Changes in Coagulation Testing During a National Shortage of Blue-Top Tubes.

OBJECTIVES: Manufacturer recalls and altered supply chains during the coronavirus disease 2019 (COVID-19) pandemic caused a nationwide shortage of blue-top tubes (BTTs). Most non-point-of-care coagulation tests use these tubes, leaving laboratories and health care facilities in short supply. The Department of Pathology and Laboratory Medicine at Cedars-Sinai Medical Center implemented interventions to conserve supply without sacrificing patient safety. METHODS: In a retrospective quality improvement analysis, we examined coagulation testing and BTT utilization over the 3-month interval during which our interventions were applied. Our study assessed the interventions' effectiveness by evaluating changes in BTT utilization, coagulation testing volume, and patient impact. RESULTS: Average daily use (ADU) of BTT before and after the intervention were 476 and 403, respectively-a 15.2% reduction. Notably, the Emergency Department had a reduction in ADU of 43.3%. Average daily volumes of coagulation assays performed decreased from 949 to 783-a 17.5% reduction. No adverse events from the Pharmacy Department were identified during the study period. CONCLUSIONS: Interventions resulting in significant reductions were in divisions with effective management and supervision. Success in navigating the BTT shortage stemmed from timely announcements, action, and effective communication. Our recommendations established more effective coagulation assay utilization, decreased overall BTT use, and prevented patients with coagulopathic disorders from experiencing adverse consequences.

Digital public health leadership in the global fight for health security.

The COVID-19 pandemic highlighted the need to prioritise mature digital health and data governance at both national and supranational levels to guarantee future health security. The Riyadh Declaration on Digital Health was a call to action to create the infrastructure needed to share effective digital health evidence-based practices and high-quality, real-time data locally and globally to provide actionable information to more health systems and countries. The declaration proposed nine key recommendations for data and digital health that need to be adopted by the global health community to address future pandemics and health threats. Here, we expand on each recommendation and provide an evidence-based roadmap for their implementation. This policy document serves as a resource and toolkit that all stakeholders in digital health and disaster preparedness can follow to develop digital infrastructure and protocols in readiness for future health threats through robust digital public health leadership.

Successfully Implementing Digital Health to Ensure Future Global Health Security During Pandemics: A Consensus Statement.

Importance: COVID-19 has highlighted widespread chronic underinvestment in digital health that hampered public health responses to the pandemic. Recognizing this, the Riyadh Declaration on Digital Health, formulated by an international interdisciplinary team of medical, academic, and industry experts at the Riyadh Global Digital Health Summit in August 2020, provided a set of digital health recommendations for the global health community to address the challenges of current and future pandemics. However, guidance is needed on how to implement these recommendations in practice. Objective: To develop guidance for stakeholders on how best to deploy digital health and data and support public health in an integrated manner to overcome the COVID-19 pandemic and future pandemics. Evidence Review: Themes were determined by first reviewing the literature and Riyadh Global Digital Health Summit conference proceedings, with experts independently contributing ideas. Then, 2 rounds of review were conducted until all experts agreed on the themes and main issues arising using a nominal group technique to reach consensus. Prioritization was based on how useful the consensus recommendation might be to a policy maker. Findings: A diverse stakeholder group of 13 leaders in the fields of public health, digital health, and health care were engaged to reach a consensus on how to implement digital health recommendations to address the challenges of current and future pandemics. Participants reached a consensus on high-priority issues identified within 5 themes: team, transparency and trust, technology, techquity (the strategic development and deployment of technology in health care and health to achieve health equity), and transformation. Each theme contains concrete points of consensus to guide the local, national, and international adoption of digital health to address challenges of current and future pandemics. Conclusions and Relevance: The consensus points described for these themes provide a roadmap for the implementation of digital health policy by all stakeholders, including governments. Implementation of these recommendations could have a significant impact by reducing fatalities and uniting countries on current and future battles against pandemics.

Characterization of Phosphopantetheinyl Hydrolase from Mycobacterium tuberculosis.

Phosphopantetheinyl hydrolase, PptH (Rv2795c), is a recently discovered enzyme from Mycobacterium tuberculosis that removes 4'-phosphopantetheine (Ppt) from holo-carrier proteins (CPs) and thereby opposes the action of phosphopantetheinyl transferases (PPTases). PptH is the first structurally characterized enzyme of the phosphopantetheinyl hydrolase family. However, conditions for optimal activity of PptH have not been defined, and only one substrate has been identified. Here, we provide biochemical characterization of PptH and demonstrate that the enzyme hydrolyzes Ppt in vitro from more than one M. tuberculosis holo-CP as well as holo-CPs from other organisms. PptH provided the only detectable activity in mycobacterial lysates that dephosphopantetheinylated acyl carrier protein M (AcpM), suggesting that PptH is the main Ppt hydrolase in M. tuberculosis. We could not detect a role for PptH in coenzyme A (CoA) salvage, and PptH was not required for virulence of M. tuberculosis during infection of mice. It remains to be determined why mycobacteria conserve a broadly acting phosphohydrolase that removes the Ppt prosthetic group from essential CPs. We speculate that the enzyme is critical for aspects of the life cycle of M. tuberculosis that are not routinely modeled. IMPORTANCE Tuberculosis (TB), caused by Mycobacterium tuberculosis, was the leading cause of death from an infectious disease before COVID, yet the in vivo essentiality and function of many of the protein-encoding genes expressed by M. tuberculosis are not known. We biochemically characterize M. tuberculosis's phosphopantetheinyl hydrolase, PptH, a protein unique to mycobacteria that removes an essential posttranslational modification on proteins involved in synthesis of lipids important for the bacterium's cell wall and virulence. We demonstrate that the enzyme has broad substrate specificity, but it does not appear to have a role in coenzyme A (CoA) salvage or virulence in a mouse model of TB.

