Electronic clinical decision support for children with minor head trauma and intracranial injuries: a sociotechnical analysis.

BACKGROUND: Current management of children with minor head trauma (MHT) and intracranial injuries is not evidence-based and may place some children at risk of harm. Evidence-based electronic clinical decision support (CDS) for management of these children may improve patient safety and decrease resource use. To guide these efforts, we evaluated the sociotechnical environment impacting the implementation of electronic CDS, including workflow and communication, institutional culture, and hardware and software infrastructure, among other factors. METHODS: Between March and May, 2020 semi-structured qualitative focus group interviews were conducted to identify sociotechnical influences on CDS implementation. Physicians from neurosurgery, emergency medicine, critical care, and pediatric general surgery were included, along with information technology specialists. Participants were recruited from nine health centers in the United States. Focus group transcripts were coded and analyzed using thematic analysis. The final themes were then cross-referenced with previously defined sociotechnical dimensions. RESULTS: We included 28 physicians and four information technology specialists in seven focus groups (median five participants per group). Five physicians were trainees and 10 had administrative leadership positions. Through inductive thematic analysis, we identified five primary themes: (1) clinical impact; (2) stakeholders and users; (3) tool content; (4) clinical practice integration; and (5) post-implementation evaluation measures. Participants generally supported using CDS to determine an appropriate level-of-care for these children. However, some had mixed feelings regarding how the tool could best be used by different specialties (e.g. use by neurosurgeons versus non-neurosurgeons). Feedback from the interviews helped refine the tool content and also highlighted potential technical and workflow barriers to address prior to implementation. CONCLUSIONS: We identified key factors impacting the implementation of electronic CDS for children with MHT and intracranial injuries. These results have informed our implementation strategy and may also serve as a template for future efforts to implement health information technology in a multidisciplinary, emergency setting.

Mobile Health Applications, Cancer Survivors, and Lifestyle Modification: An Integrative Review.

Cancer survivors' well-being is threatened by the risk of cancer recurrence and the increased risk of chronic diseases resulting from cancer treatments. Improving lifestyle behaviors attenuates these risks. Traditional approaches to lifestyle modification (ie, counseling) are expensive, require significant human resources, and are difficult to scale. Mobile health interventions offer a novel alternative to traditional approaches. However, to date, systematic reviews have yet to examine the use of mobile health interventions for lifestyle behavior improvement among cancer survivors. The objectives of this integrative review were to synthesize research findings, critically appraise the scientific literature, examine the use of theory in intervention design, and identify survivors' preferences in using mobile health interventions for lifestyle improvement. Nineteen articles met eligibility requirements. Only two studies used quantitative methods. Study quality was low, and only one study reported the use of theory in app design. Unfortunately, the evidence has not yet sufficiently matured, in quality or in rigor, to make recommendations on how to improve health behaviors or outcomes. However, six themes emerged as important considerations for intervention development for cancer survivors (app features/functionality, social relationships/support, provider relationships/support, app content, app acceptability, and barriers to use). These findings underscored the need for rigorous, efficacy studies before the use of mobile health interventions can be safely recommended for cancer survivors.

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.

A multi-level usability evaluation of mobile health applications: A case study.

OBJECTIVE: To report a methodological approach for the development of a usable mHealth application (app). MATERIALS AND METHODS: This work was guided by a 3-level stratified view of health information technology (IT) usability evaluation framework. We first describe a number of methodologies for operationalizing each level of the framework. Following the description of each methodology, we present a case study which illustrates the use of our preferred methodologies for the development of a mHealth app. At level 1 (user-task), we applied a card sorting technique to guide the information architecture of a mobile HIV symptom self-management app, entitled mVIP. At level 2 (user-task-system), we conducted a usability evaluation of mVIP in a laboratory setting through end-user usability testing and heuristic evaluation with informatics experts. At level 3 (user-task-system-environment), usability of mVIP was evaluated in a real-world setting following the use of the app during a 3-month trial. RESULTS: The 3-level usability evaluation guided our work exploring in-depth interactions between the user, task, system, and environment. Integral to the findings from the 3-level usability evaluation, we iteratively refined the app's content, functionality, and interface to meet the needs of our intended end-users. DISCUSSION AND CONCLUSION: The stratified view of the health IT usability evaluation framework is a useful methodological approach for the design, development, and evaluation of mHealth apps. The methodological recommendations for using the theoretical framework can inform future usability studies of mHealth apps.

