Association of Medicaid expansion with 2-year survival and time to treatment initiation in gastrointestinal cancer patients: A National Cancer Database study.

INTRODUCTION: We evaluated whether Medicaid expansion (ME) was associated with improved 2-year survival and time to treatment initiation (TTI) among patients with gastrointestinal (GI) cancer. METHODS: GI cancer patients diagnosed 40-64 years were queried from the National Cancer Database. Those diagnosed from 2010 to 2012 were considered pre-expansion; those diagnosed from 2014 to 2016 were considered post-expansion. Cox models estimated hazard ratios and 95% confidence intervals (CIs) for 2-year overall survival. Generalized estimating equations (GEE) estimated odds ratios (OR) and 95% CI of TTI within 30- and 90 days. Multivariable Difference-in-Difference models were used to compare expansion/nonexpansion cohorts pre-/post-expansion, adjusting for patient, clinical, and hospital factors. RESULTS: 377,063 patients were included. No significant difference in 2-year survival was demonstrated across ME and non-ME states overall or in site-based subgroup analysis. In stage-based subgroup analysis, 2-year survival significantly improved among stage II cancer, with an 8% decreased hazard of death at 2 years (0.92; 0.87-0.97). Those with stage IV had a 4% increased hazard of death at 2 years (1.04; 1.01-1.07). Multivariable GEE models showed increased TTI within 30 days (1.12; 1.09-1.16) and 90 days (1.22; 1.17-1.27). Site-based subgroup analyses indicated increased likelihood of TTI within 30 and 90 days among colon, liver, pancreas, rectum, and stomach cancers, by 30 days for small intestinal cancer, and by 90 days for esophageal cancer. In subgroup analyses, all stages experienced improved odds of TTI within 30 and 90 days. CONCLUSION: ME was not associated with significant improvement in 2-year survival for those with GI cancer. Although TTI increased after ME for both cohorts, the 30- and 90-day odds of TTI was higher for those from ME compared with non-ME states. Our findings add to growing evidence of associations with ME for those diagnosed with GI cancer.

Prevalence and Financial Burden of Digestive Diseases in a Commercially Insured Population.

BACKGROUND & AIMS: Digestive diseases represent a diverse group of clinical conditions that impact the population. Their heterogeneity in classification, presentation, acuity, chronicity, and need for drug therapy presents a challenge when comparing and contrasting the burden associated with these conditions. Prior studies use an outdated classification system and aggregate costs at the population level or focus on specific diseases, limiting the ability to characterize the overall landscape. Our aim was to provide the most up-to-date assessment of cost, utilization, and prevalence associated with digestive diseases. METHODS: We examined digestive disease claims and payment data for a commercially insured adult population between 2016 and 2018 to provide a comprehensive summary of costs, utilization, and prevalence across 38 conditions. Outcome variables included point prevalence and relative prevalence, annualized all-cause medical and drug costs, digestive disease-specific average medical cost, digestive disease-specific cost per fill, and utilization by clinical setting and by clinical condition. RESULTS: A total of 7,297,435 individuals with a digestive disease diagnosis were included in the study. The point prevalence of having a digestive disease in the total population was 24%. Annualized total costs by clinical category ranged from $10,038 (eosinophilic esophagitis) to $107,007 (hepatitis C), with medical costs accounting for most of the expenditures in a majority of conditions. Annualized total costs for common conditions included $39,653 for alcoholic liver disease, $42,554 for acute pancreatitis, $62,735 for Crohn's disease, $13,948 for functional gastrointestinal disorders, $53,214 for nonalcoholic cirrhosis, and $36,441 for ulcerative colitis. Average cost of inpatient stays ranged from $12,218 (noninfectious gastroenteritis/colitis) to $78,259 (nonalcoholic steatohepatitis). Outpatient visits ranged from $784 (gastrointestinal infection) to $4629 (gallbladder and biliary tract disease). Average drug cost per fill ranged from $83 (gastroesophageal reflux disease) to $1458 (hepatitis C). A total of 27,429,046 clinical encounters occurred across all conditions during the study period, with 90% taking place as outpatient visits. Abdominal pain was the single largest contributor to outpatient visits and emergency department to home encounters. Inpatient stays were considerably more heterogeneous, with no condition accounting for more than 12% (gallbladder and biliary tract disease) of the total. CONCLUSIONS: The results demonstrate digestive diseases are common, heterogeneous in cost and utilization, and collectively exact a significant financial burden on the U.S. adult population.

