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The Balanced Budget Act of 1997 created a designation for critical access hospitals (CAHs) to sustain care for people living in rural communities who lacked access to care due to hospital closures over the preceding decade. Twenty-five years later, 1350 CAHs serve approximately 18% of the US population and a systematic policy evaluation has yet to be performed. This policy analysis serves to define challenges faced by CAHs through a literature review addressing the four major categories of payment, quality, access to capital, and workforce. Additionally, this analysis describes how current challenges to maintain sustainability of CAHs over time are accentuated by gaps in public health infrastructure and variability in individual health care plans exhibited during the COVID-19 pandemic.

Three inherited autosomal dominant conditions-BRCA-related hereditary breast and ovarian cancer (HBOC), Lynch syndrome (LS) and familial hypercholesterolemia (FH)-have been termed the Centers for Disease Control and Prevention Tier 1 (CDCT1) genetic conditions, for which early identification and intervention have a meaningful potential for clinical actionability and a positive impact on public health1. In typical medical practice, genetic testing for these conditions is based on personal or family history, ethnic background or other demographic characteristics2. In this study of a cohort of 26,906 participants in the Healthy Nevada Project (HNP), we first evaluated whether population screening could efficiently identify carriers of these genetic conditions and, second, we evaluated the impact of genetic risk on health outcomes for these participants. We found a 1.33% combined carrier rate for pathogenic and likely pathogenic (P/LP) genetic variants for HBOC, LS and FH. Of these carriers, 21.9% of participants had clinically relevant disease, among whom 70% had been diagnosed with relevant disease before age 65. Moreover, 90% of the risk carriers had not been previously identified, and less than 19.8% of these had documentation in their medical records of inherited genetic disease risk, including family history. In a direct follow-up survey with all carriers, only 25.2% of individuals reported a family history of relevant disease. Our experience with the HNP suggests that genetic screening in patients could identify at-risk carriers, who would not be otherwise identified in routine care.

Objective In the midst of the severe acute respiratory syndrome coronavirus 2 pandemic, which causes coronavirus disease 2019, there is a recognized need to expand critical care services and beds beyond the traditional boundaries. There is considerable concern that widespread infection will result in a surge of critically ill patients that will overwhelm our present adult ICU capacity. In this setting, one proposal to add "surge capacity" has been the use of PICU beds and physicians to care for these critically ill adults. Design Narrative review/perspective. Setting Not applicable. Patients Not applicable. Interventions None. Measurements and main results The virus's high infectivity and prolonged asymptomatic shedding have resulted in an exponential growth in the number of cases in the United States within the past weeks with many (up to 6%) developing acute respiratory distress syndrome mandating critical care services. Coronavirus disease 2019 critical illness appears to be primarily occurring in adults. Although pediatric intensivists are well versed in the care of acute respiratory distress syndrome from viral pneumonia, the care of differing aged adult populations presents some unique challenges. In this statement, a team of adult and pediatric-trained critical care physicians provides guidance on common "adult" issues that may be encountered in the care of these patients and how they can best be managed in a PICU. Conclusions This concise scientific statement includes references to the most recent and relevant guidelines and clinical trials that shape management decisions. The intention is to assist PICUs and intensivists in rapidly preparing for care of adult coronavirus disease 2019 patients should the need arise.

ImportancePatient experience and patient safety are 2 major domains of health care quality; however empirical data on the association of physician vs nonphysician chief executive officers (CEOs) with public and private quality measures are rare but critical to evaluate as hospitals increasingly seek out physician CEOs.ObjectivesTo evaluate whether there is an association of CEO background with hospital quality and to investigate differences in hospital characteristics between hospitals with a physician CEO vs those with a nonphysician CEO.Design, Setting, and ParticipantsThis cross-sectional study used 2019 data from 3 sources (ie, the American Hospital Association [AHA] Annual Survey, the Hospital Consumer Assessment of Healthcare Providers and Systems [HCAHPS], and the Leapfrog Hospital Safety Grades) to identify statistical differences in hospital characteristics and outcomes. Data were analyzed from April to December 2021 .Main Outcomes and MeasuresMultivariable ordinal logistic regression was used to examine the association of physician CEOs with hospital quality assessment outcomes while controlling for other confounding factors. Characteristics from the AHA Annual Survey database were assessed as potential confounders, including hospital control, bed size, region, teaching status, and patient volume.ResultsThe AHA database contained 6162 hospitals; 1759 (29%) had HCAHPS ratings, 1824 (30%) had Leapfrog grades, and 383 (6%) had physician CEOs. A positive Spearman correlation coefficient was found between physician CEOs and HCAHPS patient willingness to recommend the hospital (ρ = 0.0756; P = .002), but the association between CEO medical background and Leapfrog safety grades or HCAHPS ratings did not reach a level of significance in the multivariable ordinal logistic regression models.Conclusions and RelevanceIn this study, a positive correlation was found between physician CEOs and HCAHPS patient willingness to recommend the hospital, but the multivariable analysis did not find an association between hospital physician CEOs and the examined quality and safety outcomes.

