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BackgroundThe ‘Productive Ward: Releasing Time to Care’ programme is a quality improvement (QI) intervention introduced in English acute hospitals a decade ago to: (1) Increase time nurses spend in direct patient care. (2) Improve safety and reliability of care. (3) Improve experience for staff and patients. (4) Make changes to physical environments to improve efficiency.ObjectiveTo explore how timing of adoption, local implementation strategies and processes of assimilation into day-to-day practice relate to one another and shape any sustained impact and wider legacies of a large-scale QI intervention.DesignMultiple methods within six hospitals including 88 interviews (with Productive Ward leads, ward staff, Patient and Public Involvement representatives and senior managers), 10 ward manager questionnaires and structured observations on 12 randomly selected wards.ResultsResource constraints and a managerial desire for standardisation meant that, over time, there was a shift away from the original vision of empowering ward staff to take ownership of Productive Ward towards a range of implementation ‘short cuts’. Nonetheless, material legacies (eg, displaying metrics data; storage systems) have remained in place for up to a decade after initial implementation as have some specific practices (eg, protected mealtimes). Variations in timing of adoption, local implementation strategies and contextual changes influenced assimilation into routine practice and subsequent legacies. Productive Ward has informed wider organisational QI strategies that remain in place today and developed lasting QI capabilities among those meaningfully involved in its implementation.ConclusionsAs an ongoing QI approach Productive Ward has not been sustained but has informed contemporary organisational QI practices and strategies. Judgements about the long-term sustainability of QI interventions should consider the evolutionary and adaptive nature of change processes.

National policies to improve health care quality have largely focused on clinical provider outcomes and, more recently, payment reform. Yet the association between hospital leadership and quality, although crucial to driving quality improvement, has not been explored in depth. The authors collected data from surveys of nationally representative groups of hospitals in the US and England to examine the relationships among hospital boards, management practices of front-line managers, and the quality of care delivered. First, they found that hospitals with more effective management practices provided higher-quality care. Second, higher-rated hospital boards had superior performance by hospital management staff. Finally, they identified two signatures of high-performing hospital boards and management practice. Hospitals with boards that paid greater attention to clinical quality had management that better monitored quality performance. Similarly, they found that hospitals with boards that used clinical quality metrics more effectively had higher performance by hospital management staff on target setting and operations.

Background:Rigorous measurement of organizational performance requires large, unbiased samples to allow inferences to the population. Studies of organizations, including hospitals, often rely on voluntary surveys subject to nonresponse bias. For example, hospital administrators with concerns about performance are more likely to opt-out of surveys about organizational quality and safety, which is problematic for generating inferences.Objective:The objective of this study was to describe a novel approach to obtaining a representative sample of organizations using individuals nested within organizations, and demonstrate how resurveying nonrespondents can allay concerns about bias from low response rates at the individual-level.Methods:We review and analyze common ways of surveying hospitals. We describe the approach and results of a double-sampling technique of surveying nurses as informants about hospital quality and performance. Finally, we provide recommendations for sampling and survey methods to increase response rates and evaluate whether and to what extent bias exists.Results:The survey of nurses yielded data on over 95% of hospitals in the sampling frame. Although the nurse response rate was 26%, comparisons of nurses' responses in the main survey and those of resurveyed nonrespondents, which yielded nearly a 90% response rate, revealed no statistically significant differences at the nurse-level, suggesting no evidence of nonresponse bias.Conclusions:Surveying organizations via random sampling of front-line providers can avoid the self-selection issues caused by directly sampling organizations. Response rates are commonly misinterpreted as a measure of representativeness; however, findings from the double-sampling approach show how low response rates merely increase the potential for nonresponse bias but do not confirm it.

Context: Despite serious and widespread efforts to improve the quality of health care, many patients still suffer preventable harm every day. Hospitals find improvement difficult to sustain, and they suffer \"project fatigue\" because so many problems need attention. No hospitals or health systems have achieved consistent excellence throughout their institutions. High-reliability science is the study of organizations in industries like commercial aviation and nuclear power that operate under hazardous conditions while maintaining safety levels that are far better than those of health care. Adapting and applying the lessons of this science to health care offer the promise of enabling hospitals to reach levels of quality and safety that are comparable to those of the best high-reliability organizations. Methods: We combined the Joint Commission's knowledge of health care organizations with knowledge from the published literature and from experts in high-reliability industries and leading safety scholars outside health care. We developed a conceptual and practical framework for assessing hospitals' readiness for and progress toward high reliability. By iterative testing with hospital leaders, we refined the framework and, for each of its fourteen components, defined stages of maturity through which we believe hospitals must pass to reach high reliability. Findings: We discovered that the ways that high-reliability organizations generate and maintain high levels of safety cannot be directly applied to today's hospitals. We defined a series of incremental changes that hospitals should undertake to progress toward high reliability. These changes involve the leadership's commitment to achieving zero patient harm, a fully functional culture of safety throughout the organization, and the widespread deployment of highly effective process improvement tools. Conclusions: Hospitals can make substantial progress toward high reliability by undertaking several specific organizational change initiatives. Further research and practical experience will be necessary to determine the validity and effectiveness of this framework for high-reliability health care.

