Trustworthiness of a machine learning early warning model in medical and surgical inpatients.

Objectives: In the general hospital wards, machine learning (ML)-based early warning systems (EWSs) can identify patients at risk of deterioration to facilitate rescue interventions. We assess subpopulation performance of a ML-based EWS on medical and surgical adult patients admitted to general hospital wards.

Materials And Methods: We assessed the scores of an EWS integrated into the electronic health record and calculated every 15 minutes to predict a composite adverse event (AE): all-cause mortality, transfer to intensive care, cardiac arrest, or rapid response team evaluation.

Opportunities for the informatics community to advance learning health systems.

Objectives: There is rapidly growing interest in learning health systems (LHSs) nationally and globally. While the critical role of informatics is recognized, the informatics community has been relatively slow to formalize LHS as a priority area.

Materials And Methods: We compiled results from a short survey of LHS leaders and American Medical Informatics Association (AMIA) members, discussion from an LHS reception at the AMIA annual meeting, and a follow-up survey to inform priorities at the intersection of LHS and informatics.

A model of academic-practice collaboration for facilitating informatics capacity and building a learning health system framework in public health.

Background And Objective: The data modernization initiative (DMI) is a multi-year, multi-billion-dollar endeavor toward a robust public health information infrastructure. The various DMI projects (interoperability, analytics, workforce, governance) present an opportunity for a learning health system (LHS) framework in public health. The objective is to share an academic-practice partnership model between the University of Minnesota (UMN) and the Minnesota Department of Health (MDH) in advancing public health informatics (PHI) and its relationship to an LHS model.

The Role of Tumor Location on Endoscopic and Surgical Management of Malignant Colon Polyps.

Background: Endoscopic polypectomy could be an appropriate, definitive treatment for pathologic T1 (pT1) colon polyps without high-risk features. Prior studies suggested worse prognosis for proximal versus distal advanced-stage colon cancers following curative treatment. However, there is limited evidence on the prognostic impact of tumor location for pT1s.

Building to learn: Information technology innovations to enable rapid pragmatic evaluation in a learning health system.

Background: Learning health systems (LHSs) iteratively generate evidence that can be implemented into practice to improve care and produce generalizable knowledge. Pragmatic clinical trials fit well within LHSs as they combine real-world data and experiences with a degree of methodological rigor which supports generalizability.

Objectives: We established a pragmatic clinical trial unit ("RapidEval") to support the development of an LHS.

Consensus modeling: Safer transfer learning for small health systems.

Predictive modeling is becoming an essential tool for clinical decision support, but health systems with smaller sample sizes may construct suboptimal or overly specific models. Models become over-specific when beside true physiological effects, they also incorporate potentially volatile site-specific artifacts. These artifacts can change suddenly and can render the model unsafe.

Exploring Large Language Models for Acronym, Symbol Sense Disambiguation, and Semantic Similarity and Relatedness Assessment.

Acronyms, abbreviations, and symbols play a significant role in clinical notes. Acronym and symbol sense disambiguation are crucial natural language processing (NLP) tasks that ensure the clarity and consistency of clinical notes and downstream NLP processing. Previous studies using traditional machine learning methods have been relatively successful in tackling this issue.

Selection and Implementation of Virtual Scribe Solutions to Reduce Documentation Burden: A Mixed Methods Pilot.

Electronic health record (EHR) documentation is a leading reason for clinician burnout. While technology-enabled solutions like virtual and digital scribes aim to improve this, there is limited evidence of their effectiveness and minimal guidance for healthcare systems around solution selection and implementation. A transdisciplinary approach, informed by clinician interviews and other considerations, was used to evaluate and select a virtual scribe solution to pilot in a rapid iterative sprint over 12 weeks.

Evaluation of a Clinical Decision Support Tool to Guide Adoption of the American Heart Association Telemetry Monitoring Practice Standards.

Background: The objectives of this study were to (1) evaluate telemetry use pre- and postimplementation of clinical decision support tools to support American Heart Association practice standards for telemetry monitoring and (2) understand the factors that may contribute to variation of telemetry monitoring in practice.

Methods And Results: First, we captured overall variability in telemetry use pre- and postimplementation of the clinical decision support intervention. We then conducted semistructured interviews with telemetry-ordering providers to identify key barriers and facilitators to adoption.

Implementing Telestroke in the Inpatient Setting: Identifying Factors for Success.

Background: Inpatient telestroke programs have emerged as a solution to provide timely stroke care in underserved areas, but their successful implementation and factors influencing their effectiveness remain underexplored. This study aimed to qualitatively evaluate the perspectives of inpatient clinicians located at spoke hospitals participating in a newly established inpatient telestroke program to identify implementation barriers and facilitators.

Methods: This was a formative evaluation relying on semistructured qualitative interviews with 16 inpatient providers (physicians and nurse practitioners) at 5 spoke sites of a hub-and-spoke inpatient telestroke program.

A population-based analysis on the incidence of metachronous colon cancer after endoscopic resection of advanced adenomas with high-grade dysplasia: does location matter?

Background: Advanced adenomas (AAs) with high-grade dysplasia (HGD) represent a risk factor for metachronous neoplasia, with guidelines recommending short-interval surveillance. Although the worse prognosis of proximal (vs distal) colon cancers (CCs) is established, there is paucity of evidence on the impact of laterality on the risk of subsequent neoplasia for these AAs.

Methods: Adults with HGD adenomas undergoing polypectomy were identified in the Surveillance, Epidemiology, and End Results database (2000-2019).

