Patient and Caregiver Perceptions of an Interface Design to Communicate Artificial Intelligence-Based Prognosis for Patients With Advanced Solid Tumors.

Purpose: Use of artificial intelligence (AI) in cancer care is increasing. What remains unclear is how best to design patient-facing systems that communicate AI output. With oncologist input, we designed an interface that presents patient-specific, machine learning-based 6-month survival prognosis information designed to aid oncology providers in preparing for and discussing prognosis with patients with advanced solid tumors and their caregivers.

An economic evaluation of the expansion of electronic case reporting in an academic healthcare setting.

Objectives: Determine the economic cost or benefit of expanding electronic case reporting (eCR) for 29 reportable conditions beyond the initial eCR implementation for COVID-19 at an academic health center.

Materials And Methods: The return on investment (ROI) framework was used to quantify the economic impact of the expansion of eCR from the perspective of an academic health system over a 5-year time horizon. Sensitivity analyses were performed to assess key factors such as personnel cost, inflation, and number of expanded conditions.

Design of an interface to communicate artificial intelligence-based prognosis for patients with advanced solid tumors: a user-centered approach.

Objectives: To design an interface to support communication of machine learning (ML)-based prognosis for patients with advanced solid tumors, incorporating oncologists' needs and feedback throughout design.

Materials And Methods: Using an interdisciplinary user-centered design approach, we performed 5 rounds of iterative design to refine an interface, involving expert review based on usability heuristics, input from a color-blind adult, and 13 individual semi-structured interviews with oncologists. Individual interviews included patient vignettes and a series of interfaces populated with representative patient data and predicted survival for each treatment decision point when a new line of therapy (LoT) was being considered.

Utility of Poison Control Center Data for Automated Opioid Overdose Surveillance.

Context: Overdosing on opioids is a national epidemic and the number one cause of death from unintentional injury in the United States. Poison control centers (PCCs) may be a source of timely data that can track opioid exposure cases, identify clusters of opioid exposure cases by geographic region, and capture opioid exposure cases that may not seek medical attention from health care facilities.

Objective: The objectives were to (a) identify data requirements for opioid overdose case ascertainment and classification and visualization in a dashboard, and (b) assess the availability and quality of the relevant PCC data for state-based opioid overdose surveillance.

Evaluation in Life Cycle of Information Technology (ELICIT) framework: Supporting the innovation life cycle from business case assessment to summative evaluation.

Objective: Our objective was to develop an evaluation framework for electronic health record (EHR)-integrated innovations to support evaluation activities at each of four information technology (IT) life cycle phases: planning, development, implementation, and operation.

Methods: The evaluation framework was developed based on a review of existing evaluation frameworks from health informatics and other domains (human factors engineering, software engineering, and social sciences); expert consensus; and real-world testing in multiple EHR-integrated innovation studies.

Results: The resulting Evaluation in Life Cycle of IT (ELICIT) framework covers four IT life cycle phases and three measure levels (society, user, and IT).

A Conceptual Framework of Data Readiness: The Contextual Intersection of Quality, Availability, Interoperability, and Provenance.

Background: Data readiness is a concept often used when referring to health information technology applications in the informatics disciplines, but it is not clearly defined in the literature. To avoid misinterpretations in research and implementation, a formal definition should be developed.

Objectives: The objective of this research is to provide a conceptual definition and framework for the term data readiness that can be used to guide research and development related to data-based applications in health care.

Developing a sampling method and preliminary taxonomy for classifying COVID-19 public health guidance for healthcare organizations and the general public.

Background: Development and dissemination of public health (PH) guidance to healthcare organizations and the general public (e.g., businesses, schools, individuals) during emergencies like the COVID-19 pandemic is vital for policy, clinical, and public decision-making.

A thematic analysis to examine the feasibility of EHR-based clinical decision support for implementing guidelines.

Objective: To identify important barriers and facilitators relating to the feasibility of implementing clinical practice guidelines (CPGs) as clinical decision support (CDS).