Top-of-License Nursing Practice, Part 2: Differentiating BSN and ADN Perceptions of Top-of-License Activities.

OBJECTIVE: The aim of this study was to describe differences in associate degree (ADN) and baccalaureate degree-prepared (BSN) nurses' perceptions of top-of-license (TOL) practice. BACKGROUND: To date, no empirical work has examined whether ADN and BSN nurses approach TOL practice nursing activities differently. METHODS: We conducted a qualitative pilot study with focus groups to explore the perceptions of a group of ADN- and BSN-prepared nurses concerning nursing activities and their relation to TOL practice. RESULTS: Subthemes emerged differentiating how ADN and BSN nurses perceived their responsibilities related to critical thinking, communication, and patient education. For professional nursing care, 5 subthemes further emerged: (a) approaches to assessment, (b) chart review, (c) psychosocial patient care, (d) documentation, and (e) handoff. CONCLUSIONS: The differences identified in approaches to TOL practice activities by educational preparation have implications for staffing patterns that can optimize the contribution of ADN- and BSN-prepared nurses. Further research is indicated.

Top-of-License Nursing Practice: Describing Common Nursing Activities and Nurses' Experiences That Hinder Top-of-License Practice, Part 1.

OBJECTIVE: The aims of this study were to describe nurses' perceptions of nursing activities and analyze for consistency with top-of-license (TOL) practice. BACKGROUND: The Advisory Board Company expert panel proposed 8 TOL core nursing responsibilities representing practice at its potential. Thus far, no empirical work has examined nursing practices relative to TOL, from staff nurses' points of view. METHODS: This qualitative study used focus groups to explore perceptions of typical nursing activities. We analyzed activities for themes that described nurses' work during typical shifts. RESULTS: Nurses' full scope of work included TOL-consistent categories, as well as categories that did not exemplify TOL practice, such as nonnursing care. A proposed model was developed, which depicts nurses' total scope of work, inclusive of all activity categories. In addition, hindrances to TOL practice were also identified. CONCLUSIONS: Findings from this study can inform leadership imperatives and the development of innovative, sustainable nursing practice models that support nursing practice at TOL.

Using ontology-based semantic similarity to facilitate the article screening process for systematic reviews.

OBJECTIVE: Systematic Reviews (SRs) are utilized to summarize evidence from high quality studies and are considered the preferred source of evidence-based practice (EBP). However, conducting SRs can be time and labor intensive due to the high cost of article screening. In previous studies, we demonstrated utilizing established (lexical) article relationships to facilitate the identification of relevant articles in an efficient and effective manner. Here we propose to enhance article relationships with background semantic knowledge derived from Unified Medical Language System (UMLS) concepts and ontologies. METHODS: We developed a pipelined semantic concepts representation process to represent articles from an SR into an optimized and enriched semantic space of UMLS concepts. Throughout the process, we leveraged concepts and concept relations encoded in biomedical ontologies (SNOMED-CT and MeSH) within the UMLS framework to prompt concept features of each article. Article relationships (similarities) were established and represented as a semantic article network, which was readily applied to assist with the article screening process. We incorporated the concept of active learning to simulate an interactive article recommendation process, and evaluated the performance on 15 completed SRs. We used work saved over sampling at 95% recall (WSS95) as the performance measure. RESULTS: We compared the WSS95 performance of our ontology-based semantic approach to existing lexical feature approaches and corpus-based semantic approaches, and found that we had better WSS95 in most SRs. We also had the highest average WSS95 of 43.81% and the highest total WSS95 of 657.18%. CONCLUSION: We demonstrated using ontology-based semantics to facilitate the identification of relevant articles for SRs. Effective concepts and concept relations derived from UMLS ontologies can be utilized to establish article semantic relationships. Our approach provided a promising performance and can easily apply to any SR topics in the biomedical domain with generalizability.