OBJECTIVE: Investigate clinical and system drivers of family satisfaction in the PICU. DESIGN: Mixed methods qualitative and quantitative (observational) study. Qualitative interviews with families were performed as a pilot to inform modality of survey distribution based on family preferences. A validated pediatric satisfaction survey deployed to family members for seven months with a corresponding chart review and administrative data collection. SETTING: Pediatric intensive care unit in a tertiary children’s hospital PATIENTS: 206 families of patients admitted to the PICU >48 hours. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Families preferred surveys distributed electronically on a tablet in the PICU setting. The Pediatric Family Satisfaction-ICU survey was used to assess comfort with medical decision-making and communication with the care team. Capture rate of all eligible patients was 69.5% and response rate of 90.8%. Overall, 64.7% of respondents were highly satisfied, while over one third were not highly satisfied; families of Hispanic ethnicity (odds ratio of lower satisfaction of families with Hispanic ethnicity: 2.09, 95% CI: 1.01, 4.33; p= 0.047) and high social stressors (odds ratio of higher satisfaction among high stressed subgroup: 0.49, 95% CI: 0.24, 0.99; p=0.047) reported statistically significant lower satisfaction. Additional free text responses were identified in 21% of respondents, with the majority of comments indicating wishes for improvements clustered around communication with the medical team or sleeping environment of families and patients. CONCLUSIONS: High capture rates of family satisfaction in the PICU can be obtained with a PICU-specific survey, limiting barriers to completion by including family preferences, and distributing in the PICU setting. Less than 2/3 of PICU families are highly satisfied; patients of Hispanic ethnicity and those with high social stressors predict low satisfaction, while illness severity, age and PICU LOS did not have statistical significance. Local improvement teams can use this approach to drive enhanced satisfaction.

Importance The I-PASS training bundle has been shown to improve communication errors during medical student and resident handoffs but has not been explored in the context of interprofessional care. Objective We examined the impact of an interprofessional I-PASS training on communication accuracy and completeness during the delivery of nurse-physician student handoffs. Design, participants, and setting A pre-post comparative design was used to evaluate the differential impact of training on sixty-three first year nursing students and sixty-nine first year medical students, as part of an interprofessional curricular intervention, at an integrated university campus. Intervention The interprofessional I-PASS training was adapted from the existing I-PASS: Medical Student Workshop by a team of interprofessional faculty and presented to nursing and medical students. Main measures Simulated patient handoffs were used to assess communication performance prior to and following training. Evaluation involved the transcription and scoring of all verbal units delivered during the nurse-physician student handoffs based on four levels of information accuracy: correct, missing, erred, and omitted. Proportional changes in verbal units produced during pre and post-training encounters were compared at the combine total and individual domain levels based on training, student type, gender, previous handoff experience, and their interactions. Results Significant improvements in the proportions of correct, erred, and omitted verbal units at the combined total levels were observed across students, all p Discussion I-PASS training can improve communication accuracy and completeness during nurse-physician student handoffs. An event-based tool and coding procedure allowed for the identification of differential training impact based on individual handoff domains and provider demographics.

OBJECTIVES: To determine the bivariable associations between abnormalities of 28 common laboratory tests and hospital mortality and determine how mortality risks changes when the ranges are evaluated in the context of commonly used laboratory test panels. DESIGN: A 2009–2016 cohort from the Health Facts® (Cerner Corporation, Kansas City, MO) database. SETTING: Hospitals caring for children in ICUs. PATIENTS: Children cared for in ICUs with laboratory data. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: There were 2,987,515 laboratory measurements in 71,563 children. The distribution of laboratory test values in 10 groups defined by population percentiles demonstrated the midrange of tests was within the normal range except for those measured predominantly when significant abnormalities are suspected. Logistic regression analysis at the patient level combined the population-based groups into ranges with non-overlapping mortality odds ratios (MORs). The most deviant test ranges associated with increased mortality risk (MORs > 5.0) included variables associated with acidosis, coagulation abnormalities and blood loss, immune function, liver function, nutritional status, and the basic metabolic profile. The test ranges most associated with survival included normal values for chloride, pH, and bicarbonate/total CO(2). When the significant test ranges from bivariable analyses were combined in commonly used test panels, they generally remained significant but were reduced as risk was distributed among the tests. CONCLUSION AND RELEVANCE: The relative importance of laboratory test ranges vary widely, with some ranges strongly associated with mortality and others strongly associated with survival. When evaluated in the context of test panels rather than isolated tests, the MORs for the test ranges decreased but generally remained significant as risk was distributed among the components of the test panels. These data are useful to develop critical values for children in ICUs, to identify risk factors previously under-appreciated, for education and training, and for future risk score development.

The Socio-Technical Probabilistic Risk Assessment, a proactive risk assessment tool imported from high-risk industries, was used to identify risks for surgical site infections (SSIs) associated with the ambulatory surgery center setting and to guide improvement efforts. This study had 2 primary objectives: (1) to identify the critical risk factors associated with SSIs resulting from procedures performed at ambulatory surgery centers and (2) to design an intervention to mitigate the probability of SSI for the highest risk factors identified. Inputs included quantitative and qualitative data sources from the evidence-based literature and from health care providers. The Socio-Technical Probabilistic Risk Assessment ranked the failure points (events) on the basis of their contribution to an SSI. The event, entitled “Failure to protect the patient effectively,” which included several failure points, was the most critical unique event with the highest contribution to SSI risk. A total of 51.87% of SSIs in this setting were caused by this failure. Consequently, we proposed an intervention aimed at all 5 major components of this failure. The intervention targets improvements in skin preparation; proper administration of antibiotics; staff training in infection control principles, including practices for the prevention of glove punctures; and procedures to ensure the removal of watches, jewelry, and artificial nails.