Experience of nurse practitioners performing colonoscopy after endoscopic training in more than 1,000 patients.

Background and study aims The demand for screening colonoscopy has continued to rise over the past two decades. As a result, the current workforce of gastroenterologists is unable to meet the needs for colorectal cancer (CRC) screening. Therefore, solutions are needed to improve this disparity, with non-physician endoscopists being a potential option. However, current literature on the performance of non-physicians in endoscopy is limited. The aim of this study was to assess the quality of colonoscopy performed by three gastrointestinal fellowship-trained nurse practitioners (NPs). Methods This was a retrospective study performed at a single tertiary academic medical center. Colonoscopies performed by three gastrointestinal-specialized NPs after having completed training of at least 140 supervised colonoscopies were reviewed for analysis. Inclusion criteria were patients undergoing colonoscopy for colorectal cancer screening purposes. Outcomes included colonoscopy quality indicators as defined by the American Society for Gastrointestinal Endoscopy/American College of Gastroenterology Taskforce (ASGE/ACG) Taskforce. Results The study included 1,012 subjects (mean age 56.2 years, female 51.5 %, African American 73.9 %) who underwent screening colonoscopies by three NPs. Cecal intubation was successful in 997 subjects (98.5 %). Mean adenoma detection rate was 35.6 %. Mean withdrawal time was 18.9 minutes. There were no adverse events including colonic perforations or delayed post-polypectomy bleeding. Conclusions Three fellowship-trained NPs in colonoscopy in the United States satisfied the quality indicators proposed by the ASGE/ACG Task force, demonstrating that adequately trained NPs can perform colonoscopy safely and effectively. With the demand for colonoscopy exceeding the supply, non-physicians could be part of the solution to meet the demands for CRC screening.

Prioritizing Health Care Solutions for Pressure Ulcers Using the Quality Function Deployment Process.

Reducing the incidence and morbidity of pressure ulcers remains a leading national priority in patient safety. However, the optimal strategy for a hospital or health system to address this safety goal is not straightforward given the number and complexity of available solutions. Leveraging techniques from systems engineering, such as the quality function deployment process, may provide a transparent and objective way to address this challenge. A detailed and practical application of quality function deployment is presented that demonstrates the value of applying engineering practices for prioritizing solutions for pressures ulcers specifically and can easily be adapted to other conditions.

Top-Funded Digital Health Companies And Their Impact On High-Burden, High-Cost Conditions.

Digital health companies hold promise to address major health care challenges, though little has been published on their impact. We identified the twenty top-funded private US-based digital health companies to analyze their products and services, related peer-reviewed evidence, and the potential for impact on patients with high-burden conditions. Data analytics (including artificial intelligence and big data) was the most common company type. Companies producing biosensors had the greatest funding. Publications were concentrated among a small number of companies. Healthy volunteers were most commonly studied. Few studies enrolled high-burden populations, and few measured their impact in terms of outcomes, cost, or access to care. These data suggest that leading digital health companies have not yet demonstrated substantial impact on disease burden or cost in the US health care system. Our findings indicate the importance of fostering an environment, with regard to policy and the consumer market, that encourages the development of evidence-based, high-impact products.

A framework for operationalizing risk: A practical approach to patient safety.

The importance of patient safety has grown tremendously; however, there are insufficient resources dedicated to its practical application. We provide an overview of the framework for addressing patient safety within the Johns Hopkins Health System, which approaches patient safety in the context of risk at the patient, provider, unit, and system levels. We present practical examples of how this approach is applied and highlight the resources needed as well as describe how it fits within the broader quality management infrastructure in the health system on its journey toward high reliability.

Latent risk assessment tool for health care leaders.

Efforts to improve quality of care and patient safety have concentrated on provider practice and frontline care processes. Little attention has focused on understanding the role that leadership decisions play in creating risk within a health care system. The framework and tool described in this article builds on Reason's construct of latent organizational failure, by assessing the latent risks of leadership decisions, and identifying appropriate mitigation strategies before the implementation of a change. Stakeholders who will be involved in or impacted by the change are engaged in the assessment to more thoroughly explore both technical and cultural risks.

A Model for the Departmental Quality Management Infrastructure Within an Academic Health System.

