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Objective: To assess the impact of the use of an ambient listening/digital scribing solution (Nuance Dragon Ambient eXperience (DAX)) on caregiver engagement, time spent on Electronic Health Record (EHR) including time after hours, productivity, attributed panel size for value-based care providers, documentation timeliness, and Current Procedural Terminology (CPT) submissions., Materials and Methods: We performed a peer-matched controlled cohort study from March to September 2022 to evaluate the impact of DAX in outpatient clinics in an integrated healthcare system. Primary outcome measurements included provider engagement survey results, reported patient safety events related to DAX use, patients' Likelihood to Recommend score, number of patients opting out of ambient listening, change in work relative values units, attributed value-based primary care panel size, documentation completion and CPT code submission deficiency rates, and note turnaround time., Results: A total of 99 providers representing 12 specialties enrolled in the study; 76 matched control group providers were included for analysis. Median utilization of DAX was 47% among active participants. We found positive trends in provider engagement, while non-participants saw worsening engagement and no practical change in productivity. There was a statistically significant worsening of after-hours EHR. There was no quantifiable effect on patient safety., Discussion: Nuance DAX use showed positive trends in provider engagement at no risk to patient safety, experience, or clinical documentation. There were no significant benefits to patient experience, documentation, or measures of provider productivity., Conclusion: Our results highlight the potential of ambient dictation as a tool for improving the provider experience. Head-to-head comparisons of EHR documentation efficiency training are needed., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Purpose: Combinatorial pharmacogenetic (PGx) panels intended to aid psychiatric prescribing are available to clinicians. Here, we evaluated the documentation of PGx panel results and subsequent prescribing patterns within a tertiary health care system., Methods: We performed a query of psychiatry service note text in our electronic health record using 71 predefined PGx terms. Patients who underwent combinatorial PGx testing were identified, and documentation of test results was analyzed. Prescription data following testing were examined for the frequency of prescriptions influenced by genes on the panel along with the medical specialties involved., Results: A total of 341 patients received combinatorial PGx testing, and documentation of results was found to be absent or incomplete for 198 patients (58%). The predominant method of documentation was through portable document formats uploaded to the electronic health record's "Media" section. Among patients with at least 1 year of follow-up, a large majority (194/228, 85%) received orders for medications affected by the tested genes, including 132 of 228 (58%) patients receiving at least 1 non-psychiatric medication influenced by the test results., Conclusion: Results from combinatorial PGx testing were poorly documented. Medications affected by these results were often prescribed after testing, highlighting the need for discrete results and clinical decision support., Competing Interests: Conflict of Interest The authors declare no conflicts of interest., (Copyright © 2023 American College of Medical Genetics and Genomics. Published by Elsevier Inc. All rights reserved.)

Background: Electronic health records (EHR) have become commonplace in medicine. A disconnect between developers and users while creating the interface often fails to create a product that captures clinical workflow, and issues become apparent with implementation. Optimization allows collaboration of clinicians and informaticists after implementation, but documentation of success has only been at the institutional level., Methods: A 4-month, department-wide EHR optimization was conducted with information technology (IT). Optimizations were developed from an intensive quality improvement process involving all levels of clinicians and clinical staff. The optimizations were then categorized as accommodations (department adjusted workflow to EHR), creations (IT developed new workflows within EHR), discoveries (department found workflows within EHR), and modifications (IT changed workflows within EHR). Departmental productivity, defined as number of visits, charges, and payments, was standardized to ratios prior to the COVID-19 pandemic and evaluated by Taylor's change point analysis. Significant improvements were defined as shifts (change points), trends (5 or more consecutive values above/below the mean), and values outside 95% CIs., Results: The 124 optimizations were categorized as 43 accommodations, 13 creations, 54 discoveries, and 14 modifications. Productivity ratios of monthly charges (0.74 to 1.28) and payments (0.83 to 1.58) significantly improved with the optimization efforts. Monthly visit ratios increased (0.65 to 0.98) but did not change significantly., Conclusion: Departmental collaboration with organizational IT for EHR optimization focused on detailed analysis of how workflows can impact productivity. Discovery optimization predominance indicates many solutions to EHR usability problems were already in the system. A large proportion of accommodation optimizations reinforced the need for better developer-user collaboration before implementation. Annals Early Access., (© 2024 Annals of Family Medicine, Inc.)