OBJECTIVE: To evaluate the translation of a paper high-risk checklist for pediatric intensive care unit (PICU) patients at risk of clinical deterioration to an automated clinical decision support tool. DESIGN: Retrospective, observational cohort study of an automated clinical decision support tool, the PICU Warning Tool, adapted from a paper checklist to predict clinical deterioration events in PICU patients within 24-hours. SETTING: Two quaternary care medical-surgical PICUs—The Children’s Hospital of Philadelphia and Cincinnati Children’s Hospital Medical Center. PATIENTS: The study included all patients admitted from July 1, 2014 through June 30, 2015, the year prior to the initiation of any focused situational awareness work at either institution. INTERVENTIONS: We replicated the predictions of the real-time PICU Warning Tool by retrospectively querying the institutional data warehouse to identify all patients that would have flagged as high-risk by the PICU Warning Tool for their index deterioration. MEASUREMENTS: The primary exposure of interest was determination of high-risk status during PICU admission via the PICU Warning Tool. The primary outcome of interest was clinical deterioration event within 24 hours of a positive screen. The date and time of the deterioration event was used as the index time point. We evaluated the sensitivity, specificity, positive and negative predictive value of the performance of the PICU Warning Tool. MAIN RESULTS: There were 6233 patients evaluated with 233 clinical deterioration events experienced by 154 individual patients. The positive predictive value of the PICU Warning Tool was 7.1% with a number needed to screen of 14 patients for each index clinical deterioration event. The most predictive of the individual criteria were elevated lactic acidosis, high mean airway pressure, and profound acidosis. CONCLUSIONS: Performance of a clinical decision support translation of a paper-based tool showed inferior test characteristics. Improved feasibility of identification of high-risk patients using automated tools must be balanced with performance.

In an unselected population of 23,713 participants who underwent clinical exome sequencing as a part of the Healthy Nevada Project (HNP) in Northern Nevada (Renown Health, Reno, Nevada) from March 15, 2018, to Sept 30, 2018 (Table S1) we find a 1.26% carrier rate for expected pathogenic and likely pathogenic genetic variants in (FH: LDLR, PCSK9, APOB), Hereditary Breast and Ovarian Cancer (HBOC: BRCA1, BRCA2) and Lynch Syndrome (LS: MLH1, MSH2, MSH6, PSM2) with over 90% of carriers undetected under current medical practice. 26% of carriers were found to have advanced disease with 70% first diagnosed before the age of 65. Less than 20% of all carriers had any documented suspicion for inherited genetic disease in the medical record and upon direct follow-up survey under 40% of carriers had family history of relevant disease. A population preventative genetic screening approach for patients under 45 may improve outcomes.

Financial incentives offered by insurers to health care providers have been identified as a key mechanism to lower costs while improving quality of care. How to effectively design incentive programs that can align the varying objectives of health care stakeholders, as well as predict program performance and stakeholders’ decision response is an unresolved research challenge. The objective of this paper was to establish the foundation for a novel approach based on multiscale decision theory (MSDT) that can effectively model and efficiently analyze such incentive programs, and the complex health care system in general. The MSDT model captures the interdependencies of stakeholders, their decision processes, uncertainties, and how incentives impact decisions and outcomes at the payer, hospital, physician and patient level. We illustrate the modeling approach by applying it to a specific incentive program, the Medicare Shared Savings Program (MSSP) for Accountable Care Organizations (ACOs), which was introduced by the Centers for Medicare and Medicaid Services (CMS) in the United States in 2012. We focus our analysis on computed tomography (CT) use by physicians, and CT scanner investment decisions by hospitals. We determine the conditions under which the incentive program leads to the desired outcomes of cost reduction and quality of care improvements. The results have policy and managerial implications for CMS, ACOs and their members, specifically hospitals and physicians.

Critical Access Hospitals (CAHs) were developed as a model to improve the access and availability of hospital services in rural counties. There has been limited research on clinical outcomes to evaluate the impact of CAHs since they were authorized through the Balanced Budget Act. This study evaluates CAH’s performance on clinical outcomes, and compares health outcomes between rural counties with CAHs and rural counties without established federally supported hospitals. The American Hospital Association’s (AHA) Annual Survey Database was used to identify CAHs within rural counties and their characteristics. The County Health Rankings (CHR) data were used to quantify health outcomes by county. US rural counties with CAHs versus remaining US rural counties without CAHs were correlated with measures of Clinical Care (p < 0.001). US rural counties with CAHs presented greater health status with regard to All Health Outcomes, p < 0.0001; Length of Life, p < 0.0001; Quality of Life, p < 0.0001; All Health Factors, p < 0.0001; Health Behaviors, p < 0.0001; Social and Economic Environment, p < 0.0001 and Physical Environment, p < 0.0001, than compared to US rural counties without CAHs. Rural counties serviced by CAHs demonstrate better overall health status scores, on several CHR metrics, as compared to rural counties without CAHs. The only exception to this conclusion being that rural counties without CAHs performed superiorly in the CHR metrics related to primary care and mental health services, demonstrating capacities in which CAHs could improve the impact on health in the counties they serve.

