Development of an Automated Triage System for Longstanding Dizzy Patients Using Artificial Intelligence.

Objective: To report the first steps of a project to automate and optimize scheduling of multidisciplinary consultations for patients with longstanding dizziness utilizing artificial intelligence.

Study Design: Retrospective case review.

Setting: Quaternary referral center.

Retrospective validation study of a machine learning-based software for empirical and organism-targeted antibiotic therapy selection.

Unlabelled: Errors in antibiotic prescriptions are frequent, often resulting from the inadequate coverage of the infection-causative microorganism. The efficacy of iAST, a machine-learning-based software offering empirical and organism-targeted antibiotic recommendations, was assessed. The study was conducted in a 12-hospital Spanish institution.

An operational guide to translational clinical machine learning in academic medical centers.

Few published data science tools are ever translated from academia to real-world clinical settings for which they were intended. One dimension of this problem is the software engineering task of turning published academic projects into tools that are usable at the bedside. Given the complexity of the data ecosystem in large health systems, this task often represents a significant barrier to the real-world deployment of data science tools for prospective piloting and evaluation.

AI model for predicting adult cochlear implant candidacy using routine behavioral audiometry.

Objective: To describe an AI model to facilitate adult cochlear implant candidacy prediction based on basic demographical data and standard behavioral audiometry.

Methods: A machine-learning approach using retrospective demographic and audiometric data to predict candidacy CNC word scores and AzBio sentence in quiet scores was performed at a tertiary academic center. Data for the model were derived from adults completing cochlear implant candidacy testing between January 2011 and March 2023.

Addressing positive multi-cancer early detection tests in head and neck Surgery: Experience with head and neck work up for high-risk referrals.

Objectives: Blood-based multi-cancer early detection (MCED) tests are now commercially available. However, there are currently no consensus guidelines available for head and neck cancer (HNC) providers to direct work up or surveillance for patients with a positive MCED test. We seek to describe cases of patients with positive MCED tests suggesting HNC and provide insights for their evaluation.

Non-linear machine learning models incorporating SNPs and PRS improve polygenic prediction in diverse human populations.

Polygenic risk scores (PRS) are commonly used to quantify the inherited susceptibility for a trait, yet they fail to account for non-linear and interaction effects between single nucleotide polymorphisms (SNPs). We address this via a machine learning approach, validated in nine complex phenotypes in a multi-ancestry population. We use an ensemble method of SNP selection followed by gradient boosted trees (XGBoost) to allow for non-linearities and interaction effects.

Blockchain for Electronic Vaccine Certificates: More Cons Than Pros?

Electronic vaccine certificates (EVC) for COVID-19 vaccination are likely to become widespread. Blockchain (BC) is an electronic immutable distributed ledger and is one of the more common proposed EVC platform options. However, the principles of blockchain are not widely understood by public health and medical professionals.

Assessing the Generalizability of a Clinical Machine Learning Model Across Multiple Emergency Departments.

Objective: To assess the generalizability of a clinical machine learning algorithm across multiple emergency departments (EDs).

Patients And Methods: We obtained data on all ED visits at our health care system's largest ED from May 5, 2018, to December 31, 2019. We also obtained data from 3 satellite EDs and 1 distant-hub ED from May 1, 2018, to December 31, 2018.

Getting to 100%: Research Priorities and Unanswered Questions to Inform the US Debate on Universal Health Insurance Coverage.

A majority of Americans favor universal health insurance, but there is uncertainty over how best to achieve this goal. Whatever the insurance design that is implemented, additional details that must be considered include breadth of services covered, restrictions and limits on volumes of services, cost-sharing for individuals, and pricing. In the hopes that research can inform this ongoing debate, we review evidence supporting different models for achieving universal coverage in the US and identify areas where additional research and stakeholder input is needed.

Forecasting the COVID-19 Pandemic: Lessons learned and future directions.

I.The Coronavirus Disease 2019 (COVID-19) has demonstrated that accurate forecasts of infection and mortality rates are essential for informing healthcare resource allocation, designing countermeasures, implementing public health policies, and increasing public awareness. However, there exist a multitude of modeling methodologies, and their relative performances in accurately forecasting pandemic dynamics are not currently comprehensively understood.

Practical development and operationalization of a 12-hour hospital census prediction algorithm.

Hospital census prediction has well-described implications for efficient hospital resource utilization, and recent issues with hospital crowding due to CoVID-19 have emphasized the importance of this task. Our team has been leading an institutional effort to develop machine-learning models that can predict hospital census 12 hours into the future. We describe our efforts at developing accurate empirical models for this task.

Public health impact of delaying second dose of BNT162b2 or mRNA-1273 covid-19 vaccine: simulation agent based modeling study.

Objective: To estimate population health outcomes with delayed second dose versus standard schedule of SARS-CoV-2 mRNA vaccination.

Design: Simulation agent based modeling study.

Setting: Simulated population based on real world US county.

Using nursing notes to improve clinical outcome prediction in intensive care patients: A retrospective cohort study.