Decreasing Opioid Addiction and Diversion Using Behavioral Economics Applied Through a Digital Engagement Solution: Protocol for a Randomized Controlled Trial.

Background: Despite strong and growing interest in ending the ongoing opioid health crisis, there has been limited success in reducing the prevalence of opioid addiction and the number of deaths associated with opioid overdoses. Further, 1 explanation for this is that existing interventions target those who are opiate-dependent but do not prevent opioid-naïve patients from becoming addicted.

Objective: Leveraging behavioral economics at the patient level could help patients successfully use, discontinue, and dispose of their opioid medications in an acute pain setting.

Development and Usability Evaluation of an Opioid Management App.

We describe the development and usability evaluation of a novel patient engagement tool (OPY) in its early stage from perspectives of both experts and end-users. The tool is aimed at engaging patients in positive behaviors surrounding the use, weaning, and disposal of opioid medications in the post-surgical setting. The messaging and design of the application were created through a behavioral economics lens.

A Symptom-Based Natural Language Processing Surveillance Pipeline for Post-COVID-19 Patients.

Post-acute sequelae of SARS CoV-2 (PASC) are a group of conditions in which patients previously infected with COVID-19 experience symptoms weeks/months post-infection. PASC has substantial societal burden, including increased healthcare costs and disabilities. This study presents a natural language processing (NLP) based pipeline for identification of PASC symptoms and demonstrates its ability to estimate the proportion of suspected PASC cases.

Advanced Care Planning Content Encoding with Natural Language Processing.

While advanced care planning (ACP) is an essential practice for ensuring patient-centered care, its adoption remains poor and the completeness of its documentation variable. Natural language processing (NLP) approaches hold promise for supporting ACP, including its use for decision support to improve ACP gaps at the point of care. ACP themes were annotated on palliative care notes across four annotators (Fleiss kappa = 0.

Post-Implementation Outcomes of a Remote Patient Monitoring Program After Emergency Department Discharge.

To better communicate and improve post-visit outcomes, a remote patient monitoring (RPM) program was implemented for patients discharged from emergency departments (ED) across 10 hospitals. The solution was offered to patients at the time of ED discharge and staffed by a group of care coordinators to respond to questions/urgent needs. Of 107,477 consecutive patients offered RPM, 28,425 patients (26.

Machine Learning for Risk Prediction of Recurrent AKI in Adult Patients After Hospital Discharge.

Recurrent AKI has been found common among hospitalized patients after discharge, and early prediction may allow timely intervention and optimized post-discharge treatment [1]. There are significant gaps in the literature regarding the risk prediction on the post-AKI population, and most current works only included a limited number of pre-selected variables [2]. In this study, we built and compared machine learning models using both knowledge-based and data-driven features in predicting the risk of recurrent AKI within 1-year of discharge.

Promoting Learning Health System Cycles by Optimizing EHR Data Clinical Concept Encoding Processes.

Electronic health records (EHRs) and other real-world data (RWD) are critical to accelerating and scaling care improvement and transformation. To efficiently leverage it for secondary uses, EHR/RWD should be optimally managed and mapped to industry standard concepts (ISCs). Inherent challenges in concept encoding usually result in inefficient and costly workflows and resultant metadata representation structures outside the EHR.

Evolving Interoperability Across a State Public Health Immunization Registry and Electronic Health Records.

The critical need for system interoperability and robust information infrastructure in public health was highlighted during the COVID-19 pandemic. An assessment of the evolving interoperability between immunization information system (IIS) in a state-based public health agency and electronic health records (EHRs) including pandemic-driven evolution/use was conducted. The Minnesota Immunization Information Connection (MIIC), the IIS for Minnesota (US) supports interoperability with EHRs using HL7v2.

How Are Leading Research Institutions Engaging with Data Sharing Tools and Programs?

With widespread electronic health record (EHR) adoption and improvements in health information interoperability in the United States, troves of data are available for knowledge discovery. Several data sharing programs and tools have been developed to support research activities, including efforts funded by the National Institutes of Health (NIH), EHR vendors, and other public- and private-sector entities. We surveyed 65 leading research institutions (77% response rate) about their use of and value derived from ten programs/tools, including NIH's Accrual to Clinical Trials, Epic Corporation's Cosmos, and the Observational Health Data Sciences and Informatics consortium.

Large Language Model-Based Chatbot vs Surgeon-Generated Informed Consent Documentation for Common Procedures.

Importance: Informed consent is a critical component of patient care before invasive procedures, yet it is frequently inadequate. Electronic consent forms have the potential to facilitate patient comprehension if they provide information that is readable, accurate, and complete; it is not known if large language model (LLM)-based chatbots may improve informed consent documentation by generating accurate and complete information that is easily understood by patients.

Objective: To compare the readability, accuracy, and completeness of LLM-based chatbot- vs surgeon-generated information on the risks, benefits, and alternatives (RBAs) of common surgical procedures.

Development and implementation of an interoperability tool across state public health agency's disease surveillance and immunization information systems.

Public health information systems have historically been siloed with limited interoperability. The State of Minnesota's disease surveillance system (Minnesota Electronic Disease Surveillance System: MEDSS, ∼12 million total reportable events) and immunization information system (Minnesota Immunization Information Connection: MIIC, ∼130 million total immunizations) lacked interoperability between them and data exchange was fully manual. An interoperability tool based on national standards (HL7 and SOAP/web services) for query and response was developed for electronic vaccination data exchange from MIIC into MEDSS by soliciting stakeholder requirements ( = 39) and mapping MIIC vaccine codes ( = 294) to corresponding MEDSS product codes ( = 48).