definitions. Subsequent linkage to routine data sets will provide information on an admission cohort which will be grouped into HAI and non-HAI cases. The case-control study will recruit eligible patients who develop HAI and twice that number without HAI as controls. Patients will be asked to complete five questionnaires: the first during their stay, and four others during the year following discharge from their recruitment admission (1, 3, 6 and 12 months). Multiple data collection methods will include clinical case note review; patient-reported outcome; linkage to electronic health records and qualitative interviews. Outcomes collected encompass infection types; morbidity and mortality; length of stay; quality of life; healthcare utilisation; repeat admissions and postdischarge prescribing. Ethics and dissemination The study has received a favourable ethical opinion from the Scotland A Research Ethics Committee (reference 16/SS/0199). All publications arising from this study will be published in open-access peer-reviewed journal. Lay-person summaries will be published on the ECONI website. Trial registration number NCT03253640; Pre-results