Emergency admission rates for ambulatory care-sensitive conditions (ACSCs) have been used by both researchers and policy makers as an indicator to assess healthcare systems.1–3 ACSCs are a set of conditions such as asthma and diabetes, where the need for emergency admissions is thought to be avoidable.4 5 They are designed to capture how ambulatory care might impact on rates of emergency admissions. However, in this issue of BMJ Quality and Safety, Lynch et al6 question the merits of distinguishing ACSCs from other emergency admissions. Examining factors associated with county-level variation in emergency admissions in Ireland, they found similar factors were associated with both all emergency admissions and ACSCs. Lynch and colleagues concluded that ‘the distinction between potentially avoidable and all other emergency admissions may not be as useful as previously believed’.6
Here, we discuss the...
Over recent years, hospitals have increasingly focused on improving value: outcomes achieved per dollar spent.1 Although prior efforts to address costs and overuse in healthcare date back decades,2 the modern movement in hospitals has largely progressed through overlapping stages, focused first on raising awareness and articulating the motivation for addressing costs and healthcare waste in education and care delivery.3–9 Some hospital leaders began exploring the effect of simply providing cost transparency to clinicians, with limited results.10 11 In concert with the launch of the ‘Choosing Wisely’ campaign in the USA in 2012, hospitalists led projects that largely sought to root out individual areas of overuse and ‘things we do for no reason’, ushering in a renewed emphasis on utilisation in hospitals.12 13 Now,...
Many emergency admissions are deemed to be potentially avoidable in a well-performing health system.Objective
To measure the impact of population and health system factors on county-level variation in potentially avoidable emergency admissions in Ireland over the period 2014–2016.Methods
Admissions data were used to calculate 2014–2016 age-adjusted emergency admission rates for selected conditions by county of residence. Negative binomial regression was used to identify which a priori factors were significantly associated with emergency admissions for these conditions and whether these factors were also associated with total/other emergency admissions. Standardised incidence rate ratios (IRRs) associated with a 1 SD change in risk factors were reported.Results
Nationally, potentially avoidable emergency admissions for the period 2014–2016 (266 395) accounted for 22% of all emergency admissions. Of the population factors, a 1 SD change in the county-level unemployment rate was associated with a 24% higher rate of potentially avoidable emergency admissions (IRR: 1.24; 95% CI 1.04 to 1.41). Significant health system factors included emergency admissions with length of stay equal to 1 day (IRR: 1.20; 95% CI 1.11 to 1.30) and private health insurance coverage (IRR: 0.92; 95% CI 0.89 to 0.96). The full model accounted for 50% of unexplained variation in potentially avoidable emergency admissions in each county. Similar results were found across total/other emergency admissions.Conclusion
The results suggest potentially avoidable emergency admissions and total/other emergency admissions are primarily driven by socioeconomic conditions, hospital admission policy and private health insurance coverage. The distinction between potentially avoidable and all other emergency admissions may not be as useful as previously believed when attempting to identify the causes of regional variation in emergency admission rates.
Reducing costs while increasing or maintaining quality is crucial to delivering high value care.Objective
To assess the impact of a hospital value-based management programme on cost and quality.Design
Time series analysis of non-psychiatric, non-rehabilitation, non-newborn patients discharged between 1 September 2011 and 31 December 2017 from a US urban, academic medical centre.Intervention
NYU Langone Health instituted an institution-wide programme in April 2014 to increase value of healthcare, defined as health outcomes achieved per dollar spent. Key features included joint clinical and operational leadership; granular and transparent cost accounting; dedicated project support staff; information technology support; and a departmental shared savings programme.Measurements
Change in variable direct costs; secondary outcomes included changes in length of stay, readmission and in-hospital mortality.Results
The programme chartered 74 projects targeting opportunities in supply chain management (eg, surgical trays), operational efficiency (eg, discharge optimisation), care of outlier patients (eg, those at end of life) and resource utilisation (eg, blood management). The study cohort included 160 434 hospitalisations. Adjusted variable costs decreased 7.7% over the study period. Admissions with medical diagnosis related groups (DRG) declined an average 0.20% per month relative to baseline. Admissions with surgical DRGs had an early increase in costs of 2.7% followed by 0.37% decrease in costs per month. Mean expense per hospitalisation improved from 13% above median for teaching hospitals to 2% above median. Length of stay decreased by 0.25% per month relative to prior trends (95% CI –0.34 to 0.17): approximately half a day by the end of the study period. There were no significant changes in 30-day same-hospital readmission or in-hospital mortality. Estimated institutional savings after intervention costs were approximately $53.9 million.Limitations
A systematic programme to increase healthcare value by lowering the cost of care without compromising quality is achievable and sustainable over several years.