Materials And Methods: We conducted a qualitative, thematic analysis of interviews from seven interviews with dyads (one clinical expert and one systems analyst) who discussed the feasibility of implementing 10 Choosing Wisely guidelines at their institutions. We conducted a content analysis to extract salient themes describing facilitators, challenges, and other feasibility considerations regarding implementing CPGs as CDS.

Measuring implementation feasibility of clinical decision support alerts for clinical practice recommendations.

Objective: The study sought to describe key features of clinical concepts and data required to implement clinical practice recommendations as clinical decision support (CDS) tools in electronic health record systems and to identify recommendation features that predict feasibility of implementation.

Materials And Methods: Using semistructured interviews, CDS implementers and clinician subject matter experts from 7 academic medical centers rated the feasibility of implementing 10 American College of Emergency Physicians Choosing Wisely Recommendations as electronic health record-embedded CDS and estimated the need for additional data collection. Ratings were combined with objective features of the guidelines to develop a predictive model for technical implementation feasibility.

State-level adoption of national guidelines for norovirus outbreaks in health care settings.

Background: Clinical decision support (CDS) systems can help investigators use best practices when responding to outbreaks, but variation in guidelines between jurisdictions can make such systems hard to develop and implement. This study aimed to identify (1) the extent to which state-level guidelines adhere to national recommendations for norovirus outbreak response in health care settings and (2) the impact of variation between states on outbreak outcomes.

Methods: State guidelines were obtained from Internet searches and direct contact with state public health officials in early 2016.

Melinda - A custom search engine that provides access to locally-developed content using the HL7 Infobutton standard.

Healthcare organizations use care pathways to standardize care, but once developed, adoption rates often remain low. One challenge for usage concerns clinicians' difficulty in accessing guidance when it is most needed. Although the HL7 'Infobutton Standard' allows clinicians easier access to external references, access to locally-developed resources often requires clinicians to deviate from their normal electronic health record (EHR) workflow to use another application.

Longitudinal Analysis of Computerized Alerts for Laboratory Monitoring of Post-liver Transplant Immunosuppressive Care.

Post-liver transplant patients require lifelong immunosuppressive care and monitoring. Computerized alerts can aid laboratory monitoring, but it is unknown how the distribution of alerts changes over time. We describe the changes over time of the distribution of computerized alerts for laboratory monitoring of post-liver transplant immunosuppressive care.

Challenges and Solutions in Optimizing Execution Performance of a Clinical Decision Support-Based Quality Measurement (CDS-QM) Framework.

Given the close relationship between clinical decision support (CDS) and quality measurement (QM), it has been proposed that a standards-based CDS Web service could be leveraged to enable QM. Benefits of such a CDS-QM framework include semantic consistency and implementation efficiency. However, earlier research has identified execution performance as a critical barrier when CDS-QM is applied to large populations.

Design, Development, and Initial Evaluation of a Terminology for Clinical Decision Support and Electronic Clinical Quality Measurement.

When coupled with a common information model, a common terminology for clinical decision support (CDS) and electronic clinical quality measurement (eCQM) could greatly facilitate the distributed development and sharing of CDS and eCQM knowledge resources. To enable such scalable knowledge authoring and sharing, we systematically developed an extensible and standards-based terminology for CDS and eCQM in the context of the HL7 Virtual Medical Record (vMR) information model. The development of this terminology entailed three steps: (1) systematic, physician-curated concept identification from sources such as the Health Information Technology Standards Panel (HITSP) and the SNOMED-CT CORE problem list; (2) concept de-duplication leveraging the Unified Medical Language System (UMLS) MetaMap and Metathesaurus; and (3) systematic concept naming using standard terminologies and heuristic algorithms.

Value Driven Outcomes (VDO): a pragmatic, modular, and extensible software framework for understanding and improving health care costs and outcomes.

Objective: To develop expeditiously a pragmatic, modular, and extensible software framework for understanding and improving healthcare value (costs relative to outcomes).

