Regulation of artificial intelligence in healthcare: Clinical Laboratory Improvement Amendments (CLIA) as a model.

Objectives: To assess the potential to adapt an existing technology regulatory model, namely the Clinical Laboratory Improvement Amendments (CLIA), for clinical artificial intelligence (AI).

Materials And Methods: We identify overlap in the quality management requirements for laboratory testing and clinical AI.

Results: We propose modifications to the CLIA model that could make it suitable for oversight of clinical AI.

Implementation of Electronic Triggers to Identify Diagnostic Errors in Emergency Departments.

Importance: Missed diagnosis can lead to preventable patient harm.

Objective: To develop and implement a portfolio of electronic triggers (e-triggers) and examine their performance for identifying missed opportunities in diagnosis (MODs) in emergency departments (EDs).

Design, Setting, And Participants: In this retrospective medical record review study of ED visits at 1321 Veterans Affairs health care sites, rules-based e-triggers were developed and implemented using a national electronic health record repository.

Reusable Generic Clinical Decision Support System Module for Immunization Recommendations in Resource-Constraint Settings.

Clinical decision support systems (CDSS) are routinely employed in clinical settings to improve quality of care, ensure patient safety, and deliver consistent medical care. However, rule-based CDSS, currently available, do not feature reusable rules. In this study, we present CDSS with reusable rules.

Active Learning Pipeline to Identify Candidate Terms for a CDSS Ontology.

Ontology is essential for achieving health information and information technology application interoperability in the biomedical fields and beyond. Traditionally, ontology construction is carried out manually by human domain experts (HDE). Here, we explore an active learning approach to automatically identify candidate terms from publications, with manual verification later as a part of a deep learning model training and learning process.

Patient-centered clinical decision support challenges and opportunities identified from workflow execution models.

Objective: To use workflow execution models to highlight new considerations for patient-centered clinical decision support policies (PC CDS), processes, procedures, technology, and expertise required to support new workflows.

Methods: To generate and refine models, we used (1) targeted literature reviews; (2) key informant interviews with 6 external PC CDS experts; (3) model refinement based on authors' experience; and (4) validation of the models by a 26-member steering committee.

Results And Discussion: We identified 7 major issues that provide significant challenges and opportunities for healthcare systems, researchers, administrators, and health IT and app developers.

Implementation of a health information technology safety classification system in the Veterans Health Administration's Informatics Patient Safety Office.

Objective: Implement the 5-type health information technology (HIT) patient safety concern classification system for HIT patient safety issues reported to the Veterans Health Administration's Informatics Patient Safety Office.

Materials And Methods: A team of informatics safety analysts retrospectively classified 1 year of HIT patient safety issues by type of HIT patient safety concern using consensus discussions. The processes established during retrospective classification were then applied to incoming HIT safety issues moving forward.

Leveraging explainable artificial intelligence to optimize clinical decision support.

Objective: To develop and evaluate a data-driven process to generate suggestions for improving alert criteria using explainable artificial intelligence (XAI) approaches.

Methods: We extracted data on alerts generated from January 1, 2019 to December 31, 2020, at Vanderbilt University Medical Center. We developed machine learning models to predict user responses to alerts.

Visualization of Patient-Generated Health Data: A Scoping Review of Dashboard Designs.

Background: Patient-centered clinical decision support (PC CDS) aims to assist with tailoring decisions to an individual patient's needs. Patient-generated health data (PGHD), including physiologic measurements captured frequently by automated devices, provide important information for PC CDS. The volume and availability of such PGHD is increasing, but how PGHD should be presented to clinicians to best aid decision-making is unclear.

A guide to mitigating audit log-related risk in medical professional liability cases.

Following the American Recovery and Reinvestment Act in 2009, use of electronic health records (EHRs) has become ubiquitous. Accordingly, one should expect most medical professional liability cases to involve review of patient records produced from EHRs. When questions arise regarding who was involved in care of a patient, what they knew and when, or the meaning, completeness, integrity, validity, timeliness, confidentiality, accuracy, or legitimacy of data, or ways that the EHR's user interface or automated clinical decision support tools may have contributed to the alleged events, one often turns to the EHR and its audit log.

A lifecycle framework illustrates eight stages necessary for realizing the benefits of patient-centered clinical decision support.

The design, development, implementation, use, and evaluation of high-quality, patient-centered clinical decision support (PC CDS) is necessary if we are to achieve the quintuple aim in healthcare. We developed a PC CDS lifecycle framework to promote a common understanding and language for communication among researchers, patients, clinicians, and policymakers. The framework puts the patient, and/or their caregiver at the center and illustrates how they are involved in all the following stages: Computable Clinical Knowledge, Patient-specific Inference, Information Delivery, Clinical Decision, Patient Behaviors, Health Outcomes, Aggregate Data, and patient-centered outcomes research (PCOR) Evidence.

