Supplementing systematic review findings with healthcare system data: pilot projects from the Agency for Healthcare Research and Quality Evidence-based Practice Center program.

Objectives: The US Agency for Healthcare Research and Quality, through the Evidence-based Practice Center (EPC) Program, aims to provide health system decision makers with the highest-quality evidence to inform clinical decisions. However, limitations in the literature may lead to inconclusive findings in EPC systematic reviews (SRs). The EPC Program conducted pilot projects to understand the feasibility, benefits, and challenges of utilizing health system data to augment SR findings to support confidence in healthcare decision-making based on real-world experiences.

COVID-19 Admission Rates and Changes in Care Quality in US Hospitals.

Importance: Unprecedented increases in hospital occupancy rates during COVID-19 surges in 2020 caused concern over hospital care quality for patients without COVID-19.

Objective: To examine changes in hospital nonsurgical care quality for patients without COVID-19 during periods of high and low COVID-19 admissions.

Design, Setting, And Participants: This cross-sectional study used data from the 2019 and 2020 Agency for Healthcare Research and Quality's Healthcare Cost and Utilization Project State Inpatient Databases.

Guiding Principles to Address the Impact of Algorithm Bias on Racial and Ethnic Disparities in Health and Health Care.

Importance: Health care algorithms are used for diagnosis, treatment, prognosis, risk stratification, and allocation of resources. Bias in the development and use of algorithms can lead to worse outcomes for racial and ethnic minoritized groups and other historically marginalized populations such as individuals with lower income.

Objective: To provide a conceptual framework and guiding principles for mitigating and preventing bias in health care algorithms to promote health and health care equity.

Clinician Trust in Artificial Intelligence: What is Known and How Trust Can Be Facilitated.

Predictive analytics based on artificial intelligence (AI) offer clinicians the opportunity to leverage big data available in electronic health records (EHR) to improve clinical decision-making, and thus patient outcomes. Despite this, many barriers exist to facilitating trust between clinicians and AI-based tools, limiting its current impact. Potential solutions are available at both the local and national level.

Awareness of Racial and Ethnic Bias and Potential Solutions to Address Bias With Use of Health Care Algorithms.

Importance: Algorithms are commonly incorporated into health care decision tools used by health systems and payers and thus affect quality of care, access, and health outcomes. Some algorithms include a patient's race or ethnicity among their inputs and can lead clinicians and decision-makers to make choices that vary by race and potentially affect inequities.

Objective: To inform an evidence review on the use of race- and ethnicity-based algorithms in health care by gathering public and stakeholder perspectives about the repercussions of and efforts to address algorithm-related bias.

From precision medicine to precision care: Choosing and using precision medicine in the context of multimorbidity.

Rapid advances in precision medicine promise dramatic reductions in morbidity and mortality for a growing array of conditions. To realize the benefits of precision medicine and minimize harm, it is necessary to address real-world challenges encountered in translating this research into practice. Foremost among these is how to choose and use precision medicine modalities in real-world practice by addressing issues related to caring for the sizable proportion of people living with multimorbidity.

Use of clinical pathways integrated into the electronic health record to address the coronavirus disease 2019 (COVID-19) pandemic.

Background: The coronavirus disease 2019 (COVID-19) pandemic has required healthcare systems to meet new demands for rapid information dissemination, resource allocation, and data reporting. To help address these challenges, our institution leveraged electronic health record (EHR)-integrated clinical pathways (E-ICPs), which are easily understood care algorithms accessible at the point of care.

Objective: To describe our institution's creation of E-ICPs to address the COVID-19 pandemic, and to assess the use and impact of these tools.

Framework for Integrating Equity Into Machine Learning Models: A Case Study.

Predictive analytic models leveraging machine learning methods increasingly have become vital to health care organizations hoping to improve clinical outcomes and the efficiency of care delivery for all patients. Unfortunately, predictive models could harm populations that have experienced interpersonal, institutional, and structural biases. Models learn from historically collected data that could be biased.

Assessing the confidence, knowledge and learning preferences of healthcare workers regarding personal protective equipment use during the coronavirus disease 2019 (COVID-19) pandemic.

We surveyed healthcare workers at an urban academic hospital in the United States about their confidence in and knowledge of appropriate personal protective equipment use during the coronavirus disease 2019 (COVID-19) pandemic. Among 461 respondents, most were confident and knowledgeable about use. Prescribers or nurses and those extremely confident about use were also the most knowledgeable.

Communicating Guideline Recommendations Using Graphic Narrative Versus Text-Based Broadcast Screensavers: Design and Implementation Study.

Background: The use of graphic narratives, defined as stories that use images for narration, is growing in health communication.

Objective: The aim of this study was to describe the design and implementation of a graphic narrative screensaver (GNS) to communicate a guideline recommendation (ie, avoiding low-value acid suppressive therapy [AST] use in hospital inpatients) and examine the comparative effectiveness of the GNS versus a text-based screensaver (TBS) on clinical practice (ie, low-value AST prescriptions) and clinician recall.

