Advancing LGBTQ+ inclusion in STEM education and AI research.

The authors emphasize diversity, equity, and inclusion in STEM education and artificial intelligence (AI) research, focusing on LGBTQ+ representation. They discuss the challenges faced by queer scientists, educational resources, the implementation of National AI Campus, and the notion of intersectionality. The authors hope to ensure supportive and respectful engagement across all communities.

Cluster Analysis reveals Socioeconomic Disparities among Elective Spine Surgery Patients.

This work demonstrates the use of cluster analysis in detecting fair and unbiased novel discoveries. Given a sample population of elective spinal fusion patients, we identify two overarching subgroups driven by insurance type. The Medicare group, associated with lower socioeconomic status, exhibited an over-representation of negative risk factors. The findings provide a compelling depiction of the interwoven socioeconomic and racial disparities present within the healthcare system, highlighting their consequential effects on health inequalities. The results are intended to guide design of fair and precise machine learning models based on intentional integration of population stratification.

ChatGPT and large language models in academia: opportunities and challenges.

The introduction of large language models (LLMs) that allow iterative "chat" in late 2022 is a paradigm shift that enables generation of text often indistinguishable from that written by humans. LLM-based chatbots have immense potential to improve academic work efficiency, but the ethical implications of their fair use and inherent bias must be considered. In this editorial, we discuss this technology from the academic's perspective with regard to its limitations and utility for academic writing, education, and programming. We end with our stance with regard to using LLMs and chatbots in academia, which is summarized as (1) we must find ways to effectively use them, (2) their use does not constitute plagiarism (although they may produce plagiarized text), (3) we must quantify their bias, (4) users must be cautious of their poor accuracy, and (5) the future is bright for their application to research and as an academic tool.

Making the case for workforce diversity in biomedical informatics to help achieve equity-centered care: a look at the AMIA First Look Program.

Developing a diverse informatics workforce broadens the research agenda and ensures the growth of innovative solutions that enable equity-centered care. The American Medical Informatics Association (AMIA) established the AMIA First Look Program in 2017 to address workforce disparities among women, including those from marginalized communities. The program exposes women to informatics, furnishes mentors, and provides career resources. In 4 years, the program has introduced 87 undergraduate women, 41% members of marginalized communities, to informatics. Participants from the 2019 and 2020 cohorts reported interest in pursuing a career in informatics increased from 57% to 86% after participation, and 86% of both years' attendees responded that they would recommend the program to others. A June 2021 LinkedIn profile review found 50% of participants working in computer science or informatics, 4% pursuing informatics graduate degrees, and 32% having completed informatics internships, suggesting AMIA First Look has the potential to increase informatics diversity.

The Perceived Impact and Usability of a Care Management and Coordination System in Delivering Services to Vulnerable Populations: Mixed Methods Study.

BACKGROUND: People with complex needs, such as those experiencing homelessness, require concurrent, seamless support from multiple social service agencies. Sonoma County, California has one of the nation's largest homeless populations among largely suburban communities. To support client-centered care, the county deployed a Care Management and Coordination System (CMCS). This system comprised the Watson Care Manager (WCM), a front-end system, and Connect 360, which is an integrated data hub that aggregates information from various systems into a single client record. OBJECTIVE: The aim of this study is to evaluate the perceived impact and usability of WCM in delivering services to the homeless population in Sonoma County. METHODS: A mixed methods study was conducted to identify ways in which WCM helps to coordinate care. Interviews, observations, and surveys were conducted, and transcripts and field notes were thematically analyzed and directed by a grounded theory approach. Responses to the Technology Acceptance Model survey were analyzed. RESULTS: A total of 16 participants were interviewed, including WCM users (n=8) and department leadership members (n=8). In total, 3 interdisciplinary team meetings were observed, and 8 WCM users were surveyed. WCM provided a central shared platform where client-related, up-to-date, comprehensive, and reliable information from participating agencies was consolidated. Factors that facilitated WCM use were users' enthusiasm regarding the tool functionalities, scalability, and agency collaboration. Constraining factors included the suboptimal awareness of care delivery goals and functionality of the system among the community, sensitivities about data sharing and legal requirements, and constrained funding from government and nongovernment organizations. Overall, users found WCM to be a useful tool that was easy to use and helped to enhance performance. CONCLUSIONS: WCM supports the delivery of care to individuals with complex needs. Integration of data and information in a CMCS can facilitate coordinated care. Future research should examine WCM and similar CMCSs in diverse populations and settings.

