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Importance: Starting in 2018, the 'Women in American Medical Informatics Association (AMIA) Podcast' was women-focused, in 2021 the podcast was rebranded and relaunched as the "For Your Informatics Podcast" (FYI) to expand the scope of the podcast to include other historically underrepresented groups. That expansion of the scope, together with a rebranding and marketing campaign, led to a larger audience and engagement of the AMIA community. Objectives: The goals of this case report are to characterize our rebranding and expanding decisions, and to assess how they impacted our listenership and engagement to achieve the Podcast goals of increasing diversity among the Podcast team, guests, audience, and improve audience engagement. Materials and Methods: This descriptive case study is focused on the FYI Podcast team's processes to develop a revised mission, vision, and values, increase the diversity of guests, augment listenership through social media, and track the reach through the number of followers, downloads, and impressions. Results: As of December 2023, 35 FYI Podcast episodes are available with 685 social media followers, over 20 000 downloads, and nearly 145 000 impressions. In addition to introductions to informatics and loyal listeners within AMIA, the FYI Podcast episodes have been used by students as teaching material in a graduate biomedical informatics curriculum, and as introductory material for student clubs and programs. Discussion: The Podcast relaunching led to 98% of guests from underrepresented groups and growth in listenership by 329% since May 2021. Conclusion: The FYI Podcast supports AMIA's diversity mission, and gives voices to underrepresented groups, engages the clinical informatics community in critical conversations on justice, equity, diversity and inclusion, and supports education.

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BACKGROUND: Standardizing and formalizing consent processes and forms can prevent ambiguities, convey a more precise meaning, and support machine interpretation of consent terms. OBJECTIVES: Our goal was to introduce a systematic approach to standardizing and digitizing pediatric consent forms, which are complex due to legal requirements for child and legal guardian involvement. METHODS: First, we reviewed the consent requirements from the Arizona regulation, and we used 21 pediatric treatment consents from five Arizona health care organizations to propose and evaluate an implementation-agnostic Consent for Treatment Framework. Second, we assessed the adequacy of the Fast Healthcare Interoperability Resources (FHIR) to support the proposed framework. RESULTS: The resulting Consent for Treatment Framework supports compliance with the state consent requirements and has been validated with pediatric consent forms. We also demonstrated that the FHIR standard has the required expressiveness to compute the framework's specifications and express the 21 consent forms. CONCLUSION: Health care organizations can apply the shared open-source code and FHIR implementation guidelines to standardize the design of machine-interpretable pediatric treatment consent forms. The resulting FHIR-based executable models may support compliance with the law and support interoperability and data sharing.

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Objectives: Learn how substance use disorder (SUD) treatment organizations view and respond to changes in confidentiality and disclosure law following adoption of CARES Act Sec. 3221 and prior to promulgation of revised implementing regulations. Methods: Online survey followed by informal interviews. Representatives of SUD organizations reported their degree of awareness of Sec. 3221 provisions and their organizations' views on amendments to disclosure practices; current and future changes of organizational policies; difficulties anticipated in implementing new rules; and preferences for resources. Results: Forty informant surveys on 30 organizations completed. Participants (62.5 percent) indicated being somewhat knowledgeable about Sec. 3221. Evenly divided positive and concerned views on Sec. 3221 reflect tension between preserving confidentiality of patient records and improving coordination of care. Most (76.7 percent) reported organizational discussions on Sec. 3221. Some (30 percent) identified changes to make in near future. Over a third expected few or no barriers to implementing changes to privacy and disclosure practices, while most (64.7 percent) expected hindrances including complexity and tensions in the law, staff education, cost, technological adjustments, and changes in the ways SUD organizations interact with external organizations and individuals. To overcome barriers noted, participants expressed desire (66.7 percent) for teaching tools such as webinars and templates to follow. Conclusions: SUD treatment organizations began thinking of and planning for proposed changes well before expected implementation of Sec. 3221. Their concerns reflected practicalities of implementation, determining content of law, and wondering about the extent to which it solves problems (improving coordination of care among various providers) or endangered other goals (protecting confidentiality of SUD patient records).

