Publications by authors named "Guilherme Del Fiol"

154 Publications

User interface approaches implemented with automated patient deterioration surveillance tools: protocol for a scoping review.

BMJ Open 2022 Jan 13;12(1):e055525. Epub 2022 Jan 13.

College of Pharmacy, Idaho State University, Pocatello, Idaho, USA

Introduction: Early identification of patients who may suffer from unexpected adverse events (eg, sepsis, sudden cardiac arrest) gives bedside staff valuable lead time to care for these patients appropriately. Consequently, many machine learning algorithms have been developed to predict adverse events. However, little research focuses on how these systems are implemented and how system design impacts clinicians' decisions or patient outcomes. This protocol outlines the steps to review the designs of these tools.

Methods And Analysis: We will use scoping review methods to explore how tools that leverage machine learning algorithms in predicting adverse events are designed to integrate into clinical practice. We will explore the types of user interfaces deployed, what information is displayed, and how clinical workflows are supported. Electronic sources include Medline, Embase, CINAHL Complete, Cochrane Library (including CENTRAL), and IEEE Xplore from 1 January 2009 to present. We will only review primary research articles that report findings from the implementation of patient deterioration surveillance tools for hospital clinicians. The articles must also include a description of the tool's user interface. Since our primary focus is on how the user interacts with automated tools driven by machine learning algorithms, electronic tools that do not extract data from clinical data documentation or recording systems such as an EHR or patient monitor, or otherwise require manual entry, will be excluded. Similarly, tools that do not synthesise information from more than one data variable will also be excluded. This review will be limited to English-language articles. Two reviewers will review the articles and extract the data. Findings from both researchers will be compared with minimise bias. The results will be quantified, synthesised and presented using appropriate formats.

Ethics And Dissemination: Ethics review is not required for this scoping review. Findings will be disseminated through peer-reviewed publications.
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http://dx.doi.org/10.1136/bmjopen-2021-055525DOI Listing
January 2022

The potential for leveraging machine learning to filter medication alerts.

J Am Med Inform Assoc 2022 Jan 5. Epub 2022 Jan 5.

Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA.

Objective: To evaluate the potential for machine learning to predict medication alerts that might be ignored by a user, and intelligently filter out those alerts from the user's view.

Materials And Methods: We identified features (eg, patient and provider characteristics) proposed to modulate user responses to medication alerts through the literature; these features were then refined through expert review. Models were developed using rule-based and machine learning techniques (logistic regression, random forest, support vector machine, neural network, and LightGBM). We collected log data on alerts shown to users throughout 2019 at University of Utah Health. We sought to maximize precision while maintaining a false-negative rate <0.01, a threshold predefined through discussion with physicians and pharmacists. We developed models while maintaining a sensitivity of 0.99. Two null hypotheses were developed: H1-there is no difference in precision among prediction models; and H2-the removal of any feature category does not change precision.

Results: A total of 3,481,634 medication alerts with 751 features were evaluated. With sensitivity fixed at 0.99, LightGBM achieved the highest precision of 0.192 and less than 0.01 for the pre-defined maximal false-negative rate by subject-matter experts (H1) (P < 0.001). This model could reduce alert volume by 54.1%. We removed different combinations of features (H2) and found that not all features significantly contributed to precision. Removing medication order features (eg, dosage) most significantly decreased precision (-0.147, P = 0.001).

Conclusions: Machine learning potentially enables the intelligent filtering of medication alerts.
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http://dx.doi.org/10.1093/jamia/ocab292DOI Listing
January 2022

Application of community - engaged dissemination and implementation science to improve health equity.

Prev Med Rep 2021 Dec 26;24:101620. Epub 2021 Oct 26.

Center for Health Outcomes and Population Equity, University of Utah and Huntsman Cancer Institute, 2000 Circle of Hope Dr, Salt Lake City, UT 84112, United States.

Community engagement is critical to accelerate and improve implementation of evidence-based interventions to reduce health inequities. Community-engaged dissemination and implementation research (CEDI) emphasizes engaging stakeholders (e.g., community members, practitioners, community organizations, etc.) with diverse perspectives, experience, and expertise to provide tacit community knowledge regarding the local context, priorities, needs, and assets. Importantly, CEDI can help improve health inequities through incorporating unique perspectives from communities experiencing health inequities that have historically been left out of the research process. The community-engagement process that exists in practice can be highly variable, and characteristics of the process are often underreported, making it difficult to discern how engagement of community partners was used to improve implementation. This paper describes the community-engagement process for a multilevel, pragmatic randomized trial to increase the reach and impact of evidence-based tobacco cessation treatment among Community Health Center patients; describes how engagement activities and the resulting partnership informed the development of implementation strategies and improved the research process; and presents lessons learned to inform future CEDI research.
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http://dx.doi.org/10.1016/j.pmedr.2021.101620DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8684008PMC
December 2021

Patient Interactions With an Automated Conversational Agent Delivering Pretest Genetics Education: Descriptive Study.

J Med Internet Res 2021 11 18;23(11):e29447. Epub 2021 Nov 18.

Department of Communication, University of Utah, Salt Lake City, UT, United States.

Background: Cancer genetic testing to assess an individual's cancer risk and to enable genomics-informed cancer treatment has grown exponentially in the past decade. Because of this continued growth and a shortage of health care workers, there is a need for automated strategies that provide high-quality genetics services to patients to reduce the clinical demand for genetics providers. Conversational agents have shown promise in managing mental health, pain, and other chronic conditions and are increasingly being used in cancer genetic services. However, research on how patients interact with these agents to satisfy their information needs is limited.

Objective: Our primary aim is to assess user interactions with a conversational agent for pretest genetics education.

Methods: We conducted a feasibility study of user interactions with a conversational agent who delivers pretest genetics education to primary care patients without cancer who are eligible for cancer genetic evaluation. The conversational agent provided scripted content similar to that delivered in a pretest genetic counseling visit for cancer genetic testing. Outside of a core set of information delivered to all patients, users were able to navigate within the chat to request additional content in their areas of interest. An artificial intelligence-based preprogrammed library was also established to allow users to ask open-ended questions to the conversational agent. Transcripts of the interactions were recorded. Here, we describe the information selected, time spent to complete the chat, and use of the open-ended question feature. Descriptive statistics were used for quantitative measures, and thematic analyses were used for qualitative responses.

