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The authors of “Impact of Electronic Health Records on Information Practices in Mental Health Contexts: Scoping Review” have effectively brought to our attention the failure of the electronic health record (EHR) to represent the human context. Because mental health or behavioral disorders (and functional status in general) emerge from an interaction between the individual’s characteristics and the social context, it is essentially a failure to represent the human context. The assessment and treatment of these disorders must reflect how the person lives, their degree of social connectedness, their personal motivation, and their cultural background. This type of information is best communicated both through narrative and in collaboration with other providers and the patient—largely because human social memory is organized around situation models and natural episodes. Neither functionality is currently available in most EHRs. Narrative communication is effective for several reasons: (1) it supports the communication of goals between providers; (2) it allows the author to express their belief in others’ perspectives (theory of mind), for example, those who will be reading these notes; and (3) it supports the incorporation of the patient’s personal perspective. The failure of the EHR to support mental health information data and information practices is, therefore, essentially a failure to support the basic communication functions necessary for the narrative. The authors have rightly noted the problems of the EHR in this domain, but perhaps they did not completely link the problems to the lack of functionality to support narrative communication. Suggestions for adding design elements are discussed.

Background Recruitment of sufficient participants for clinical trials remains challenging. Primary care is an important avenue for patient recruitment but is underutilized. We developed and pilot tested a questionnaire to measure relevant barriers and facilitators to primary care providers’ involvement in recruiting patients for clinical trials. Methods Prior research informed the development of the questionnaire. The initial instrument was revised using feedback obtained from cognitive interviews. We invited all primary care providers practicing within the University of Utah Health system to complete the revised questionnaire. We used a mixed-mode design to collect paper responses via in-person recruitment and email contacts to collect responses online. Descriptive statistics, exploratory factor analysis, Cronbach’s alpha, and multivariable regression analyses were conducted. Results Sixty-seven primary care providers participated in the survey. Exploratory factor analysis suggested retaining five factors, representing the importance of clinical trial recruitment in providers’ professional identity, clinic-level interventions to facilitate referral, patient-related barriers, concerns about patient health management, and knowledge gaps. The five factors exhibited good or high internal consistency reliability. Professional identity and clinic-level intervention factors were significant predictors of providers’ intention to participate in clinical trial recruitment activities. Conclusions Results of this exploratory analysis provide preliminary evidence of the internal structure, internal consistency reliability, and predictive validity of the questionnaire to measure factors relevant to primary care providers’ involvement in clinical trial recruitment.

Patient-centered care promotes the involvement of patients in decision-making related to their health care. The adoption and implementation of shared decision-making (SDM) into routine care are constrained by several obstacles, including technical and time constraints, clinician and patient attitudes and perceptions, and processes that exist outside the standardized clinical workflow. We aimed to understand the integration and implementation characteristics of reported SDM interventions integrated into an electronic health record (EHR) system. We conducted a scoping review using the methodological framework by Arksey and O'Malley with guidance from the Joanna Briggs Institute. Eligibility criteria included original research and reviews focusing on SDM situations in a real-world clinical setting and EHR integration of SDM tools and processes. We excluded retrospective studies, conference abstracts, simulation studies, user design studies, opinion pieces, and editorials. To identify eligible studies, we searched the following databases on January 11, 2021: MEDLINE, Embase, CINAHL Complete, Cochrane Library including CENTRAL, PsycINFO, Scopus, and Web of Science Core Collection. We systematically categorized descriptive data and key findings in a tabular format using predetermined data charting forms. Results were summarized using tables and associated narratives related to the review questions. Of the 2153 studies, 18 (0.84%) were included in the final review. There was a high degree of variation across studies, including SDM definitions, standardized measures, technical integration, and implementation strategies. SDM tools that targeted established health care processes promoted their use. Integrating SDM templates and tools into an EHR appeared to improve the targeted outcomes of most (17/18, 94%) studies. Most SDM interventions were designed for clinicians. Patient-specific goals and values were included in 56% (10/18) of studies. The 2 most common study outcome measures were SDM-related measures and SDM tool use. Understanding how to integrate SDM tools directly into a clinician's workflow within the EHR is a logical approach to promoting SDM into routine clinical practice. This review contributes to the literature by illuminating features of SDM tools that have been integrated into an EHR system. Standardization of SDM tools and processes, including the use of patient decision aids, is needed for consistency across SDM studies. The implementation approaches for SDM applications showed varying levels of planning and effort to promote SDM intervention awareness. Targeting accepted and established clinical processes may enhance the adoption and use of SDM tools. Future studies designed as randomized controlled trials are needed to expand the quality of the evidence base. This includes the study of integration methods into EHR systems as well as implementation methods and strategies deployed to operationalize the uptake of the SDM-integrated tools. Emphasizing patients' goals and values is another key area for future studies.

Deep neural network models are emerging as an important method in healthcare delivery, following the recent success in other domains such as image recognition. Due to the multiple non-linear inner transformations, deep neural networks are viewed by many as black boxes. For practical use, deep learning models require explanations that are intuitive to clinicians. In this study, we developed a deep neural network model to predict outcomes following major cardiovascular procedures, using temporal image representation of past medical history as input. We created a novel explanation for the prediction of the model by defining impact scores that associate clinical observations with the outcome. For comparison, a logistic regression model was fitted to the same dataset. We compared the impact scores and log odds ratios by calculating three types of correlations, which provided a partial validation of the impact scores. The deep neural network model achieved an area under the receiver operating characteristics curve (AUC) of 0.787, compared to 0.746 for the logistic regression model. Moderate correlations were found between the impact scores and the log odds ratios. Impact scores generated by the explanation algorithm has the potential to shed light on the “black box” deep neural network model and could facilitate its adoption by clinicians.