Nursing Informatics Competency Assessment for the Nurse Leader: The Delphi Study.

OBJECTIVE: The aim of this study was to identify nursing informatics competencies perceived as relevant and required by nurse leaders. BACKGROUND: To participate as a full partner in healthcare leadership among rapidly advancing health information technologies (HITs), nurse leaders must attain knowledge of informatics competencies related to their clinical leadership roles and responsibilities. Despite this increased need to engage in HIT-related decision making, a gap remains in validated informatics competencies specific to the needs of nurse leaders. METHODS: An environmental scan and 3-round survey using Delphi methods used with nurse leaders for competency identification were used. RESULTS: Between 26 and 41 participants responded to each Delphi round. Most nurse leaders acquired HIT knowledge through on-the-job training. We identified 74 competencies from an initial list of 108 competencies. CONCLUSION: This work can advance nursing practice to move beyond "on-the-job informatics training" to a more competency-based model of nursing informatics education and practice.

Nursing Informatics Competency Assessment for the Nurse Leader: Instrument Refinement, Validation, and Psychometric Analysis.

OBJECTIVE: The aim of this article is to refine and validate a new instrument, Nursing Informatics Competency Assessment for the Nurse Leader (NL). BACKGROUND: Because health information technology rapidly advances, the NL requires greater levels of informatics knowledge. METHODS: Item reduction and psychometric analysis methodology RESULTS: A total of 357 national NLs completed the survey. Exploratory factor analysis resulted in a final 6-factor solution that contained 26 items: (1) strategic implementation management, (2) advanced information management and education, (3) executive planning, (4) ethical and legal concepts, (5) information systems concepts, and (6) requirements and system selection. Cronbach's α were .96, .91, .90, .83, .92, .81, respectively. CONCLUSION: We established a valid and reliable nursing informatics competency assessment instrument with sufficient specificity to guide NLs to recognize the competencies required in their role, create solutions to address potential gaps, and enhance delivery of patient care.

Time motion studies in healthcare: what are we talking about?

Time motion studies were first described in the early 20th century in industrial engineering, referring to a quantitative data collection method where an external observer captured detailed data on the duration and movements required to accomplish a specific task, coupled with an analysis focused on improving efficiency. Since then, they have been broadly adopted by biomedical researchers and have become a focus of attention due to the current interest in clinical workflow related factors. However, attempts to aggregate results from these studies have been difficult, resulting from a significant variability in the implementation and reporting of methods. While efforts have been made to standardize the reporting of such data and findings, a lack of common understanding on what "time motion studies" are remains, which not only hinders reviews, but could also partially explain the methodological variability in the domain literature (duration of the observations, number of tasks, multitasking, training rigor and reliability assessments) caused by an attempt to cluster dissimilar sub-techniques. A crucial milestone towards the standardization and validation of time motion studies corresponds to a common understanding, accompanied by a proper recognition of the distinct techniques it encompasses. Towards this goal, we conducted a review of the literature aiming at identifying what is being referred to as "time motion studies". We provide a detailed description of the distinct methods used in articles referenced or classified as "time motion studies", and conclude that currently it is used not only to define the original technique, but also to describe a broad spectrum of studies whose only common factor is the capture and/or analysis of the duration of one or more events. To maintain alignment with the existing broad scope of the term, we propose a disambiguation approach by preserving the expanded conception, while recommending the use of a specific qualifier "continuous observation time motion studies" to refer to variations of the original method (the use of an external observer recording data continuously). In addition, we present a more granular naming for sub-techniques within continuous observation time motion studies, expecting to reduce the methodological variability within each sub-technique and facilitate future results aggregation.