Results: We invited 103 patients to participate, of which 88.3% (91/103) were offered access to the conversational agent, 39% (36/91) started the chat, and 32% (30/91) completed the chat. Most users who completed the chat indicated that they wanted to continue with genetic testing (21/30, 70%), few were unsure (9/30, 30%), and no patient declined to move forward with testing. Those who decided to test spent an average of 10 (SD 2.57) minutes on the chat, selected an average of 1.87 (SD 1.2) additional pieces of information, and generally did not ask open-ended questions. Those who were unsure spent 4 more minutes on average (mean 14.1, SD 7.41; P=.03) on the chat, selected an average of 3.67 (SD 2.9) additional pieces of information, and asked at least one open-ended question.

Conclusions: The pretest chat provided enough information for most patients to decide on cancer genetic testing, as indicated by the small number of open-ended questions. A subset of participants were still unsure about receiving genetic testing and may require additional education or interpersonal support before making a testing decision. Conversational agents have the potential to become a scalable alternative for pretest genetics education, reducing the clinical demand on genetics providers.
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http://dx.doi.org/10.2196/29447DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8663668PMC
November 2021

CASIDE: A data model for interoperable cancer survivorship information based on FHIR.

J Biomed Inform 2021 Dec 13;124:103953. Epub 2021 Nov 13.

atlanTTic Research Center, Department of Telematics Engineering, University of Vigo, Vigo, Spain.

Cancer survivorship has traditionally received little research attention although it is associated with a variety of long-term consequences and also many other comorbidities. There is an urgent need to increase research on this area, and the secondary use of healthcare data has the potential to provide valuable insights on survivors' health trajectories. However, cancer survivors' data is often stored in silos and collected inconsistently. In this study we present CASIDE, an interoperable data model for cancer survivorship information that aims to accelerate the secondary use of healthcare data and data sharing across institutions. It is designed to provide a holistic view of the cancer survivor, taking into account not just the clinical data but also the patient's own perspective, and is built upon the emerging Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR) standard. Advantages of adopting FHIR and challenges in information modelling using this standard are discussed. CASIDE is a generalizable approach that is already being used as a support tool for the development of downstream applications to support clinical decision making and can contribute to translational collaborative research on cancer survivorship.
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http://dx.doi.org/10.1016/j.jbi.2021.103953DOI Listing
December 2021

Successful Multi-Level HPV Vaccination Intervention at a Rural Healthcare Center in the Era of COVID-19.

Front Digit Health 2021 19;3:719138. Epub 2021 Aug 19.

Kaiser Permanente Center for Health Research, Portland, OR, United States.

To develop and test a human papillomavirus (HPV) vaccination intervention that includes healthcare team training activities and patient reminders to reduce missed opportunities and improves the rate of appointment scheduling for HPV vaccination in a rural medical clinic in the United States. The multi-level and multi-component intervention included healthcare team training activities and the distribution of patient education materials along with technology-based patient HPV vaccination reminders for parents/caregivers and young adult patients. Missed vaccination opportunities were assessed pre- and post-intervention ( = 402 and = 99, respectively) by retrospective chart review and compared using Pearson χ. The patient parent/caregiver and young adult patient population ( = 80) was surveyed following the reminder messages and penalized logistic regression quantified unadjusted odds of scheduling a visit. Missed opportunities for HPV vaccination declined significantly from the pre-intervention to the post-intervention period (21.6 vs. 8.1%, respectively, = 0.002). Participants who recalled receipt of a vaccination reminder had 7.0 (95% 2.4-22.8) times higher unadjusted odds of scheduling a visit compared with those who did not recall receiving a reminder. The unadjusted odds of confirming that they had scheduled or were intending to schedule a follow-up appointment to receive the HPV vaccine was 4.9 (95% 1.51-20.59) times greater among those who had not received the vaccine for themselves or for their child. Results from this intervention are promising and suggest that vaccination interventions consisting of provider and support staff education and parent/caregiver and patient education materials, and reminders can reduce missed opportunities for vaccinations in rural settings.
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http://dx.doi.org/10.3389/fdgth.2021.719138DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8521914PMC
August 2021

A Shared Decision-making Tool for Drug Interactions Between Warfarin and Nonsteroidal Anti-inflammatory Drugs: Design and Usability Study.

JMIR Hum Factors 2021 Oct 26;8(4):e28618. Epub 2021 Oct 26.

University of Utah, Salt Lake City, UT, United States.

Background: Exposure to life-threatening drug-drug interactions (DDIs) occurs despite the widespread use of clinical decision support. The DDI between warfarin and nonsteroidal anti-inflammatory drugs is common and potentially life-threatening. Patients can play a substantial role in preventing harm from DDIs; however, the current model for DDI decision-making is clinician centric.

Objective: This study aims to design and study the usability of DDInteract, a tool to support shared decision-making (SDM) between a patient and provider for the DDI between warfarin and nonsteroidal anti-inflammatory drugs.

Methods: We used an SDM framework and user-centered design methods to guide the design and usability of DDInteract-an SDM electronic health record app to prevent harm from clinically significant DDIs. The design involved iterative prototypes, qualitative feedback from stakeholders, and a heuristic evaluation. The usability evaluation included patients and clinicians. Patients participated in a simulated SDM discussion using clinical vignettes. Clinicians were asked to complete eight tasks using DDInteract and to assess the tool using a survey adapted from the System Usability Scale.

Results: The designed DDInteract prototype includes the following features: a patient-specific risk profile, dynamic risk icon array, patient education section, and treatment decision tree. A total of 4 patients and 11 clinicians participated in the usability study. After an SDM session where patients and clinicians review the tool concurrently, patients generally favored pain treatments with less risk of gastrointestinal bleeding. Clinicians successfully completed the tasks with a mean of 144 (SD 74) seconds and rated the usability of DDInteract as 4.32 (SD 0.52) of 5.

Conclusions: This study expands the use of SDM to DDIs. The next steps are to determine if DDInteract can improve shared decision-making quality and to implement it across health systems using interoperable technology.
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http://dx.doi.org/10.2196/28618DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8579222PMC
October 2021

Physicians' strategies for using family history data: having the data is not the same as using the data.

JAMIA Open 2020 Oct 8;3(3):378-385. Epub 2020 Oct 8.

Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA.

Objective: To identify needs in a clinical decision support tool development by exploring how primary care providers currently collect and use family health history (FHH).

Design: Survey questionnaires and semi-structured interviews were administered to a mix of primary and specialty care clinicians within the University of Utah Health system (40 surveys, 12 interviews).

Results: Three key themes emerged regarding providers' collection and use of FHH: (1) Strategies for collecting FHH vary by level of effort; (2) Documentation practices extend beyond the electronic health record's dedicated FHH module; and (3) Providers desire feedback from genetic services consultation and are uncertain how to refer patients to genetic services.

Conclusion: Study findings highlight the varying degrees of engagement that providers have with collecting FHH. Improving the integration of FHH into workflow, and providing decision support, as well as links and tools to help providers better utilize genetic counseling may improve patient care.
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http://dx.doi.org/10.1093/jamiaopen/ooaa035DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7660959PMC
October 2020

Enhancing the use of EHR systems for pragmatic embedded research: lessons from the NIH Health Care Systems Research Collaboratory.

J Am Med Inform Assoc 2021 Nov;28(12):2626-2640

Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA.

Objective: We identified challenges and solutions to using electronic health record (EHR) systems for the design and conduct of pragmatic research.

Materials And Methods: Since 2012, the Health Care Systems Research Collaboratory has served as the resource coordinating center for 21 pragmatic clinical trial demonstration projects. The EHR Core working group invited these demonstration projects to complete a written semistructured survey and used an inductive approach to review responses and identify EHR-related challenges and suggested EHR enhancements.

Results: We received survey responses from 20 projects and identified 21 challenges that fell into 6 broad themes: (1) inadequate collection of patient-reported outcome data, (2) lack of structured data collection, (3) data standardization, (4) resources to support customization of EHRs, (5) difficulties aggregating data across sites, and (6) accessing EHR data.

Discussion: Based on these findings, we formulated 6 prerequisites for PCTs that would enable the conduct of pragmatic research: (1) integrate the collection of patient-centered data into EHR systems, (2) facilitate structured research data collection by leveraging standard EHR functions, usable interfaces, and standard workflows, (3) support the creation of high-quality research data by using standards, (4) ensure adequate IT staff to support embedded research, (5) create aggregate, multidata type resources for multisite trials, and (6) create re-usable and automated queries.

Conclusion: We are hopeful our collection of specific EHR challenges and research needs will drive health system leaders, policymakers, and EHR designers to support these suggestions to improve our national capacity for generating real-world evidence.
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http://dx.doi.org/10.1093/jamia/ocab202DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8633608PMC
November 2021

Interoperable genetic lab test reports: mapping key data elements to HL7 FHIR specifications and professional reporting guidelines.

J Am Med Inform Assoc 2021 Nov;28(12):2617-2625

Department of Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, Utah, USA.

Objective: In many cases, genetic testing labs provide their test reports as portable document format files or scanned images, which limits the availability of the contained information to advanced informatics solutions, such as automated clinical decision support systems. One of the promising standards that aims to address this limitation is Health Level Seven International (HL7) Fast Healthcare Interoperability Resources Clinical Genomics Implementation Guide-Release 1 (FHIR CG IG STU1). This study aims to identify various data content of some genetic lab test reports and map them to FHIR CG IG specification to assess its coverage and to provide some suggestions for standard development and implementation.

Materials And Methods: We analyzed sample reports of 4 genetic tests and relevant professional reporting guidelines to identify their key data elements (KDEs) that were then mapped to FHIR CG IG.

Results: We identified 36 common KDEs among the analyzed genetic test reports, in addition to other unique KDEs for each genetic test. Relevant suggestions were made to guide the standard implementation and development.

Discussion And Conclusion: The FHIR CG IG covers the majority of the identified KDEs. However, we suggested some FHIR extensions that might better represent some KDEs. These extensions may be relevant to FHIR implementations or future FHIR updates.The FHIR CG IG is an excellent step toward the interoperability of genetic lab test reports. However, it is a work-in-progress that needs informative and continuous input from the clinical genetics' community, specifically professional organizations, systems implementers, and genetic knowledgebase providers.
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http://dx.doi.org/10.1093/jamia/ocab201DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8633597PMC
November 2021

New Standards for Clinical Decision Support: A Survey of The State of Implementation.

Yearb Med Inform 2021 Aug 3;30(1):159-171. Epub 2021 Sep 3.

Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.

Objectives: To review the current state of research on designing and implementing clinical decision support (CDS) using four current interoperability standards: Fast Healthcare Interoperability Resources (FHIR); Substitutable Medical Applications and Reusable Technologies (SMART); Clinical Quality Language (CQL); and CDS Hooks.

Methods: We conducted a review of original studies describing development of specific CDS tools or infrastructures using one of the four targeted standards, regardless of implementation stage. Citations published any time before the literature search was executed on October 21, 2020 were retrieved from PubMed. Two reviewers independently screened articles and abstracted data according to a protocol designed by team consensus.

Results: Of 290 articles identified via PubMed search, 44 were included in this study. More than three quarters were published since 2018. Forty-three (98%) used FHIR; 22 (50%) used SMART; two (5%) used CQL; and eight (18%) used CDS Hooks. Twenty-four (55%) were in the design stage, 15 (34%) in the piloting stage, and five (11%) were deployed in a real-world setting. Only 12 (27%) of the articles reported an evaluation of the technology under development. Three of the four articles describing a deployed technology reported an evaluation. Only two evaluations with randomized study components were identified.

Conclusion: The diversity of topics and approaches identified in the literature highlights the utility of these standards. The infrequency of reported evaluations, as well as the high number of studies in the design or piloting stage, indicate that these technologies are still early in their life cycles. Informaticists will require a stronger evidence base to understand the implications of using these standards in CDS design and implementation.
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http://dx.doi.org/10.1055/s-0041-1726502DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8416232PMC
August 2021

A qualitative investigation of biomedical informatics interoperability standards for genetic test reporting: benefits, challenges, and motivations from the testing laboratory's perspective.

Genet Med 2021 11 25;23(11):2178-2185. Epub 2021 Aug 25.

Department of Biomedical Informatcs, School of Medicine, University of Utah, Salt Lake City, UT, USA.