Keyphrase Identification Using Minimal Labeled Data with Hierarchical Contexts and Transfer Learning.

Background: Interoperable clinical decision support system (CDSS) rules provide a pathway to interoperability, a well-recognized challenge in health information technology. Building an ontology facilitates creating interoperable CDSS rules, which can be achieved by identifying the keyphrases (KP) from the existing literature. Ontology construction is traditionally a manual effort by human domain experts, and the newly advanced natural language processing techniques, such as KP identification, can be a critical complementary automatic part of building ontology.

Developing electronic clinical quality measures to assess the cancer diagnostic process.

Objective: Measures of diagnostic performance in cancer are underdeveloped. Electronic clinical quality measures (eCQMs) to assess quality of cancer diagnosis could help quantify and improve diagnostic performance.

Materials And Methods: We developed 2 eCQMs to assess diagnostic evaluation of red-flag clinical findings for colorectal (CRC; based on abnormal stool-based cancer screening tests or labs suggestive of iron deficiency anemia) and lung (abnormal chest imaging) cancer.

Using AI-generated suggestions from ChatGPT to optimize clinical decision support.

Objective: To determine if ChatGPT can generate useful suggestions for improving clinical decision support (CDS) logic and to assess noninferiority compared to human-generated suggestions.

Methods: We supplied summaries of CDS logic to ChatGPT, an artificial intelligence (AI) tool for question answering that uses a large language model, and asked it to generate suggestions. We asked human clinician reviewers to review the AI-generated suggestions as well as human-generated suggestions for improving the same CDS alerts, and rate the suggestions for their usefulness, acceptance, relevance, understanding, workflow, bias, inversion, and redundancy.

Assessing the Value of ChatGPT for Clinical Decision Support Optimization.

Objective: To determine if ChatGPT can generate useful suggestions for improving clinical decision support (CDS) logic and to assess noninferiority compared to human-generated suggestions.

Methods: We supplied summaries of CDS logic to ChatGPT, an artificial intelligence (AI) tool for question answering that uses a large language model, and asked it to generate suggestions. We asked human clinician reviewers to review the AI-generated suggestions as well as human-generated suggestions for improving the same CDS alerts, and rate the suggestions for their usefulness, acceptance, relevance, understanding, workflow, bias, inversion, and redundancy.

A multi-site randomized trial of a clinical decision support intervention to improve problem list completeness.

Objective: To improve problem list documentation and care quality.

Materials And Methods: We developed algorithms to infer clinical problems a patient has that are not recorded on the coded problem list using structured data in the electronic health record (EHR) for 12 clinically significant heart, lung, and blood diseases. We also developed a clinical decision support (CDS) intervention which suggests adding missing problems to the problem list.

Identifying a Clinical Informatics or Electronic Health Record Expert Witness for Medical Professional Liability Cases.

Background: The health care field is experiencing widespread electronic health record (EHR) adoption. New medical professional liability (i.e.

Clinical decision support malfunctions related to medication routes: a case series.

Objective: To identify common medication route-related causes of clinical decision support (CDS) malfunctions and best practices for avoiding them.

Materials And Methods: Case series of medication route-related CDS malfunctions from diverse healthcare provider organizations.

Results: Nine cases were identified and described, including both false-positive and false-negative alert scenarios.

i-CLIMATE: a "clinical climate informatics" action framework to reduce environmental pollution from healthcare.

Addressing environmental pollution and climate change is one of the biggest sociotechnical challenges of our time. While information technology has led to improvements in healthcare, it has also contributed to increased energy usage, destructive natural resource extraction, piles of e-waste, and increased greenhouse gases. We introduce a framework "Information technology-enabled Clinical cLimate InforMAtics acTions for the Environment" (i-CLIMATE) to illustrate how clinical informatics can help reduce healthcare's environmental pollution and climate-related impacts using 5 actionable components: (1) create a circular economy for health IT, (2) reduce energy consumption through smarter use of health IT, (3) support more environmentally friendly decision-making by clinicians and health administrators, (4) mobilize healthcare workforce environmental stewardship through informatics, and (5) Inform policies and regulations for change.

Translating electronic health record-based patient safety algorithms from research to clinical practice at multiple sites.

Introduction: Researchers are increasingly developing algorithms that impact patient care, but algorithms must also be implemented in practice to improve quality and safety.

Objective: We worked with clinical operations personnel at two US health systems to implement algorithms to proactively identify patients without timely follow-up of abnormal test results that warrant diagnostic evaluation for colorectal or lung cancer. We summarise the steps involved and lessons learned.