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. Data were obtained for all nonfederal, acute care hospitals in 36 states with admissions in 2019 and 2020, and patients without a diagnosis of COVID-19 or pneumonia who were at risk for selected quality indicators were included. The data analysis was performed between January 1, 2023, and March 15, 2024. Exposure: Each hospital and week in 2020 was categorized based on the number of COVID-19 admissions per 100 beds: less than 1.0, 1.0 to 4.9, 5.0 to 9.9, 10.0 to 14.9, and 15.0 or greater. Main Outcomes and Measures: The main outcomes were rates of adverse outcomes for selected quality indicators, including pressure ulcers and in-hospital mortality for acute myocardial infarction, heart failure, acute stroke, gastrointestinal hemorrhage, hip fracture, and percutaneous coronary intervention. Changes in 2020 compared with 2019 were calculated for each level of the weekly COVID-19 admission rate, adjusting for case-mix and hospital-month fixed effects. Changes during weeks with high COVID-19 admissions (≥15 per 100 beds) were compared with changes during weeks with low COVID-19 admissions (<1 per 100 beds). Results: The analysis included 19 111 629 discharges (50.3% female; mean [SD] age, 63.0 [18.0] years) from 3283 hospitals in 36 states. In weeks 18 to 48 of 2020, 35 851 hospital-weeks (36.7%) had low COVID-19 admission rates, and 8094 (8.3%) had high rates. Quality indicators for patients without COVID-19 significantly worsened in 2020 during weeks with high vs low COVID-19 admissions. Pressure ulcer rates increased by 0.09 per 1000 admissions (95% CI, 0.01-0.17 per 1000 admissions; relative change, 24.3%), heart failure mortality increased by 0.40 per 100 admissions (95% CI, 0.18-0.63 per 100 admissions; relative change, 21.1%), hip fracture mortality increased by 0.40 per 100 admissions (95% CI, 0.04-0.77 per 100 admissions; relative change, 29.4%), and a weighted mean of mortality for the selected indicators increased by 0.30 per 100 admissions (95% CI, 0.14-0.45 per 100 admissions; relative change, 10.6%). Conclusions and Relevance: In this cross-sectional study, COVID-19 surges were associated with declines in hospital quality, highlighting the importance of identifying and implementing strategies to maintain care quality during periods of high hospital use.

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. Evidence Review: The Agency for Healthcare Research and Quality and the National Institute for Minority Health and Health Disparities convened a diverse panel of experts to review evidence, hear from stakeholders, and receive community feedback. Findings: The panel developed a conceptual framework to apply guiding principles across an algorithm's life cycle, centering health and health care equity for patients and communities as the goal, within the wider context of structural racism and discrimination. Multiple stakeholders can mitigate and prevent bias at each phase of the algorithm life cycle, including problem formulation (phase 1); data selection, assessment, and management (phase 2); algorithm development, training, and validation (phase 3); deployment and integration of algorithms in intended settings (phase 4); and algorithm monitoring, maintenance, updating, or deimplementation (phase 5). Five principles should guide these efforts: (1) promote health and health care equity during all phases of the health care algorithm life cycle; (2) ensure health care algorithms and their use are transparent and explainable; (3) authentically engage patients and communities during all phases of the health care algorithm life cycle and earn trustworthiness; (4) explicitly identify health care algorithmic fairness issues and trade-offs; and (5) establish accountability for equity and fairness in outcomes from health care algorithms. Conclusions and Relevance: Multiple stakeholders must partner to create systems, processes, regulations, incentives, standards, and policies to mitigate and prevent algorithmic bias. Reforms should implement guiding principles that support promotion of health and health care equity in all phases of the algorithm life cycle as well as transparency and explainability, authentic community engagement and ethical partnerships, explicit identification of fairness issues and trade-offs, and accountability for equity and fairness.

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. It will take a broad and diverse coalition of stakeholders, from health-care systems, EHR vendors, and clinical educators to regulators, researchers and the patient community, to help facilitate this trust so that the promise of AI in health care can be realized.

