Accuracy of Noninvasive Blood Pressure Monitoring in Critically Ill Adults.

Background: Blood pressure (BP) is routinely invasively monitored by an arterial catheter in the intensive care unit (ICU). However, the available data comparing the accuracy of noninvasive methods to arterial catheters for measuring BP in the ICU are limited by small numbers and diverse methodologies. Purpose: To determine agreement between invasive arterial blood pressure monitoring (IABP) and noninvasive blood pressure (NIBP) in critically ill patients. Methods: This was a single center, observational study of critical ill adults in a tertiary care facility evaluating agreement (≤10% difference) between simultaneously measured IABP and NIBP. We measured clinical features at time of BP measurement inclusive of patient demographics, laboratory data, severity of illness, specific interventions (mechanical ventilation and dialysis), and vasopressor dose to identify particular clinical scenarios in which measurement agreement is more or less likely. Results: Of the 1852 critically ill adults with simultaneous IABP and NIBP readings, there was a median difference of 6 mm Hg in mean arterial pressure (MAP), interquartile range (1-12), P < .01. A logistic regression analysis identified 5 independent predictors of measurement discrepancy: increasing doses of norepinephrine (adjusted odds ratio [aOR] 1.10 [95% confidence interval, CI 1.08-1.12] P = .03 for every change in 5 µg/min), lower MAP value (aOR 0.98 [0.98-0.99] P < .01 for every change in 1 mm Hg), higher body mass index (aOR 1.04 [1.01-1.09] P = .01 for an increase in 1), increased patient age (aOR 1.31 [1.30-1.37] P < .01 for every 10 years), and radial arterial line location (aOR 1.74 [1.16-2.47] P = .04). Conclusions: There was broad agreement between IABP and NIBP in critically ill patients over a range of BPs and severity of illness. Several variables are associated with measurement discrepancy; however, their predictive capacity is modest. This may guide future study into which patients may specifically benefit from an arterial catheter.

A text mining approach to categorize patient safety event reports by medication error type.

Patient safety reporting systems give healthcare provider staff the ability to report medication related safety events and errors; however, many of these reports go unanalyzed and safety hazards go undetected. The objective of this study is to examine whether natural language processing can be used to better categorize medication related patient safety event reports. 3,861 medication related patient safety event reports that were previously annotated using a consolidated medication error taxonomy were used to develop three models using the following algorithms: (1) logistic regression, (2) elastic net, and (3) XGBoost. After development, models were tested, and model performance was analyzed. We found the XGBoost model performed best across all medication error categories. 'Wrong Drug', 'Wrong Dosage Form or Technique or Route', and 'Improper Dose/Dose Omission' categories performed best across the three models. In addition, we identified five words most closely associated with each medication error category and which medication error categories were most likely to co-occur. Machine learning techniques offer a semi-automated method for identifying specific medication error types from the free text of patient safety event reports. These algorithms have the potential to improve the categorization of medication related patient safety event reports which may lead to better identification of important medication safety patterns and trends.

Variations in Physician Telemedicine Provision.

This cross-sectional study assesses variation in the provision of telemedicine services among primary care physicians and quantifies the extent to which this variation may be explained by the individual physician vs temporal, patient, or visit factors.

The impact of COVID-19 on primary care accessibility and the role of telehealth for patients with chronic conditions.

