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BACKGROUND: Many COVID-19 studies are constructed to report hospitalization outcomes, with few large multi-center population-based reports on the time course of intra-hospitalization characteristics, including daily oxygenation support requirements. Comprehensive epidemiologic profiles of oxygenation methods used by day and by week during hospitalization across all severities are important to illustrate the clinical and economic burden of COVID-19 hospitalizations. METHODS: This was a retrospective, multi-center observational cohort study of 15,361 consecutive hospitalizations of patients with COVID-19 at 25 adult acute care hospitals in Texas participating in the Society of Critical Care Medicine Discovery Viral Respiratory Illness Universal Study COVID-19 registry. RESULTS: At initial hospitalization, the majority required nasal cannula (44.0%), with an increasing proportion of invasive mechanical ventilation in the first week and particularly the weeks to follow. After 4 weeks of acute illness, 69.9% of adults hospitalized with COVID-19 required intermediate (eg, high-flow nasal cannula, noninvasive ventilation) or advanced respiratory support (ie, invasive mechanical ventilation), with similar proportions that extended to hospitalizations that lasted ≥ 6 weeks. CONCLUSIONS: Data representation of intra-hospital processes of care drawn from hospitals with varied size, teaching and trauma designations is important to presenting a balanced perspective of care delivery mechanisms employed, such as daily oxygen method utilization. [ABSTRACT FROM AUTHOR]

A detailed understanding of the molecular and immunological changes that occur longitudinally across tumors exposed to immune checkpoint inhibitors is a significant knowledge gap in oncology. To address this unmet need, we created a statewide biospecimen collection and clinical informatics system to enable longitudinal tumor and immune profiling and to enhance translational research. The Texas Immuno-Oncology Biorepository (TIOB) consents patients to collect, process, store, and analyze serial biospecimens of tissue, blood, urine, and stool from a diverse population of over 100,000 cancer patients treated each year across the Baylor Scott & White Health system. Here we sought to demonstrate that these samples were fit for purpose with regard to downstream multi-omic assays. Plasma, urine, peripheral blood mononuclear cells, and stool samples from 11 enrolled patients were collected from various cancer types. RNA isolated from extracellular vesicles derived from plasma and urine was sufficient for transcriptomics. Peripheral blood mononuclear cells demonstrated excellent yield and viability. Ten of 11 stool samples produced RNA quality to enable microbiome characterization. Sample acquisition and processing methods are known to impact sample quality and performance. We demonstrate that consistent acquisition methodology, sample preparation, and sample storage employed by the TIOB can produce high-quality specimens, suited for employment in a wide array of multi-omic platforms, enabling comprehensive immune and molecular profiling. [ABSTRACT FROM AUTHOR]

Background: Opioids are a mainstay for acute pain management, but their side effects can adversely impact patient recovery. Multimodal analgesia (MMA) is recommended for treatment of postoperative pain and has been incorporated in enhanced recovery after surgery (ERAS) protocols. The objective of this quality improvement study was to implement an MMA care pathway as part of an ERAS program for colorectal surgery and to measure the effect of this intervention on patient outcomes and costs., Methods: This pre-post study included 856 adult inpatients who underwent an elective colorectal surgery at three hospitals within an integrated healthcare system. The impact of ERAS program implementation on opioid prescribing practices, outcomes, and costs was examined after adjusting for clinical and demographic confounders., Results: Improvements were seen in MMA compliance (34.0% vs 65.5%, P  < 0.0001) and ERAS compliance (50.4% vs 57.6%, P  < 0.0001). Reductions in mean days on opioids (4.2 vs 3.2), daily (51.6 vs 33.4 mg) and total (228.8 vs 112.7 mg) morphine milligram equivalents given during hospitalization, and risk-adjusted length of stay (4.3 vs 3.6 days, P  < 0.05) were also observed., Conclusions: Implementing ERAS programs that include MMA care pathways as standard of care may result in more judicious use of opioids and reduce patient recovery time., Competing Interests: Dr. Wan is the Global Head of Evidence Generation and Data Sciences at Mallinckrodt Pharmaceuticals. Ms. Böing previously served as the Senior Manager for Health Economics and Outcomes Research (HEOR) and Real-World Evidence at Mallinckrodt Pharmaceuticals and is currently the Director of Global HEOR for Rare Disease at Ipsen. Dr. Collinsworth is currently a Senior Research Scientist at 3M. The other authors declare no potential conflicts of interest., (Copyright © 2023 Baylor University Medical Center.)