Methods: During a 2-year period, the GNS and the TBS were displayed on inpatient clinical workstations.

Development, implementation, and use of an "equity lens" integrated into an institutional quality scorecard.

Few healthcare provider organizations systematically track their healthcare equity, and fewer enable direct interaction with such data by their employees. From May to August 2019, we enhanced the data architecture and reporting functionality of our existing institutional quality scorecard to allow direct comparisons of quality measure performance by gender, age, race, ethnicity, language, zip code, and payor. The Equity Lens was made available to over 4000 staff in September 2019 for 82 institutional quality measures.

Hand Hygiene Compliance Rate During the COVID-19 Pandemic.

This quality improvement study assesses hand hygiene compliance rates in a hospital with an automated hand hygiene monitoring system during the COVID-19 pandemic.

A Mobile App to Facilitate Socially Distanced Hospital Communication During COVID-19: Implementation Experience.

Background: COVID-19 has significantly altered health care delivery, requiring clinicians and hospitals to adapt to rapidly changing hospital policies and social distancing guidelines. At our large academic medical center, clinicians reported that existing information on distribution channels, including emails and hospital intranet posts, was inadequate to keep everyone abreast with these changes. To address these challenges, we adapted a mobile app developed in-house to communicate critical changes in hospital policies and enable direct telephonic communication between clinical team members and hospitalized patients, to support social distancing guidelines and remote rounding.

2020 Focused Updates to the Asthma Management Guidelines: A Report from the National Asthma Education and Prevention Program Coordinating Committee Expert Panel Working Group.

The 2020 Focused Updates to the Asthma Management Guidelines: A Report from the National Asthma Education and Prevention Program Coordinating Committee Expert Panel Working Group was coordinated and supported by the National Heart, Lung, and Blood Institute (NHLBI) of the National Institutes of Health. It is designed to improve patient care and support informed decision making about asthma management in the clinical setting. This update addresses six priority topic areas as determined by the state of the science at the time of a needs assessment, and input from multiple stakeholders:A rigorous process was undertaken to develop these evidence-based guidelines.

Growth of Ambulatory Virtual Visits and Differential Use by Patient Sociodemographics at One Urban Academic Medical Center During the COVID-19 Pandemic: Retrospective Analysis.

Background: Despite widespread interest in the use of virtual (ie, telephone and video) visits for ambulatory patient care during the COVID-19 pandemic, studies examining their adoption during the pandemic by race, sex, age, or insurance are lacking. Moreover, there have been limited evaluations to date of the impact of these sociodemographic factors on the use of telephone versus video visits. Such assessments are crucial to identify, understand, and address differences in care delivery across patient populations, particularly those that could affect access to or quality of care.

AHRQ EPC Series on Improving Translation of Evidence: Use of a Clinical Pathway for C. Difficile Treatment to Facilitate the Translation of Research Findings into Practice.

Background: In 2018 the Agency for Healthcare Research and Quality (AHRQ) Evidence-based Practice Center (EPC) Program issued a call for strategies to disseminate AHRQ EPC systematic reviews. In this pilot, findings from the 2016 AHRQ EPC report on Clostridioides difficile infection were translated into a treatment pathway and disseminated via a cloud-based platform and electronic health record (EHR).

Methods: An existing 10-step framework was used for developing and disseminating evidence-based clinical pathways.

Determining the Association Between End-of-Life Care Resources and Patient Outcomes in Pennsylvania ICUs.

Objectives: As ICUs are increasingly a site of end-of-life care, many have adopted end-of-life care resources. We sought to determine the association of such resources with outcomes of ICU patients.

Design: Retrospective cohort study.

A Machine Learning Algorithm to Predict Severe Sepsis and Septic Shock: Development, Implementation, and Impact on Clinical Practice.

Objectives: Develop and implement a machine learning algorithm to predict severe sepsis and septic shock and evaluate the impact on clinical practice and patient outcomes.

Design: Retrospective cohort for algorithm derivation and validation, pre-post impact evaluation.

Setting: Tertiary teaching hospital system in Philadelphia, PA.

Clinician Perception of a Machine Learning-Based Early Warning System Designed to Predict Severe Sepsis and Septic Shock.

Objective: To assess clinician perceptions of a machine learning-based early warning system to predict severe sepsis and septic shock (Early Warning System 2.0).

Design: Prospective observational study.

Next Steps in Improving Healthcare Value: AHRQ Evidence-based Practice Center Program-Applying the Knowledge to Practice to Data Cycle to Strengthen the Value of Patient Care.

For more than 20 years, the Agency for Healthcare Research and Quality (AHRQ) Evidence-based Practice Center (EPC) Program has been identifying and synthesizing evidence to inform evidence-based healthcare. Recognizing that many healthcare settings continue to face challenges in disseminating and implementing evidence into practice, AHRQ's EPC program has also embarked on initiatives to facilitate the translation of evidence into practice and to measure and monitor how practice changes impact health outcomes. The program has structured its efforts around the three phases of the Learning Healthcare System cycle: knowledge, practice, and data.