Assessing Medication CDS Usability: Pilot Results from 10 Outpatient Clinics.

This study aimed to develop a human factors assessment for medication-related clinical decision support (CDS) based on a previously validated tool that assessed the integration of human factors principles in CDS, the instrument for evaluating human factors principles in medication-related decision support alerts (I-MeDeSA), and pilot it with 10 outpatient clinics across the United States.The human factors assessment was developed based on past validations of I-MeDeSA. Examples included changing the wording of questions and reformatting answer choices to check-box options, allowing for multiple answer choices.

Adoption of artificial intelligence in healthcare: survey of health system priorities, successes, and challenges.

Importance: The US healthcare system faces significant challenges, including clinician burnout, operational inefficiencies, and concerns about patient safety. Artificial intelligence (AI), particularly generative AI, has the potential to address these challenges, but its adoption, effectiveness, and barriers to implementation are not well understood.

Objective: To evaluate the current state of AI adoption in US healthcare systems, assess successes and barriers to implementation during the early generative AI era.

The problem with the existing reporting standards for adverse event and medical error research.

The Enhancing the Quality and Transparency of Health Research (EQUATOR) Network indexes over 600 reporting guidelines designed to improve the reproducibility of manuscripts across medical fields and study designs. Although several such reporting guidelines touch on adverse events that may occur in the context of a study, there is a large body of research whose primary focus is on adverse events, near-misses and medical errors that do not currently have a dedicated reporting guideline to help set reporting standards and facilitate comparisons across studies. As part of the process prescribed by EQUATOR for developing such a reporting guideline, we performed a needs assessment, evaluating whether existing standards address key features of a proposed reporting guideline in development, entitled (SESAME).

Automating detection of diagnostic error of infectious diseases using machine learning.

Diagnostic error, a cause of substantial morbidity and mortality, is largely discovered and evaluated through self-report and manual review, which is costly and not suitable to real-time intervention. Opportunities exist to leverage electronic health record data for automated detection of potential misdiagnosis, executed at scale and generalized across diseases. We propose a novel automated approach to identifying diagnostic divergence considering both diagnosis and risk of mortality.

Building the Future of Infectious Diseases: A Call to Action for Quality Improvement Research and Measurement.

Quality is central to value-based care, and measurement is essential for assessing performance and understanding improvement over time. Both value-based care and methods for quality measurement are evolving. Infectious diseases (ID) has been less engaged than other specialties in quality measure development, and ID providers must seize the opportunity to engage with quality measure development and research.

A Linguistic Analysis Examining the Impact of COVID-19 on Pneumonia Diagnosis and Disease Models.

Written clinical language embodies and reflects the clinician's mental models of disease. Prior to the COVID-19 pandemic, pneumonia was shifting away from concern for healthcare-associated pneumonia and toward recognition of heterogeneity of pathogens and host response. How these models are reflected in clinical language or whether they were impacted by the pandemic has not been studied.

How Safe are Outpatient Electronic Health Records? An Evaluation of Medication-Related Decision Support using the Ambulatory Electronic Health Record Evaluation Tool.

Background: The purpose of the Ambulatory Electronic Health Record (EHR) Evaluation Tool is to provide outpatient clinics with an assessment that they can use to measure the ability of the EHR system to detect and prevent common prescriber errors. The tool consists of a medication safety test and a medication reconciliation module.

Objectives: The goal of this study was to perform a broad evaluation of outpatient medication-related decision support using the Ambulatory EHR Evaluation Tool.

Improving Sepsis Outcomes in the Era of Pay-for-Performance and Electronic Quality Measures: A Joint IDSA/ACEP/PIDS/SHEA/SHM/SIDP Position Paper.

The Centers for Medicare & Medicaid Services (CMS) introduced the Severe Sepsis/Septic Shock Management Bundle (SEP-1) as a pay-for-reporting measure in 2015 and is now planning to make it a pay-for-performance measure by incorporating it into the Hospital Value-Based Purchasing Program. This joint IDSA/ACEP/PIDS/SHEA/SHM/SIPD position paper highlights concerns with this change. Multiple studies indicate that SEP-1 implementation was associated with increased broad-spectrum antibiotic use, lactate measurements, and aggressive fluid resuscitation for patients with suspected sepsis but not with decreased mortality rates.

Healthcare-associated infections and conditions in the era of digital measurement.

As the third edition of the is released with the latest recommendations for the prevention and management of healthcare-associated infections (HAIs), a new approach to reporting HAIs is just beginning to unfold. This next generation of HAI reporting will be fully electronic and based largely on existing data in electronic health record (EHR) systems and other electronic data sources. It will be a significant change in how hospitals report HAIs and how the Centers for Disease Control and Prevention (CDC) and other agencies receive this information.

How can artificial intelligence decrease cognitive and work burden for front line practitioners?