In this study, we perform a full genome-wide association study (GWAS) to identify statistically significantly associated single nucleotide polymorphisms (SNPs) with three red blood cell (RBC) components and follow it with two independent PheWASs to examine associations between phenotypic data (case-control status of diagnoses or disease), significant SNPs, and RBC component levels. We first identified associations between the three RBC components: mean platelet volume (MPV), mean corpuscular volume (MCV), and platelet counts (PC), and the genotypes of approximately 500,000 SNPs on the Illumina Infimum® DNA Human OmniExpress-24 BeadChip using a single cohort of 4,700 Northern Nevadans. Twenty-one SNPs in five major genomic regions were found to be statistically significantly associated with MPV, two regions with MCV, and one region with PC, with p-8. Twenty-nine SNPs and nine chromosomal regions were identified in 30 previous GWASs, with effect sizes of similar magnitude and direction as found in our cohort. The two strongest associations were SNP rs1354034 with MPV (p=2.4x10-13) and rs855791 with MCV (p=5.2x10-12). We then examined possible associations between these significant SNPs and incidence of 1,488 phenotype groups mapped from International Classification of Disease version 9 and 10 (ICD9 and ICD10) codes collected in the extensive electronic health record (EHR) database associated with Healthy Nevada Project consented participants. Further leveraging data collected in the EHR, we performed an additional PheWAS to identify associations between continuous red blood cell (RBC) component measures and incidence of specific diagnoses. The first PheWAS illuminated whether SNPs associated with RBC components in our cohort were linked with other hematologic phenotypic diagnoses or diagnoses of other nature. Although no SNPs from our GWAS were identified as strongly associated to other phenotypic components, a number of associations were identified with p-values ranging between 1x10-3 and 1x10-4 with traits such as respiratory failure, sleep disorders, hypoglycemia, hyperglyceridemia, GERD and IBS. The second PheWAS examined possible phenotypic predictors of abnormal RBC component measures: a number of hematologic phenotypes such as thrombocytopenia, anemias, hemoglobinopathies and pancytopenia were found to be strongly associated to RBC component measures; additional phenotypes such as (morbid) obesity, malaise and fatigue, alcoholism, and cirrhosis were also identified to be possible predictors of RBC component measures.Author SummaryThe combination of electronic health records and genomic data have the capability to revolutionize personalized medicine. Each separately contains invaluable data; however, combined, the two are able to identify new discoveries that may have long-term health benefits. The Healthy Nevada Project is a non-profit initiative between Renown Medical Center and the Desert Research Institute in Reno, NV. The project has so far collected a cohort of 6,500 Northern Nevadans, with extensive medical electronic health records in the Renown Health database. Combining the genotypes of these participants with the clinical data, this study’s aim is to find associations between genotypes (genes) and phenotypes (diagnoses and lab records). Here, we identify and examine clinical associations with red blood cell components such as platelet counts and mean platelet volume. These are components that have clinical relevance for several diseases, such as anemia, atherothrombosis and cancer. Our results from genome wide association studies mirror previous studies, and identify new associations. The extensive electronic health records enabled us to perform phenome wide associations to discover strong associations with hematologic components, as well as other important traits and diagnoses.

Familial hypercholesterolemia (FH) carries up to a 22-fold higher risk of cardiovascular disease. The Dutch Lipid Clinical Criteria (DLCC), Simon Broome (SB) Criteria, and the US Make Early Diagnosis to Prevent Early Deaths (MEDPED) Criteria are used to diagnose FH. The Healthy Nevada Project (HNP)

BackgroundNevada ranks in the bottom half of overall health rankings in the United States. The majority of residents of Northern Nevada live in Washoe County, which is confounded with high age-adjusted death rates for heart disease, cancer and chronic lower respiratory disease.MethodsSaliva as a source of DNA was collected from adults in Northern Nevada as the first phase of a much larger (100,000 participants) effort to contribute to comprehensive population health studies in Nevada. The personal genetics company 23andMe was used to genotype the first 10,250 participants and deliver their custom ancestry, traits, wellness, and carrier status reports.ResultsThe study was announced by Governor Brian Sandoval on September 15, 2016 and within two days the registration of 9,700 volunteers for an appointment was complete. Processing of 9,344 participants was achieved in 3 months, with a no-show rate of just over 11%. The participant population was skewed to female and was less racially diverse than the population.ConclusionDNA genotyping was administered free-of-charge and the patient population was representative of the socio-economic diversity in northern Nevada – indicating that free genetic testing is of interest to a broad swath of the population and a powerful motivator for comprehensive population health study research.

Background.To understand how structural and process elements may affect the risk for surgical site infections (SSIs) in the ambulatory surgery center (ASC) environment, the researchers employed a tool known as socio-technical probabilistic risk assessment (ST-PRA). ST-PRA is particularly helpful for estimating risks in outcomes that are very rare, such as the risk of SSI in ASCs.Objective.Study objectives were to (1) identify the risk factors associated with SSIs resulting from procedures performed at ASCs and (2) design an intervention to mitigate the likelihood of SSIs for the most common risk factors that were identified by the ST-PRA for a particular surgical procedure.Methods.ST-PRA was used to study the SSI risk in the ASC setting. Both quantitative and qualitative data sources were utilized, and sensitivity analysis was performed to ensure the robustness of the results.Results.The event entitled “fail to protect the patient effectively” accounted for 51.9% of SSIs in the ambulatory care setting. Critical components of this event included several failure risk points related to skin preparation, antibiotic administration, staff training, proper response to glove punctures during surgery, and adherence to surgical preparation rules related to the wearing of jewelry, watches, and artificial nails. Assuming a 75% reduction in noncompliance on any combination of 2 of these 5 components, the risk for an SSI decreased from 0.0044 to between 0.0027 and 0.0035.Conclusion.An intervention that targeted the 5 major components of the major risk point was proposed, and its implications were discussed.