TechQuity is an imperative for health and technology business: Let's work together to achieve it.

Open discussions of social justice and health inequities may be an uncommon focus within information technology science, business, and health care delivery partnerships. However, the COVID-19 pandemic-which disproportionately affected Black, indigenous, and people of color-has reinforced the need to examine and define roles that technology partners should play to lead anti-racism efforts through our work. In our perspective piece, we describe the imperative to prioritize TechQuity-equity and social justice as a technology business strategy-through collaborating in partnerships that focus on eliminating racial and social inequities.

Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases.

Artificial intelligence (AI) represents a valuable tool that could be widely used to inform clinical and public health decision-making to effectively manage the impacts of a pandemic. The objective of this scoping review was to identify the key use cases for involving AI for pandemic preparedness and response from the peer-reviewed, preprint, and grey literature. The data synthesis had two parts: an in-depth review of studies that leveraged machine learning (ML) techniques and a limited review of studies that applied traditional modeling approaches. ML applications from the in-depth review were categorized into use cases related to public health and clinical practice, and narratively synthesized. One hundred eighty-three articles met the inclusion criteria for the in-depth review. Six key use cases were identified: forecasting infectious disease dynamics and effects of interventions; surveillance and outbreak detection; real-time monitoring of adherence to public health recommendations; real-time detection of influenza-like illness; triage and timely diagnosis of infections; and prognosis of illness and response to treatment. Data sources and types of ML that were useful varied by use case. The search identified 1167 articles that reported on traditional modeling approaches, which highlighted additional areas where ML could be leveraged for improving the accuracy of estimations or projections. Important ML-based solutions have been developed in response to pandemics, and particularly for COVID-19 but few were optimized for practical application early in the pandemic. These findings can support policymakers, clinicians, and other stakeholders in prioritizing research and development to support operationalization of AI for future pandemics.

Leveraging Informatics and Technology to Support Public Health Response: Framework and Illustrations using COVID-19.

OBJECTIVE: To develop a conceptual model and novel, comprehensive framework that encompass the myriad ways informatics and technology can support public health response to a pandemic. METHOD: The conceptual model and framework categorize informatics solutions that could be used by stakeholders (e.g., government, academic institutions, healthcare providers and payers, life science companies, employers, citizens) to address public health challenges across the prepare, respond, and recover phases of a pandemic, building on existing models for public health operations and response. RESULTS: Mapping existing solutions, technology assets, and ideas to the framework helped identify public health informatics solution requirements and gaps in responding to COVID-19 in areas such as applied science, epidemiology, communications, and business continuity. Two examples of technologies used in COVID-19 illustrate novel applications of informatics encompassed by the framework. First, we examine a hub from The Weather Channel, which provides COVID-19 data via interactive maps, trend graphs, and details on case data to individuals and businesses. Second, we examine IBM Watson Assistant for Citizens, an AI-powered virtual agent implemented by healthcare providers and payers, government agencies, and employers to provide information about COVID-19 via digital and telephone-based interaction. DISCUSSION: Early results from these novel informatics solutions have been positive, showing high levels of engagement and added value across stakeholders. CONCLUSION: The framework supports development, application, and evaluation of informatics approaches and technologies in support of public health preparedness, response, and recovery during a pandemic. Effective solutions are critical to success in recovery from COVID-19 and future pandemics.

What is TechQuity?

Three seminal reports, the 2001 Institute of Medicine's Crossing the Quality Chasm, the 2003 report Unequal Treatment,1 and the 2020 National Academy of Medicine's (formerly Institute of Medicine) Artificial Intelligence in Healthcare2 represented inflection points in highlighting the substantial disparities in access, clinical care, and outcomes, and recommended that equity in health care and health technology must be achieved to deliver quality care.3 Though Crossing the Quality Chasm set up the STEEEP framework, which explicitly called out equity as one of six health care quality domains (alongside safety, timeliness, effectiveness, efficiency, and patient-centered care) the issue of inequities in health care delivery was truly laid bare in Unequal Treatment, which also called upon health care institutions and providers to develop strategies to confront disparities in care.4 Artificial Intelligence in Healthcare introduced the "Quintiple Aim" where "Equity and Inclusion" was added to the "Quadruple Aim. Equity Dashboards The application of analytics to demonstrate health care quality in the domains of safety, timeliness, effectiveness, efficiency, and patient-centeredness has been common in diverse dashboards for hospital ratings and other key health care certifications (e.g., National Committee for Quality Assurance, Joint Commission);however, equity has often been overlooked.17 Peter Drucker, a famous business thinker and writer for the modern company, stated that "if you can't measure it, you can't improve it. [...]we must move AI from being a "black box" to a "clear box" with AI factsheets like nutrition labels where buyers and end-users of AI algorithms can transparently see who trained the AI, what datasets were used, and what specific AI algorithms and models were used.28 We must assure transparent, ethical, fair, and equitable AI. Institute of Medicine, Board on Health Sciences Policy, Committee on Understanding and Eliminating Racial and Ethnic Disparities in Health Care.