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. Design, Setting, and Participants: Qualitative methods were used to analyze responses. Responses were initially open coded and then consolidated to create a codebook, with themes and subthemes identified and finalized by consensus. This qualitative study was conducted from May 4, 2021, through December 7, 2022. Forty-two organization representatives (eg, clinical professional societies, universities, government agencies, payers, and health technology organizations) and individuals responded to the request for information. Main Outcomes and Measures: Identification of algorithms with the potential for race- and ethnicity-based biases and qualitative themes. Results: Forty-two respondents identified 18 algorithms currently in use with the potential for bias, including, for example, the Simple Calculated Osteoporosis Risk Estimation risk prediction tool and the risk calculator for vaginal birth after cesarean section. The 7 qualitative themes, with 31 subthemes, included the following: (1) algorithms are in widespread use and have significant repercussions, (2) bias can result from algorithms whether or not they explicitly include race, (3) clinicians and patients are often unaware of the use of algorithms and potential for bias, (4) race is a social construct used as a proxy for clinical variables, (5) there is a lack of standardization in how race and social determinants of health are collected and defined, (6) bias can be introduced at all stages of algorithm development, and (7) algorithms should be discussed as part of shared decision-making between the patient and clinician. Conclusions and Relevance: This qualitative study found that participants perceived widespread and increasing use of algorithms in health care and lack of oversight, potentially exacerbating racial and ethnic inequities. Increasing awareness for clinicians and patients and standardized, transparent approaches for algorithm development and implementation may be needed to address racial and ethnic biases related to algorithms.

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. SETTING: Urban academic medical center with adult and pediatric hospitals, emergency departments, and ambulatory practices. METHODS: Using the E-ICP processes and infrastructure established at our institution as a foundation, we developed a suite of COVID-19-specific E-ICPs along with a process for frequent reassessment and updating. We examined the development and use of our COVID-19-specific pathways for a 6-month period (March 1-September 1, 2020), and we have described their impact using case studies. RESULTS: In total, 45 COVID-19-specific pathways were developed, pertaining to triage, diagnosis, and management of COVID-19 in diverse patient settings. Orders available in E-ICPs included those for isolation precautions, testing, treatments, admissions, and transfers. Pathways were accessed 86,400 times, with 99,081 individual orders were placed. Case studies demonstrate the impact of COVID-19 E-ICPs on stewardship of resources, testing optimization, and data reporting. CONCLUSIONS: E-ICPs provide a flexible and unified mechanism to meet the evolving demands of the COVID-19 pandemic, and they continue to be a critical tool leveraged by clinicians and hospital administrators alike for the management of COVID-19. Lessons learned may be generalizable to other urgent and nonurgent clinical conditions.

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.

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. Precision medicine needs to be delivered in the broader context of precision care to account for factors that influence outcomes for specific therapeutics. Precision care integrates a person-centered approach with precision medicine to inform decision making and care planning by taking multimorbidity, functional status, values, goals, preferences, social and societal context into account. Designing dissemination and implementation of precision medicine around precision care would improve person-centered quality and outcomes of care, target interventions to those most likely to benefit thereby improving access to new therapeutics, minimize the risk of withdrawal from the market from unanticipated harms of therapy, and advance health equity by tailoring interventions and care to meet the needs of diverse individuals and populations. Precision medicine delivered in the context of precision care would foster respectful care aligned with preferences, values, and goals, engendering trust, and providing needed information to make informed decisions. Accelerating adoption requires attention to the full continuum of translational research: developing new approaches, demonstrating their usefulness, disseminating and implementing findings, while engaging patients throughout the process. This encompasses basic science, preclinical and clinical research and implementation into practice, ultimately improving health. This article examines challenges to the adoption of precision medicine in the context of multimorbidity. Although the potential of precision medicine is enormous, proactive efforts are needed to avoid unintended consequences and foster its equitable and effective adoption.

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. In addition, bias impacts a model's development, application, and interpretation. We present a strategy to evaluate for and mitigate biases in machine learning models that potentially could create harm. We recommend analyzing for disparities between less and more socially advantaged populations across model performance metrics (eg, accuracy, positive predictive value), patient outcomes, and resource allocation and then identify root causes of the disparities (eg, biased data, interpretation) and brainstorm solutions to address the disparities. This strategy follows the lifecycle of machine learning models in health care, namely, identifying the clinical problem, model design, data collection, model training, model validation, model deployment, and monitoring after deployment. To illustrate this approach, we use a hypothetical case of a health system developing and deploying a machine learning model to predict the risk of mortality in 6 months for patients admitted to the hospital to target a hospital's delivery of palliative care services to those with the highest mortality risk. The core ethical concepts of equity and transparency guide our proposed framework to help ensure the safe and effective use of predictive algorithms in health care to help everyone achieve their best possible health.