Quality improvement and epidemiology studies often rely on database codes to measure performance or impact of adjusted risk factors, but how validity issues can bias those estimates is seldom quantified.Objectives
To evaluate whether and how much interhospital administrative coding variations influence a typical performance measure (adjusted mortality) and potential incentives based on it.Design
National cross-sectional study comparing hospital mortality ranking and simulated pay-for-performance incentives before/after recoding discharge abstracts using medical records.Setting
Twenty-four public and private hospitals located in FranceParticipants
All inpatient stays from the 78 deadliest diagnosis-related groups over 1 year.Interventions
Elixhauser and Charlson comorbidities were derived, and mortality ratios were computed for each hospital. Thirty random stays per hospital were then recoded by two central reviewers and used in a Bayesian hierarchical model to estimate hospital-specific and comorbidity-specific predictive values. Simulations then estimated shifts in adjusted mortality and proportion of incentives that would be unfairly distributed by a typical pay-for-performance programme in this situation.Main outcome measures
Positive and negative predictive values of routine coding of comorbidities in hospital databases, variations in hospitals’ mortality league table and proportion of unfair incentives.Results
A total of 70 402 hospital discharge abstracts were analysed, of which 715 were recoded from full medical records. Hospital comorbidity-level positive predictive values ranged from 64.4% to 96.4% and negative ones from 88.0% to 99.9%. Using Elixhauser comorbidities for adjustment, 70.3% of hospitals changed position in the mortality league table after correction, which added up to a mean 6.5% (SD 3.6) of a total pay-for-performance budget being allocated to the wrong hospitals. Using Charlson, 61.5% of hospitals changed position, with 7.3% (SD 4.0) budget misallocation.Conclusions
Variations in administrative data coding can bias mortality comparisons and budget allocation across hospitals. Such heterogeneity in data validity may be corrected using a centralised coding strategy from a random sample of observations.
Unprecedented numbers of physicians are practicing past age 65. Unlike other safety-conscious industries, such as aviation, medicine lacks robust systems to ensure late-career physician (LCP) competence while promoting career longevity.Objective
To describe the attitudes of key stakeholders about the oversight of LCPs and principles that might shape policy development.Design
Thematic content analysis of interviews and focus groups.Participants
40 representatives of stakeholder groups including state medical board leaders, institutional chief medical officers, senior physicians (>65 years old), patient advocates (patients or family members in advocacy roles), nurses and junior physicians. Participants represented a balanced sample from all US regions, surgical and non-surgical specialties, and both academic and non-academic institutions.Results
Stakeholders describe lax professional self-regulation of LCPs and believe this represents an important unsolved challenge. Patient safety and attention to physician well-being emerged as key organising principles for policy development. Stakeholders believe that healthcare institutions rather than state or certifying boards should lead implementation of policies related to LCPs, yet expressed concerns about resistance by physicians and the ability of institutions to address politically complex medical staff challenges. Respondents recommended a coaching and professional development framework, with environmental changes, to maximise safety and career longevity of physicians as they age.Conclusions
Key stakeholders express a desire for wider adoption of LCP standards, but foresee significant culture change and practical challenges ahead. Participants recommended that institutions lead this work, with support from regulatory stakeholders that endorse standards and create frameworks for policy adoption.
Integration of evidence into practice is suboptimal. Clinical pathways, defined as multidisciplinary care plans, are a method for translating evidence into local settings and have been shown to improve the value of patient care.Objective
To describe the development of a clinical pathways programme across a large academic healthcare system.Methods
We use a 10-step framework (grounded in the Knowledge-to-Action framework and ADAPTE Collaboration methodology for guideline adaptation) to support pathway development and dissemination, including facilitating clinical owner and stakeholder engagement, developing pathway prototypes based on rapid reviews of the existing literature, developing tools for dissemination and impact assessment. We use a cloud-based technology platform (Dorsata, Washington, DC) to assist with development and dissemination across our geographically distributed care settings and providers. Content is viewable through desktop and mobile applications. We measured programme adoption and penetration by examining number of pathways developed as well as mobile application use and pathway views.Results
From 1 February 2016 to 30 April 2018, a total of 202 pathways were disseminated. The three most common clinical domains represented were oncology (46.5%, n=94), pulmonary/critical care (8.9%, n=18) and cardiovascular medicine (7.4%, n=15). Users opting to register for a personal account totalled 1279; the three largest groups were physicians (45.1%, n=504), advanced practice providers (19.5%, n=245) and nurses (19.1%, n=240). Pathway views reached an average of 2150 monthly views during the last 3 months of the period. The majority of pathways reference at least one evidence-based source (93.6%, n=180).Conclusions
A healthcare system can successfully use a framework and technology platform to support the development and dissemination of pathways across a multisite institution.