Background: Despite efforts to view electronic health records (EHR) data through an equity lens, crucial contextual information regarding patients' social environments remains limited. Integrating EHR data and Geographic Information Systems (GIS) technology can give deeper insights into the relationships between patients' social environments, health outcomes, and geographic factors. This study aims to identify regions with the fastest and slowest access to outpatient physical therapy services using bivariate choropleth maps to provide contextual insights that may contribute to health disparity in access., Methods: This was a retrospective cohort study of patients' access timelines for the first visit to outpatient physical therapy services (n = 10,363). The three timelines evaluated were (1) referral-to-scheduled appointment time, (2) scheduled appointment to first visit time, and (3) referral to first visit time. Hot and coldspot analyses (CI 95%) determined the fastest and slowest access times with patient-level characteristics and bivariate choropleth maps that were developed to visualize associations between access patterns and disadvantaged areas using Area Deprivation Index scores. Data were collected between January 1, 2016 and January 1, 2020. EHR data were geocoded via GIS technology to calculate geospatial statistics (G i ∗ statistic from ArcGIS Pro) in an urban area., Results: Statistically significant differences were found for all three access timelines between coldspot (i.e., fast access group) and hotspot (i.e., slow access group) comparisons (p < .05). The hotspot regions had higher deprivation scores; higher proportions of residents who were older, privately insured, female, lived further from clinics; and a higher proportion of Black patients with orthopaedic diagnoses compared to the coldspot regions., Conclusions: Our study identified and described local areas with higher densities of patients that experienced longer access times to outpatient physical therapy services. Integration of EHR and GIS data is a more robust method to identify health disparities in access to care. With this approach, we can better understand the intricate interplay between social, economic, and environmental factors contributing to health disparities in access to care., (© 2024. The Author(s).)

Remote patient monitoring (RPM) provides real-time clinical patient data to the medical team. The foundational element of RPM is communication, including data processing and integration in the electronic health record and communication of data between patients and clinicians. Patient portals are integral to this communication and their use can result in improved health outcomes and patient safety. Patient portals promote engagement of patients in their care, increase access to the medical team, and integrate RPM system data. RPM systems can monitor a spectrum of parameters related to chronic conditions, from vital signs (eg, heart and respiration rates, blood pressure, blood oxygen and glucose levels) to advanced cardiovascular measures. Some RPM systems are capable of automated monitoring. Health care insurance coverage of RPM systems varies widely, which has health equity implications, particularly for high-risk patients with endocrine and cardiovascular conditions. Additional challenges to widespread adoption of RPM include its contribution to administrative burden for physicians, patient data privacy issues, and variable effectiveness of RPM systems in the management of different chronic conditions., (Written permission from the American Academy of Family Physicians is required for reproduction of this material in whole or in part in any form or medium.)

