A Clinical Decision Support System for Addressing Health-Related Social Needs in Emergency Department: Defining End User Needs and Preferences.

Background:  Health-related social needs (HRSNs) are the unmet social and economic needs (e.g., housing instability) that affect individuals' health and well-being.

Preliminary findings regarding the association between patient demographics and ED experience scores across a regional health system: A cross sectional study using natural language processing of patient comments.

Objective: Existing literature shows associations between patient demographics and reported experiences of care, but this relationship is poorly understood. Our objective was to use natural language processing of patient comments to gain insight into associations between patient demographics and experiences of care.

Methods: This is a cross-sectional study of 14,848 unique emergency department (ED) patient visits from 1/1/2020 to 12/31/2020.

Using human factors methods to mitigate bias in artificial intelligence-based clinical decision support.

Objectives: To highlight the often overlooked role of user interface (UI) design in mitigating bias in artificial intelligence (AI)-based clinical decision support (CDS).

Materials And Methods: This perspective paper discusses the interdependency between AI-based algorithm development and UI design and proposes strategies for increasing the safety and efficacy of CDS.

Results: The role of design in biasing user behavior is well documented in behavioral economics and other disciplines.

Comparing the performance of screening surveys versus predictive models in identifying patients in need of health-related social need services in the emergency department.

Background: Health-related social needs (HRSNs), such as housing instability, food insecurity, and financial strain, are increasingly prevalent among patients. Healthcare organizations must first correctly identify patients with HRSNs to refer them to appropriate services or offer resources to address their HRSNs. Yet, current identification methods are suboptimal, inconsistently applied, and cost prohibitive.

Design and development of a machine-learning-driven opioid overdose risk prediction tool integrated in electronic health records in primary care settings.

Background: Integrating advanced machine-learning (ML) algorithms into clinical practice is challenging and requires interdisciplinary collaboration to develop transparent, interpretable, and ethically sound clinical decision support (CDS) tools. We aimed to design a ML-driven CDS tool to predict opioid overdose risk and gather feedback for its integration into the University of Florida Health (UFHealth) electronic health record (EHR) system.

Methods: We used user-centered design methods to integrate the ML algorithm into the EHR system.

Acceptance of Automated Social Risk Scoring in the Emergency Department: Clinician, Staff, and Patient Perspectives.

Introduction: Healthcare organizations are under increasing pressure from policymakers, payers, and advocates to screen for and address patients' health-related social needs (HRSN). The emergency department (ED) presents several challenges to HRSN screening, and patients are frequently not screened for HRSNs. Predictive modeling using machine learning and artificial intelligence, approaches may address some pragmatic HRSN screening challenges in the ED.

Association between Opioid-Benzodiazepine Trajectories and Injurious Fall Risk among US Medicare Beneficiaries.

Concurrent opioid (OPI) and benzodiazepine (BZD) use may exacerbate injurious fall risk (e.g., falls and fractures) compared to no use or use alone.

New insights about community benefit evaluation: Using the Community Health Implementation Evaluation Framework to assess what hospitals are measuring.

Background: Nonprofit hospitals are required to conduct community health needs assessments (CHNA) every 3 years and develop corresponding implementation plans. Implemented strategies must address the identified community needs and be evaluated for impact.

Purpose: Using the Community Health Implementation Evaluation Framework (CHIEF), we assessed whether and how nonprofit hospitals are evaluating the impact of their CHNA-informed community benefit initiatives.

Health-related social needs information in the emergency department: clinician and patient perspectives on availability and use.

Background: Patient health-related social needs (HRSN) complicate care and drive poor outcomes in emergency department (ED) settings. This study sought to understand what HRSN information is available to ED physicians and staff, and how HRSN-related clinical actions may or may not align with patient expectations.

Methods: We conducted a qualitative study using in-depth semi-structured interviews guided by HRSN literature, the 5 Rights of Clinical Decision Support (CDS) framework, and the Contextual Information Model.

Examining primary care provider experiences with using a clinical decision support tool for pain management.

Objective: To evaluate primary care provider (PCP) experiences using a clinical decision support (CDS) tool over 16 months following a user-centered design process and implementation.

Materials And Methods: We conducted a qualitative evaluation of the Chronic Pain OneSheet (OneSheet), a chronic pain CDS tool. OneSheet provides pain- and opioid-related risks, benefits, and treatment information for patients with chronic pain to PCPs.

Enhancing an enterprise data warehouse for research with data extracted using natural language processing.

Objective: This study aims to develop a generalizable architecture for enhancing an enterprise data warehouse for research (EDW4R) with results from a natural language processing (NLP) model, which allows discrete data derived from clinical notes to be made broadly available for research use without need for NLP expertise. The study also quantifies the additional value that information extracted from clinical narratives brings to EDW4R.