Under Medicare's Hospital Readmissions Reduction Program, two thirds of U.S. hospitals will receive penalties of up to 1% of Medicare reimbursements. But the program could exacerbate disparities in care and create disincentives to providing care for the very ill. October 1, 2012, marked the beginning of the Hospital Readmissions Reduction Program (HRRP), an ambitious effort by the Centers for Medicare and Medicaid Services (CMS) to reduce the frequency of rehospitalization of Medicare patients. The program consists primarily of financial penalties levied against hospitals with readmission rates that are deemed to be excessive. To assign penalties, CMS calculated expected readmission rates for all hospitalizations for acute myocardial infarction, congestive heart failure, and pneumonia from July 2008 through June 2011, adjusting for age, sex, and coexisting conditions such as diabetes and hypertension. These expected rates were then compared with the actual . . .

Background Administrative data is a useful tool for research and quality improvement; however, validity of research findings based on these data depends on their reliability. Diagnoses assigned by physicians are subsequently converted by nosologists to ICD-10 codes (International Statistical Classification of Diseases and Related Health Problems, 10th Revision). Several groups have reported ICD-9 coding errors in inpatient data that have implications for research, quality improvement, and policymaking, but few have assessed ICD-10 code validity in ambulatory care databases. Our objective was to evaluate pulmonary embolism (PE) ICD-10 code accuracy in our large, integrated hospital system, and the validity of using these codes for operational and health services research using ED ambulatory care databases. Methods Ambulatory care data for patients (age ≥ 18 years) with a PE ICD-10 code (I26.0 and I26.9) were obtained from the records of four urban EDs between July 2013 to January 2015. PE diagnoses were confirmed by reviewing medical records and imaging reports. In cases where chart diagnosis and ICD-10 code were discrepant, chart review was considered correct. Physicians’ written discharge diagnoses were also searched using ‘pulmonary embolism’ and ‘PE’, and patients who were diagnosed with PE but not coded as PE were identified. Coding discrepancies were quantified and described. Results One thousand, four hundred and fifty-three ED patients had a PE ICD-10 code. Of these, 257 (17.7%) were false positive, with an incorrectly assigned PE code. Among the 257 false positives, 193 cases had ambiguous ED diagnoses such as ‘rule out PE’ or ‘query PE’, while 64 cases should have had non-PE codes. An additional 117 patients (8.90%) with a PE discharge diagnosis were incorrectly assigned a non-PE ICD-10 code (false negative group). The sensitivity of PE ICD-10 codes in this dataset was 91.1% (95%CI, 89.4–92.6) with a specificity of 99.9% (95%CI, 99.9–99.9). The positive and negative predictive values were 82.3% (95%CI, 80.3–84.2) and 99.9% (95%CI, 99.9–99.9), respectively. Conclusions Ambulatory care data, like inpatient data, are subject to coding errors. This confirms the importance of ICD-10 code validation prior to use. The largest proportion of coding errors arises from ambiguous physician documentation; therefore, physicians and data custodians must ensure that quality improvement processes are in place to promote ICD-10 coding accuracy.