The residual risk (RR) of transfusion-transmitted infections, including the human immunodeficiency virus and hepatitis B and C viruses, is typically estimated by the incidence[Formula: see text]window period model, which relies on the following restrictive assumptions: Each screening test, with probability 1, (1) detects an infected unit outside of the test's window period; (2) fails to detect an infected unit within the window period; and (3) correctly identifies an infection-free unit. These assumptions need not hold in practice due to random or systemic errors and individual variations in the window period. We develop a probability model that accurately estimates the RR by relaxing these assumptions, and quantify their impact using a published cost-effectiveness study and also within an optimization model. These assumptions lead to inaccurate estimates in cost-effectiveness studies and to sub-optimal solutions in the optimization model. The testing solution generated by the optimization model translates into fewer expected infections without an increase in the testing cost.

Because the economic crisis in the United States continues to have an impact on healthcare organizations, industry leaders must optimize their decision making. Discrete-event computer simulation is a quality tool with a demonstrated track record of improving the precision of analysis for process redesign. However, the use of simulation to consolidate practices and design efficiencies into an unfinished medical office building was a unique task. A discrete-event computer simulation package was used to model the operations and forecast future results for four orthopedic surgery practices. The scenarios were created to allow an evaluation of the impact of process change on the output variables of exam room utilization, patient queue size, and staff utilization. The model helped with decisions regarding space allocation and efficient exam room use by demonstrating the impact of process changes in patient queues at check-in/out, x-ray, and cast room locations when compared to the status quo model. The analysis impacted decisions on facility layout, patient flow, and staff functions in this newly consolidated practice. Simulation was found to be a useful tool for process redesign and decision making even prior to building occupancy.

In their provision of acute care services, emergency department (ED) providers in critical access hospitals (CAHs) are uniquely challenged by limitations in personnel, facility resources, and available expertise. We sought to define the impact of team behaviors among ED staff in CAHs on clinical performance by studying the relationship between team and clinical performance scores across 100 simulated scenarios. Team and clinical performance scores were calculated for each scenario using standardized checklists to quantify goal achievement and aggregated across teams and scenario types. These scores were compared using bivariate analysis and correlated with clinical and team performance outcomes using Pearson's correlation (r coefficient). There was a positive correlation between team and clinical performance across all scenarios; the relationship was statistically significant (p

OBJECTIVES: Hospitals are being required to report publically their adjusted mortality rates, which are then being used to rank hospitals. Our objectives were to assess the statistical reliability of the determination of a hospital's adjusted mortality rate, of comparisons of that rate with the rates of other hospitals, and of the use of those rates to rank the hospitals. METHODS: A cross-sectional study of 473 383 patients discharged from 42 US children's hospitals in 2008 was performed. Hospital-specific observed/expected (O/E) mortality rate ratios and corresponding hospital rankings, with 95% confidence intervals (CIs), were examined. RESULTS: Hospitals' O/E mortality rate ratios exhibited wide 95% CIs, and no hospital was clearly distinguishable from the other hospitals' aggregated mean mortality performance. Only 2 hospitals' mortality performance fell outside the comparator hospitals' 95% CI. Those hospitals' 95% CIs overlapped with the overall comparator set's 95% CI, which suggests that there were no statistically significant hospital outliers. Fourteen (33.3%) of the 42 hospitals had O/E ratios that were not statistically different from being in the 95% CI of the top 10% of hospitals. Hospital-specific mortality rate rankings displayed even broader 95% CIs; the typical hospital had a 95% CI range that spanned 22 rank-order positions. CONCLUSION: Children's hospital-specific measures of adjusted mortality rate ratios and rankings have substantial amounts of statistical imprecision, which limits the usefulness of such measures for comparisons of quality of care.

To quantify the difference in weekday versus weekend occupancy, and the opportunity to smooth inpatient occupancy to reduce crowding at children's hospitals.Daily inpatient census data for 39 freestanding, tertiary-care children's hospitals were used to calculate occupancy and to model the impact of reducing variation in occupancy and the change in the number of patients, patient-days, and hospitals exposed to high occupancy pre- and post-smoothing. We also calculated the proportion of weekly admissions that would require different scheduling to achieve within-week smoothing.Overall, hospitals' mean occupancy ranged from 70.9% to 108.1% on weekdays, and 65.7% to 94.9% on weekends. Weekday occupancy exceeded weekend occupancy with a median difference of 8.2% points. The mean post-smoothing reduction in weekly maximum occupancy across all hospitals was 6.6% points. Through smoothing, 39,607 patients from the 39 hospitals were removed from exposure to occupancy levels95%. To achieve within-week smoothing, a median 2.6% of admissions would have to be scheduled on a different day of the week; this equates to a median of 7.4 patients per week (range: 2.3-14.4).Hospitals do have substantial unused capacity, and smoothing occupancy over the course of a week could be a useful strategy that hospitals can use to reduce crowding and protect patients from crowded conditions.