Objective: The aim of the study was to (1) characterize organizational differences in primary care physicians' electronic health record (EHR) behavior; (2) assess within-organization consistency in EHR behaviors; and (3) identify whether organizational consistency is associated with physician-level efficiency., Data Sources: EHR metadata capturing averaged weekly measures of EHR time and documentation composition from 75,124 US primary care physicians across 299 organizations between September 2020 and May 2021 were taken. EHR time measures include active time in orders, chart review, notes, messaging, time spent outside of scheduled hours, and total EHR time. Documentation composition measures include note length and percentage use of templated text or copy/paste. Efficiency is measured as the percent of visits with same-day note completion., Study Design: All analyses are cross-sectional. Across-organization differences in EHR use and documentation composition are presented via 90th-to-10th percentile ratios of means and SDs. Multilevel modeling with post-estimation variance partitioning assesses the extent of an organizational signature-the proportion of variation in our measures attributable to organizations (versus specialty and individual behaviors). We measured organizational internal consistency for each measure via organization-level SD, which we grouped into quartiles for regression. Association between internally consistent (i.e., low SD) organizational EHR use and physician-level efficiency was assessed with multi-variable OLS models., Data Collection: Extraction from Epic's Signal platform used for measuring provider EHR efficiency., Principal Findings: EHR time per visit for physicians at a 90th percentile organization is 1.94 times the average EHR time at a 10th percentile organization. There is little evidence, on average, of an organizational signature. However, physicians in organizations with high internal consistency in EHR use demonstrate increased efficiency. Physicians in organizations with the highest internal consistency (top quartile) have a 3.77 percentage point higher same-day visit closure rates compared with peers in bottom quartile organizations (95% confidence interval: 0.0142-0.0612)., Conclusions: Results suggest unrealized opportunities for organizations and policymakers to support consistency in how physicians engage in EHR-supported work., (© 2023 The Authors. Health Services Research published by Wiley Periodicals LLC on behalf of Health Research and Educational Trust.)

Electronic health records (EHRs) and other real-world data (RWD) are critical to accelerating and scaling care improvement and transformation. To efficiently leverage it for secondary uses, EHR/RWD should be optimally managed and mapped to industry standard concepts (ISCs). Inherent challenges in concept encoding usually result in inefficient and costly workflows and resultant metadata representation structures outside the EHR. Using three related projects to map data to ISCs, we describe the development of standard, repeatable processes for precisely and unambiguously representing EHR data using appropriate ISCs within the EHR platform lifecycle and mappings specific to SNOMED-CT for Demographics, Specialty and Services. Mappings in these 3 areas resulted in ISC mappings of 779 data elements requiring 90 new concept requests to SNOMED-CT and 738 new ISCs mapped into the workflow within an accessible, enterprise-wide EHR resource with supporting processes.

Healthcare quality as defined by the National Academy of Medicine is "the degree to which health care services for individuals and populations increase the likelihood of desired health outcomes [1]". While building QI dashboard data quality to improve the maternal health of our patient population issues were discovered that hindered that the progress of the project. This paper will discuss the challenges and difficulties faced while creating an OB quality dashboard at a regional perinatal.

With increased application of natural language processing (NLP) in medicine, many NLP models are being developed for uncovering relevant clinical features from electronic health records. Temporal information plays a key role in understanding the context, significance, and interpretation of medical concepts extracted from clinical notes. This is particularly true in situations where the behavior, value, or status of a medical concept changes over time. In this paper, we introduce a systematic framework, NLP annotation-Relaxation-Generation (NRG). NRG compiles incidents of medical concept changes from status annotations and timestamps of multiple clinical notes. We demonstrate the effectiveness of the NRG pipeline by applying it to two medical concepts related to patients with inflammatory bowel disease: extra-intestinal manifestations and medications. We show that the NRG pipeline offers not only insights into medical concept changes over time, but can help convey longitudinal changes in clinical features at both individual and population level., (©2023 AMIA - All rights reserved.)

