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The COVID-19 pandemic has led to an exponential increase in SARS-CoV-2 infections and associated deaths, and represents a significant challenge to healthcare professionals and facilities. Individual countries have taken several prevention and containment actions to control the spread of infection, including measures to guarantee safety of both healthcare professionals and patients who are at increased risk of infection from COVID-19. Faecal microbiota transplantation (FMT) has a well-established role in the treatment of Clostridioides difficile infection. In the time of the pandemic, FMT centres and stool banks are required to adopt a workflow that continues to ensure reliable patient access to FMT while maintaining safety and quality of procedures. In this position paper, based on the best available evidence, worldwide FMT experts provide guidance on issues relating to the impact of COVID-19 on FMT, including patient selection, donor recruitment and selection, stool manufacturing, FMT procedures, patient follow-up and research activities.

Differences in Medicare patients’ reported diagnoses partly reflect their providers’ proclivity for making diagnoses. Proposed risk-adjustment factors could allow payments and performance measures to be scaled to counteract regional differences in diagnostic intensity. In the U.S. health care system, payments and performance measures are often adjusted to account for differences in patients’ baseline health and demographic characteristics. The idea behind such risk adjustments is to create a level playing field, so that providers aren’t penalized for serving sicker or harder-to-treat patients and insurers aren’t penalized for covering them. For example, the private insurance companies that participate in Medicare Advantage and the Affordable Care Act (ACA) exchanges receive risk-adjusted payments from the U.S. government, with the rationale that insurers should be reimbursed more for enrollees with higher expected costs. The intent of risk adjustment . . .

Background Healthcare stakeholders are increasingly seeking comparative provider performance data to enhance data-driven decision-making and quality improvement. Traditional visualisations, like caterpillar plots, are often difficult for end users to understand and interpret. This study aimed to (1) obtain general feedback from end users on a newly proposed design solution for visualising a risk-adjusted hospital comparison and to develop an understanding of the key criteria they rely on in the evaluation process; (2) test the hypothesis that end users will better understand key messages and rate perceived usability higher with the new design solution than with a caterpillar plot. Methods An end user-centred mixed methods study, involving end users of risk-adjusted hospital comparisons across all levels of the Swiss healthcare system, was conducted to evaluate the new design solution. In the qualitative phase, 14 end users from health authorities, insurers, hospital associations, and hospitals were surveyed in 10 semi-structured individual and group interviews, which were analysed using thematic analysis. In the quantitative phase, a non-clinical randomised controlled online trial (A/B testing) was conducted. In total, 200 of the targeted end users, comprising cantonal quality managers, hospital directors, and those responsible for quality and/or the ‘National Prevalence Measurement’ in hospitals, completed the questionnaire. The data were analysed using comparative descriptive and bivariate statistics. Results Thematic analysis revealed three key criteria that end users relied on when evaluating a risk-adjusted hospital comparison: (1) ‘clarity by design’, highlighting strategies for effectively conveying key messages of hospital comparisons; (2) ‘usability by design’, focusing on end user-centred functionalities and presentation elements; (3) ‘suitability for quality development’, addressing the conditions for creating a trustworthy and useful comparison to drive quality improvement. Quantitative analysis confirmed the hypothesis that end users understand key messages better and perceived usability is higher with the new design than with the caterpillar plot. Conclusions The new design solution improves hospital comparison outputs for end users by combining clear displays with additional interactive features. The identified criteria underlying the evaluation should inform further design projects and research dealing with the visualisation of hospital comparisons. Clinical trial number Not applicable.