Blood products are an essential component of any health system, and their safety, in terms of being free of “transfusion-transmitted infections” (TTIs), i.e., diseases that include Human Immunodeficiency Virus, Hepatitis Viruses, Human T-cell Lymphotropic Virus, Syphilis, West Nile Virus, and Chagas’ Disease, is crucial. However, blood screening tests are imperfectly reliable, with the possibility of false-negatives and false-positives. The budget-constrained decision-maker needs to (i) select a set of screening tests to administer to each unit of donated blood, and (ii) construct a “decision rule” with which to classify each blood unit as infection-free versus infected. The objective is to minimize the TTI risk for blood classified as infection-free, which depends on the efficacies of the entire set of tests selected and the decision rule adopted. This risk structure leads to a nonlinear optimization problem. Our analysis provides efficient optimal algorithms for a special case where only mono-infections...

OBJECTIVE: The primary objective of this study was to obtain a broad understanding of inpatient deaths across academic children's hospitals. METHODS: A nonconcurrent cohort study of children hospitalized in 37 academic children's hospitals in 2005 was performed. The primary outcome was death. Patient characteristics including age, gender, race, diagnostic grouping, and insurance status and epidemiological measures including standardized mortality rate and standardized mortality ratios (SMRs) were used. RESULTS: A total of 427 615 patients were discharged during the study period, of whom 4529 (1.1%) died. Neonates had the highest mortality rate (4.03%; odds ratio: 8.66; P < .001), followed by patients >18 years of age (1.4%; odds ratio: 2.86; P < .001). The SMRs ranged from 0.46 (all patient-refined, diagnosis-related group 663, other anemias and disorders of blood) to 30.0 (all patient-refined, diagnosis-related group 383, cellulitis and other bacterial skin infections). When deaths were compared according to institution, there was considerable variability in both the number of children who died and the SMRs at those institutions. CONCLUSIONS: Patient characteristics, such as age, severity, and diagnosis, were all substantive factors associated with the death of children. Opportunities to improve the environment of care by reducing variability within and between hospitals may improve mortality rates for hospitalized children.

The objective of this study was to identify the patient, institutional, and utilization characteristics associated with outcome in hospitalized pediatric patients with myocarditis. This was a nonconcurrent cohort study of all consecutive pediatric discharges from the 35 academic children's hospitals that are members of the Pediatric Health Information System (PHIS): patients from birth through age 21 years discharged from participating hospitals between January 1, 2005, and December 31, 2005. Patient-level, institution-level, and utilization variables were examined. A total of 427,615 patients were discharged, and 216 (0.05%) were diagnosed with myocarditis. Common etiologies were idiopathic (82%), related to other diseases (6%), and bacterial or viral (3%). Myocarditis patients required considerable support including intravenous immunoglobulin (IVIG; 49.1%), milrinone (45%), epinephrine (35%), mechanical ventilation (25%), extracorporeal membrane oxygenation (7%), and cardiac transplantation (5%). Even in patients with extreme illness scores, IVIG use did not impact survival (P = 0.67). Overall survival of myocarditis patients was 92%. Myocarditis patients who died presented with a higher severity of illness and required frequent use of extracorporeal membrane oxygenation and other ICU therapies. In conclusion, pediatric patients with myocarditis have considerable variability in their presentations and outcomes, use more resources, and die more often than children with other diagnoses. Attempts at using characteristics that uniformly predict illness severity or survival were not successful. Despite increased use in the sickest patients, IVIG conferred no survival advantage.

OBJECTIVE. The purpose of this work was to determine the excess charges, both overall and according to category, and lengths of stay attributable to adverse patient-care events during pediatric hospitalization.METHODS. Agency for Healthcare Research and Quality pediatric-specific quality indicators were used to identify adverse events in 431524 discharges from 38 freestanding, academic, not-for-profit, tertiary care pediatric hospitals in the United States participating in the Pediatric Health Information System database in 2006. All of the discharges from any of the 38 hospitals participating in the Pediatric Health Information System between January 1 and December 31, 2006, were eligible for inclusion. The primary outcomes were excess lengths of stay and charges (both overall and according to pharmacy, laboratory, imaging, clinical, supply, and other categories) were attributable to adverse patient-safety events as determined by 12 pediatric-specific quality indicators.RESULTS. Statistically significant excess lengths of stay attributable to pediatric-specific quality indicator events ranged from 2.8 days for accidental puncture and laceration to 23.5 days for postoperative sepsis, and statistically significant excess overall charges ranged from $34884 for accidental puncture and laceration to $337226 for in-hospital mortality after pediatric heart surgery. Each charge category had significant charge increases caused by pediatric-specific quality indicator events, with the largest being laboratory and other charges, ranging from $7622 to $78048 and $11094 to $97805, respectively.CONCLUSIONS. Some adverse events experienced during pediatric hospitalization have the potential to increase lengths of stay and charges considerably, and pediatric-specific quality indicators are useful in calculating these effects.

To identify factors influencing the duration of cerebrospinal fluid shunt survival after initial placement and after subsequent revisions.We conducted a retrospective cohort study using the Pediatric Health Information System database, which contains resource use data from 37 tertiary care children's hospitals. Children younger than 18 years who underwent initial cerebrospinal fluid placement between January 1, 2000, and December 31, 2005, were eligible.During the study period, 20.2, 7.5, and 6.9% of 7399 patients required one, two, or three or more shunt revisions, respectively. Shunt survival rates were lower with each subsequent shunt revision. In multivariable Cox proportional hazards analysis, children undergoing shunt placement in the Northeast census region had a longer duration of shunt survival between initial placement and both the first (adjusted hazard ratio, 0.74; 95% confidence interval, 0.55-0.99) and second (adjusted hazard ratio, 0.66; 95% confidence interval, 0.51-0.86) revisions. Young age and a principal diagnosis of obstructive hydrocephalus were also associated with a higher risk of failure after initial placement; age-related variation in shunt survival persisted after the first but not the second revision. Among patients with multiple shunt revisions, those with early revision (i.e., revision60 d after placement) had a shorter shunt survival time after subsequent revisions (adjusted hazard ratio for second revision, 1.30; 95% confidence interval, 1.11-1.52).Regional variation in the risk of ventricular shunt revision exists, and young infants are at the highest risk for shunt failure. Risk factors for the duration of shunt survival differ between the initial and subsequent revisions.