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. The numbers of new AST prescriptions were examined in the four quarters before, the three quarters during, and the one quarter after screensavers were implemented. Additionally, an electronic survey was sent to resident physicians 1 year after the intervention to assess screensaver recall. RESULTS: Designing an aesthetically engaging graphic that could be rapidly understood was critical in the development of the GNS. The odds of receiving an AST prescription on medicine and medicine subspecialty services after the screensavers were implemented were lower for all four quarters (ie, GNS and TBS broadcast together, only TBS broadcast, only GNS broadcast, and no AST screensavers broadcast) compared to the quarter prior to implementation (odds ratio [OR] 0.85, 95% CI 0.78-0.92; OR 0.89, 95% CI 0.82-0.97; OR 0.87, 95% CI 0.80-0.95; and OR 0.81, 95% CI 0.75-0.89, respectively; P<.001 for all comparisons). There were no statistically significant decreases for other high-volume services, such as the surgical services. These declines appear to have begun prior to screensaver implementation. When surveyed about the screensaver content 1 year later, resident physicians recalled both the GNS and TBS (43/70, 61%, vs 54/70, 77%; P=.07) and those who recalled the screensaver were more likely to recall the main message of the GNS compared to the TBS (30/43, 70%, vs 1/54, 2%; P<.001). CONCLUSIONS: It is feasible to use a graphic narrative embedded in a broadcast screensaver to communicate a guideline recommendation, but further study is needed to determine the impact of graphic narratives on clinical practice.

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. During the first 11 months, 235 unique individuals used the tool; users were most commonly from the quality and equity departments. Two early use cases evaluated hypertension control and readmissions by race, identifying potential inequities. This is the first description of an interactive equity lens integrated into an institutional quality scorecard made available to healthcare system employees. Early evidence suggests the tool is used and can inform quality improvement initiatives.

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. OBJECTIVE: This study aimed to describe the unique benefits and challenges of adapting an app developed in-house to facilitate communication and remote rounding during COVID-19. METHODS: We adapted moblMD, a mobile app available on the iOS and Android platforms. In conjunction with our Hospital Incident Command System, resident advisory council, and health system innovation center, we identified critical, time-sensitive policies for app usage. A shared collaborative document was used to align app-based communication with more traditional communication channels. To minimize synchronization efforts, we particularly focused on high-yield policies, and the time of last review and the corresponding reviewer were noted for each protocol. To facilitate social distancing and remote patient rounding, the app was also populated with a searchable directory of numbers to patient bedside phones and hospital locations. We monitored anonymized user activity from February 1 to July 31, 2020. RESULTS: On its first release, 1104 clinicians downloaded moblMD during the observation period, of which 46% (n=508) of downloads occurred within 72 hours of initial release. COVID-19 policies in the app were reviewed most commonly during the first week (801 views). Users made sustained use of hospital phone dialing features, including weekly peaks of 2242 phone number dials, 1874 directory searches, and 277 patient room phone number searches through the last 2 weeks of the observation period. Furthermore, clinicians submitted 56 content- and phone number-related suggestions through moblMD. CONCLUSIONS: We rapidly developed and deployed a communication-focused mobile app early during COVID-19, which has demonstrated initial and sustained value among clinicians in communicating with in-patients and each other during social distancing. Our internal innovation benefited from our team's familiarity with institutional structures, short feedback loops, limited security and privacy implications, and a path toward sustainability provided by our innovation center. Challenges in content management were overcome through synchronization efforts and timestamping review. As COVID-19 continues to alter health care delivery, user activity metrics suggest that our solution will remain important in our efforts to continue providing safe and up-to-date clinical care.

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. The Agency for Healthcare Research and Quality's (AHRQ) Evidence-Based Practice Centers conducted systematic reviews on these topics, which were used by the Expert Panel Working Group as a basis for developing recommendations and guidance. The Expert Panel used GRADE (Grading of Recommendations, Assessment, Development and Evaluation), an internationally accepted framework, in consultation with an experienced methodology team for determining the certainty of evidence and the direction and strength of recommendations based on the evidence. Practical implementation guidance for each recommendation incorporates findings from NHLBI-led patient, caregiver, and clinician focus groups. To assist clincians in implementing these recommendations into patient care, the new recommendations have been integrated into the existing Expert Panel Report-3 (EPR-3) asthma management step diagram format.