Understanding the Technical Implementation of a Clinical Decision Support SmartApp: A Qualitative Analysis.

CONCERN is a SmartApp that identifies patients at risk for deterioration. This study aimed to understand the technical components and processes that should be included in our Implementation Toolkit. In focus groups with technical experts five themes emerged: 1) implementation challenges, 2) implementation facilitators, 3) project management, 4) stakeholder engagement, and 5) security assessments. Our results may aid other teams in implementing healthcare SmartApps.

Identifying Barriers to The Implementation of Communicating Narrative Concerns Entered by Registered Nurses, An Early Warning System SmartApp.

BACKGROUND: Nurses are at the frontline of detecting patient deterioration. We developed Communicating Narrative Concerns Entered by Registered Nurses (CONCERN), an early warning system for clinical deterioration that generates a risk prediction score utilizing nursing data. CONCERN was implemented as a randomized clinical trial at two health systems in the Northeastern United States. Following the implementation of CONCERN, our team sought to develop the CONCERN Implementation Toolkit to enable other hospital systems to adopt CONCERN. OBJECTIVE: The aim of this study was to identify the optimal resources needed to implement CONCERN and package these resources into the CONCERN Implementation Toolkit to enable the spread of CONCERN to other hospital sites. METHODS: To accomplish this aim, we conducted qualitative interviews with nurses, prescribing providers, and information technology experts in two health systems. We recruited participants from July 2022 to January 2023. We conducted thematic analysis guided by the Donabedian model. Based on the results of the thematic analysis, we updated the α version of the CONCERN Implementation Toolkit. RESULTS: There was a total of 32 participants included in our study. In total, 12 themes were identified, with four themes mapping to each domain in Donabedian's model (i.e., structure, process, and outcome). Eight new resources were added to the CONCERN Implementation Toolkit. CONCLUSIONS: This study validated the α version of the CONCERN Implementation Toolkit. Future studies will focus on returning the results of the Toolkit to the hospital sites to validate the ß version of the CONCERN Implementation Toolkit. As the development of early warning systems continues to increase and clinician workflows evolve, the results of this study will provide considerations for research teams interested in implementing early warning systems in the acute care setting.

Assessment of the Health IT Usability Evaluation Model (Health-ITUEM) for evaluating mobile health (mHealth) technology.

BACKGROUND: Over two decades of research has been conducted using mobile devices for health related behaviors yet many of these studies lack rigor. There are few evaluation frameworks for assessing the usability of mHealth, which is critical as the use of this technology proliferates. As the development of interventions using mobile technology increase, future work in this domain necessitates the use of a rigorous usability evaluation framework. METHODS: We used two exemplars to assess the appropriateness of the Health IT Usability Evaluation Model (Health-ITUEM) for evaluating the usability of mHealth technology. In the first exemplar, we conducted 6 focus group sessions to explore adolescents' use of mobile technology for meeting their health Information needs. In the second exemplar, we conducted 4 focus group sessions following an Ecological Momentary Assessment study in which 60 adolescents were given a smartphone with pre-installed health-related applications (apps). DATA ANALYSIS: We coded the focus group data using the 9 concepts of the Health-ITUEM: Error prevention, Completeness, Memorability, Information needs, Flexibility/Customizability, Learnability, Performance speed, Competency, Other outcomes. To develop a finer granularity of analysis, the nine concepts were broken into positive, negative, and neutral codes. A total of 27 codes were created. Two raters (R1 and R2) initially coded all text and a third rater (R3) reconciled coding discordance between raters R1 and R2. RESULTS: A total of 133 codes were applied to Exemplar 1. In Exemplar 2 there were a total of 286 codes applied to 195 excerpts. Performance speed, Other outcomes, and Information needs were among the most frequently occurring codes. CONCLUSION: Our two exemplars demonstrated the appropriateness and usefulness of the Health-ITUEM in evaluating mobile health technology. Further assessment of this framework with other study populations should consider whether Memorability and Error prevention are necessary to include when evaluating mHealth technology.