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BACKGROUND: Patient-controlled granular information sharing (PC-GIS) allows a patient to select specific health information "granules," such as diagnoses and medications; choose with whom the information is shared; and decide how the information can be used. Previous studies suggest that health professionals have mixed or concerned opinions about the process and impact of PC-GIS for care and research. Further understanding of behavioral health professionals' views on PC-GIS are needed for successful implementation and use of this technology. OBJECTIVE: The aim of this study was to evaluate changes in health professionals' opinions on PC-GIS before and after a demonstrative case study. METHODS: Four focus groups were conducted at two integrated health care facilities: one serious mental illness facility and one general behavioral health facility. A total of 28 participants were given access to outcomes of a previous study where patients had control over medical record sharing. Participants were surveyed before and after focus groups on their views about PC-GIS. Thematic analysis of focus group output was paired with descriptive statistics and exploratory factor analysis of surveys. RESULTS: Behavioral health professionals showed a significant opinion shift toward concern after the focus group intervention, specifically on the topics of patient understanding (P=.001), authorized electronic health record access (P=.03), patient-professional relationship (P=.006), patient control acceptance (P<.001), and patient rights (P=.02). Qualitative methodology supported these results. The themes of professional considerations (2234/4025, 55.5% of codes) and necessity of health information (260/766, 33.9%) identified key aspects of PC-GIS concerns. CONCLUSIONS: Behavioral health professionals agreed that a trusting patient-professional relationship is integral to the optimal implementation of PC-GIS, but were concerned about the potential negative impacts of PC-GIS on patient safety and quality of care.

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BACKGROUND: Patient-directed selection and sharing of health information "granules" is known as granular information sharing. In a previous study, patients with behavioral health conditions categorized their own health information into sensitive categories (eg, mental health) and chose the health professionals (eg, pharmacists) who should have access to those records. Little is known about behavioral health professionals' perspectives of patient-controlled granular information sharing (PC-GIS). OBJECTIVE: This study aimed to assess behavioral health professionals' (1) understanding of and opinions about PC-GIS; (2) accuracy in assessing redacted medical information; (3) reactions to patient rationale for health data categorization, assignment of sensitivity, and sharing choices; and (4) recommendations to improve PC-GIS. METHODS: Four 2-hour focus groups and pre- and postsurveys were conducted at 2 facilities. During the focus groups, outcomes from a previous study on patients' choices for medical record sharing were discussed. Thematic analysis was applied to focus group transcripts to address study objectives. RESULTS: A total of 28 health professionals were recruited. Over half (14/25, 56%) were unaware or provided incorrect definitions of granular information sharing. After PC-GIS was explained, all professionals demonstrated understanding of the terminology and process. Most (26/32 codes, 81%) recognized that key medical data had been redacted from the study case. A majority (41/62 codes, 66%) found the patient rationale for categorization and data sharing choices to be unclear. Finally, education and other approaches to inform and engage patients in granular information sharing were recommended. CONCLUSIONS: This study provides detailed insights from behavioral health professionals on granular information sharing. Outcomes will inform the development, deployment, and evaluation of an electronic consent tool for granular health data sharing.

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BACKGROUND: Value sets are lists of terms (e.g., opioid medication names) and their corresponding codes from standard clinical vocabularies (e.g., RxNorm) created with the intent of supporting health information exchange and research. Value sets are manually-created and often exhibit errors. OBJECTIVES: The aim of the study is to develop a semi-automatic, data-centric natural language processing (NLP) method to assess medication-related value set correctness and evaluate it on a set of opioid medication value sets. METHODS: We developed an NLP algorithm that utilizes value sets containing mostly true positives and true negatives to learn lexical patterns associated with the true positives, and then employs these patterns to identify potential errors in unseen value sets. We evaluated the algorithm on a set of opioid medication value sets, using the recall, precision and F1-score metrics. We applied the trained model to assess the correctness of unseen opioid value sets based on recall. To replicate the application of the algorithm in real-world settings, a domain expert manually conducted error analysis to identify potential system and value set errors. RESULTS: Thirty-eight value sets were retrieved from the Value Set Authority Center, and six (two opioid, four non-opioid) were used to develop and evaluate the system. Average precision, recall, and F1-score were 0.932, 0.904, and 0.909, respectively on uncorrected value sets; and 0.958, 0.953, and 0.953, respectively after manual correction of the same value sets. On 20 unseen opioid value sets, the algorithm obtained average recall of 0.89. Error analyses revealed that the main sources of system misclassifications were differences in how opioids were coded in the value sets-while the training value sets had generic names mostly, some of the unseen value sets had new trade names and ingredients. CONCLUSION: The proposed approach is data-centric, reusable, customizable, and not resource intensive. It may help domain experts to easily validate value sets.