Objectives: The objective of this study is to quantify how long patients took to complete their rescheduled primary care appointment pre-pandemic (2019) and during an initial pandemic period (2020). In doing so, the study evaluates telehealth's role in helping primary care patients - particularly in patients with chronic conditions - withstand COVID's significant disruption in care. Methods: Cancelled and completed primary care appointments for adult patients were extracted from the beginning of the pandemic (March 1 to July 31, 2020) and a similar period pre-pandemic (March 1 to July 31, 2019). Days to the subsequent completed visit after cancellation (through June 30, 2021) and appointment modality (in-person, phone, video) were examined. Statistical testing was done to determine statistical significance, and a linear regression was run to control for effects of other study variables. Results: Pre-pandemic patients with chronic conditions needed 52.3 days on average to reschedule their cancelled in-person appointment. During the early pandemic period, chronic condition patients who saw their provider in-person took on average 78.8 days. During the same pre-pandemic period, patients with chronic conditions had their average wait time decrease to 51.5 days when rescheduling via telehealth. These differences were similar for patients without chronic conditions. Conclusions: This analysis shows that telehealth created return to care timelines comparable to the pre-pandemic period which is especially important for patients with chronic conditions. Public interest summary: Telehealth visits (i.e., talking with a physician via phone or video call) help patients continue to receive the medical care they need - especially during disruptive periods such as the COVID pandemic. Access to telehealth is the strongest predictor in determining how soon a patient will complete their reschedule primary care appointment. Because telehealth is so important, health care providers and systems need to continue to offer patients the ability to talk with their physician via phone or video call.

Identifying Electronic Health Record Contributions to Diagnostic Error in Ambulatory Settings Through Legal Claims Analysis.

This qualitative study analyzes closed legal claims data to determine whether problems with electronic health records are associated with diagnostic errors, in which part of the diagnostic process errors occur, and the specific types of errors that occur.

Electronic Health Record Use Issues and Diagnostic Error: A Scoping Review and Framework.

BACKGROUND: Diagnostic errors are a major source of patient harm, most of which are caused by cognitive errors and biases. Despite research showing the relationship between software systems and cognitive processes, the impact of the electronic health record (EHR) on diagnostic error remains unknown. METHODS: We conducted a scoping review of the scientific literature to (1) survey the association between aspects of the EHR and diagnostic error, and (2) through a human-systems integration lens, identify the types of EHR issues and their impact on the stages of the diagnostic process. RESULTS: We analyzed 11 research articles for the relationship between EHR use and diagnostic error. These articles highlight specific technical, usability, and workflow issues with the EHR that pose risks for diagnostic error at every stage of the diagnostic process. DISCUSSION: Although technical problems such as EHR interoperability and data integrity pose critical issues for the diagnostic process, usability and workflow issues such as poor display design, and inability to track test results also hamper clinicians' ability to track, process, and act in the diagnostic process. Current research methods have limited coverage over clinical settings, are not standardized, and rarely include measures of patient harm. CONCLUSIONS: The available evidence shows that EHRs pose risks for diagnostic error throughout the diagnostic process, with most issues involving their incompatibility with providers' cognitive processing. A structured and systematic model of collecting and reporting on these errors is needed to understand how the EHR shapes the diagnostic process and improve diagnostic accuracy.

Using Community Detection Techniques to Identify Themes in COVID-19-Related Patient Safety Event Reports.

OBJECTIVES: The COVID-19 pandemic has transformed how healthcare is delivered to patients. As the pandemic progresses and healthcare systems continue to adapt, it is important to understand how these changes in care have changed patient care. This study aims to use community detection techniques to identify and facilitate analysis of themes in patient safety event (PSE) reports to better understand COVID-19 pandemic's impact on patient safety. With this approach, we also seek to understand how community detection techniques can be used to better identify themes and extract information from PSE reports. METHODS: We used community detection techniques to group 2082 PSE reports from January 1, 2020, to January 31, 2021, that mentioned COVID-19 into 65 communities. We then grouped these communities into 8 clinically relevant themes for analysis. RESULTS: We found the COVID-19 pandemic is associated with the following clinically relevant themes: (1) errors due to new and unknown COVID-19 protocols/workflows; (2) COVID-19 patients developing pressure ulcers; (3) unsuccessful/incomplete COVID-19 testing; (4) inadequate isolation of COVID-19 patients; (5) inappropriate/inadequate care for COVID-19 patients; (6) COVID-19 patient falls; (7) delays or errors communicating COVID-19 test results; and (8) COVID-19 patients developing venous thromboembolism. CONCLUSIONS: Our study begins the long process of understanding new challenges created by the pandemic and highlights how machine learning methods can be used to understand these and similar challenges. Using community detection techniques to analyze PSE reports and identify themes within them can help give healthcare systems the necessary information to improve patient safety and the quality of care they deliver.

