Outcomes and Complications of Mitral and Tricuspid Transcatheter Edge-to-edge Repair.

In the realm of innovative medical procedures, TEER (transcatheter edge-to-edge repair) has emerged as a promising field, showcasing significant growth and advancements. Mitral TEER has been performed for the last two decades; in contrast, tricuspid TEER is newer, with long-term outcomes pending. This article aims to provide a comprehensive review of the current literature, with a primary focus on outcomes and potential complications associated with both procedures.

Predictors and implications of renal injury after CD19 chimeric antigen receptor T-cell therapy.

Chimeric Antigen Receptor (CAR) T cells targeting CD19 induce durable remissions in patients with relapsed or refractory non-Hodgkin lymphoma (NHL), but many patients experience treatmentrelated toxicity. Cytokine release syndrome and immune effector cell-associated neurologic syndrome are extensively characterized. However, limited data exist on the burden, predictors, and implications of acute kidney injury (AKI) after CAR T cell therapy.

A scoping review of patient safety checklists in dentistry.

Objectives: Not much is known about safety checklists use in dentistry. We aim to examine, assess, and provide a comprehensive understanding of the current knowledge concerning the use of checklists to improve patient safety in dentistry.

Methods: We conducted a comprehensive literature search using Medline and Embase for studies that use or describe the development of dental patient safety checklists.

Using precision periodontal health care chart (PPHCC) to improve periodontal health.

Objectives: Developing a Precision Periodontal Health Care Chart (PPHCC) in the electronic dental record (EDR) system and evaluating its clinical usability and effects on clinical outcomes.

Materials And Methods: A survey with ten questions based on the System Usability Scale (SUS) and six questions about assessing clinical impact was used to evaluate the satisfaction of periodontitis patients and care providers with PPHCC before and after non-surgical periodontal therapy (NSPT). The clinical outcomes, including probing depth (PD), interdental clinical attachment loss (CAL), and bleeding on probing (BOP), in patients who used PPHCC (PC) were compared to those in patients without using PPHCC (control).

The Nature of Adverse Events in Dentistry.

Objectives: Learning from clinical data on the subject of safety with regards to patient care in dentistry is still largely in its infancy. Current evidence does not provide epidemiological estimates on adverse events (AEs) associated with dental care. The goal of the dental practice study was to quantify and describe the nature and severity of harm experienced in association with dental care, and to assess for disparities in the prevalence of AEs.

The burden of diagnostic error in dentistry: A study on periodontal disease misclassification.

Background: Periodontal disease constitutes a widely prevalent category of non-communicable diseases and ranks among the top 10 causes of disability worldwide. Little however is known about diagnostic errors in dentistry. In this work, by retrospectively deploying an electronic health record (EHR)-based trigger tool, followed by gold standard manual review, we provide epidemiological estimates on the rate of diagnostic misclassification in dentistry through a periodontal use case.

Systematically assessing the quality of dental electronic health record data for an investigation into oral health care disparities.

Objectives: This work describes the process by which the quality of electronic health care data for a public health study was determined. The objectives were to adapt, develop, and implement data quality assessments (DQAs) based on the National Institutes of Health Pragmatic Trials Collaboratory (NIHPTC) data quality framework within the three domains of completeness, accuracy, and consistency, for an investigation into oral health care disparities of a preventive care program.

Methods: Electronic health record data for eligible children in a dental accountable care organization of 30 offices, in Oregon, were extracted iteratively from January 1, 2014, through March 31, 2022.

Learning from data in dentistry: Summary of the third annual OpenWide conference.

The overarching goal of the third scientific oral health symposium was to introduce the concept of a learning health system to the dental community and to identify and discuss cutting-edge research and strategies using data for improving the quality of dental care and patient safety. Conference participants included clinically active dentists, dental researchers, quality improvement experts, informaticians, insurers, EHR vendors/developers, and members of dental professional organizations and dental service organizations. This report summarizes the main outputs of the third annual OpenWide conference held in Houston, Texas, on October 12, 2022, as an affiliated meeting of the American Dental Association (ADA) 2022 annual conference.

Improvements in appropriate placement of dental sealants after implementation of a clinical decision support system.

Background: Dental sealants are effective for the prevention of caries in children at elevated risk levels, and increasing the proportion of children and adolescents who have dental sealants on 1 or more molars is a Healthy People 2030 objective. Electronic health record (EHR)-based clinical decision support systems (CDSSs) have the ability to improve patient care. A dental quality measure related to dental sealant placement for children at elevated risk of caries was targeted for improvement using a CDSS.

Evaluating and improving the usability of a mHealth platform to assess postoperative dental pain.

Objectives: The use of interactive mobile health (mHealth) applications to monitor patient-reported postoperative pain outcomes is an emerging area in dentistry that requires further exploration. This study aimed to evaluate and improve the usability of an existing mHealth application.

Materials And Methods: The usability of the application was assessed iteratively using a 3-phase approach, including a rapid cognitive walkthrough (Phase I), lab-based usability testing (Phase II), and pilot testing (Phase III).

Identifying predictors of the tooth loss phenotype in a large periodontitis patient cohort using a machine learning approach.

Objectives: This study aimed to identify predictors associated with the tooth loss phenotype in a large periodontitis patient cohort in the university setting.