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The objective is to report on the design and evaluation of the inaugural Women in AMIA Leadership Program. A year-long leadership curriculum was developed. Survey responses were summarized with descriptive statistics and quotes selected. Twenty-four scholars participated in the program. There was a significant increase in perceived achievement of learning objectives after the program (P < .0001). The largest improvement was in leadership confidence and presence in work interactions (modal answer Neutral in presurvey from 21 responses rose to Agree in postsurvey from 24 responses). Most (92% of 13) scholars clarified leadership vision and goals and (83% of 18) would be Very Likely to recommend the program to others. The goals of the program-developing women's leader identity, increasing networks, and accumulating experience for future programs-were achieved. The second leadership program is on its way in the United States and Australia. This study may benefit organizations seeking to develop leadership programs for women in informatics and digital health.

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This pilot study compares medical record data sensitivity (e.g., depression is sensitive) and categorization perspective (e.g., depression categorized as mental health information) of patients with behavioral health conditions and healthcare providers using a mixed-methods approach employing patient's own EHR. Perspectives of 25 English- and Spanish-speaking patients were compared with providers. Data categorization comparisons resulted in 66.3% agreements, 14.5% partial agreements, and 19.3% disagreements. Sensitivity comparisons obtained 54.5% agreement, 11.9% partial agreement, and 33.6% disagreements. Patients and providers disagreed in classification of genetic data, mental health, drug abuse, and physical health information. Factors influencing patients' sensitivity determination were sensitive category comprehension, own experience, stigma towards category labels (e.g., drug abuse), and perception of information applicability (e.g., alcohol dependency). Knowledge of patients' sensitivity perceptions and reconciliation with providers could expedite the development of granular and personalized consent technology.

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Clinician task performance is significantly impacted by the navigational efficiency of the system interface. Here we propose and evaluate a navigational complexity framework useful for examining differences in electronic health record (EHR) interface systems and their impact on task performance. The methodological approach includes 1) expert-based methods-specifically, representational analysis (focused on interface elements), keystroke level modeling (KLM), and cognitive walkthrough; and 2) quantitative analysis of interactive behaviors based on video-captured observations. Medication administration record (MAR) tasks completed by nurses during preoperative (PreOp) patient assessment were studied across three Mayo Clinic regional campuses and three different EHR systems. By analyzing the steps executed within the interfaces involved to complete the MAR tasks, we characterized complexities in EHR navigation. These complexities were reflected in time spent on task, click counts, and screen transitions, and were found to potentially influence nurses' performance. Two of the EHR systems, employing a single screen format, required less time to complete (mean 101.5, range 106-97 s), respectively, compared to one system employing multiple screens (176 s, 73% increase). These complexities surfaced through trade-offs in cognitive processes that could potentially influence nurses' performance. Factors such as perceptual-motor activity, visual search, and memory load impacted navigational complexity. An implication of this work is that small tractable changes in interface design can substantially improve EHR navigation, overall usability, and workflow.

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BACKGROUND: Consent2Share (C2S) is an open source software created by the Office of the National Coordinator Data Segmentation for Privacy initiative to support electronic health record (EHR) granular segmentation. To date, there are no published formal evaluations of Consent2Share. METHOD: Structured data (e.g. medications) codified using standard clinical terminologies (e.g. RxNorm) was extracted from the EHR of 36 patients with behavioral health conditions from study sites. EHRs were available through a health information exchange and two sites. The EHR data was already classified into data types (e.g. procedures and services). Both Consent2Share and health providers classified EHR data based on value sets (e.g. mental health) and sensitivity (e.g. not sensitive. Descriptive statistics and Chi-square analysis were used to compare differences between data categorizations. RESULTS: From the resulting 1,080 medical records items, 584 were distinct. Significant differences were found between sensitivity classifications by Consent2Share and providers (χ2 (2, N = 584) = 114.74, p = <0.0001). Sensitivity comparisons led to 56.0 % of agreements, 31.2 % disagreements, and 12.8 % partial agreements. Most (97.8 %) disagreements resulted from information classified as not sensitive by Consent2Share, but sensitive by provider (e.g. behavioral health prevention education service). In terms of data types, most disagreements (57.1 %) focused on procedures and services information (e.g. ligation of fallopian tube). When considering value sets, most disagreements focused on genetic data (100.0 %), followed by sexual and reproductive health (88.9 %). CONCLUSIONS: There is a need to further validate Consent2Share before broad use in health care settings. The outcomes from this pilot study will help guide improvements in segmentation logic of tools like Consent2Share and may set the stage for a new generation of personalized consent engines.