Materials And Methods: In 2012, a multidisciplinary team was assembled by the leadership of the University of Utah Health Sciences Center and charged with rapidly developing a pragmatic and actionable analytics framework for understanding and enhancing healthcare value. Based on an analysis of relevant prior work, a value analytics framework known as Value Driven Outcomes (VDO) was developed using an agile methodology.

Clinical Decision Support-based Quality Measurement (CDS-QM) Framework: Prototype Implementation, Evaluation, and Future Directions.

Electronic quality measurement (QM) and clinical decision support (CDS) are closely related but are typically implemented independently, resulting in significant duplication of effort. While it seems intuitive that technical approaches could be re-used across these two related use cases, such reuse is seldom reported in the literature, especially for standards-based approaches. Therefore, we evaluated the feasibility of using a standards-based CDS framework aligned with anticipated EHR certification criteria to implement electronic QM.

Evaluation of need for ontologies to manage domain content for the Reportable Conditions Knowledge Management System.

The Reportable Condition Knowledge Management System (RCKMS) is envisioned to be a single, comprehensive, authoritative, real-time portal to author, view and access computable information about reportable conditions. The system is designed for use by hospitals, laboratories, health information exchanges, and providers to meet public health reporting requirements. The RCKMS Knowledge Representation Workgroup was tasked to explore the need for ontologies to support RCKMS functionality.

Point-of-Care Testing as an Influenza Surveillance Tool: Methodology and Lessons Learned from Implementation.

Objectives. Disease surveillance combines data collection and analysis with dissemination of findings to decision makers. The timeliness of these activities affects the ability to implement preventive measures.

Comparison of computerized surveillance and manual chart review for adverse events.

Objective: To understand how the source of information affects different adverse event (AE) surveillance methods.

Design: Retrospective analysis of inpatient adverse drug events (ADEs) and hospital-associated infections (HAIs) detected by either a computerized surveillance system (CSS) or manual chart review (MCR).

Measurement: Descriptive analysis of events detected using the two methods by type of AE, type of information about the AE, and sources of the information.

Evaluation of knowledge resources for public health reporting logic: Implications for knowledge authoring and management.

To control disease, laboratories and providers are required to report conditions to public health authorities. Reporting logic is defined in a variety of resources, but there is no single resource available for reporters to access the list of reportable events and computable reporting logic for any jurisdiction. In order to develop evidence-based requirements for authoring such knowledge, we evaluated reporting logic in the Council of State and Territorial Epidemiologist (CSTE) position statements to assess its readiness for automated systems and identify features that should be considered when designing an authoring interface; we evaluated codes in the Reportable Condition Mapping Tables (RCMT) relative to the nationally-defined reporting logic, and described the high level business processes and knowledge required to support laboratory-based public health reporting.

Characterization of public health alerts and their suitability for alerting in electronic health record systems.

Public health agencies including federal, state, and local governments routinely send out public health advisories and alerts via e-mail and text messages to health care providers to increase awareness of public health events and situations. Agencies must ensure that practitioners have timely and accessible information at the critical point-of-care. Electronic health record (EHR) systems have the potential to alert physicians of emerging health conditions deemed important for public health at the most critical time of need.

Public health communication with frontline clinicians during the first wave of the 2009 influenza pandemic.

Context: During public health emergencies, office-based frontline clinicians are critical partners in the detection, treatment, and control of disease. Communication between public health authorities and frontline clinicians is critical, yet public health agencies, medical societies, and healthcare delivery organizations have all called for improvements.

Objectives: Describe communication processes between public health and frontline clinicians during the first wave of the 2009 novel influenza A(H1N1) pandemic; assess clinicians' use of and knowledge about public health guidance; and assess clinicians' perceptions and preferences about communication during a public health emergency.

Development of an electronic public health case report using HL7 v2.5 to meet public health needs.

Clinicians are required to report selected conditions to public health authorities within a stipulated amount of time. The current reporting process is mostly paper-based and inefficient and may lead to delays in case investigation. As electronic medical records become more prevalent, electronic case reporting is becoming increasingly feasible.