Methods: Information on periodontitis patients and nineteen factors identified at the initial visit was extracted from electronic health records. The primary outcome is tooth loss phenotype (presence or absence of tooth loss).

Patients' Experiences of Dental Diagnostic Failures: A Qualitative Study Using Social Media.

Objective: Despite the many advancements made in patient safety over the past decade, combating diagnostic errors (DEs) remains a crucial, yet understudied initiative toward improvement. This study sought to understand the perception of dental patients who have experienced a dental diagnostic failure (DDF) and to identify patient-centered strategies to help reduce future occurrences of DDF.

Methods: Through social media recruitment, we conducted a screening survey, initial assessment, and 67 individual patient interviews to capture the effects of misdiagnosis, missed diagnosis, or delayed diagnosis on patient lives.

Patient and dentist perspectives on collecting patient reported outcomes after painful dental procedures in the National Dental PBRN.

Background: Dental Patient Reported Outcomes (PROs) relate to a dental patient's subjective experience of their oral health. How practitioners and patients value PROs influences their successful use in practice.

Methods: Semi-structured interviews were conducted with 22 practitioners and 32 patients who provided feedback on using a mobile health (mHealth) platform to collect the pain experience after dental procedures.

Use GPT-J Prompt Generation with RoBERTa for NER Models on Diagnosis Extraction of Periodontal Diagnosis from Electronic Dental Records.

This study explored the usability of prompt generation on named entity recognition (NER) tasks and the performance in different settings of the prompt. The prompt generation by GPT-J models was utilized to directly test the gold standard as well as to generate the seed and further fed to the RoBERTa model with the spaCy package. In the direct test, a lower ratio of negative examples with higher numbers of examples in prompt achieved the best results with a F1 score of 0.

Development and validation of a rule-based algorithm to identify periodontal diagnosis using structured electronic health record data.

Aim: To develop and validate an automated electronic health record (EHR)-based algorithm to suggest a periodontal diagnosis based on the 2017 World Workshop on the Classification of Periodontal Diseases and Conditions.

Materials And Methods: Using material published from the 2017 World Workshop, a tool was iteratively developed to suggest a periodontal diagnosis based on clinical data within the EHR. Pertinent clinical data included clinical attachment level (CAL), gingival margin to cemento-enamel junction distance, probing depth, furcation involvement (if present) and mobility.

Evaluating the Impact of an mHealth Platform for Managing Acute Postoperative Dental Pain: Randomized Controlled Trial.

Background: Postoperative dental pain is pervasive and can affect a patient's quality of life. Adopting a patient-centric approach to pain management involves having contemporaneous information about the patient's experience of pain and using it to personalize care.

Objective: In this study, we evaluated the use of a mobile health (mHealth) platform to collect pain-related patient-reported outcomes over 7 days after the patients underwent pain-inducing dental procedures; we then relayed the information to the dentist and determined its impact on the patient's pain experience.

Assessing the safety of deep sedation in outpatient pediatric oral health care.

Background: Children are the patient subgroup with the lowest error tolerance regarding deep sedation (DS)-supported care. This study assessed the safety of DS-supported pediatric dental treatment carried out in an outpatient setting through retrospective review of patient charts.

Methods: An automated script was developed to identify charts of pediatric patients who underwent DS-supported dental procedures from 2017 through 2019 at a dental clinic.

Dental CLAIRES: Contrastive LAnguage Image REtrieval Search for Dental Research.

Learning about diagnostic features and related clinical information from dental radiographs is important for dental research. However, the lack of expert-annotated data and convenient search tools poses challenges. Our primary objective is to design a search tool that uses a user's query for oral-related research.

Designing audit and feedback dashboards for dentists to monitor their opioid prescribing.

Background And Objective: Prescription drug abuse is a major factor leading to drug overdose deaths in the US and dentists are one of the leading prescribers of opioid pain medication. Knowing that Audit & Feedback (A&F) dashboards are an effective tool and are used as quality improvement interventions, we aimed to develop such dashboards personalized for dental providers which could allow them to monitor their own opioid prescribing performance.

Methods: In this paper we report on the process for designing the A&F dashboards for dentists which were developed by using an iterative human-centered design process.

Identifying predictors of tooth loss using a rule-based machine learning approach: A retrospective study at university-setting clinics.

Background: This study aimed to identify predictors associated with tooth loss in a large periodontitis patient cohort in the university setting using the machine learning approach.

Methods: Information on periodontitis patients and 18 factors identified at the initial visit was extracted from electronic health records. A two-step machine learning pipeline was proposed to develop the tooth loss prediction model.

Identifying Contributing Factors Associated With Dental Adverse Events Through a Pragmatic Electronic Health Record-Based Root Cause Analysis.

Objective: This study assessed contributing factors associated with dental adverse events (AEs).

Methods: Seven electronic health record-based triggers were deployed identifying potential AEs at 2 dental institutions. From 4106 flagged charts, 2 reviewers examined 439 charts selected randomly to identify and classify AEs using our dental AE type and severity classification systems.

Informatics approaches to improve the quality of dental care.

Despite technological advances, challenges exist in US dental care, including variations in quality of care, access and untreated dental needs. The implementation of learning health systems (LHSs) in dentistry can help to address these challenges. LHSs use robust informatics infrastructure including data and technology to continuously measure and improve the quality and safety of care and can help to reduce costs and improve patient outcomes.