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This study explores behavioral health professionals' perceptions of granular data. Semi-structured in-person interviews of 20 health professionals were conducted at two different sites. Qualitative and quantitative analysis was performed. While most health professionals agreed that patients should control who accesses their personal medical record (70%), there are certain types of health information that should never be restricted (65%). Emergent themes, including perceived reasons that patients might share or withhold certain types of health information (65%), care coordination (12%), patient comprehension (11%), stigma (5%), trust (3%), sociocultural understanding (3%), and dissatisfaction with consent processes (1%), are explored. The impact of care role (prescriber or non-prescriber) on data-sharing perception is explored as well. This study informs the discussion on developing technology that helps balance provider and patient data-sharing and access needs.

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Preoperative care is a critical, yet complex, time-sensitive process. Optimization of workflow is challenging for many reasons, including a lack of standard workflow analysis methods. We sought to comprehensively characterize electronic health record-mediated preoperative nursing workflow. We employed a structured methodological framework to investigate and explain variations in the workflow. Video recording software captured 10 preoperative cases at Arizona and Florida regional referral centers. We compared the distribution of work for electronic health record tasks and off-screen tasks through quantitative analysis. Suboptimal patterns and reasons for variation were explored through qualitative analysis. Although both settings used the same electronic health record system, electronic health record tasks and off-screen tasks time distribution and patterns were notably different across two sites. Arizona nurses spent a longer time completing preoperative assessment. Electronic health record tasks occupied a higher proportion of time in Arizona, while off-screen tasks occupied a higher proportion in Florida. The contextual analysis helped to identify the variation associated with the documentation workload, preparation of the patient, and regional differences. These findings should seed hypotheses for future optimization efforts and research supporting standardization and harmonization of workflow across settings, post-electronic health record conversion.

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Integrated mental and physical care environments require data sharing, but little is known about health professionals' perceptions of patient-controlled health data sharing. We describe mental health professionals' views on patient-controlled data sharing using semi-structured interviews and a mixed-method analysis with thematic coding. Health information rights, specifically those of patients and health care professionals, emerged as a key theme. Behavioral health professionals identified patient motivations for non-sharing sensitive mental health records relating to substance use, emergency treatment, and serious mental illness (94%). We explore conflicts between professional need for timely access to health information and patient desire to withhold some data categories. Health professionals' views on data sharing are integral to the redesign of health data sharing and informed consent. As well, they seek clarity about the impact of patient-controlled sharing on health professionals' roles and scope of practice.

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OBJECTIVE: Sensitive health information possesses risks, such as stigma and discrimination, when disclosed. Few studies have used a patient's own electronic health records (EHRs) to explore what types of information are considered sensitive andhow such perceptions affect data sharing preferences. After a systematic literature review, we designed and piloted a mixed-method approach that employs an individual's own records to assess content sensitivity and preferences for granular data sharing for care and research. METHODS: A systematic literature review of methodologies employed to assess data sharing willingness and perceptions on data sensitivity was conducted. A methodology was designed to organize and categorize sensitive health information from EHRs. Patients were asked permission to access their EHRs, including those available through the state's health information exchange. A semi-structured interview script with closed card sorting was designed and personalized to each participant's own EHRs using 30 items from each patient record. This mixed method combines the quantitative outcomes from the card sorting exercises with themes captured from interview audio recording analysis. RESULTS: Eight publications on patients' perspectives on data sharing and sensitivity were found. Based on our systematic review, the proposed method meets a need to use EHRs to systematize the study of data privacy issues. Twenty-five patients with behavioral health conditions, English and Spanish-speaking, were recruited. On average, participants recognized 82.7% of the 30 items from their own EHRs. Participants considered mental health (76.0%), sexual and reproductive health (75.0%) and alcohol use and alcoholism (50.0%) sensitive information. Participants were willing to share information related to other addictions (100.0%), genetic data (95.8%) and general physical health information (90.5%). CONCLUSION: The findings indicate diversity in patient views on EHR sensitivity and data sharing preferences and the need for more granular and patient-centered electronic consent mechanisms to accommodate patient needs. More research is needed to validate the generalizability of the proposed methodology.

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Patient order management (POM) is a mission-critical task for perioperative workflow. Interface complexity within different EHR systems result in poor usability, increasing documentation burden. POM interfaces were compared across two systems prior to (Cerner SurgiNet) and subsequent to an EHR conversion (Epic). Here we employ a navigational complexity framework useful for examining differences in EHR interface systems. The methodological approach includes 1) expert-based methods-specifically, functional analysis, keystroke level model (KLM) and cognitive walkthrough, and 2) quantitative analysis of observed interactive user behaviors. We found differences in relation to navigational complexity with the SurgiNet interface displaying a higher number of unused POM functions, with 12 in total whereas Epic displayed 7 total functions. As reflected in all measures, Epic facilitated a more streamlined task-focused user experience. The approach enabled us to scrutinize the impact of different EHR interfaces on task performance and usability barriers subsequent to system implementation.