Sustainable Development for Mobile Health Apps Using the Human-Centered Design Process.

Well-documented scientific evidence indicates that mobile health (mHealth) apps can improve the quality of life, relieve symptoms, and restore health for patients. In addition to improving patients' health outcomes, mHealth apps reduce health care use and the cost burdens associated with disease management. Currently, patients and health care providers have a wide variety of choices among commercially available mHealth apps. However, due to the high resource costs and low user adoption of mHealth apps, the cost-benefit relationship remains controversial. When compared to traditional expert-driven approaches, applying human-centered design (HCD) may result in more useable, acceptable, and effective mHealth apps. In this paper, we summarize current HCD practices in mHealth development studies and make recommendations to improve the sustainability of mHealth. These recommendations include consideration of factors regarding culture norms, iterative evaluations on HCD practice, use of novelty in mHealth app, and consideration of privacy and reliability across the entire HCD process. Additionally, we suggest a sociotechnical lens toward HCD practices to promote the sustainability of mHealth apps. Future research should consider standardizing the HCD practice to help mHealth researchers and developers avoid barriers associated with inadequate HCD practices.

Computational challenges and human factors influencing the design and use of clinical research participant eligibility pre-screening tools.

BACKGROUND: Clinical trials are the primary mechanism for advancing clinical care and evidenced-based practice, yet challenges with the recruitment of participants for such trials are widely recognized as a major barrier to these types of studies. Data warehouses (DW) store large amounts of heterogenous clinical data that can be used to enhance recruitment practices, but multiple challenges exist when using a data warehouse for such activities, due to the manner of collection, management, integration, analysis, and dissemination of the data. A critical step in leveraging the DW for recruitment purposes is being able to match trial eligibility criteria to discrete and semi-structured data types in the data warehouse, though trial eligibility criteria tend to be written without concern for their computability. We present the multi-modal evaluation of a web-based tool that can be used for pre-screening patients for clinical trial eligibility and assess the ability of this tool to be practically used for clinical research pre-screening and recruitment. METHODS: The study used a validation study, usability testing, and a heuristic evaluation to evaluate and characterize the operational characteristics of the software as well as human factors affecting its use. RESULTS: Clinical trials from the Division of Cardiology and the Department of Family Medicine were used for this multi-modal evaluation, which included a validation study, usability study, and a heuristic evaluation. From the results of the validation study, the software demonstrated a positive predictive value (PPV) of 54.12% and 0.7%, respectively, and a negative predictive value (NPV) of 73.3% and 87.5%, respectively, for two types of clinical trials. Heuristic principles concerning error prevention and documentation were characterized as the major usability issues during the heuristic evaluation. CONCLUSIONS: This software is intended to provide an initial list of eligible patients to a clinical study coordinators, which provides a starting point for further eligibility screening by the coordinator. Because this software has a high "rule in" ability, meaning that it is able to remove patients who are not eligible for the study, the use of an automated tool built to leverage an existing enterprise DW can be beneficial to determining eligibility and facilitating clinical trial recruitment through pre-screening. While the results of this study are promising, further refinement and study of this and related approaches to automated eligibility screening, including comparison to other approaches and stakeholder perceptions, are needed and future studies are planned to address these needs.

Stakeholder Mapping on the Development of Digital Health Interventions for Self-Management Among Patients with Chronic Obstructive Pulmonary Disease in China.

Chronic Obstructive Pulmonary Disease (COPD) is a progressive lung disease consisting of chronic bronchitis and emphysema. Digital Health Interventions (DHIs) can improve COPD patients' self-management. However, the market penetration of DHIs is lower than expected. Using stakeholder mapping, healthcare providers identified opportunities for design and development of sustainable DHIs. Two different stakeholder maps were identified. These maps demonstrated the importance of utilizing structured mapping techniques to understand roles of different stakeholders, and addressing regulatory and practice needs to ultimately support patient self-management.