Objective: Health data standardized to a common data model (CDM) simplifies and facilitates research. This study examines the factors that make standardizing observational health data to the Observational Medical Outcomes Partnership (OMOP) CDM successful., Materials and Methods: Twenty-five data partners (DPs) from 11 countries received funding from the European Health Data Evidence Network (EHDEN) to standardize their data. Three surveys, DataQualityDashboard results, and statistics from the conversion process were analyzed qualitatively and quantitatively. Our measures of success were the total number of days to transform source data into the OMOP CDM and participation in network research., Results: The health data converted to CDM represented more than 133 million patients. 100%, 88%, and 84% of DPs took Surveys 1, 2, and 3. The median duration of the 6 key extract, transform, and load (ETL) processes ranged from 4 to 115 days. Of the 25 DPs, 21 DPs were considered applicable for analysis of which 52% standardized their data on time, and 48% participated in an international collaborative study., Discussion: This study shows that the consistent workflow used by EHDEN proves appropriate to support the successful standardization of observational data across Europe. Over the 25 successful transformations, we confirmed that getting the right people for the ETL is critical and vocabulary mapping requires specific expertise and support of tools. Additionally, we learned that teams that proactively prepared for data governance issues were able to avoid considerable delays improving their ability to finish on time., Conclusion: This study provides guidance for future DPs to standardize to the OMOP CDM and participate in distributed networks. We demonstrate that the Observational Health Data Sciences and Informatics community must continue to evaluate and provide guidance and support for what ultimately develops the backbone of how community members generate evidence., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Rapidly increasing adoption of electronic health record (EHR) systems has caused automated medical specialty classification to become an important research field. Medical specialty classification not only improves EHR system retrieval efficiency and helps general practitioners identify urgent patient issues but also is useful in studying the practice and validity of clinical referral patterns. However, currently available medical note data are imbalanced and insufficient. In addition, medical specialty classification is a multicategory problem, and it is not easy to remove sensitive information from numerous medical notes and tag them. To solve those problems, we propose a data augmentation method based on adversarial attacks. The semiadversarial examples generated during the dynamic process of adversarial attacking are added to the training set as augmented examples, which can effectively expand the coverage of the training data on the decision space. Besides, as nouns in medical notes are critical information, we design a classification framework incorporating probabilistic information of nouns, with confidence recalculation after the softmax layer. We validate our proposed method on an 18-class dataset with extremely unbalanced data, and comparison experiments with four benchmarks show that our method improves accuracy and F 1 score to the optimal level, by an average of 14.9%., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2023 Huan Zhang et al.)

Purpose: Persistent inequities in genomic medicine and research contribute to health disparities. This analysis uses a context-specific and equity-focused strategy to evaluate enrollment patterns for Genomic Answers for Kids (GA4K), a large, metropolitan-wide genomic study on children., Methods: Electronic health records for 2247 GA4K study participants were used to evaluate the distribution of individuals by demographics (race, ethnicity, and payor type) and location (residential address). Addresses were geocoded to produce point density and 3-digit zip code maps showing local and regional enrollment patterns. Health system reports and census data were used to compare participant characteristics with reference populations at different spatial scales., Results: Racial and ethnic minoritized and populations with low-income were underrepresented in the GA4K study cohort. Geographic variation demonstrates inequity in enrollment and participation among children from historically segregated and socially disadvantaged communities., Conclusion: Our findings illustrate inequity in enrollment related to both GA4K study design and structural inequalities, which we suspect may exist for similar US-based studies. Our methods provide a scalable framework for continually evaluating and improving study design to ensure equitable participation in and benefits from genomic research and medicine. The use of high-resolution, place-based data represents a novel and practical means of identifying and characterizing inequities and targeting community engagement., Competing Interests: Conflict of Interest The authors declare no conflicts of interest., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

We introduce a new logic, called Temporal Cohort Logic (TCL), for cohort specification and discovery in clinical and population health research. TCL is created to fill a conceptual gap in formalizing temporal reasoning in biomedicine, in a similar role that temporal logics play for computer science and its applications. We provide formal syntax and semantics for TCL and illustrate the various logical constructs using examples related to human health. Relationships and distinctions with existing temporal logical frameworks are discussed. Applications in electronic health record (EHR) and in neurophysiological data resource are provided. Our approach differs from existing temporal logics, in that we explicitly capture Allen's interval algebra as modal operators in a language of temporal logic (rather than addressing it in the semantic structure). This has two major implications. First, it provides a formal logical framework for reasoning about time in biomedicine, allowing general (i.e., higher-levels of abstraction) investigation into the properties of this approach (such as proof systems, completeness, expressiveness, and decidability) independent of a specific query language or a database system. Second, it puts our approach in the context of logical developments in computer science, allowing potential translation of existing results into the setting of TCL and its variants or subsystems so as to illuminate opportunities and computational challenges involved in temporal reasoning for biomedicine., (©2022 AMIA - All rights reserved.)