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The impact of EHRs conversion on clinicians' daily work is crucial to evaluate the success of the intervention for Hospitals and to yield valuable insights into quality improvement. To assess the impact of different EHR systems on the preoperative nursing workflow, we used a structured framework combining quantitative time and motion study and qualitative cognitive analysis to characterize, visualize and explain the differences before and after an EHR conversion. The results showed that the EHR conversion brought a significant decrease in the patient case time and a reduced percentage of time using EHR. PreOp nurses spent a higher proportion of time caring for the patient, while the important tasks were completed in a more continuous pattern after the EHR conversion. The workflow variance was due to different nurse's cognitive process and the task time change was reduced because of some new interface features in the new EHR systems.

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OBJECTIVE: It is difficult to assess self-management behaviors (SMBs) and incorporate them into a personalized self-care plan. We aimed to develop and apply SMB phenotyping algorithms from data collected by diabetes devices and a mobile health (mHealth) application to create patient-specific SMBs reports to guide individualized interventions. Follow-up interventions aimed to understand patient's reasoning behind discovered SMB choices. METHODS: This study deals with adults on continuous subcutaneous insulin infusion using a continuous glucose monitor (CGM) who self-tracked SMBs with an mHealth application for 1 month. Patient-generated data were quantified and an SMB report was designed and populated for each participant. A diabetes educator used the report to conduct personalized, data-driven educational interventions. Thematic analysis of the intervention was conducted. RESULTS: Twenty-two participants recorded 118 alcohol, 251 exercise, 2,661 meal events, and 1,900 photos. A patient-specific SMB report was created from this data and used to conduct the educational intervention. High variability of SMB was observed between patients. There was variability in the percentage of alcohol events accompanied by a blood glucose check, median 79% (38-100% range), and frequency of changing the bolus waveform, median 11 (7-95 range). Interventions confirmed variability of SMBs. Main emerging themes from thematic analysis were: challenges and barriers, motivators, current SMB techniques, and future plans to improve glycemic control. CONCLUSION: The ability to quantify SMBs and understand patients' rationale may help improve diabetes self-care and related outcomes. This study describes our first steps in piloting a patient-specific diabetes educational intervention, as opposed to the current "one size fits all" approach.

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OBJECTIVE: To systematically examine clinical workflows before and after a major electronic health record (EHR) implementation, we performed this study. EHR implementation and/or conversion are associated with many challenges, which are barriers to optimal care. Clinical workflows may be significantly affected by EHR implementations and conversions, resulting in provider frustration and reduced efficiency. PATIENTS AND METHODS: Our institution completed a large EHR conversion and workflow standardization converting from 3 EHRs (GE Centricity and 2 versions of Cerner) to a system-wide Epic platform. To study this quantitatively and qualitatively, we collected and curated clinical workflows through rapid ethnography, workflow observation, video ethnography, and log-file analyses of hundreds of providers, patients, and more than 100,000 log files. The study included 5 geographic sites in 4 states (Arizona, Minnesota, Florida, and Wisconsin). This project began in April 2016, and will be completed by December 2019. Our study began on May 1, 2016, and is ongoing. RESULTS: Salient themes include the importance of prioritizing clinical areas with the most intensive EHR use, the value of tools to identify bottlenecks in workflow that cause delays, and desire for additional training to optimize navigation. Video microanalyses identified marked differences in patterns of workflow and EHR navigation patterns across sites. Log-file analyses and social network analyses identified differences in personnel roles, which led to differences in patient-clinician interaction, time spent using the EHR, and paper-based artifacts. CONCLUSION: Assessing and curating workflow data before and after EHR conversion may provide opportunities for unexpected efficiencies in workflow optimization and information-system redesign. This project may be a model for capturing significant new knowledge in using EHRs to improve patient care, workflow efficiency, and outcomes.

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Little is known about data sharing preferences for care and research of behavioral health patients. Eighty-six behavioral health patients (n = 37 Latinos; n = 32 with serious mental illness) completed questionnaires, in either English or Spanish, with items assessing their views on privacy and sensitivity of health record information. Most patients (82.5%) considered mental health information as sensitive. In general, there was a direct correspondence between perceived sensitivity of information and willingness to share with all or some providers. A main motivation for sharing data with providers was improving the patient's own care (77.8%). Most participants (96.5%) indicated they would be extremely to somewhat willing to share their data for research with their care facilities and universities. Follow-up patient interviews are being conducted to further elucidate these findings.

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