A Data-Driven Pipeline to Discover Treatment Variations and the Associated Contributing Factors Balanced with Optimal Granularity.

Evidence-based medicine utilizes research evidence from clinical trials to support treatment decisions. To leverage the advantage of electronic health records and big data analysis methods, we developed a data-driven analytic pipeline that uses 1) agglomerative hierarchical clustering to define different granularity of treatment variation, 2) feature selection and multinomial multivariate logistic regression analysis to identify variables (factors) associated with treatment variation, and 3) prognosis analysis to compare patient outcome across top treatment groups. We tested our approach on the diffuse large B-cell lymphoma patient population from the MIMIC-IV dataset and found that our approach helps determine the optimal granularity of treatment variation and identify factors associated with treatment variation but not realized in randomized controlled trials due to unbalanced patient cohorts. We also found some patient cohorts' characteristics that could serve to inspire hypothesis generation, such as the influence of ethnicity on the treatment plans and subsequent prognoses.

DocFlow: A Visual Analytics System for Question-based Document Retrieval and Categorization.

A systematic review (SR) is essential with up-to-date research evidence to support clinical decisions and practices. However, the growing literature volume makes it challenging for SR reviewers and clinicians to discover useful information efficiently. Many human-in-the-loop information retrieval approaches (HIR) have been proposed to rank documents semantically similar to users' queries and provide interactive visualizations to facilitate document retrieval. Given that the queries are mainly composed of keywords and keyphrases retrieving documents that are semantically similar to a query does not necessarily respond to the clinician's need. Clinicians still have to review many documents to find the solution. The problem motivates us to develop a visual analytics system, DocFlow, to facilitate information-seeking. One of the features of our DocFlow is accepting natural language questions. The detailed description enables retrieving documents that can answer users' questions. Additionally, clinicians often categorize documents based on their backgrounds and with different purposes (e.g., populations, treatments). Since the criteria are unknown and cannot be pre-defined in advance, existing methods can only achieve categorization by considering the entire information in documents. In contrast, by locating answers in each document, our DocFlow can intelligently categorize documents based on users' questions. The second feature of our DocFlow is a flexible interface where users can arrange a sequence of questions to customize their rules for document retrieval and categorization. The two features of this visual analytics system support a flexible information-seeking process. The case studies and the feedback from domain experts demonstrate the usefulness and effectiveness of our DocFlow.

PhraseMap: Attention-Based Keyphrases Recommendation for Information Seeking.

Many Information Retrieval (IR) approaches have been proposed to extract relevant information from a large corpus. Among these methods, phrase-based retrieval methods have been proven to capture more concrete and concise information than word-based and paragraph-based methods. However, due to the complex relationship among phrases and a lack of proper visual guidance, achieving user-driven interactive information-seeking and retrieval remains challenging. In this study, we present a visual analytic approach for users to seek information from an extensive collection of documents efficiently. The main component of our approach is a PhraseMap, where nodes and edges represent the extracted keyphrases and their relationships, respectively, from a large corpus. To build the PhraseMap, we extract keyphrases from each document and link the phrases according to word attention determined using modern language models, i.e., BERT. As can be imagined, the graph is complex due to the extensive volume of information and the massive amount of relationships. Therefore, we develop a navigation algorithm to facilitate information seeking. It includes (1) a question-answering (QA) model to identify phrases related to users' queries and (2) updating relevant phrases based on users' feedback. To better present the PhraseMap, we introduce a resource-controlled self-organizing map (RC-SOM) to evenly and regularly display phrases on grid cells while expecting phrases with similar semantics to stay close in the visualization. To evaluate our approach, we conducted case studies with three domain experts in diverse literature. The results and feedback demonstrate its effectiveness, usability, and intelligence.