Purpose: Genetic laboratory test reports can often be of limited computational utility to the receiving clinical information systems, such as clinical decision support systems. Many health-care interoperability (HC) standards aim to tackle this problem, but the perceived benefits, challenges, and motivations for implementing HC interoperability standards from the labs' perspective has not been systematically assessed.

Methods: We surveyed genetic testing labs across the United States and conducted a semistructured interview with responding lab representatives. We conducted a thematic analysis of the interview transcripts to identify relevant themes. A panel of experts discussed and validated the identified themes.

Results: Nine labs participated in the interview, and 24 relevant themes were identified within five domains. These themes included the challenge of complex and changing genetic knowledge, the motivation of competitive advantage, provided financial incentives, and the benefit of supporting the learning health system.

Conclusion: Our study identified the labs' perspective on various aspects of implementing HC interoperability standards in producing and communicating genetic test reports. Interviewees frequently reported that increased adoption of HC standards may be motivated by competition and programs incentivizing and regulating the incorporation of interoperability standards for genetic test data, which could benefit quality control, research, and other areas.
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http://dx.doi.org/10.1038/s41436-021-01301-yDOI Listing
November 2021

Implementing lung cancer screening in primary care: needs assessment and implementation strategy design.

Transl Behav Med 2021 Aug 23. Epub 2021 Aug 23.

Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84108, USA.

Lung cancer screening with low-dose computed tomography (CT) could help avert thousands of deaths each year. Since the implementation of screening is complex and underspecified, there is a need for systematic and theory-based strategies. Explore the implementation of lung cancer screening in primary care, in the context of integrating a decision aid into the electronic health record. Design implementation strategies that target hypothesized mechanisms of change and context-specific barriers. The study had two phases. The Qualitative Analysis phase included semi-structured interviews with primary care physicians to elicit key task behaviors (e.g., ordering a low-dose CT) and understand the underlying behavioral determinants (e.g., social influence). The Implementation Strategy Design phase consisted of defining implementation strategies and hypothesizing causal pathways to improve screening with a decision aid. Three key task behaviors and four behavioral determinants emerged from 14 interviews. Implementation strategies were designed to target multiple levels of influence. Strategies included increasing provider self-efficacy toward performing shared decision making and using the decision aid, improving provider performance expectancy toward ordering a low-dose CT, increasing social influence toward performing shared decision making and using the decision aid, and addressing key facilitators to using the decision aid. This study contributes knowledge about theoretical determinants of key task behaviors associated with lung cancer screening. We designed implementation strategies according to causal pathways that can be replicated and tested at other institutions. Future research is needed to evaluate the effectiveness of these strategies and to determine the contexts in which they can be effectively applied.
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http://dx.doi.org/10.1093/tbm/ibab115DOI Listing
August 2021

A theory-based meta-regression of factors influencing clinical decision support adoption and implementation.

J Am Med Inform Assoc 2021 10;28(11):2514-2522

Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA.

Objective: The purpose of the study was to explore the theoretical underpinnings of effective clinical decision support (CDS) factors using the comparative effectiveness results.

Materials And Methods: We leveraged search results from a previous systematic literature review and updated the search to screen articles published from January 2017 to January 2020. We included randomized controlled trials and cluster randomized controlled trials that compared a CDS intervention with and without specific factors. We used random effects meta-regression procedures to analyze clinician behavior for the aggregate effects. The theoretical model was the Unified Theory of Acceptance and Use of Technology (UTAUT) model with motivational control.

Results: Thirty-four studies were included. The meta-regression models identified the importance of effort expectancy (estimated coefficient = -0.162; P = .0003); facilitating conditions (estimated coefficient = 0.094; P = .013); and performance expectancy with motivational control (estimated coefficient = 1.029; P = .022). Each of these factors created a significant impact on clinician behavior. The meta-regression model with the multivariate analysis explained a large amount of the heterogeneity across studies (R2 = 88.32%).

Discussion: Three positive factors were identified: low effort to use, low controllability, and providing more infrastructure and implementation strategies to support the CDS. The multivariate analysis suggests that passive CDS could be effective if users believe the CDS is useful and/or social expectations to use the CDS intervention exist.

Conclusions: Overall, a modified UTAUT model that includes motivational control is an appropriate model to understand psychological factors associated with CDS effectiveness and to guide CDS design, implementation, and optimization.
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http://dx.doi.org/10.1093/jamia/ocab160DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8510321PMC
October 2021

Establishing a multidisciplinary initiative for interoperable electronic health record innovations at an academic medical center.

JAMIA Open 2021 Jul 31;4(3):ooab041. Epub 2021 Jul 31.

Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA.

Objective: To establish an enterprise initiative for improving health and health care through interoperable electronic health record (EHR) innovations.

Materials And Methods: We developed a unifying mission and vision, established multidisciplinary governance, and formulated a strategic plan. Key elements of our strategy include establishing a world-class team; creating shared infrastructure to support individual innovations; developing and implementing innovations with high anticipated impact and a clear path to adoption; incorporating best practices such as the use of Fast Healthcare Interoperability Resources (FHIR) and related interoperability standards; and maximizing synergies across research and operations and with partner organizations.

Results: University of Utah Health launched the ReImagine EHR initiative in 2016. Supportive infrastructure developed by the initiative include various FHIR-related tooling and a systematic evaluation framework. More than 10 EHR-integrated digital innovations have been implemented to support preventive care, shared decision-making, chronic disease management, and acute clinical care. Initial evaluations of these innovations have demonstrated positive impact on user satisfaction, provider efficiency, and compliance with evidence-based guidelines. Return on investment has included improvements in care; over $35 million in external grant funding; commercial opportunities; and increased ability to adapt to a changing healthcare landscape.

Discussion: Key lessons learned include the value of investing in digital innovation initiatives leveraging FHIR; the importance of supportive infrastructure for accelerating innovation; and the critical role of user-centered design, implementation science, and evaluation.

Conclusion: EHR-integrated digital innovation initiatives can be key assets for enhancing the EHR user experience, improving patient care, and reducing provider burnout.
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http://dx.doi.org/10.1093/jamiaopen/ooab041DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8325485PMC
July 2021

Provider Preferences for Patient-Generated Health Data Displays in Pediatric Asthma: A Participatory Design Approach.

Appl Clin Inform 2021 05 21;12(3):664-674. Epub 2021 Jul 21.