A qualitative study of physician perspectives on adaptation to electronic health records.

BACKGROUND: Electronic Health Records (EHRs) have the potential to improve many aspects of care and their use has increased in the last decade. Because of this, acceptance and adoption of EHRs is less of a concern than adaptation to use. To understand this issue more deeply, we conducted a qualitative study of physician perspectives on EHR use to identify factors that facilitate adaptation. METHODS: We conducted semi-structured interviews with 9 physicians across a range of inpatient disciplines at a large Academic Medical Center. Interviews were conducted by phone, lasting approximately 30 min, and were transcribed verbatim for analysis. We utilized inductive and deductive methods in our analysis. RESULTS: We identified 4 major themes related to EHR adaptation: impact of EHR changes on physicians, how physicians managed these changes, factors that facilitated adaptation to using the EHR and adapting to using the EHR in the patient encounter. Within these themes, physicians felt that a positive mindset toward change, providing upgrade training that was tailored to their role, and the opportunity to learn from colleagues were important facilitators of adaptation. CONCLUSIONS: As EHR use moves beyond implementation, physicians continue to be required to adapt to the technology and to its frequent changes. Our study provides actionable findings that allow healthcare systems to focus on factors that facilitate the adaptation process for physicians.

Building an Informed Consent Tool Starting with the Patient: The Patient-Centered Virtual Multimedia Interactive Informed Consent (VIC).

Patient safety and quality of care are at risk if the informed consent process does not emphasize patient comprehension. In this paper, we describe how we designed, developed, and evaluated an mHealth tool for advancing the informed consent process. Our tool enables the informed consent process to be performed on tablets (e.g., iPads) utilizing virtual coaching with text-to-speech automated translation as well as an interactive multimedia elements (e.g., graphics, video clips, animations, presentations, etc.). We designed our tool to enhance patient comprehension and quality of care, while improving the efficiency of obtaining patient consent. We present the Used-Centered Design approach we adopted to develop the tool and the results of the different methods we used during the development of the tool. Also, we describe the results of the usability study which we conducted to evaluate the effectiveness, efficiency, and user satisfaction with our mHealth App to enhance the informed consent process. Using the UCD approach we were able to design, develop, and evaluate a highly interactive mHealth App to deliver the informed consent process.

Enabling health care decisionmaking through clinical decision support and knowledge management.

OBJECTIVES: To catalogue study designs used to assess the clinical effectiveness of CDSSs and KMSs, to identify features that impact the success of CDSSs/KMSs, to document the impact of CDSSs/KMSs on outcomes, and to identify knowledge types that can be integrated into CDSSs/KMSs. DATA SOURCES: MEDLINE(®), CINAHL(®), PsycINFO(®), and Web of Science(®). REVIEW METHODS: We included studies published in English from January 1976 through December 2010. After screening titles and abstracts, full-text versions of articles were reviewed by two independent reviewers. Included articles were abstracted to evidence tables by two reviewers. Meta-analyses were performed for seven domains in which sufficient studies with common outcomes were included. RESULTS: We identified 15,176 articles, from which 323 articles describing 311 unique studies including 160 reports on 148 randomized control trials (RCTs) were selected for inclusion. RCTs comprised 47.5 percent of the comparative studies on CDSSs/KMSs. Both commercially and locally developed CDSSs effectively improved health care process measures related to performing preventive services (n = 25; OR 1.42, 95% confidence interval [CI] 1.27 to 1.58), ordering clinical studies (n = 20; OR 1.72, 95% CI 1.47 to 2.00), and prescribing therapies (n = 46; OR 1.57, 95% CI 1.35 to 1.82). Fourteen CDSS/KMS features were assessed for correlation with success of CDSSs/KMSs across all endpoints. Meta-analyses identified six new success features: Integration with charting or order entry system. Promotion of action rather than inaction. No need for additional clinician data entry. Justification of decision support via research evidence. Local user involvement. Provision of decision support results to patients as well as providers. Three previously identified success features were confirmed: Automatic provision of decision support as part of clinician workflow. Provision of decision support at time and location of decisionmaking. Provision of a recommendation, not just an assessment. Only 29 (19.6%) RCTs assessed the impact of CDSSs on clinical outcomes, 22 (14.9%) assessed costs, and 3 assessed KMSs on any outcomes. The primary source of knowledge used in CDSSs was derived from structured care protocols. CONCLUSIONS: Strong evidence shows that CDSSs/KMSs are effective in improving health care process measures across diverse settings using both commercially and locally developed systems. Evidence for the effectiveness of CDSSs on clinical outcomes and costs and KMSs on any outcomes is minimal. Nine features of CDSSs/KMSs that correlate with a successful impact of clinical decision support have been newly identified or confirmed.