Diagnostic errors in medicine represent a significant public health problem but continue to be challenging to measure accurately, reliably, and efficiently. The recently developed Symptom-Disease Pair Analysis of Diagnostic Error (SPADE) approach measures misdiagnosis related harms using electronic health records or administrative claims data. The approach is clinically valid, methodologically sound, statistically robust, and operationally viable without the requirement for manual chart review. This paper clarifies aspects of the SPADE analysis to assure that researchers apply this method to yield valid results with a particular emphasis on defining appropriate comparator groups and analytical strategies for balancing differences between these groups. We discuss four distinct types of comparators (intra-group and inter-group for both look-back and look-forward analyses), detailing the rationale for choosing one over the other and inferences that can be drawn from these comparative analyses. Our aim is that these additional analytical practices will improve the validity of SPADE and related approaches to quantify diagnostic error in medicine., (© 2023 Walter de Gruyter GmbH, Berlin/Boston.)

The electronic medical record has fundamentally altered the way surgeons participate and practice medicine. There is now a wealth of data, once hidden behind paper records, that is, now available to surgeons to provide superior care to their patients. This article reviews the history of the electronic medical record, discusses use cases of additional data resources, and highlights the pitfalls of this relatively new technology., Competing Interests: Disclosure No authors have received any financial benefit for participating in the development of this article. There are no conflicts of interest., (Copyright © 2022 Elsevier Inc. All rights reserved.)

The exceptionally rapid development of highly flexible, reusable artificial intelligence (AI) models is likely to usher in newfound capabilities in medicine. We propose a new paradigm for medical AI, which we refer to as generalist medical AI (GMAI). GMAI models will be capable of carrying out a diverse set of tasks using very little or no task-specific labelled data. Built through self-supervision on large, diverse datasets, GMAI will flexibly interpret different combinations of medical modalities, including data from imaging, electronic health records, laboratory results, genomics, graphs or medical text. Models will in turn produce expressive outputs such as free-text explanations, spoken recommendations or image annotations that demonstrate advanced medical reasoning abilities. Here we identify a set of high-impact potential applications for GMAI and lay out specific technical capabilities and training datasets necessary to enable them. We expect that GMAI-enabled applications will challenge current strategies for regulating and validating AI devices for medicine and will shift practices associated with the collection of large medical datasets., (© 2023. Springer Nature Limited.)

Physician turnover places a heavy burden on the healthcare industry, patients, physicians, and their families. Having a mechanism in place to identify physicians at risk for departure could help target appropriate interventions that prevent departure. We have collected physician characteristics, electronic health record (EHR) use patterns, and clinical productivity data from a large ambulatory based practice of non-teaching physicians to build a predictive model. We use several techniques to identify possible intervenable variables. Specifically, we used gradient boosted trees to predict the probability of a physician departing within an interval of 6 months. Several variables significantly contributed to predicting physician departure including tenure (time since hiring date), panel complexity, physician demand, physician age, inbox, and documentation time. These variables were identified by training, validating, and testing the model followed by computing SHAP (SHapley Additive exPlanation) values to investigate which variables influence the model's prediction the most. We found these top variables to have large interactions with other variables indicating their importance. Since these variables may be predictive of physician departure, they could prove useful to identify at risk physicians such who would benefit from targeted interventions., Competing Interests: The authors have declared that no competing interests exist., (Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.)