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.

Background This study sought to understand the process of clinical decision-making for suspected pneumonia by emergency departments (ED) providers in Veterans Affairs (VA) Medical Centers. The long-term goal of this work is to create clinical decision support tools to reduce unwarranted variation in diagnosis and treatment of suspected pneumonia. Methods Semi-structured qualitative interviews were conducted with 16 ED clinicians from 9 VA facilities demonstrating variation in antibiotic and hospitalization decisions. Interviews of ED providers focused on understanding decision making for provider-selected pneumonia cases and providers’ organizational contexts. Results Thematic analysis identified four salient themes: i) ED decision-making for suspected pneumonia is a social process; ii) the “diagnosis drives treatment” paradigm is poorly suited to pneumonia decision-making in the ED; iii) The unpredictability of the ED requires deliberate and effortful information management by providers in CAP decision-making; and iv) the emotional stakes and high uncertainty of pneumonia care drive conservative decision making. Conclusions Ensuring CDS reflects the realities of clinical work as a socially organized process with high uncertainty may ultimately improve communication between ED and admitting providers, continuity of care and patient outcomes.

Background Clinical experts’ cognitive mechanisms for managing complexity have implications for the design of future innovative healthcare systems. The purpose of the study is to examine the constituents of decision complexity and explore the cognitive strategies clinicians use to control and adapt to their information environment. Methods We used Cognitive Task Analysis (CTA) methods to interview 10 Infectious Disease (ID) experts at the University of Utah and Salt Lake City Veterans Administration Medical Center. Participants were asked to recall a complex, critical and vivid antibiotic-prescribing incident using the Critical Decision Method (CDM), a type of Cognitive Task Analysis (CTA). Using the four iterations of the Critical Decision Method, questions were posed to fully explore the incident, focusing in depth on the clinical components underlying the complexity. Probes were included to assess cognitive and decision strategies used by participants. Results The following three themes emerged as the constituents of decision complexity experienced by the Infectious Diseases experts: 1) the overall clinical picture does not match the pattern, 2) a lack of comprehension of the situation and 3) dealing with social and emotional pressures such as fear and anxiety. All these factors contribute to decision complexity. These factors almost always occurred together, creating unexpected events and uncertainty in clinical reasoning. Five themes emerged in the analyses of how experts deal with the complexity. Expert clinicians frequently used 1) watchful waiting instead of over- prescribing antibiotics, engaged in 2) theory of mind to project and simulate other practitioners’ perspectives, reduced very complex cases into simple 3) heuristics , employed 4) anticipatory thinking to plan and re-plan events and consulted with peers to share knowledge, solicit opinions and 5) seek help on patient cases. Conclusion The cognitive strategies to deal with decision complexity found in this study have important implications for design future decision support systems for the management of complex patients.

Abstract 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. Designing strategies and tools for implementing lung cancer screening can be informed by primary care physicians.

Background Tobacco use remains the leading cause of death and disability in the USA and is disproportionately concentrated among low socioeconomic status (SES) populations. Community Health Centers (CHCs) are a key venue for reaching low SES populations with evidence-based tobacco cessation treatment such as Quitlines. Electronic health record (EHR)-based interventions at the point-of-care, text messaging (TM), and phone counseling have the potential to increase Quitline reach and are feasible to implement within CHCs. However, there is a lack of data to inform how, when, and in what combination these strategies should be implemented. The aims of this cluster-randomized trial are to evaluate multi-level implementation strategies to increase the Reach (i.e., proportion of tobacco-using patients who enroll in the Quitline) and Impact (i.e., Reach × Efficacy [efficacy is defined as the proportion of tobacco-using patients who enroll in Quitline treatment that successfully quit]) and to evaluate characteristics of healthcare system, providers, and patients that may influence tobacco-use outcomes. Methods This study is a multilevel, three-phase, Sequential Multiple Assignment Randomized Trial (SMART), conducted in CHCs ( N  = 33 clinics; N  = 6000 patients). In the first phase, clinics will be randomized to two different EHR conditions. The second and third phases are patient-level randomizations based on prior treatment response. Patients who enroll in the Quitline receive no further interventions. In phase two, patients who are non-responders (i.e., patients who do not enroll in Quitline) will be randomized to receive either TM or continued-EHR. In phase three, patients in the TM condition who are non-responders will be randomized to receive either continued-TM or TM + phone coaching. Discussion This project will evaluate scalable, multi-level interventions to directly address strategic national priorities for reducing tobacco use and related disparities by increasing the Reach and Impact of evidence-based tobacco cessation interventions in low SES populations. Trial registration This trial was registered at ClinicalTrials.gov ( NCT03900767 ) on April 4th, 2019.

Poor clinical trial (CT) recruitment is a significant barrier to translating basic science discoveries into medical practice. Improving support for primary care provider (PCP) referral of patients to CTs may be an important part of the solution. However, implementing CT referral support in primary care is not only technically challenging, but also presents challenges at the person and organization levels. The objectives of this study were (1) to characterize provider and clinical supervisor attitudes and perceptions regarding CT research, recruitment, and referrals in primary care and (2) to identify perceived workflow strategies and facilitators relevant to designing a technology-supported primary care CT referral program. Focus groups were conducted with PCPs, directors, and supervisors. Analysis indicated widespread support for the intrinsic scientific value of CTs, while at the same time deep concerns regarding protecting patient well-being, perceived loss of control when patients participate in trials, concern about the impact of point-of-care referrals on clinic workflow, the need for standard processes, and the need for CT information that enables referring providers to quickly confirm that the burdens are justified by the benefits at both patient and provider levels. PCP suggestions pertinent to implementing a CT referral decision support system are reported. The results from this work contribute to developing an implementation approach to support increased referral of patients to CTs.