Effect of clinical decision-support systems: a systematic review.

BACKGROUND: Despite increasing emphasis on the role of clinical decision-support systems (CDSSs) for improving care and reducing costs, evidence to support widespread use is lacking. PURPOSE: To evaluate the effect of CDSSs on clinical outcomes, health care processes, workload and efficiency, patient satisfaction, cost, and provider use and implementation. DATA SOURCES: MEDLINE, CINAHL, PsycINFO, and Web of Science through January 2011. STUDY SELECTION: Investigators independently screened reports to identify randomized trials published in English of electronic CDSSs that were implemented in clinical settings; used by providers to aid decision making at the point of care; and reported clinical, health care process, workload, relationship-centered, economic, or provider use outcomes. DATA EXTRACTION: Investigators extracted data about study design, participant characteristics, interventions, outcomes, and quality. DATA SYNTHESIS: 148 randomized, controlled trials were included. A total of 128 (86%) assessed health care process measures, 29 (20%) assessed clinical outcomes, and 22 (15%) measured costs. Both commercially and locally developed CDSSs improved health care process measures related to performing preventive services (n= 25; odds ratio [OR], 1.42 [95% CI, 1.27 to 1.58]), ordering clinical studies (n= 20; OR, 1.72 [CI, 1.47 to 2.00]), and prescribing therapies (n= 46; OR, 1.57 [CI, 1.35 to 1.82]). Few studies measured potential unintended consequences or adverse effects. LIMITATIONS: Studies were heterogeneous in interventions, populations, settings, and outcomes. Publication bias and selective reporting cannot be excluded. CONCLUSION: Both commercially and locally developed CDSSs are effective at improving health care process measures across diverse settings, but evidence for clinical, economic, workload, and efficiency outcomes remains sparse. This review expands knowledge in the field by demonstrating the benefits of CDSSs outside of experienced academic centers. PRIMARY FUNDING SOURCE: Agency for Healthcare Research and Quality.

Development and evaluation of an ontology for guiding appropriate antibiotic prescribing.

OBJECTIVES: To develop and apply formal ontology creation methods to the domain of antimicrobial prescribing and to formally evaluate the resulting ontology through intrinsic and extrinsic evaluation studies. METHODS: We extended existing ontology development methods to create the ontology and implemented the ontology using Protégé-OWL. Correctness of the ontology was assessed using a set of ontology design principles and domain expert review via the laddering technique. We created three artifacts to support the extrinsic evaluation (set of prescribing rules, alerts and an ontology-driven alert module, and a patient database) and evaluated the usefulness of the ontology for performing knowledge management tasks to maintain the ontology and for generating alerts to guide antibiotic prescribing. RESULTS: The ontology includes 199 classes, 10 properties, and 1636 description logic restrictions. Twenty-three Semantic Web Rule Language rules were written to generate three prescribing alerts: (1) antibiotic-microorganism mismatch alert; (2) medication-allergy alert; and (3) non-recommended empiric antibiotic therapy alert. The evaluation studies confirmed the correctness of the ontology, usefulness of the ontology for representing and maintaining antimicrobial treatment knowledge rules, and usefulness of the ontology for generating alerts to provide feedback to clinicians during antibiotic prescribing. CONCLUSIONS: This study contributes to the understanding of ontology development and evaluation methods and addresses one knowledge gap related to using ontologies as a clinical decision support system component-a need for formal ontology evaluation methods to measure their quality from the perspective of their intrinsic characteristics and their usefulness for specific tasks.

Creating an ontology for an antibiotic prescribing decision support system.

One of the most effective and immediate solutions to the growing antimicrobial resistance problem is to address the role of inappropriate prescribing. Ontologies can support judicious prescribing, but use of an ontology as part of a CDSS to support antibiotic therapeutic planning has not been fully explored. The proposed research project will create and evaluate an ontology for supporting a CDSS module that generates an antibiotic-mismatch alert to guide appropriate antibiotic prescribing.