Medical technologies represent a major share of hospital budgets and have been driving up healthcare costs in the United States. Typically, hospitals have limited resources and have to be selective with their purchases. Despite the importance and complexity of investment decisions, most hospitals do not use structured methods or comprehensive data. In response, we developed and field-tested a data-to-decision (D2D) approach. We interviewed executives and physicians from two hospitals to learn about the role of technology and capital investment decision processes. We developed a system dynamics (SD) model for the adoption of a Da Vinci® surgical system. Hospital executives evaluated this technology along with other alternatives in a session we conducted using the Simple Multi-Attribute Rating Technique (SMART).

There have been an increasing number of descriptive analyses of pediatric medical errors as the release of the Institute of Medicine reports over 5 years ago (IOM 2000, IOM 2001, IOM 2004). These include single and multi-institution studies, studies of medication errors, and several broad characterizations of medical errors in hospitalized children. The incidence of medical errors in hospitalized children is estimated to range from 0.8 to 1.3 percent and is highly dependent upon the definitions and classifications used (Brennan et al. 1991; Localio et al. 1991; Smith, Langlois, and Buechner 1991; Rosen et al. 1992; Silber et al. 1992; Roos et al. 1995; Duke, Butt, and South 1997; Gawande et al. 1999; McCormick et al. 2000; Miller et al. 2001; Connors et al. 2001; Kaushal et al. 2001; Davis et al. 2002; Miller, Elixhauser, and Zhan 2003; Proctor et al. 2003; Slonim et al. 2003; Kanter, Turenne, and Slonim 2004; Miller and Zhan 2004; Sedman et al. 2005). The study of pediatric medical errors has been limited by the relatively small numbers of children hospitalized at individual institutions and the need for detailed prospective and retrospective reviews. One method of overcoming the methodological challenges of small sample sizes and the potential idiosyncrasies of data from a single institution is to use administrative datasets that contain large volumes of discharges from multiple institutions (McCormick et al. 2000; Miller et al. 2001; Miller, Elixhauser, and Zhan 2003; Proctor et al. 2003; Slonim et al. 2003; Kanter, Turenne, and Slonim 2004; Miller and Zhan 2004; Sedman et al. 2005). Miller and colleagues developed an effective method for screening the large number of discharges in these datasets to identify potential medical errors (Miller et al. 2001; Miller, Elixhauser, and Zhan 2003; Miller and Zhan 2004). This method uses events known as patient safety indicators (PSIs). For pediatric patients, analyses of PSIs have included both community hospitals and Children's hospitals (Miller et al. 2001; Miller, Elixhauser, and Zhan 2003; Miller and Zhan 2004). However, when synthesized, the prior literature on medical errors in children during hospitalization is subject to two important caveats, both of which are informed by this study. First, inpatient error estimates generated using the PSI method are likely to be higher in freestanding, academic Children's Hospitals where children with complex conditions receive their care, because children with more complex health care needs may be more prone to medical errors (Miller, Elixhauser, and Zhan 2003; Slonim et al. 2003). Therefore, a description of the types and frequencies of PSIs in a unique dataset of these institutions may help to prioritize efforts aimed at reducing their occurrence in specific subgroups of patients (Slonim et al. 2003). These associations and the incidence rates, however, may be misrepresented if the analytic approach inadequately considers the statistical methods used in the analysis. The second caveat addresses the analytic approach and the methodological challenge that occurs when modeling a multi-institutional dataset (Normand, Glickman, and Gastoonis 1997; y,Snijders and Bosker 1999; McCulloch and Searle 2000; Williams 2000; Leyland and Goldstein 2001; Palta 2003; Loehlin 2004; Luke 2004; Skrondal and Hesketh 2004; Zaslavasky, Zaborski, and Cleary 2004). Patients being treated in the same hospital may be more similar to each other than those treated in other hospitals. Independence, one of the key assumptions for regression, is violated under these circumstances. Therefore, unmeasured hospital characteristics that introduce variability into the results require additional effort to appropriately control. Several techniques can account for the “clustering” which exists in the dataset. Clustering represents the correlations associated with data that are organized at specific sites or levels. The techniques used to adjust for hospital clustering, which allow for measuring both patient and hospital level characteristics are, as a group, referred to as hierarchical or mixed models (Goldstein 1987, Goldstein, 1995; Gatsonis et al. 1995; Daniels and Gastonis 1999; Landrum and Normand 1999; Landrum et al. 1999; Burgess and Lourdes 2000; Bryck and Raudnebush 2002; Hox 2002). The justifications for performing these models arise from empirical, statistical, and theoretical foundations, but their usefulness in practice remains questionable. The empirical justification arises from the possibility that the rates of PSIs differ at the individual hospital level (Goldstein 1987; Snijders 1999; Williams 2000; Bryck and Raudenbush 2002; Palta 2003; Loehlin 2004; Luke 2004; Skrondal 2004). Second, a statistical justification arises from the fact that the cases are not independent, but are clustered by hospital and may have correlated errors. Third, a theoretical justification would suggest that a multilevel model will more accurately estimate the extent to which both the patient-level and hospital-level characteristics influence PSI rates. Consequently, we studied the incidence rates of PSIs in children hospitalized at a sample of academic Children's Hospitals and accounted for the effects of hospital “clustering” using three different statistical modeling techniques. We compared these models to a “base-state” that excluded institutional variables to determine the effect that each of these techniques may have on the characteristics associated with PSIs.