Objective: Electronic health record documentation by intensive care unit (ICU) clinicians may predict patient outcomes. However, it is unclear whether physician and nursing notes differ in their ability to predict short-term ICU prognosis. We aimed to investigate and compare the ability of physician and nursing notes, written in the first 48 hours of admission, to predict ICU length of stay and mortality using 3 analytical methods.

Using machine learning to improve the accuracy of patient deterioration predictions: Mayo Clinic Early Warning Score (MC-EWS).

Objective: We aimed to develop a model for accurate prediction of general care inpatient deterioration.

Materials And Methods: Training and internal validation datasets were built using 2-year data from a quaternary hospital in the Midwest. Model training used gradient boosting and feature engineering (clinically relevant interactions, time-series information) to predict general care inpatient deterioration (resuscitation call, intensive care unit transfer, or rapid response team call) in 24 hours.

Emotion Regulation Strategies, Workload Conditions, and Burnout in Healthcare Residents.

Background: Burnout syndrome is very prevalent among healthcare residents. Initiatives addressing workload conditions have had limited impact on burnout. The present study aims to explore the contribution of two emotion regulation strategies, namely emotion suppression and cognitive reevaluation, to residents' burnout, while accounting for workload factors.

Implementation of Artificial Intelligence-Based Clinical Decision Support to Reduce Hospital Readmissions at a Regional Hospital.

Background: Hospital readmissions are a key quality metric, which has been tied to reimbursement. One strategy to reduce readmissions is to direct resources to patients at the highest risk of readmission. This strategy necessitates a robust predictive model coupled with effective, patient-centered interventions.

What's in a name? A comparison of attitudes towards artificial intelligence (AI) versus augmented human intelligence (AHI).

Background: "Artificial intelligence" (AI) is often referred to as "augmented human intelligence" (AHI). The latter term implies that computers support-rather than replace-human decision-making. It is unclear whether the terminology used affects attitudes and perceptions in practice.

The right parasternal window: when Doppler-beam alignment may be life-saving in patients with aortic valve stenosis.

: The need for multiple transducer positions, especially from right parasternal windows, is consistently mentioned in the recommendations for the accurate measurement of peak velocities across a stenotic aortic valve, but yet poorly adopted.We performed a subanalysis of the largest prospective series on the right parasternal acoustic windows in patients with aortic stenosis (330 consecutive) to calculate the degree of misalignment and estimate the potential outcome implication of this often-forgotten approach.The right parasternal view was highly feasible with an average estimated misalignment from the apical view of 14 ± 16 degree; in 10 cases, an estimated misalignment >40 degree.

A lesson in implementation: A pre-post study of providers' experience with artificial intelligence-based clinical decision support.

Background: To explore attitudes about artificial intelligence (AI) among staff who utilized AI-based clinical decision support (CDS).

Methods: A survey was designed to assess staff attitudes about AI-based CDS tools. The survey was anonymously and voluntarily completed by clinical staff in three primary care outpatient clinics before and after implementation of an AI-based CDS system aimed to improve glycemic control in patients with diabetes as part of a quality improvement project.

Notice to comply: A systematic review of clinician compliance with guidelines surrounding acute hospital-based infection management.

Purpose: To identify and characterize studies evaluating clinician compliance with infection-related guidelines, and to explore trends in guideline design and implementation strategies.

Data Sources: PubMed database, April 2017. Followed the PRISMA Statement for systematic reviews.

The fifth vital sign? Nurse worry predicts inpatient deterioration within 24 hours.

Introduction: Identification of hospitalized patients with suddenly unfavorable clinical course remains challenging. Models using objective data elements from the electronic health record may miss important sources of information available to nurses.

Methods: We recorded nurses' perception of patient potential for deterioration in 2 medical and 2 surgical adult hospital units using a 5-point score at the start of the shift (the Worry Factor [WF]), and any time a change or an increase was noted by the nurse.

The Component Structure of the Dizziness Handicap Inventory (DHI): A Reappraisal.

Objective: The Dizziness Handicap Inventory (DHI) is a 25-item self-report questionnaire developed to measure the disabling and handicapping impact of dizziness. The present investigation was conducted in an effort to re-assess the factor structure of the DHI.

Study Design: Retrospective study.

Development of data integration and visualization tools for the Department of Radiology to display operational and strategic metrics.

Visualizing process metrics can help identify targets for improvement initiatives. Dashboards and scorecards are tools to visualize important metrics in an easily interpretable manner. We describe the development of two visualization systems: a dashboard to provide real-time situational awareness to frontline coordinators, and a scorecard to display aggregate monthly performance metrics for strategic process improvement efforts.

Identifying patients at risk of inhospital death or hospice transfer for early goals of care discussions in a US referral center: the HELPS model derived from retrospective data.

Objective: Create a score to identify patients at risk of death or hospice placement who may benefit from goals of care discussion earlier in the hospitalisation.

Design: Retrospective cohort study to develop a risk index using multivariable logistic regression.

Setting: Two tertiary care hospitals in Southeastern Minnesota.