BackgroundHospital standardised mortality ratio (HSMR) is a simple ratio that is plagued by sparsity, dimensionality, overdispersion, exclusions and controversy.ObjectiveDescribe Hospital Outcome Prediction Equation V.7 (HOPE-7) methodology.SettingState of Victoria (Australia), population 6.8 million.MethodsMultiphase process: (a) principal diagnoses aggregated into 406 clinical diagnosis groups (CDGs); (b) low case fatality rate (CFR<0.02%) CDGs set aside; (c) remaining CDGs ranked according to predicted risk; (d) final generalised linear model fitted to (75%) training dataset; (e) low-risk cases reinserted and allocated zero risk; (e) model performance in validation dataset assessed for calibration (Hosmer-Lemeshow goodness-of-fit (H10), Brier score, calibration plot), discrimination (area under the receiver operator characteristic (AUCROC) and area under the precision recall (AUCPRC) curves) and classification (dispersion value (φ), SD random effect (τ)). Ideal model: Brier score~0, H10 p value>0.05, AUCROC>0.80, AUCPRC>0.30, φ~1 and τ~0. Classification assessed by proportion of outlier CFR reclassified as inlier HSMR.Results315 hospitals treated 12.97 million adult separations and 152 (48.3%) reported 63 806 in-hospital deaths, 0.49 (95% CI 0.48 to 0.50) per 100 separations. 10 722 principal diagnoses allocated to 198 non-significant CDGs, 45 low-risk CDGs (5.05 million cases) assigned zero risk and 163 significant CDGs aggregated to 20 risk ranks. Final model (development cohort 9.73 million) included demographic variables (age, birth sex, emergency, aged-care resident, hospital transfer, relationship status), one interaction term (emergency transfer) and 20 diagnosis-risk categories. Validation metrics (cohort 3.24 million): Brier score 0.015; H10 p value 0.09; AUCROC 0.90 (95% CI 0.87 to 0.92); AUCPRC 0.28 (95% CI 0.25 to 0.31); φ=4.31 and τ=0.24. Study hospitals generated 2192 hospital quarters with 2053 (95.7%) outlier CFR values, of which 1975 (96.2%) reclassified as HSMR inliers.ConclusionsHOPE-7 is a parsimonious and pragmatic HSMR model based on administrative data common to many jurisdictions that displayed satisfactory calibration, classification and discrimination metrics and addressed frequent HSMR limitations.

Background The Centers for Medicare and Medicaid Services (CMS) has implemented the CMS-Hierarchical Condition Category (CMS-HCC) model to risk adjust Medicare capitation payments. This study intends to assess the performance of the CMS-HCC risk adjustment method and to compare it to the Charlson and Elixhauser comorbidity measures in predicting in-hospital and six-month mortality in Medicare beneficiaries. Methods The study used the 2005-2006 Chronic Condition Data Warehouse (CCW) 5% Medicare files. The primary study sample included all community-dwelling fee-for-service Medicare beneficiaries with a hospital admission between January 1 st , 2006 and June 30 th , 2006. Additionally, four disease-specific samples consisting of subgroups of patients with principal diagnoses of congestive heart failure (CHF), stroke, diabetes mellitus (DM), and acute myocardial infarction (AMI) were also selected. Four analytic files were generated for each sample by extracting inpatient and/or outpatient claims for each patient. Logistic regressions were used to compare the methods. Model performance was assessed using the c-statistic, the Akaike's information criterion (AIC), the Bayesian information criterion (BIC) and their 95% confidence intervals estimated using bootstrapping. Results The CMS-HCC had statistically significant higher c-statistic and lower AIC and BIC values than the Charlson and Elixhauser methods in predicting in-hospital and six-month mortality across all samples in analytic files that included claims from the index hospitalization. Exclusion of claims for the index hospitalization generally led to drops in model performance across all methods with the highest drops for the CMS-HCC method. However, the CMS-HCC still performed as well or better than the other two methods. Conclusions The CMS-HCC method demonstrated better performance relative to the Charlson and Elixhauser methods in predicting in-hospital and six-month mortality. The CMS-HCC model is preferred over the Charlson and Elixhauser methods if information about the patient's diagnoses prior to the index hospitalization is available and used to code the risk adjusters. However, caution should be exercised in studies evaluating inpatient processes of care and where data on pre-index admission diagnoses are unavailable.

BACKGROUND:Functional status measures are important patient-centered indicators of inpatient rehabilitation facility (IRF) quality of care. We developed a risk-adjusted self-care functional status measure for the IRF Quality Reporting Program. This paper describes the development and performance of the measure’s risk-adjustment model. METHODS:Our sample included IRF Medicare fee-for-service patients from the Centers for Medicare & Medicaid Services’ 2008–2010 Post-Acute Care Payment Reform Demonstration. Data sources included the Continuity Assessment Record and Evaluation Item Set, IRF-Patient Assessment Instrument, and Medicare claims. Self-care scores were based on 7 Continuity Assessment Record and Evaluation items. The model was developed using discharge self-care score as the dependent variable, and generalized linear modeling with generalized estimation equation to account for patient characteristics and clustering within IRFs. Patient demographics, clinical characteristics at IRF admission, and clinical characteristics related to the recent hospitalization were tested as risk adjusters. RESULTS:A total of 4769 patient stays from 38 IRFs were included. Approximately 57% of the sample was female; 38.4%, 75–84 years; and 31.0%, 65–74 years. The final model, containing 77 risk adjusters, explained 53.7% of variance in discharge self-care scores (P<0.0001). Admission self-care function was the strongest predictor, followed by admission cognitive function and IRF primary diagnosis group. The range of expected and observed scores overlapped very well, with little bias across the range of predicted self-care functioning. CONCLUSIONS:Our risk-adjustment model demonstrated strong validity for predicting discharge self-care scores. Although the model needs validation with national data, it represents an important first step in evaluation of IRF functional outcomes.