College of Nursing, University of Utah, Salt Lake City, Utah, United States.

Objective: There is a lack of evidence on how to best integrate patient-generated health data (PGHD) into electronic health record (EHR) systems in a way that supports provider needs, preferences, and workflows. The purpose of this study was to investigate provider preferences for the graphical display of pediatric asthma PGHD to support decisions and information needs in the outpatient setting.

Methods: In December 2019, we conducted a formative evaluation of information display prototypes using an iterative, participatory design process. Using multiple types of PGHD, we created two case-based vignettes for pediatric asthma and designed accompanying displays to support treatment decisions. Semi-structured interviews and questionnaires with six participants were used to evaluate the display usability and determine provider preferences.

Results: We identified provider preferences for display features, such as the use of color to indicate different levels of abnormality, the use of patterns to trend PGHD over time, and the display of environmental data. Preferences for display content included the amount of information and the relationship between data elements.

Conclusion: Overall, provider preferences for PGHD include a desire for greater detail, additional sources, and visual integration with relevant EHR data. In the design of PGHD displays, it appears that the visual synthesis of multiple PGHD elements facilitates the interpretation of the PGHD. Clinicians likely need more information to make treatment decisions when PGHD displays are introduced into practice. Future work should include the development of interactive interface displays with full integration of PGHD into EHR systems.
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http://dx.doi.org/10.1055/s-0041-1732424DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8294945PMC
May 2021

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

Appl Clin Inform 2021 05 21;12(3):675-685. Epub 2021 Jul 21.

Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan, United States.

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.

Methods: PubMed, the National Institutes of Health RePORTER, Scopus, the Cochrane Library, and Duke University Library databases for business and information sciences were queried for formal mentions of the term "data readiness." Manuscripts found in the search were reviewed, and relevant information was extracted, evaluated, and assimilated into a framework for data readiness.

Results: Of the 264 manuscripts found in the database searches, 20 were included in the final synthesis to define data readiness. In these 20 manuscripts, the term data readiness was revealed to encompass the constructs of data quality, data availability, interoperability, and data provenance.

Discussion: Based upon our review of the literature, we define data readiness as the application-specific intersection of data quality, data availability, interoperability, and data provenance. While these concepts are not new, the combination of these factors in a novel data readiness model may help guide future informatics research and implementation science.

Conclusion: This analysis provides a definition to guide research and development related to data-based applications in health care. Future work should be done to validate this definition, and to apply the components of data readiness to real-world applications so that specific metrics may be developed and disseminated.
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http://dx.doi.org/10.1055/s-0041-1732423DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8294946PMC
May 2021

A qualitative study of prevalent laboratory information systems and data communication patterns for genetic test reporting.

Genet Med 2021 11 6;23(11):2171-2177. Epub 2021 Jul 6.

Department of Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, UT, USA.

Purpose: The availability of genetic test data within the electronic health record (EHR) is a pillar of the US vision for an interoperable health IT infrastructure and a learning health system. Although EHRs have been highly investigated, evaluation of the information systems used by the genetic labs has received less attention-but is necessary for achieving optimal interoperability. This study aimed to characterize how US genetic testing labs handle their information processing tasks.

Methods: We followed a qualitative research method that included interviewing lab representatives and a panel discussion to characterize the information flow models.

Results: Ten labs participated in the study. We identified three generic lab system models and their relevant characteristics: a backbone system with additional specialized systems for interpreting genetic results, a brokering system that handles housekeeping and communication, and a single primary system for results interpretation and report generation.

Conclusion: Labs have heterogeneous workflows and generally have a low adoption of standards when sending genetic test reports back to EHRs. Core interpretations are often delivered as free text, limiting their computational availability for clinical decision support tools. Increased provision of genetic test data in discrete and standard-based formats by labs will benefit individual and public health.
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http://dx.doi.org/10.1038/s41436-021-01251-5DOI Listing
November 2021

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

J Biomed Inform 2021 08 28;120:103852. Epub 2021 Jun 28.

Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Semedy, Inc, Needham, MA, United States.

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. Yet, the rapidly evolving nature of these events poses significant challenges for guidance development and dissemination strategies predicated on well-understood concepts and clearly defined access and distribution pathways. Taxonomies are an important but underutilized tool for guidance authoring, dissemination and updating in such dynamic scenarios.

Objective: To design a rapid, semi-automated method for sampling and developing a PH guidance taxonomy using widely available Web crawling tools and streamlined manual content analysis.

Methods: Iterative samples of guidance documents were taken from four state PH agency websites, the US Center for Disease Control and Prevention, and the World Health Organization. Documents were used to derive and refine a preliminary taxonomy of COVID-19 PH guidance via content analysis.

Results: Eight iterations of guidance document sampling and taxonomy revisions were performed, with a final corpus of 226 documents. The preliminary taxonomy contains 110 branches distributed between three major domains: stakeholders (24 branches), settings (25 branches) and topics (61 branches). Thematic saturation measures indicated rapid saturation (≤5% change) for the domains of "stakeholders" and "settings", and "topic"-related branches for clinical decision-making. Branches related to business reopening and economic consequences remained dynamic throughout sampling iterations.

Conclusion: The PH guidance taxonomy can support public health agencies by aligning guidance development with curation and indexing strategies; supporting targeted dissemination; increasing the speed of updates; and enhancing public-facing guidance repositories and information retrieval tools. Taxonomies are essential to support knowledge management activities during rapidly evolving scenarios such as disease outbreaks and natural disasters.
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http://dx.doi.org/10.1016/j.jbi.2021.103852DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8236411PMC
August 2021

Predictive analytics for step-up therapy: Supervised or semi-supervised learning?

J Biomed Inform 2021 07 17;119:103842. Epub 2021 Jun 17.

Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States.

Background: Step-up therapy is a patient management approach that aims to balance the efficacy, costs and risks posed by different lines of medications. While the initiation of first line medications is a straightforward decision, stepping-up a patient to the next treatment line is often more challenging and difficult to predict. By identifying patients who are likely to move to the next line of therapy, prediction models could be used to help healthcare organizations with resource planning and chronic disease management.