ObjectiveHospital electronic prescribing (ePrescribing) systems offer a wide range of patient safety benefits. Like other hospital health information technology interventions, however, they may also introduce new areas of risk. Despite recent advances in identifying these risks, the development and use of ePrescribing systems is still leading to numerous unintended consequences, which may undermine improvement and threaten patient safety. These negative consequences need to be analysed in the design, implementation and use of these systems. We therefore aimed to understand the roots of these reported threats and identify candidate avoidance/mitigation strategies.MethodsWe analysed a longitudinal, qualitative study of the implementation and adoption of ePrescribing systems in six English hospitals, each being conceptualised as a case study. Data included semistructured interviews, observations of implementation meetings and system use, and a collection of relevant documents. We analysed data first within and then across the case studies.ResultsOur dataset included 214 interviews, 24 observations and 18 documents. We developed a taxonomy of factors underlying unintended safety threats in: (1) suboptimal system design, including lack of support for complex medication administration regimens, lack of effective integration between different systems, and lack of effective automated decision support tools; (2) inappropriate use of systems—in particular, too much reliance on the system and introduction of workarounds; and (3) suboptimal implementation strategies resulting from partial roll-outs/dual systems and lack of appropriate training. We have identified a number of system and organisational strategies that could potentially avoid or reduce these risks.ConclusionsImperfections in the design, implementation and use of ePrescribing systems can give rise to unintended consequences, including safety threats. Hospitals and suppliers need to implement short- and long-term strategies in terms of the technology and organisation to minimise the unintended safety risks.

Quality problem. Despite its success in other industries, process standardization in health care has been slow to gain traction or to demonstrate a positive impact on the safety of care. Intervention. The High 5s project is a global patient safety initiative of the World Health Organization (WHO) to facilitate the development, implementation and evaluation of Standard Operating Protocols (SOPs) within a global learning community to achieve measurable, significant and sustainable reductions in challenging patient safety problems. Goals. The project seeks to answer two questions: (i) Is it feasible to implement standardized health care processes in individual hospitals, among multiple hospitals within individual countries and across country boundaries? (ii) If so, what is the impact of standardization on the safety problems that the project is targeting? Method. The two key areas in which the High 5s project is innovative are its use of process standardization both in hospitals within a country and in multiple participating countries, and its carefully designed multi-pronged approach to evaluation. Status. Three SOPs—correct surgery, medication reconciliation, concentrated injectable medicines—have been developed and are being implemented and evaluated in multiple hospitals in seven participating countries. Nearly 5 years into the implementation, it is clear that this is just the beginning of what can be seen as an exercise in behavior management, asking whether health care workers can adapt their behaviors and environments to standardize care processes in widely varying hospital settings.

Health policy experts are focusing on the prevention of hospital readmissions as a way to improve quality and reduce costs. This study showed wide variation in hospital readmission rates but only a weak association between discharge planning and readmission. The publication of discharge-planning data is unlikely to reduce readmission rates. Health policy experts are focusing on the prevention of hospital readmissions as a way to improve quality and reduce costs. This study showed wide variation in hospital readmission rates but only a weak association between discharge planning and readmission. The U.S. health care system faces challenges on two fronts: pressure to improve quality 1 and the necessity to reduce costs. 2 Unfortunately, quality-improvement efforts often increase costs even when they are “cost-effective,” and efforts to constrain costs can lead to concerns about reductions in the quality of care. Thus, improving care in clinical areas where efforts can lead simultaneously to better outcomes for patients and lower costs represents an important step forward. Preventing readmissions is one such opportunity. Previous studies have indicated large variations in readmission rates among hospitals 3 – 5 and noted substantial problems with the transition of care from the . . .

Structural factors can influence hospital costs beyond case-mix differences. However, accepted measures on how to distinguish hospitals with regard to cost-related organizational and regional differences are lacking in Switzerland. Therefore, the objective of this study was to identify and assess a comprehensive set of hospital attributes in relation to average case-mix adjusted costs of hospitals. Using detailed hospital and patient-level data enriched with regional information, we derived a list of 23 cost predictors, examined how they are associated with costs, each other, and with different hospital types, and identified principal components within them. Our results showed that attributes describing size, complexity, and teaching-intensity of hospitals (number of beds, discharges, departments, and rate of residents) were positively related to costs and showed the largest values in university (i.e., academic teaching) and central general hospitals. Attributes related to rarity and financial risk of patient mix (ratio of rare DRGs, ratio of children, and expected loss potential based on DRG mix) were positively associated with costs and showed the largest values in children’s and university hospitals. Attributes characterizing the provision of essential healthcare functions in the service area (ratio of emergency/ ambulance admissions, admissions during weekends/ nights, and admissions from nursing homes) were positively related to costs and showed the largest values in central and regional general hospitals. Regional attributes describing the location of hospitals in large agglomerations (in contrast to smaller agglomerations and rural areas) were positively associated with costs and showed the largest values in university hospitals. Furthermore, the four principal components identified within the hospital attributes fully explained the observed cost variations across different hospital types. These uncovered relationships may serve as a foundation for objectifying discussions about cost-related heterogeneity in Swiss hospitals and support policymakers to include structural characteristics into cost benchmarking and hospital reimbursement.