A detailed understanding of the molecular and immunological changes that occur longitudinally across tumors exposed to immune checkpoint inhibitors is a significant knowledge gap in oncology. To address this unmet need, we created a statewide biospecimen collection and clinical informatics system to enable longitudinal tumor and immune profiling and to enhance translational research. The Texas Immuno-Oncology Biorepository (TIOB) consents patients to collect, process, store, and analyze serial biospecimens of tissue, blood, urine, and stool from a diverse population of over 100,000 cancer patients treated each year across the Baylor Scott & White Health system. Here we sought to demonstrate that these samples were fit for purpose with regard to downstream multi-omic assays. Plasma, urine, peripheral blood mononuclear cells, and stool samples from 11 enrolled patients were collected from various cancer types. RNA isolated from extracellular vesicles derived from plasma and urine was sufficient for transcriptomics. Peripheral blood mononuclear cells demonstrated excellent yield and viability. Ten of 11 stool samples produced RNA quality to enable microbiome characterization. Sample acquisition and processing methods are known to impact sample quality and performance. We demonstrate that consistent acquisition methodology, sample preparation, and sample storage employed by the TIOB can produce high-quality specimens, suited for employment in a wide array of multi-omic platforms, enabling comprehensive immune and molecular profiling., Competing Interests: Dr. Kelly has received institutional research support from Bristol Myers Squibb and Eli Lilly and has served on advisory boards for BMS, Merck, EMD Serono, Astra Zeneca, Daiichi Sankyo, Eisai, Astellas, Ipsen, Novartis, Takeda, Novocure, and Eli Lilly. The other authors have no conflicts to disclose., (Copyright © 2022 Baylor University Medical Center.)

Introduction: Health systems can be supported by collaborative networks focused on data sharing and comparative analytics to identify and rapidly disseminate promising care practices. Standardized data collection, quality assessment, and cleansing is a necessary process to facilitate meaningful analytics for operations, quality improvement, and research. We developed a framework for aligning data from health care delivery systems using the High Value Healthcare Collaborative central registry., Framework: The centralized data registry model allows for multiple layers of data quality assessment. Our framework uses an iterative approach, starting with clear specifications, maintaining ongoing dialogue with diverse stakeholders, and regular checkpoints to assess data conformance, completeness, and plausibility., Lessons Learned: We found that an iterative communication process is critical for a central registry to ensure: 1) clarity of data specifications, 2) appropriate data quality, and 3) thorough understanding of data source, purpose, and context. Engaging teams from all participating institutions and incorporating diverse stakeholders of clinicians, information technologists, data analysts, operations managers, and health services researchers in all decision making processes supports development of high quality datasets for comparative analytics across multiple institutions., Conclusion: A standard data specification and submission process alone does not guarantee aligned data for a collaborative registry. Implementing an iterative data quality improvement framework with extensive communication proved to be effective for aligning data from multiple institutions to support meaningful analytics.

The third paper in a series on how learning health systems can use routinely collected electronic health data (EHD) to advance knowledge and support continuous learning, this review describes how analytical methods for individual-level electronic health data EHD, including regression approaches, interrupted time series (ITS) analyses, instrumental variables, and propensity score methods, can also be used to address the question of whether the intervention "works." The two major potential sources of bias in non-experimental studies of health care interventions are that the treatment groups compared do not have the same probability of treatment or exposure and the potential for confounding by unmeasured covariates. Although very different, the approaches presented in this chapter are all based on assumptions about data, causal relationships, and biases. For instance, regression approaches assume that the relationship between the treatment, outcome, and other variables is properly specified, all of the variables are available for analysis (i.e., no unobserved confounders) and measured without error, and that the error term is independent and identically distributed. The instrumental variables approach requires identifying an instrument that is related to the assignment of treatment but otherwise has no direct on the outcome. Propensity score methods approaches, on the other hand, assume that there are no unobserved confounders. The epidemiological designs discussed also make assumptions, for instance that individuals can serve as their own control. To properly address these assumptions, analysts should conduct sensitivity analyses within the assumptions of each method to assess the potential impact of what cannot be observed. Researchers also should analyze the same data with different analytical approaches that make alternative assumptions, and to apply the same methods to different data sets. Finally, different analytical methods, each subject to different biases, should be used in combination and together with different designs, to limit the potential for bias in the final results.

Learning health systems use routinely collected electronic health data (EHD) to advance knowledge and support continuous learning. Even without randomization, observational studies can play a central role as the nation's health care system embraces comparative effectiveness research and patient-centered outcomes research. However, neither the breadth, timeliness, volume of the available information, nor sophisticated analytics, allow analysts to confidently infer causal relationships from observational data. However, depending on the research question, careful study design and appropriate analytical methods can improve the utility of EHD. The introduction to a series of four papers, this review begins with a discussion of the kind of research questions that EHD can help address, noting how different evidence and assumptions are needed for each. We argue that when the question involves describing the current (and likely future) state of affairs, causal inference is not relevant, so randomized clinical trials (RCTs) are not necessary. When the question is whether an intervention improves outcomes of interest, causal inference is critical, but appropriately designed and analyzed observational studies can yield valid results that better balance internal and external validity than typical RCTs. When the question is one of translation and spread of innovations, a different set of questions comes into play: How and why does the intervention work? How can a model be amended or adapted to work in new settings? In these "delivery system science" settings, causal inference is not the main issue, so a range of quantitative, qualitative, and mixed research designs are needed. We then describe why RCTs are regarded as the gold standard for assessing cause and effect, how alternative approaches relying on observational data can be used to the same end, and how observational studies of EHD can be effective complements to RCTs. We also describe how RCTs can be a model for designing rigorous observational studies, building an evidence base through iterative studies that build upon each other (i.e., confirmation across multiple investigations).