Objective: To compared supervised learning versus semi-supervised learning to predict which rheumatoid arthritis patients will move from the first line of therapy (i.e., conventional synthetic disease-modifying antirheumatic drugs) to the next line of therapy (i.e., disease-modifying antirheumatic drugs or targeted synthetic disease-modifying antirheumatic drugs) within one year.

Materials And Methods: Five groups of features were extracted from an administrative claims database: demographics, medications, diagnoses, provider characteristics, and procedures. Then, a variety of supervised and semi-supervised learning methods were implemented to identify the most optimal method of each approach and assess the contribution of each feature group. Finally, error analysis was conducted to understand the behavior of misclassified patients.

Results: XGBoost yielded the highest F-measure (42%) among the supervised approaches and one-class support vector machine achieved the highest F-measure (65%) among the semi-supervised approaches. The semi-supervised approach had significantly higher F-measure (65% vs. 42%; p < 0.01), precision (51% vs. 33%; p < 0.01), and recall (89% vs. 59%; p < 0.01) than the supervised approach. Excluding demographic, drug, diagnosis, provider, and procedure features reduced theF-measure from 65% to 61%, 57%, 54%, 51% and 49% respectively (p < 0.01). The error analysis showed that a substantial portion of false positive patients will change their line of therapy shortly after the prediction period.

Conclusion: This study showed that supervised learning approaches are not an optimal option for a difficult clinical decision regarding step-up therapy. More specifically, negative class labels in step-up therapy data are not a robust ground truth, because the costs and risks associated with higher line of therapy impact objective decision making of patients and providers. The proposed semi-supervised learning approach can be applied to other step-up therapy applications.
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http://dx.doi.org/10.1016/j.jbi.2021.103842DOI Listing
July 2021

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

JAMIA Open 2021 Apr 16;4(2):ooab031. Epub 2021 Jun 16.

University of Michigan Medical School, Ann Arbor, Michigan, USA.

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.

Results: We identified five themes: concern about data quality impacts implementation planning; the availability of data in a computable format is a primary factor for implementation feasibility; customized strategies are needed to mitigate uncertainty and ambiguity when translating CPGs to an electronic health record-based tool; misalignment of expected CDS with pre-existing clinical workflows impact implementation; and individual level factors of end-users must be considered when selecting and implementing CDS tools.

Discussion: The themes reveal several considerations for CPG as CDS implementations regarding data quality, knowledge representation, and sociotechnical issues. Guideline authors should be aware that using CDS to implement CPGs is becoming increasingly popular and should consider providing clear guidelines to aid implementation. The complex nature of CPG as CDS implementation necessitates a unified effort to overcome these challenges.

Conclusion: Our analysis highlights the importance of cooperation and co-development of standards, strategies, and infrastructure to address the difficulties of implementing CPGs as CDS. The complex interactions between the concepts revealed in the interviews necessitates the need that such work should not be conducted in silos. We also implore that implementers disseminate their experiences.
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http://dx.doi.org/10.1093/jamiaopen/ooab031DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8206400PMC
April 2021

Contemporary clinical decision support standards using Health Level Seven International Fast Healthcare Interoperability Resources.

J Am Med Inform Assoc 2021 07;28(8):1796-1806

Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA.

Objective: To facilitate the development of standards-based clinical decision support (CDS) systems, we review the current set of CDS standards that are based on Health Level Seven International Fast Healthcare Interoperability Resources (FHIR). Widespread adoption of these standards may help reduce healthcare variability, improve healthcare quality, and improve patient safety.

Target Audience: This tutorial is designed for the broad informatics community, some of whom may be unfamiliar with the current, FHIR-based CDS standards.

Scope: This tutorial covers the following standards: Arden Syntax (using FHIR as the data model), Clinical Quality Language, FHIR Clinical Reasoning, SMART on FHIR, and CDS Hooks. Detailed descriptions and selected examples are provided.
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http://dx.doi.org/10.1093/jamia/ocab070DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8324242PMC
July 2021

Comparing models of delivery for cancer genetics services among patients receiving primary care who meet criteria for genetic evaluation in two healthcare systems: BRIDGE randomized controlled trial.

BMC Health Serv Res 2021 Jun 2;21(1):542. Epub 2021 Jun 2.

Perlmutter Cancer Center, NYU Langone Health, 160 E. 34th Street, New York, NY, 10016, USA.

Background: Advances in genetics and sequencing technologies are enabling the identification of more individuals with inherited cancer susceptibility who could benefit from tailored screening and prevention recommendations. While cancer family history information is used in primary care settings to identify unaffected patients who could benefit from a cancer genetics evaluation, this information is underutilized. System-level population health management strategies are needed to assist health care systems in identifying patients who may benefit from genetic services. In addition, because of the limited number of trained genetics specialists and increasing patient volume, the development of innovative and sustainable approaches to delivering cancer genetic services is essential.

Methods: We are conducting a randomized controlled trial, entitled Broadening the Reach, Impact, and Delivery of Genetic Services (BRIDGE), to address these needs. The trial is comparing uptake of genetic counseling, uptake of genetic testing, and patient adherence to management recommendations for automated, patient-directed versus enhanced standard of care cancer genetics services delivery models. An algorithm-based system that utilizes structured cancer family history data available in the electronic health record (EHR) is used to identify unaffected patients who receive primary care at the study sites and meet current guidelines for cancer genetic testing. We are enrolling eligible patients at two healthcare systems (University of Utah Health and New York University Langone Health) through outreach to a randomly selected sample of 2780 eligible patients in the two sites, with 1:1 randomization to the genetic services delivery arms within sites. Study outcomes are assessed through genetics clinic records, EHR, and two follow-up questionnaires at 4 weeks and 12 months after last genetic counseling contactpre-test genetic counseling.

Discussion: BRIDGE is being conducted in two healthcare systems with different clinical structures and patient populations. Innovative aspects of the trial include a randomized comparison of a chatbot-based genetic services delivery model to standard of care, as well as identification of at-risk individuals through a sustainable EHR-based system. The findings from the BRIDGE trial will advance the state of the science in identification of unaffected patients with inherited cancer susceptibility and delivery of genetic services to those patients.

Trial Registration: BRIDGE is registered as NCT03985852 . The trial was registered on June 6, 2019 at clinicaltrials.gov .
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http://dx.doi.org/10.1186/s12913-021-06489-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8170651PMC
June 2021

Infobuttons for Genomic Medicine: Requirements and Barriers.