Evaluation of a Text Message-Based COVID-19 Vaccine Outreach Program Among Older Patients: Cross-sectional Study.

BACKGROUND: COVID-19 vaccines are vital tools in the defense against infection and serious disease due to SARS-CoV-2. There are many challenges to implementing mass vaccination campaigns for large, diverse populations from crafting vaccine promotion messages to reaching individuals in a timely and effective manner. During this unprecedented period, with COVID-19 mass vaccination campaigns essential for protecting vulnerable patient populations and attaining herd immunity, health care systems were faced with the dual challenges of vaccine outreach and distribution. OBJECTIVE: The aim of this cross-sectional study was to assess the effectiveness of a COVID-19 vaccine text outreach approach for patients aged 65 years and older. Our goal was to determine whether this approach was successful in scheduling patients for COVID-19 vaccine appointments. METHODS: We developed SMS text messages using the Tavoca platform. These messages informed patients of their vaccine eligibility and allowed them to indicate their interest in scheduling an appointment via a specific method (email or phone) or indicate their lack of interest in the vaccine. We tracked the status of these messages and how patients responded. Messages were sent to patients aged 65 years and older (N=30,826) at a nonprofit health care system in Washington, DC. Data were collected and examined from January 14 to May 10, 2021. Data were analyzed using multivariate multinomial and binary logistic regression models in SAS (version 9.4; SAS Institute Inc). RESULTS: Approximately 57% of text messages were delivered to patients, but many messages received no response from patients (40%). Additionally, 42.1% (12,978/30,826) of messages were not delivered. Of the patients who expressed interest in the vaccine (2938/30,826, 9.5%), Black or African American patients preferred a phone call rather than an email for scheduling their appointment (odds ratio [OR] 1.69, 95% CI 1.29-2.21) compared to White patients. Patients aged 70-74 years were more likely to schedule an appointment (OR 1.38, 95% CI 1.01-1.89) than those aged 65-69 years, and Black or African American patients were more likely to schedule an appointment (OR 2.90, 95% CI 1.72-4.91) than White patients. CONCLUSIONS: This study provides insights into some advantages and challenges of using a text messaging vaccine outreach for patients aged 65 years and older. Lessons learned from this vaccine campaign underscore the importance of using multiple outreach methods and sharing of patient vaccination status between health systems, along with a patient-centered approach to address vaccine hesitancy and access issues.

Rapid development of visualization dashboards to enhance situation awareness of COVID-19 telehealth initiatives at a multihospital healthcare system.

The COVID-19 pandemic has led to the rapid expansion of telehealth services as healthcare organizations aim to mitigate community transmission while providing safe patient care. As technology adoption rapidly increases, operational telehealth teams must maintain awareness of critical information, such as patient volumes and wait times, patient and provider experience, and telehealth platform performance. Using a model of situation awareness as a conceptual foundation and a user-centered design approach we describe our process for rapidly developing and disseminating dashboard visualizations to support telehealth operations. We used a 5-step process to gain domain knowledge, identify user needs, identify data sources, design and develop visualizations, and iteratively refine these visualizations. Through this process we identified 3 distinct stakeholder groups and designed and developed visualization dashboards to meet their needs. Feedback from users demonstrated the dashboard's support situation awareness and informed important operational decisions. Lessons learned are shared to provide other organizations with insights from our process.

Risky Business: Predicting patient mortality at a glance.

Clinicians are constantly forecasting patient trajectories to make critical point of care decisions intended to influence clinical outcomes. Little is known, however, about how providers interpret mortality risk against validated scoring systems. This research aims to understand how providers forecast mortality specifically for that of patients with sepsis. Defined as life-threatening organ dysfunction caused by a dysregulated host response to infection, sepsis is commonly hard to diagnose, progresses rapidly, and lacks a "gold standard" test. Participants were nurses and doctors from the general medical and surgical floors of six different hospitals. Each was presented with ten different patient cases, categorized into low and high severity sepsis, and were asked about care decisions, along with estimations of mortality risk. The resulting data provides a unique look into the differences of risk forecasting between profession and patient severity.