Artificial intelligence (AI) is transforming the practice of medicine. Systems assessing chest radiographs, pathology slides, and early warning systems embedded in electronic health records (EHRs) are becoming ubiquitous in medical practice. Despite this, medical students have minimal exposure to the concepts necessary to utilize and evaluate AI systems, leaving them under prepared for future clinical practice. We must work quickly to bolster undergraduate medical education around AI to remedy this. In this commentary, we propose that medical educators treat AI as a critical component of medical practice that is introduced early and integrated with the other core components of medical school curricula. Equipping graduating medical students with this knowledge will ensure they have the skills to solve challenges arising at the confluence of AI and medicine., Competing Interests: Declaration of interests E.Ö. reports having a patent pending for the University of Michigan for an AI-based approach for the dynamic prediction of health states for patients with occupational injuries., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Despite technological advances in the analysis of digital images for medical consultations, many health information systems lack the ability to correlate textual descriptions of image findings linked to the actual images. Images and reports often reside in separate silos in the medical record throughout the process of image viewing, report authoring, and report consumption. Forward-thinking centers and early adopters have created interactive reports with multimedia elements and embedded hyperlinks in reports that connect the narrative text with the related source images and measurements. Most of these solutions rely on proprietary single-vendor systems for viewing and reporting in the absence of any encompassing industry standards to facilitate interoperability with the electronic health record (EHR) and other systems. International standards have enabled the digitization of image acquisition, storage, viewing, and structured reporting. These provide the foundation to discuss enhanced reporting. Lessons learned in the digital transformation of radiology and pathology can serve as a basis for interactive multimedia reporting (IMR) across image-centric medical specialties. This paper describes the standard-based infrastructure and communications to fulfill recently defined clinical requirements through a consensus from an international workgroup of multidisciplinary medical specialists, informaticists, and industry participants. These efforts have led toward the development of an Integrating the Healthcare Enterprise (IHE) profile that will serve as a foundation for interoperable interactive multimedia reporting., (© 2022. The Author(s).)

Objective: Clinicians spend significant time working in the electronic health record (EHR). The US is an outlier in EHR time, suggesting that EHR-related work may be driven in part by the legal environment and threat of malpractice. To assess this, we evaluate the association between state-level malpractice climate and clinician time spent in the EHR., Materials and Methods: We use EHR metadata from 351 ambulatory care health systems in the United States using Epic from January-August 2019 combined with state-level data on malpractice incidence and payouts. We used descriptive statistics to measure variation in clinician EHR time, including total EHR time, documentation time per day, and after-hours EHR time per day. Multi-variable regression evaluated the association between clinicians in high malpractice states and EHR use., Results: We found no association between location in a state in the top-quartile of malpractice payouts and time spent in the EHR per day, time spent in the EHR outside of scheduled hours, or time spent documenting per day, except for a subgroup of the clinicians in the highest malpractice specialties, where there was a small increase in EHR time per day (B = 6.08 min, P < 0.001) and time spent documenting notes (B = 2.77 min, P < 0.001)., Discussion: State-level differences in malpractice incidence are unlikely to be a significant driver of EHR work for most clinicians., Conclusion: Policymakers seeking to address EHR documentation burden should examine burden driven by other socio-technical demands on clinician time, such as billing or quality measurement., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: The high volume of research focusing on extracting patient information from electronic health records (EHRs) has led to an increase in the demand for annotated corpora, which are a precious resource for both the development and evaluation of natural language processing (NLP) algorithms. The absence of a multipurpose clinical corpus outside the scope of the English language, especially in Brazilian Portuguese, is glaring and severely impacts scientific progress in the biomedical NLP field., Methods: In this study, a semantically annotated corpus was developed using clinical text from multiple medical specialties, document types, and institutions. In addition, we present, (1) a survey listing common aspects, differences, and lessons learned from previous research, (2) a fine-grained annotation schema that can be replicated to guide other annotation initiatives, (3) a web-based annotation tool focusing on an annotation suggestion feature, and (4) both intrinsic and extrinsic evaluation of the annotations., Results: This study resulted in SemClinBr, a corpus that has 1000 clinical notes, labeled with 65,117 entities and 11,263 relations. In addition, both negation cues and medical abbreviation dictionaries were generated from the annotations. The average annotator agreement score varied from 0.71 (applying strict match) to 0.92 (considering a relaxed match) while accepting partial overlaps and hierarchically related semantic types. The extrinsic evaluation, when applying the corpus to two downstream NLP tasks, demonstrated the reliability and usefulness of annotations, with the systems achieving results that were consistent with the agreement scores., Conclusion: The SemClinBr corpus and other resources produced in this work can support clinical NLP studies, providing a common development and evaluation resource for the research community, boosting the utilization of EHRs in both clinical practice and biomedical research. To the best of our knowledge, SemClinBr is the first available Portuguese clinical corpus., (© 2022. The Author(s).)