Appl Clin Inform 2021 03 12;12(2):383-390. Epub 2021 May 12.

Genomic Medicine Institute, Geisinger, Danville, Pennsylvania, United Sates.

Objectives: The study aimed to understand potential barriers to the adoption of health information technology projects that are released as free and open source software (FOSS).

Methods: We conducted a survey of research consortia participants engaged in genomic medicine implementation to assess perceived institutional barriers to the adoption of three systems: ClinGen electronic health record (EHR) Toolkit, DocUBuild, and MyResults.org. The survey included eight barriers from the Consolidated Framework for Implementation Research (CFIR), with additional barriers identified from a qualitative analysis of open-ended responses.

Results: We analyzed responses from 24 research consortia participants from 18 institutions. In total, 14 categories of perceived barriers were evaluated, which were consistent with other observed barriers to FOSS adoption. The most frequent perceived barriers included lack of adaptability of the system, lack of institutional priority to implement, lack of trialability, lack of advantage of alternative systems, and complexity.

Conclusion: In addition to understanding potential barriers, we recommend some strategies to address them (where possible), including considerations for genomic medicine. Overall, FOSS developers need to ensure systems are easy to trial and implement and need to clearly articulate benefits of their systems, especially when alternatives exist. Institutional champions will remain a critical component to prioritizing genomic medicine projects.
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http://dx.doi.org/10.1055/s-0041-1729164DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8116174PMC
March 2021

Patient-generated health data and electronic health record integration: a scoping review.

JAMIA Open 2020 Dec 5;3(4):619-627. Epub 2020 Dec 5.

University of Utah, College of Nursing, Salt Lake City, Utah, USA.

Objectives: Patient-generated health data (PGHD) are clinically relevant data captured by patients outside of the traditional care setting. Clinical use of PGHD has emerged as an essential issue. This study explored the evidence to determine the extent of and describe the characteristics of PGHD integration into electronic health records (EHRs).

Methods: In August 2019, we conducted a systematic scoping review. We included studies with complete, partial, or in-progress PGHD and EHR integration within a clinical setting. The retrieved articles were screened for eligibility by 2 researchers, and data from eligible articles were abstracted, coded, and analyzed.

Results: A total of 19 studies met inclusion criteria after screening 9463 abstracts. Most of the study designs were pilots and all were published between 2013 and 2019. Types of PGHD were biometric and patient activity (57.9%), questionnaires and surveys (36.8%), and health history (5.3%). Diabetes was the most common patient condition (42.1%) for PGHD collection. Active integration (57.9%) was slightly more common than passive integration (31.6%). We categorized emergent themes into the 3 steps of PGHD flow. Themes emerged concerning resource requirements, data delivery to the EHR, and preferences for review.

Discussion: PGHD integration into EHRs appears to be at an early stage. PGHD have the potential to close health care gaps and support personalized medicine. Efforts are needed to understand how to optimize PGHD integration into EHRs considering resources, standards for EHR delivery, and clinical workflows.
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http://dx.doi.org/10.1093/jamiaopen/ooaa052DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7969964PMC
December 2020

A systematic review of theoretical constructs in CDS literature.

BMC Med Inform Decis Mak 2021 03 17;21(1):102. Epub 2021 Mar 17.

Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA.

Background: Studies that examine the adoption of clinical decision support (CDS) by healthcare providers have generally lacked a theoretical underpinning. The Unified Theory of Acceptance and Use of Technology (UTAUT) model may provide such a theory-based explanation; however, it is unknown if the model can be applied to the CDS literature.

Objective: Our overall goal was to develop a taxonomy based on UTAUT constructs that could reliably characterize CDS interventions.

Methods: We used a two-step process: (1) identified randomized controlled trials meeting comparative effectiveness criteria, e.g., evaluating the impact of CDS interventions with and without specific features or implementation strategies; (2) iteratively developed and validated a taxonomy for characterizing differential CDS features or implementation strategies using three raters.

Results: Twenty-five studies with 48 comparison arms were identified. We applied three constructs from the UTAUT model and added motivational control to characterize CDS interventions. Inter-rater reliability was as follows for model constructs: performance expectancy (κ = 0.79), effort expectancy (κ = 0.85), social influence (κ = 0.71), and motivational control (κ = 0.87).

Conclusion: We found that constructs from the UTAUT model and motivational control can reliably characterize features and associated implementation strategies. Our next step is to examine the quantitative relationships between constructs and CDS adoption.
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http://dx.doi.org/10.1186/s12911-021-01465-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7968272PMC
March 2021

Patient-Generated Health Data in Pediatric Asthma: Exploratory Study of Providers' Information Needs.

JMIR Pediatr Parent 2021 Jan 26;4(1):e25413. Epub 2021 Jan 26.

College of Nursing, University of Utah, Salt Lake City, UT, United States.

Background: Adolescents are using mobile health apps as a form of self-management to collect data on symptoms, medication adherence, and activity. Adding functionality to an electronic health record (EHR) to accommodate disease-specific patient-generated health data (PGHD) may support clinical care. However, little is known on how to incorporate PGHD in a way that informs care for patients. Pediatric asthma, a prevalent health issue in the United States with 6 million children diagnosed, serves as an exemplar condition to examine information needs related to PGHD.

Objective: In this study we aimed to identify and prioritize asthma care tasks and decisions based on pediatric asthma guidelines and identify types of PGHD that might support the activities associated with the decisions. The purpose of this work is to provide guidance to mobile health app developers and EHR integration.

Methods: We searched the literature for exemplar asthma mobile apps and examined the types of PGHD collected. We identified the information needs associated with each decision in accordance with consensus-based guidelines, assessed the suitability of PGHD to meet those needs, and validated our findings with expert asthma providers.

Results: We mapped guideline-derived information needs to potential PGHD types and found PGHD that may be useful in meeting information needs. Information needs included types of symptoms, symptom triggers, medication adherence, and inhaler technique. Examples of suitable types of PGHD were Asthma Control Test calculations, exposures, and inhaler use. Providers suggested uncontrolled asthma as a place to focus PGHD efforts, indicating that they preferred to review PGHD at the time of the visit.