To establish evidence-based guidelines for the use of bedside ultrasound by intensivists and specialists in the ICU and equivalent care sites for diagnostic and therapeutic purposes for organs of the chest, abdomen, pelvis, neck, and extremities.The Grading of Recommendations, Assessment, Development and Evaluation system was used to determine the strength of recommendations as either strong or conditional/weak and to rank the "levels" of quality of evidence into high (A), moderate (B), or low (C) and thus generating six "grades" of recommendation (1A-1B-1C-2A-2B-2C). Grading of Recommendations, Assessment, Development and Evaluation (GRADE) was used for all questions with clinically relevant outcomes. RAND appropriateness method, incorporating modified Delphi technique, was used in steps of GRADE that required panel judgment and for those based purely on expert consensus. The process was conducted by teleconference and electronic-based discussion, following clear rules for establishing consensus and agreement/disagreement. Individual panel members provided full disclosure and were judged to be free of any commercial bias. The process was conducted independent of industry funding.Twenty-four statements regarding the use of ultrasound were considered-three did not achieve agreement and nine were approved as conditional recommendations (strength class 2). The remaining 12 statements were approved as strong recommendations (strength class 1). Each recommendation was also linked to its level of quality of evidence. Key strong recommendations included the use of ultrasonography for ruling-in pleural effusion and assisting its drainage, ascites drainage, ruling-in pneumothorax, central venous cannulation, particularly for internal jugular and femoral sites, and for diagnosis of deep venous thrombosis. Conditional recommendations were given to the use of ultrasound by the intensivist for diagnosis of acalculous cholecystitis, renal failure, and interstitial and parenchymal lung diseases. No recommendations were made regarding static (vs dynamic) ultrasound guidance of vascular access or the use of needle guide devices.There was strong agreement among a large cohort of international experts regarding several recommendations for the use of ultrasound in the ICU. Evidence-based recommendations regarding the appropriate use of this technology are a step toward improving patient outcomes in relevant patients.

Disparities by race are known for children with asthma in the outpatient setting and post-hospital discharge. We sought to investigate if racial disparities also occur during hospitalization for children with asthma. The Pediatric Health Information System, a 35-hospital dataset from fiscal year 2003, was analyzed for patient-level, institution-level, utilization, and outcome variables that were associated with racial disparities. Of 17,406 admissions, blacks represented 51% and whites, 40%. Black patients were more likely to be insured by government sources (p < 0.001) and to be admitted in the mild severity category (p < 0.001). Blacks had a shorter mean length of stay (p < 0.01) but overall were more likely to receive bronchodilators (p < 0.0001) and steroids (p < 0.05). Length of stay and charges varies by insurance, with significant racial differences seen in government and "other" insurance types. Readmissions varied, with more whites readmitted within 72 hours (p < 0.002), but more blacks readmitte...

Ensuring patient safety is becoming increasingly important for intensive care unit practitioners. The intensive care unit is particularly prone to medical errors because of the complexity of the patients, interdependence of the practitioners, and dependence on team functioning. This review provides historical perspectives, research foundations, and a practical “how to” guide to improving care in the intensive care unit. It also considers the organizational structure, the processes of care, and the occurrence of adverse outcomes in this setting. Effective intensive care unit quality and safety programs capitalize on institutional resources and have multidisciplinary input with clear leadership, input from quality improvement initiatives, a responsible yet nonpunitive culture, and data-driven assessment and monitoring to reduce medical errors. Intensive care unit practitioners need to capitalize on the benefits that patients and their families bring to the patient safety discourse. This provides opportunities for better understanding the risks of the intensive care unit and improving the consent process.

To determine the national rate of hospital-reported medical errors in premature neonates and describe the patient and organizational characteristics associated with their occurrence.Nonconcurrent, cohort study.The Healthcare Cost and Utilization Project (HCUP) contains discharge data collected at community hospitals sited in20 states.All neonatal discharges from the 1997 edition of HCUP were included in these analyses. The definition of prematurity included any hospitalized neonate with a birth weight2500 g, which corresponds to approximately 37 wks gestation. Medical error was defined as an International Classification of Diseases-9 discharge diagnosis of 996-999 in any of the diagnosis fields associated with the discharge.None.The national rate of hospital-reported medical errors in premature neonates is 1.2 per 100 discharges. There was a significant linear increase in the rate of medical errors based on birth weight (Cochran-Armitage test for trend, p.001). After we controlled for case mix and organizational characteristics using a logistic regression model, medical errors continued to be associated with birth weight, gender, insurance status, and hospital characteristics.The rate of hospital-reported medical errors in premature neonates is lower than that reported in both the adult and pediatric populations. Specific patient and organizational characteristics are associated with an increased risk of medical errors. These characteristics may help to identify opportunities to improve patient safety efforts in this vulnerable population.