As quality improvement and patient safety come to play a larger role in health care, academic medical centers and health systems are poised to take a leadership role in addressing these issues. Academic medical centers can leverage their large integrated footprint and have the ability to innovate in this field. However, a robust quality management infrastructure is needed to support these efforts. In this context, quality and safety are often described at the executive level and at the unit level. Yet, the role of individual departments, which are often the dominant functional unit within a hospital, in realizing health system quality and safety goals has not been addressed. Developing a departmental quality management infrastructure is challenging because departments are diverse in composition, size, resources, and needs.In this article, the authors describe the model of departmental quality management infrastructure that has been implemented at the Johns Hopkins Hospital. This model leverages the fractal approach, linking departments horizontally to support peer and organizational learning and connecting departments vertically to support accountability to the hospital, health system, and board of trustees. This model also provides both structure and flexibility to meet individual departmental needs, recognizing that independence and interdependence are needed for large academic medical centers. The authors describe the structure, function, and support system for this model as well as the practical and essential steps for its implementation. They also provide examples of its early success.

Mining high-dimensional administrative claims data to predict early hospital readmissions.

BACKGROUND: Current readmission models use administrative data supplemented with clinical information. However, the majority of these result in poor predictive performance (area under the curve (AUC)<0.70). OBJECTIVE: To develop an administrative claim-based algorithm to predict 30-day readmission using standardized billing codes and basic admission characteristics available before discharge. MATERIALS AND METHODS: The algorithm works by exploiting high-dimensional information in administrative claims data and automatically selecting empirical risk factors. We applied the algorithm to index admissions in two types of hospitalized patient: (1) medical patients and (2) patients with chronic pancreatitis (CP). We trained the models on 26,091 medical admissions and 3218 CP admissions from The Johns Hopkins Hospital (a tertiary research medical center) and tested them on 16,194 medical admissions and 706 CP admissions from Johns Hopkins Bayview Medical Center (a hospital that serves a more general patient population), and vice versa. Performance metrics included AUC, sensitivity, specificity, positive predictive values, negative predictive values, and F-measure. RESULTS: From a pool of up to 5665 International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) diagnoses, 599 ICD-9-CM procedures, and 1815 Current Procedural Terminology codes observed, the algorithm learned a model consisting of 18 attributes from the medical patient cohort and five attributes from the CP cohort. Within-site and across-site validations had an AUC≥0.75 for the medical patient cohort and an AUC≥0.65 for the CP cohort. CONCLUSIONS: We have created an algorithm that is widely applicable to various patient cohorts and portable across institutions. The algorithm performed similarly to state-of-the-art readmission models that require clinical data.

25-Year summary of US malpractice claims for diagnostic errors 1986-2010: an analysis from the National Practitioner Data Bank.

BACKGROUND: We sought to characterise the frequency, health outcomes and economic consequences of diagnostic errors in the USA through analysis of closed, paid malpractice claims. METHODS: We analysed diagnosis-related claims from the National Practitioner Data Bank (1986-2010). We describe error type, outcome severity and payments (in 2011 US dollars), comparing diagnostic errors to other malpractice allegation groups and inpatient to outpatient within diagnostic errors. RESULTS: We analysed 350 706 paid claims. Diagnostic errors (n=100 249) were the leading type (28.6%) and accounted for the highest proportion of total payments (35.2%). The most frequent outcomes were death, significant permanent injury, major permanent injury and minor permanent injury. Diagnostic errors more often resulted in death than other allegation groups (40.9% vs 23.9%, p<0.001) and were the leading cause of claims-associated death and disability. More diagnostic error claims were outpatient than inpatient (68.8% vs 31.2%, p<0.001), but inpatient diagnostic errors were more likely to be lethal (48.4% vs 36.9%, p<0.001). The inflation-adjusted, 25-year sum of diagnosis-related payments was US$38.8 billion (mean per-claim payout US$386 849; median US$213 250; IQR US$74 545-484 500). Per-claim payments for permanent, serious morbidity that was 'quadriplegic, brain damage, lifelong care' (4.5%; mean US$808 591; median US$564 300), 'major' (13.3%; mean US$568 599; median US$355 350), or 'significant' (16.9%; mean US$419 711; median US$269 255) exceeded those where the outcome was death (40.9%; mean US$390 186; median US$251 745). CONCLUSIONS: Among malpractice claims, diagnostic errors appear to be the most common, most costly and most dangerous of medical mistakes. We found roughly equal numbers of lethal and non-lethal errors in our analysis, suggesting that the public health burden of diagnostic errors could be twice that previously estimated. Healthcare stakeholders should consider diagnostic safety a critical health policy issue.