The second paper in a series on how learning health systems can use routinely collected electronic health data (EHD) to advance knowledge and support continuous learning, this review summarizes study design approaches, including choosing appropriate data sources, and methods for design and analysis of natural and quasi-experiments. The primary strength of study design approaches described in this section is that they study the impact of a deliberate intervention in real-world settings, which is critical for external validity. These evaluation designs address estimating the counterfactual - what would have happened if the intervention had not been implemented. At the individual level, epidemiologic designs focus on identifying situations in which bias is minimized. Natural and quasi-experiments focus on situations where the change in assignment breaks the usual links that could lead to confounding, reverse causation, and so forth. And because these observational studies typically use data gathered for patient management or administrative purposes, the possibility of observation bias is minimized. The disadvantages are that one cannot necessarily attribute the effect to the intervention (as opposed to other things that might have changed), and the results do not indicate what about the intervention made a difference. Because they cannot rely on randomization to establish causality, program evaluation methods demand a more careful consideration of the "theory" of the intervention and how it is expected to play out. A logic model describing this theory can help to design appropriate comparisons, account for all influential variables in a model, and help to ensure that evaluation studies focus on the critical intermediate and long-term outcomes as well as possible confounders.

Context: Electronic health records (EHRs) have been promoted as a key driver of improved patient care and outcomes and as an essential component of learning health systems. However, to date, many EHRs are not optimized to support delivery of quality and safety initiatives, particularly in Intensive Care Units (ICUs). Delirium is a common and severe problem for ICU patients that may be prevented or mitigated through the use of evidence-based care processes (daily awakening and breathing trials, formal delirium screening, and early mobility-collectively known as the "ABCDE bundle"). This case study describes how an integrated health care delivery system modified its inpatient EHR to accelerate the implementation and evaluation of ABCDE bundle deployment as a safety and quality initiative., Case Description: In order to facilitate uptake of the ABCDE bundle and measure delivery of the care processes within the bundle, we worked with clinical and technical experts to create structured data fields for documentation of bundle elements and to identify where these fields should be placed within the EHR to streamline staff workflow. We created an "ABCDE" tab in the existing patient viewer that allowed providers to easily identify which components of the bundle the patient had and had not received. We examined the percentage of ABCDE bundle elements captured in these structured data fields over time to track compliance with data entry procedures and to improve documentation of care processes., Major Themes: Modifying the EHR to support ABCDE bundle deployment was a complex and time-consuming process. We found that it was critical to gain buy-in from senior leadership on the importance of the ABCDE bundle to secure information technology (IT) resources, understand the different workflows of members of multidisciplinary care teams, and obtain continuous feedback from staff on the EHR revisions during the development cycle. We also observed that it was essential to provide ongoing training to staff on proper use of the new EHR documentation fields. Lastly, timely reporting on ABCDE bundle performance may be essential to improved practice adoption and documentation of care processes., Conclusion: The creation of learning health systems is contingent on an ability to modify EHRs to meet emerging care delivery and quality improvement needs. Although this study focuses on the prevention and mitigation of delirium in ICUs, our process for identifying key data elements and making modifications to the EHR, as well as the lessons learned from the IT components of this program, are generalizable to other health care settings and conditions.

Context: Collaborative networks support the goals of a learning health system by sharing, aggregating, and analyzing data to facilitate identification of best practices care across delivery organizations. This case study describes the infrastructure and process developed by an integrated health delivery system to successfully prepare and submit a complex data set to a large national collaborative network., Case Description: We submitted four years of data for a diverse population of patients in specific clinical areas: diabetes, chronic heart failure, sepsis, and hip, knee, and spine. The most recent submission included 19 tables, more than 376,000 unique patients, and almost 5 million patient encounters. Data was extracted from multiple clinical and administrative systems., Lessons Learned: We found that a structured process with documentation was key to maintaining communication, timelines, and quality in a large-scale data submission to a national collaborative network. The three key components of this process were the experienced project team, documentation, and communication. We used a formal QA and feedback process to track and review data. Overall, the data submission was resource intensive and required an incremental approach to data quality., Conclusion: Participation in collaborative networks can be time and resource intense, however it can serve as a catalyst to increase the technical data available to the learning health system.