Publicly reported quality data can help consumers make informed choices about where to seek medical care. The Centers for Medicare and Medicaid Services developed a composite Hospital Compare Overall Star Rating for US acute-care hospitals in 2016. However, patterns of performance and improvement have not been previously described.Objective
To characterise high-quality and low-quality hospitals as assessed by Star Ratings.Design
We performed a retrospective cross-sectional study of 3429 US acute-care hospitals assigned Overall Star Ratings in both 2016 and 2017. We used multivariable logistic regression models to identify characteristics associated with receiving 4 or 5 stars.Results
Small hospitals were more likely to receive 4 or 5 stars in 2016 (33% of small hospitals, 26% of medium hospitals and 21% of large hospitals, OR for medium 0.78, p=0.02, and for large, 0.61, p=0.003). Non-profit status (OR 1.37, p=0.01), midwest region (OR=2.30, p<0.001), west region (OR 1.30 in 2016, p=0.06) and system membership (OR 1.33, p=0.003) were associated with higher odds of achieving a higher Star Rating. Hospitals with the most Medicaid patients were markedly less likely to receive 4 or 5 stars (OR for highest quartile=0.32, p<0.001), and hospitals with the highest proportion of Medicare patients were somewhat less likely to do so (OR for highest quartile=0.68, p=0.01). These associations remained largely consistent over the first two years of reporting and were also associated with the highest likelihood of improvement.Conclusions
Small hospitals with fewer Medicaid patients had the highest odds of performing well on Star Ratings. Further monitoring of these trends is needed as patients, clinicians and policymakers strive to use this information to promote high-quality care.
Artificial intelligence (AI) has enormous potential to improve the safety of healthcare, from increasing diagnostic accuracy,1 to optimising treatment planning,2 to forecasting outcomes of care.3 However, integrating AI technologies into the delivery of healthcare is likely to introduce a range of new risks and amplify existing ones. For instance, failures in widely used software have the potential to quickly affect large numbers of patients4; hidden assumptions in underlying data and models can lead to AI systems delivering dangerous recommendations that are insensitive to local care processes,5 6 and opaque AI techniques such as deep learning can make explaining and learning from failure extremely difficult.7 8 To maximise the benefits of AI in healthcare and to build trust among patients and practitioners, it will therefore be essential to robustly govern the risks that AI...
Clinicians’ satisfaction with encounter decision aids is an important component in facilitating implementation of these tools. We aimed to determine the impact of decision aids supporting shared decision making (SDM) during the clinical encounter on clinician outcomes.Methods
We searched nine databases from inception to June 2017. Randomised clinical trials (RCTs) of decision aids used during clinical encounters with an unaided control group were eligible for inclusion. Due to heterogeneity among included studies, we used a narrative evidence synthesis approach.Results
Twenty-five papers met inclusion criteria including 22 RCTs and 3 qualitative or mixed-methods studies nested in an RCT, together representing 23 unique trials. These trials evaluated healthcare decisions for cardiovascular prevention and treatment (n=8), treatment of diabetes mellitus (n=3), treatment of osteoporosis (n=2), treatment of depression (n=2), antibiotics to treat acute respiratory infections (n=3), cancer prevention and treatment (n=4) and prenatal diagnosis (n=1). Clinician outcomes were measured in only a minority of studies. Clinicians’ satisfaction with decision making was assessed in only 8 (and only 2 of them showed statistically significantly greater satisfaction with the decision aid); only three trials asked if clinicians would recommend the decision aid to colleagues and only five asked if clinicians would use decision aids in the future. Outpatient consultations were not prolonged when a decision aid was used in 9 out of 13 trials. The overall strength of the evidence was low, with the major risk of bias related to lack of blinding of participants and/or outcome assessors.Conclusion
Decision aids can improve clinicians’ satisfaction with medical decision making and provide helpful information without affecting length of consultation time. Most SDM trials, however, omit outcomes related to clinicians’ perspective on the decision making process or the likelihood of using a decision aid in the future.
‘The Problem with...’ series covers controversial topics related to efforts to improve healthcare quality, including widely recommended but deceptively difficult strategies for improvement and pervasive problems that seem to resist solution.
Writing to Improve Healthcare, edited and authored by David P. Stevens, is a timely and important book that is designed to help quality improvers publish their quality improvement (QI) work. (Dr Stevens was the previous Editor-in-Chief of this journal, when it was called Quality & Safety in Healthcare.) The book is unique in that it applies a healthcare improvement perspective to the traditional manuscript preparation and publication process. This is useful for the novice writer and for authors accustomed to writing more traditional clinical research studies or writing for other biomedical fields. Indeed, while some prospective authors of QI work may not be first-time writers, this may be the first time that they are writing to improve healthcare.
The book is organised longitudinally, beginning with why one should consider writing about their QI work, ending with the submission process, and all of the steps in between. This approach, combined...