Background: The American Medical Association updated guidance in 2021 for frequently used billing codes for outpatient evaluation and management (E/M) visits. The intent was to account for provider time outside of face-to-face encounters and to reduce onerous documentation requirements., Objective: To analyze E/M visit use, documentation length, and time spent in the electronic health record (EHR) before and after the guideline change., Design: Observational, retrospective, pre-post study., Setting: U.S.-based ambulatory practices using the Epic Systems EHR., Participants: 303 547 advanced practice providers and physicians across 389 organizations., Measurements: Data from September 2020 through April 2021 containing weekly provider-level E/M code and EHR use metadata were extracted from the Epic Signal database. We descriptively analyzed overall and specialty-specific changes in E/M visit use, note length, and time spent in the EHR before and after the new guidelines using provider-level paired t tests., Results: Following the new guidelines, level 3 visits decreased by 2.41 percentage points (95% CI, -2.48 to -2.34 percentage points) to 38.5% of all E/M visits, a 5.9% relative decrease from fall 2020. Level 4 visits increased by 0.89 percentage points (CI, 0.82 to 0.96 percentage points) to 40.9% of E/M visits, a 2.2% relative increase. Level 5 visits (the highest acuity level) increased by 1.85 percentage points (CI, 1.81 to 1.89 percentage points) to 10.1% of E/M visits, a 22.6% relative increase. These changes varied by specialty. We found no meaningful changes in measures of note length or time spent in the EHR., Limitation: The Epic ambulatory client base may underrepresent smaller and independent practices., Conclusion: Immediate changes in E/M coding contrast with null findings for changes in both note length and EHR time. Provider organizations are positioned to respond more rapidly to billing process changes than to changes in care delivery and associated EHR use behaviors. Fully realizing the intended benefits of this guideline change will require more time, facilitation, and scaling of best practices that more directly address EHR documentation practices and associated burden., Primary Funding Source: None.

Objective: To explore Veterans Health Administration clinicians' perspectives on the idea of redesigning electronic consultation (e-consult) delivery in line with a hub-and-spoke (centralized) model., Materials and Methods: We conducted a qualitative study in VA New England Healthcare System (VISN 1). Semi-structured phone interviews were conducted with 35 primary care providers and 38 specialty care providers, including 13 clinical leaders, at 6 VISN 1 sites varying in size, specialist availability, and e-consult volume. Interviews included exploration of the hub-and-spoke (centralized) e-consult model as a system redesign option. Qualitative content analysis procedures were applied to identify and describe salient categories., Results: Participants saw several potential benefits to scaling up e-consult delivery from a decentralized model to a hub-and-spoke model, including expanded access to specialist expertise and increased timeliness of e-consult responses. Concerns included differences in resource availability and management styles between sites, anticipated disruption to working relationships, lack of incentives for central e-consultants, dedicated staff's burnout and fatigue, technological challenges, and lack of motivation for change., Discussion: Based on a case study from one of the largest integrated healthcare systems in the United States, our work identifies novel concerns and offers insights for healthcare organizations contemplating a scale-up of their e-consult systems., Conclusions: Scaling up e-consults in line with the hub-and-spoke model may help pave the way for a centralized and efficient approach to care delivery, but the success of this transformation will depend on healthcare systems' ability to evaluate and address barriers to leveraging economies of scale for e-consults., (Published by Oxford University Press on behalf of the American Medical Informatics Association 2021. This work is written by US Government employees and is in the public domain in the US.)