Artificial intelligence (AI) has tremendous potential to improve the cognitive and work burden of clinicians across a range of clinical activities, which could lead to reduced burnout and better clinical care. The recent explosion of generative AI nicely illustrates this potential. Developers and organizations deploying AI have a responsibility to ensure AI is designed and implemented with end-user input, has mechanisms to identify and potentially reduce bias, and that the impact on cognitive and work burden is measured, monitored, and improved.

Risk Factors for Severe COVID-19 Among Health Care Workers.

Objective: Evaluate potential risk factors for severe coronavirus disease 2019 (COVID-19) among health care workers (HCWs) at the University of Virginia Medical Center in Charlottesville, Virginia.

Methods: We conducted a retrospective manual chart review of data from HCWs who were diagnosed with COVID-19 from March 2020 to March 2021. Using data from patient medical histories, we ascertained risk factors for COVID-19-related emergency department encounter, hospitalization, or death.

Examining medication ordering errors using AHRQ network of patient safety databases.

Background: Studies examining the effects of computerized order entry (CPOE) on medication ordering errors demonstrate that CPOE does not consistently prevent these errors as intended. We used the Agency for Healthcare Research and Quality (AHRQ) Network of Patient Safety Databases (NPSD) to investigate the frequency and degree of harm of reported events that occurred at the ordering stage, characterized by error type.

Materials And Methods: This was a retrospective observational study of safety events reported by healthcare systems in participating patient safety organizations from 6/2010 through 12/2020.

Development and evaluation of an interoperable natural language processing system for identifying pneumonia across clinical settings of care and institutions.

Objective: To evaluate the feasibility, accuracy, and interoperability of a natural language processing (NLP) system that extracts diagnostic assertions of pneumonia in different clinical notes and institutions.

Materials And Methods: A rule-based NLP system was designed to identify assertions of pneumonia in 3 types of clinical notes from electronic health records (EHRs): emergency department notes, radiology reports, and discharge summaries. The lexicon and classification logic were tailored for each note type.

Bending the patient safety curve: how much can AI help?

This paper reviews the current state of patient safety and the application of artificial intelligence (AI) techniques to patient safety. This paper defines patient safety broadly, not just inpatient care but across the continuum of care, including diagnostic errors, misdiagnosis, adverse events, injuries, and measurement issues. It outlines the major current uses of AI in patient safety and the relative adoption of these techniques in hospitals and health systems.

A flexible framework for visualizing and exploring patient misdiagnosis over time.

Diagnosis is a complex and ambiguous process and yet, it is the critical hinge point for all subsequent clinical reasoning and decision-making. Tracking the quality of the patient diagnostic process has the potential to provide valuable insights in improving the diagnostic accuracy and to reduce downstream errors but needs to be informative, timely, and efficient at scale. However, due to the rate at which healthcare data are captured on a daily basis, manually reviewing the diagnostic history of each patient would be a severely taxing process without efficient data reduction and representation.

Patient Perceptions of Hospital Experiences: Implications for Innovations in Patient Safety.

Objective: The purpose of this study was to qualitatively examine safety experiences of hospitalized patients and families.

Methods: We conducted 5 focus groups at 2 sites with patients and family members of patients who had been hospitalized at least once within the preceding 2 years. Using a semistructured focus group script, participants were asked to describe hospital experiences, including any safety risks or problems, and to discuss trust in the hospital care team or members of the care team.

Assessing hospital electronic health record vendor performance across publicly reported quality measures.

Objective: Little is known regarding variation among electronic health record (EHR) vendors in quality performance. This issue is compounded by selection effects in which high-quality hospitals coalesce to a subset of market leading vendors. We measured hospital performance, stratified by EHR vendor, across 4 quality metrics.

Engaging Patients in the Use of Real-Time Electronic Clinical Data to Improve the Safety and Reliability of Their Own Care.

Objectives: There is considerable evidence that providing patients with access to their health information is beneficial, but there is limited evidence regarding the effect of providing real-time patient safety-related information on health outcomes. The aim of this study was to evaluate the association between use of an electronic patient safety dashboard (Safety Advisor) and health outcomes.

Methods: The Safety Advisor was implemented in 6 adult medicine units at one hospital in the United States.

The Development and Piloting of the Ambulatory Electronic Health Record Evaluation Tool: Lessons Learned.

Background: Substantial research has been performed about the impact of computerized physician order entry on medication safety in the inpatient setting; however, relatively little has been done in ambulatory care, where most medications are prescribed.

Objective: To outline the development and piloting process of the Ambulatory Electronic Health Record (EHR) Evaluation Tool and to report the quantitative and qualitative results from the pilot.

Methods: The Ambulatory EHR Evaluation Tool closely mirrors the inpatient version of the tool, which is administered by The Leapfrog Group.