Conclusions: We identified a manageable list of information requirements derived from clinical guidelines that can be used to guide the design and integration of PGHD into EHRs to support pediatric asthma management and advance mobile health app development. Mobile health app developers should examine PGHD information needs to inform EHR integration efforts.
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http://dx.doi.org/10.2196/25413DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8414476PMC
January 2021

Evaluation of Revised US Preventive Services Task Force Lung Cancer Screening Guideline Among Women and Racial/Ethnic Minority Populations.

JAMA Netw Open 2021 01 4;4(1):e2033769. Epub 2021 Jan 4.

Department of Population Health Sciences, University of Utah, Salt Lake City.

Importance: Lung cancer incidence and mortality disproportionately affect women and racial/ethnic minority populations, yet screening guidelines for the past several years were derived from clinical trials of predominantly White men. To reflect current evidence, the US Preventive Services Task Force (USPSTF) has revised the eligibility criteria, which may help to ameliorate sex- and race/ethnicity-related disparities in lung cancer screening.

Objective: To determine the changes associated with the revised USPSTF guideline for lung cancer screening eligibility among female, Black, and Hispanic populations using a large nationwide survey.

Design, Setting, And Participants: This cross-sectional study included respondents to the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System who were 50 to 80 years of age with a smoking history in 19 states that used the optional lung cancer screening module. The change in eligibility among female, male, Black, Hispanic, and White participants was examined. Eligibility by sex and race/ethnicity was compared with a reference population. Data were collected from January 1, 2017, to December 31, 2018, and analyzed from May 8 to June 11, 2020.

Exposures: Self-reported sex, race/ethnicity, age, and smoking history.

Main Outcomes And Measures: Lung cancer screening eligibility using the revised USPSTF criteria. The previous criteria included current or past smokers (within 15 years) who were 55 to 80 years of age and had a smoking history of more than 30 pack-years. In the revised criteria, age was modified to 50 to 80 years; smoking history, to 20 pack-years.

Results: Among 40 869 respondents aged 50 to 80 years with a smoking history, 21 265 (52.0%) were women, 3430 (8.4%) were Black, and 1226 (30.0%) were Hispanic (mean [SD] age, 65.6 [7.9] years). The revised criteria increased eligibility for the following populations: men (29.4% to 38.3% [8.9% difference]; P < .001), women (25.9% to 36.4% [10.5% difference]; P < .001), White individuals (31.1% to 40.9% [9.8% difference]; P < .001), Black individuals (16.3% to 28.8% [12.5% difference]; P < .001), and Hispanic individuals (10.5% to 18.7% [8.2% difference]; P < .001). The odds of eligibility were lower for women compared with men (adjusted odds ratio [AOR], 0.88; 95% CI, 0.79-0.99; P = .04) and for Black (AOR, 0.43; 95% CI, 0.33-0.56; P < .001) and Hispanic populations (AOR, 0.70; 95% CI, 0.62-0.80; P < .001) compared with the White population.

Conclusions And Relevance: The revised USPSTF guideline may likely increase lung cancer screening rates for female, Black, and Hispanic populations. However, despite these potential improvements, lung cancer screening inequities may persist without tailored eligibility criteria.
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http://dx.doi.org/10.1001/jamanetworkopen.2020.33769DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7804914PMC
January 2021

A scoping review of transfer learning research on medical image analysis using ImageNet.

Comput Biol Med 2021 01 13;128:104115. Epub 2020 Nov 13.

Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.

Objective: Employing transfer learning (TL) with convolutional neural networks (CNNs), well-trained on non-medical ImageNet dataset, has shown promising results for medical image analysis in recent years. We aimed to conduct a scoping review to identify these studies and summarize their characteristics in terms of the problem description, input, methodology, and outcome.

Materials And Methods: To identify relevant studies, MEDLINE, IEEE, and ACM digital library were searched for studies published between June 1st 2012 and January 2nd, 2020. Two investigators independently reviewed articles to determine eligibility and to extract data according to a study protocol defined a priori.

Results: After screening of 8421 articles, 102 met the inclusion criteria. Of 22 anatomical areas, eye (18%), breast (14%), and brain (12%) were the most commonly studied. Data augmentation was performed in 72% of fine-tuning TL studies versus 15% of the feature-extracting TL studies. Inception models were the most commonly used in breast related studies (50%), while VGGNet was the common in eye (44%), skin (50%) and tooth (57%) studies. AlexNet for brain (42%) and DenseNet for lung studies (38%) were the most frequently used models. Inception models were the most frequently used for studies that analyzed ultrasound (55%), endoscopy (57%), and skeletal system X-rays (57%). VGGNet was the most common for fundus (42%) and optical coherence tomography images (50%). AlexNet was the most frequent model for brain MRIs (36%) and breast X-Rays (50%). 35% of the studies compared their model with other well-trained CNN models and 33% of them provided visualization for interpretation.

Discussion: This study identified the most prevalent tracks of implementation in the literature for data preparation, methodology selection and output evaluation for various medical image analysis tasks. Also, we identified several critical research gaps existing in the TL studies on medical image analysis. The findings of this scoping review can be used in future TL studies to guide the selection of appropriate research approaches, as well as identify research gaps and opportunities for innovation.
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http://dx.doi.org/10.1016/j.compbiomed.2020.104115DOI Listing
January 2021

Feeling and thinking: can theories of human motivation explain how EHR design impacts clinician burnout?

J Am Med Inform Assoc 2021 04;28(5):1042-1046

Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA.

The psychology of motivation can help us understand the impact of electronic health records (EHRs) on clinician burnout both directly and indirectly. Informatics approaches to EHR usability tend to focus on the extrinsic motivation associated with successful completion of clearly defined tasks in clinical workflows. Intrinsic motivation, which includes the need for autonomy, sense-making, creativity, connectedness, and mastery is not well supported by current designs and workflows. This piece examines existing research on the importance of 3 psychological drives in relation to healthcare technology: goal-based decision-making, sense-making, and agency/autonomy. Because these motives are ubiquitous, foundational to human functioning, automatic, and unconscious, they may be overlooked in technological interventions. The results are increased cognitive load, emotional distress, and unfulfilling workplace environments. Ultimately, we hope to stimulate new research on EHR design focused on expanding functionality to support intrinsic motivation, which, in turn, would decrease burnout and improve care.
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http://dx.doi.org/10.1093/jamia/ocaa270DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8068417PMC
April 2021
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