Objective

To quantifying the interdependency within the regulatory environment governing human subject research, including Institutional Review Boards (IRBs), federally mandated Medicare coverage analysis and contract negotiations.

Methods

Over 8000 IRB, coverage analysis and contract applications initiated between 2013 and 2016 were analyzed using traditional and machine learning analytics for a quality improvement effort to improve the time required to authorize the start of human research studies.

Results

Staffing ratios, study characteristics such as the number of arms, source of funding and number and type of ancillary reviews significantly influenced the timelines. Using key variables, a predictive algorithm identified outliers for a workflow distinct from the standard process. Improved communication between regulatory units, integration of common functions, and education outreach improved the regulatory approval process.

Conclusions

Understanding and improving the interdependencies between IRB, coverage analysis and contract negotiation offices requires a systems approach and might benefit from predictive analytics.

The SARS-CoV-2 (COVID-19) pandemic has placed unprecedented demands on entire health systems and driven them to their capacity, so that health care professionals have been confronted with the difficult problem of ensuring appropriate staffing and resources to a high number of critically ill patients. In light of such high-demand circumstances, we describe an open web-accessible simulation-based decision support tool for a better use of finite hospital resources. The aim is to explore risk and reward under differing assumptions with a model that diverges from most existing models which focus on epidemic curves and related demand of ward and intensive care beds in general. While maintaining intuitive use, our tool allows randomized "what-if" scenarios which are key for real-time experimentation and analysis of current decisions' down-stream effects on required but finite resources over self-selected time horizons. While the implementation is for COVID-19, the approach generalizes to other diseases and high-demand circumstances.

Educational tools for application of team science competencies in clinical research are needed. Our interdisciplinary group developed and evaluated acceptability of a virtual world game-based learning tool simulating a multisite clinical trial; performance hinges on effective intrateam communication. Initial implementation with clinical research trainees (n=40) indicates high satisfaction and perceived relevance to team science and research career goals. Game-based learning may play an important role in team science training.

Background

With up to 240 million people chronically infected with hepatitis B worldwide, including an estimated 2 million in the United States, widespread screening is needed to link the infected to care and decrease the possible consequences of untreated infection, including liver cancer, cirrhosis and death. Screening is currently fraught with challenges in both the developed and developing world. New point-of-care tests may have advantages over standard-of-care tests in terms of cost-effectiveness and linkage to care. Stochastic modeling is applied here for relative utility assessment of point-of-care tests and standard-of-care tests for screening.

Methods

We analyzed effects of point-of-care versus standard-of-care testing using Markov models for disease progression in individual patients. Simulations of large cohorts with distinctly quantified models permitted the assessment of particular screening schemes. The validity of the trends observed is supported by sensitivity analyses for the simulation parameters.

Results

Increased utilization of point-of-care screening was shown to decrease hepatitis B-related mortalities and increase life expectancy at low projected expense.

Conclusions

The results suggest that standard-of-care screening should be substituted by point-of-care tests resulting in improved linkage to care and decrease in long-term complications.

Introduction

The time required to obtain Institutional Review Board (IRB) approval is a frequent subject of efforts to reduce unnecessary delays in initiating clinical trials. This study was conducted by and for IRB directors to better understand factors affecting approval times as a first step in developing a quality improvement framework.

Methods

807 IRB-approved clinical trials from 5 University of California campuses were analyzed to identify operational and clinical trial characteristics influencing IRB approval times.

Results

High workloads, low staff ratios, limited training, and the number and types of ancillary reviews resulted in longer approval times. Biosafety reviews and the need for billing coverage analysis were ancillary reviews that contributed to the longest delays. Federally funded and multisite clinical trials had shorter approval times. Variability in between individual committees at each institution reviewing phase 3 multisite clinical trials also contributed to delays for some protocols. Accreditation was not associated with shorter approval times.

Conclusions

Reducing unnecessary delays in obtaining IRB approval will require a quality improvement framework that considers operational and study characteristics as well as the larger institutional regulatory environment.

Unlabelled

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease in children. In order to advance the field of NAFLD, noninvasive imaging methods for measuring liver fat are needed. Advanced magnetic resonance imaging (MRI) has shown great promise for the quantitative assessment of hepatic steatosis but has not been validated in children. Therefore, this study was designed to evaluate the correlation and diagnostic accuracy of MRI-estimated liver proton density fat fraction (PDFF), a biomarker for hepatic steatosis, compared to histologic steatosis grade in children. The study included 174 children with a mean age of 14.0 years. Liver PDFF estimated by MRI was significantly (P < 0.01) correlated (0.725) with steatosis grade. The correlation of MRI-estimated liver PDFF and steatosis grade was influenced by both sex and fibrosis stage. The correlation was significantly (P < 0.01) stronger in girls (0.86) than in boys (0.70). The correlation was significantly (P < 0.01) weaker in children with stage 2-4 fibrosis (0.61) than children with no fibrosis (0.76) or stage 1 fibrosis (0.78). The diagnostic accuracy of commonly used threshold values to distinguish between no steatosis and mild steatosis ranged from 0.69 to 0.82. The overall accuracy of predicting the histologic steatosis grade from MRI-estimated liver PDFF was 56%. No single threshold had sufficient sensitivity and specificity to be considered diagnostic for an individual child.

Conclusions

Advanced magnitude-based MRI can be used to estimate liver PDFF in children, and those PDFF values correlate well with steatosis grade by liver histology. Thus, magnitude-based MRI has the potential for clinical utility in the evaluation of NAFLD, but at this time no single threshold value has sufficient accuracy to be considered diagnostic for an individual child.