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. OBJECTIVE: The aim of this study was to examine changes in ambulatory visit volume and type (ie, in-person vs virtual and telephone vs video visits) by patient sociodemographics during the COVID-19 pandemic at one urban academic medical center. METHODS: We compared volumes and patient sociodemographics (age, sex, race, insurance) for visits during the first 11 weeks following the COVID-19 national emergency declaration (March 15 to May 31, 2020) to visits in the corresponding weeks in 2019. Additionally, for visits during the COVID-19 study period, we examined differences in visit type (ie, in-person versus virtual, and telephone versus video visits) by sociodemographics using multivariate logistic regression. RESULTS: Total visit volumes in the COVID-19 study period comprised 51.4% of the corresponding weeks in 2019 (n=80,081 vs n=155,884 visits). Although patient sociodemographics between the COVID-19 study period in 2020 and the corresponding weeks in 2019 were similar, 60.5% (n=48,475) of the visits were virtual, compared to 0% in 2019. Of the virtual visits, 61.2% (n=29,661) were video based, and 38.8% (n=18,814) were telephone based. In the COVID-19 study period, virtual (vs in-person) visits were more likely among patients with race categorized as other (vs White) and patients with Medicare (vs commercial) insurance and less likely for men, patients aged 0-17 years, 65-74 years, or ≥75 years (compared to patients aged 18-45 years), and patients with Medicaid insurance or insurance categorized as other. Among virtual visits, compared to telephone visits, video visits were more likely to be adopted by patients aged 0-17 years (vs 18-45 years), but less likely for all other age groups, men, Black (vs White) patients, and patients with Medicare or Medicaid (vs commercial) insurance. CONCLUSIONS: Virtual visits comprised the majority of ambulatory visits during the COVID-19 study period, of which a majority were by video. Sociodemographic differences existed in the use of virtual versus in-person and video versus telephone visits. To ensure equitable care delivery, we present five policy recommendations to inform the further development of virtual visit programs and their reimbursement.

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. The development of the EHR intervention was informed by the Five Rights model for clinical decision support and human-computer interaction design heuristics. The researchers used observations and time measurements to describe the impact of the EPC report on pathway development and examined provider adoption using counts of pathway views. RESULTS: Two main themes emerged: (1) discrepancies between the EPC report and existing guidelines prompted critical discussions about available treatments, and (2) lack of guideline and pathway syntheses in the EPC report necessitated a rapid literature review. Pathway development required 340 hours: 205 for the rapid literature review, 63 for pathway development and EHR intervention design, and 5 for technical implementation of the intervention. Pathways were viewed 1,069 times through the cloud-based platform and 47 times through a hyperlink embedded in key EHR ordering screens. CONCLUSION: Pathways can be an approach for disseminating AHRQ EPC report findings within health care systems; however, reports should include guideline and pathway syntheses to meet their full potential. Embedding hyperlinks to pathway content within the EHR may be a viable and low-effort solution for promoting awareness of evidence-based resources.

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. PATIENTS: All non-ICU admissions; algorithm derivation July 2011 to June 2014 (n = 162,212); algorithm validation October to December 2015 (n = 10,448); silent versus alert comparison January 2016 to February 2017 (silent n = 22,280; alert n = 32,184). INTERVENTIONS: A random-forest classifier, derived and validated using electronic health record data, was deployed both silently and later with an alert to notify clinical teams of sepsis prediction. MEASUREMENT AND MAIN RESULT: Patients identified for training the algorithm were required to have International Classification of Diseases, 9th Edition codes for severe sepsis or septic shock and a positive blood culture during their hospital encounter with either a lactate greater than 2.2 mmol/L or a systolic blood pressure less than 90 mm Hg. The algorithm demonstrated a sensitivity of 26% and specificity of 98%, with a positive predictive value of 29% and positive likelihood ratio of 13. The alert resulted in a small statistically significant increase in lactate testing and IV fluid administration. There was no significant difference in mortality, discharge disposition, or transfer to ICU, although there was a reduction in time-to-ICU transfer. CONCLUSIONS: Our machine learning algorithm can predict, with low sensitivity but high specificity, the impending occurrence of severe sepsis and septic shock. Algorithm-generated predictive alerts modestly impacted clinical measures. Next steps include describing clinical perception of this tool and optimizing algorithm design and delivery.