An electronic health record based on structured narrative.

OBJECTIVE: To develop an electronic health record that facilitates rapid capture of detailed narrative observations from clinicians, with partial structuring of narrative information for integration and reuse. DESIGN: We propose a design in which unstructured text and coded data are fused into a single model called structured narrative. Each major clinical event (e.g., encounter or procedure) is represented as a document that is marked up to identify gross structure (sections, fields, paragraphs, lists) as well as fine structure within sentences (concepts, modifiers, relationships). Marked up items are associated with standardized codes that enable linkage to other events, as well as efficient reuse of information, which can speed up data entry by clinicians. Natural language processing is used to identify fine structure, which can reduce the need for form-based entry. VALIDATION: The model is validated through an example of use by a clinician, with discussion of relevant aspects of the user interface, data structures and processing rules. DISCUSSION: The proposed model represents all patient information as documents with standardized gross structure (templates). Clinicians enter their data as free text, which is coded by natural language processing in real time making it immediately usable for other computation, such as alerts or critiques. In addition, the narrative data annotates and augments structured data with temporal relations, severity and degree modifiers, causal connections, clinical explanations and rationale. CONCLUSION: Structured narrative has potential to facilitate capture of data directly from clinicians by allowing freedom of expression, giving immediate feedback, supporting reuse of clinical information and structuring data for subsequent processing, such as quality assurance and clinical research.

Medication reconciliation using natural language processing and controlled terminologies.

Medication reconciliation (MR) is a process that seeks to assure that the medications a patient is supposed to take are the same as what they are actually taking. We have developed a method in which medication information (consisting of both coded data and narrative text) is extracted from twelve sources from two clinical information systems and assembled into a chronological sequence of medication history, plans, and orders that correspond to periods before, during and after a hospital admission. We use natural language processing, a controlled terminology, and a medication classification system to create matrices that can be used to determine the initiation, changes and discontinuation of medications over time. We applied the process to a set of 17 patient records and successfully abstracted and summarized the medication data. This approach has implications for efforts to improve medication history-taking, order entry, and automated auditing of patient records for quality assurance.

Heuristic evaluation of eNote: an electronic notes system.

eNote is an electronic health record (EHR) system based on semi-structured narrative documents. A heuristic evaluation was conducted with a sample of five usability experts. eNote performed highly in: 1)consistency with standards and 2)recognition rather than recall. eNote needs improvement in: 1)help and documentation, 2)aesthetic and minimalist design, 3)error prevention, 4)helping users recognize, diagnosis, and recover from errors, and 5)flexibility and efficiency of use. The heuristic evaluation was an efficient method of evaluating our interface.

Clinicians' perceptions of usability of eNote.

The purpose of this evaluation was to assess perceptions of usability of a new semi-structured electronic clinical note. Two focus groups were held, one with attending physicians and one with residents. Physicians described their experiences with eNote and their perceptions about the system. Transcripts of the focus groups underwent content analysis, and four major themes emerged. These were "time", "hardware-system issues", "eNote application issues", and "patients' perceptions."

PubMatrix: a tool for multiplex literature mining.

BACKGROUND: Molecular experiments using multiplex strategies such as cDNA microarrays or proteomic approaches generate large datasets requiring biological interpretation. Text based data mining tools have recently been developed to query large biological datasets of this type of data. PubMatrix is a web-based tool that allows simple text based mining of the NCBI literature search service PubMed using any two lists of keywords terms, resulting in a frequency matrix of term co-occurrence. RESULTS: For example, a simple term selection procedure allows automatic pair-wise comparisons of approximately 1-100 search terms versus approximately 1-10 modifier terms, resulting in up to 1,000 pair wise comparisons. The matrix table of pair-wise comparisons can then be surveyed, queried individually, and archived. Lists of keywords can include any terms currently capable of being searched in PubMed. In the context of cDNA microarray studies, this may be used for the annotation of gene lists from clusters of genes that are expressed coordinately. An associated PubMatrix public archive provides previous searches using common useful lists of keyword terms. CONCLUSIONS: In this way, lists of terms, such as gene names, or functional assignments can be assigned genetic, biological, or clinical relevance in a rapid flexible systematic fashion. http://pubmatrix.grc.nia.nih.gov/