Materials And Methods: Clinical notes written during one month at an academic health center were used to evaluate the performance of an existing NLP model and to quantify its value added to the structured data.

Management of Fall Risk Among Older Adults in Diverse Primary Care Settings.

Objectives: Falls are persistent among community-dwelling older adults despite existing prevention guidelines. We described how urban and rural primary care staff and older adults manage fall risk and factors important to integration of computerized clinical decision support (CCDS).

Methods: Interviews, contextual inquiries, and workflow observations were analyzed using content analysis and synthesized into a journey map.

Generalizability and portability of natural language processing system to extract individual social risk factors.

Objective: The objective of this study is to validate and report on portability and generalizability of a Natural Language Processing (NLP) method to extract individual social factors from clinical notes, which was originally developed at a different institution.

Materials And Methods: A rule-based deterministic state machine NLP model was developed to extract financial insecurity and housing instability using notes from one institution and was applied on all notes written during 6 months at another institution. 10% of positively-classified notes by NLP and the same number of negatively-classified notes were manually annotated.

Usability Testing of an Interoperable Computerized Clinical Decision Support Tool for Fall Risk Management in Primary Care.

Background: Falls are a widespread and persistent problem for community-dwelling older adults. Use of fall prevention guidelines in the primary care setting has been suboptimal. Interoperable computerized clinical decision support systems have the potential to increase engagement with fall risk management at scale.

A large language model for electronic health records.

There is an increasing interest in developing artificial intelligence (AI) systems to process and interpret electronic health records (EHRs). Natural language processing (NLP) powered by pretrained language models is the key technology for medical AI systems utilizing clinical narratives. However, there are few clinical language models, the largest of which trained in the clinical domain is comparatively small at 110 million parameters (compared with billions of parameters in the general domain).

Recent tapering from long-term opioid therapy and odds of opioid-related hospital use.

Purpose: The number of patients tapered from long-term opioid therapy (LTOT) has increased in recent years in the United States. Some patients tapered from LTOT report improved quality of life, while others face increased risks of opioid-related hospital use. Research has not yet established how the risk of opioid-related hospital use changes across LTOT dose and subsequent tapering.

Student perceptions of a teaching electronic medical record in Health Administration education.

Given the ubiquity of electronic health records (EHR), health administrators should be formally trained on the use and evaluation of EHR data for common operational tasks and managerial decision-making. A teaching electronic medical record (tEMR) is a fully operational electronic medical record that uses de-identified electronic patient data and provides a framework for students to familiarize themselves with the data, features, and functionality of an EHR. Although purported to be of value in health administration programs, specific benefits of using a tEMR in health administration education is unknown.

A framework for a consistent and reproducible evaluation of manual review for patient matching algorithms.

Healthcare systems are hampered by incomplete and fragmented patient health records. Record linkage is widely accepted as a solution to improve the quality and completeness of patient records. However, there does not exist a systematic approach for manually reviewing patient records to create gold standard record linkage data sets.

Evaluation of federated learning variations for COVID-19 diagnosis using chest radiographs from 42 US and European hospitals.

Objective: Federated learning (FL) allows multiple distributed data holders to collaboratively learn a shared model without data sharing. However, individual health system data are heterogeneous. "Personalized" FL variations have been developed to counter data heterogeneity, but few have been evaluated using real-world healthcare data.

A novel method for evaluating physician communication: A pilot study testing the feasibility of parent-assisted audio recordings via Zoom.

Objective: Quality of physician consultations are best assessed via direct observation, but require intensive in-clinic research staffing. To evaluate physician consultation quality remotely, we pilot tested the feasibility of parents using their personal mobile phones to facilitate audio recordings of pediatric visits.

Methods: Across four academic pediatric primary care clinics, we invited all physicians with a patient panel (n=20).

Assessing the use of a clinical decision support tool for pain management in primary care.

Objective: Given time constraints, poorly organized information, and complex patients, primary care providers (PCPs) can benefit from clinical decision support (CDS) tools that aggregate and synthesize problem-specific patient information. First, this article describes the design and functionality of a CDS tool for chronic noncancer pain in primary care. Second, we report on the retrospective analysis of real-world usage of the tool in the context of a pragmatic trial.

Inpatient opioid receipt and care experiences for vaginal delivery.

Objectives: To examine the relationship between care experiences and inpatient opioid receipt during and after delivery for women hospitalized for vaginal delivery (VD).

Study Design: We used a pooled cross-sectional design with inverse probability weighting to examine the association between inpatient opioid receipt and care experiences of women hospitalized for VD at a single health care system in a Midwestern state. We used 4 Hospital Consumer Assessment of Healthcare Providers and Systems scores (2 pain care items and 2 global items) as measures of care experiences of women hospitalized for VD.