BACKGROUND:The Centers for Medicare & Medicaid Services (CMS) profile hospitals using a set of 30-day risk-standardized mortality and readmission rates as a basis for public reporting. These measures are affected by hospital patient volume, raising concerns about uniformity of standards applied to providers with different volumes. OBJECTIVES:To quantitatively determine whether CMS uniformly profile hospitals that have equal performance levels but different volumes. RESEARCH DESIGN:Retrospective analysis of patient-level and hospital-level data using hierarchical logistic regression models with hospital random effects. Simulation of samples including a subset of hospitals with different volumes but equal poor performance (hospital effects=+3 SD in random-effect logistic model). SUBJECTS:A total of 1,085,568 Medicare fee-for-service patients undergoing 1,494,993 heart failure admissions in 4930 hospitals between July 1, 2005 and June 30, 2008. MEASURES:CMS methodology was used to determine the rank and proportion (by volume) of hospitals reported to perform “Worse than US National Rate.” RESULTS:Percent of hospitals performing “Worse than US National Rate” was ∼40 times higher in the largest (fifth quintile by volume) compared with the smallest hospitals (first quintile). A similar gradient was seen in a cohort of 100 hospitals with simulated equal poor performance (0%, 0%, 5%, 20%, and 85% in quintiles 1 to 5) effectively leaving 78% of poor performers undetected. CONCLUSIONS:Our results illustrate the disparity of impact that the current CMS method of hospital profiling has on hospitals with higher volumes, translating into lower thresholds for detection and reporting of poor performance.

This research is designed to examine the impact of varying patient population distributions on the in-control performance of the risk-adjusted Bernoulli CUSUM chart. The in-control performance of the chart is compared based on sampling the Parsonnet scores with replacement from five realistic subsets of a given distribution. Five patient mixes with different Parsonnet score distributions are created from a real patient population. The outcome measures for this research are the in-control average run lengths (ARLs) given varying patient populations. Our simulation results show that the in-control ARLs of the risk-adjusted Bernoulli CUSUM chart with fixed control limits and a given risk-adjustment equation vary significantly for different patient population distributions, and the in-control ARLs decrease as the mean of the Parsonnet scores increases. The simulation results imply that the control limits should vary based on the particular patient population of interest in order to control the in-control performance of the risk-adjusted Bernoulli CUSUM method.

ObjectivesThis study sought to describe the knowledge and perceptions of pregnant women in Miami-Dade County concerning Zika virus (ZIKV) in their community, to characterize their testing behaviors, and to identify any barriers that would keep them from seeking testing.MethodsThe Florida Department of Health in Miami-Dade County partnered with the Healthy Start Coalition of Miami-Dade to administer an assessment survey in eight OBGYN clinics from June to August 2017. The survey captured past ZIKV testing practices, attitudes towards testing, barriers to testing, risk perception of ZIKV in the participants’ community, and ZIKV-related knowledge. Descriptive analyses were performed on variables of interest. Chi squared tests examined associations between categorical variables.ResultsA total of 363 participants were included in the analysis. Of these, 203 (55.9%) thought they should be tested for ZIKV, and less than half of the participants reported having been previously tested (152, 41.9%). Participants with some high school education were significantly more likely than those with higher education levels to see ZIKV as a “big problem” in the community (p = 0.0026). There was a significant association (p ≤ 0.0001) between women who thought that they should be tested, and those who perceived ZIKV to be a medium or big problem in their community.Conclusions for PracticeHealth interventions that focus on increasing ZIKV knowledge should also place greater emphasis on risk communication when targeting the pregnant population. Having a higher risk perception may be more predictive of testing behaviors than having a lack of barriers or a high level of ZIKV-related knowledge.