A new development in the practice of medicine is Artificial Intelligence-based predictive analytics that forewarn clinicians of future deterioration of their patients. This proactive opportunity, though, is different from the reactive stance that clinicians traditionally take. Implementing these tools requires new ideas about how to educate clinician users to facilitate trust and adoption and to promote sustained use. Our real-world hospital experience implementing a predictive analytics monitoring system that uses electronic health record and continuous monitoring data has taught us principles that we believe to be applicable to the implementation of other such analytics systems within the health care environment. These principles are mentioned below:• To promote trust, the science must be understandable.• To enhance uptake, the workflow should not be impacted greatly.• To maximize buy-in, engagement at all levels is important.• To ensure relevance, the education must be tailored to the clinical role and hospital culture.• To lead to clinical action, the information must integrate into clinical care.• To promote sustainability, there should be periodic support interactions after formal implementation., Competing Interests: The author owns stock in AMP3D which makes the CoMET technology used in the Prediction Assistant monitoring system., (The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).)

Objective: To derive 7 proposed core electronic health record (EHR) use metrics across 2 healthcare systems with different EHR vendor product installations and examine factors associated with EHR time., Materials and Methods: A cross-sectional analysis of ambulatory physicians EHR use across the Yale-New Haven and MedStar Health systems was performed for August 2019 using 7 proposed core EHR use metrics normalized to 8 hours of patient scheduled time., Results: Five out of 7 proposed metrics could be measured in a population of nonteaching, exclusively ambulatory physicians. Among 573 physicians (Yale-New Haven N = 290, MedStar N = 283) in the analysis, median EHR-Time8 was 5.23 hours. Gender, additional clinical hours scheduled, and certain medical specialties were associated with EHR-Time8 after adjusting for age and health system on multivariable analysis. For every 8 hours of scheduled patient time, the model predicted these differences in EHR time (P < .001, unless otherwise indicated): female physicians +0.58 hours; each additional clinical hour scheduled per month -0.01 hours; practicing cardiology -1.30 hours; medical subspecialties -0.89 hours (except gastroenterology, P = .002); neurology/psychiatry -2.60 hours; obstetrics/gynecology -1.88 hours; pediatrics -1.05 hours (P = .001); sports/physical medicine and rehabilitation -3.25 hours; and surgical specialties -3.65 hours., Conclusions: For every 8 hours of scheduled patient time, ambulatory physicians spend more than 5 hours on the EHR. Physician gender, specialty, and number of clinical hours practicing are associated with differences in EHR time. While audit logs remain a powerful tool for understanding physician EHR use, additional transparency, granularity, and standardization of vendor-derived EHR use data definitions are still necessary to standardize EHR use measurement., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

We used an online survey to measure how personal acquaintance with referring primary care providers (PCPs) affects specialists' experience of care coordination as use of a shared electronic health record (EHR) increases. Only 9% of specialists rated Overall Coordination as 9 or 10 out of 10. Personal acquaintance positively impacted Overall Coordination and all measured coordination subdomains. This effect was attenuated, but persisted, even at higher levels of EHR sharing. The impact of a shared EHR alone was limited to Overall Coordination and the Data Transfer subdomains. Health systems can improve coordination through investment in clinician relationships, while research should address the gaps in coordination even with widespread personal acquaintance and shared EHRs., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

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., Competing Interests: L.V.R. reports grants from NIH/NHGRI during the conduct of the study and also has a Provisional Patent related to the design of Ancillary Genomic Systems that is no longer being pursued. J.B.S. reports a grant from NIH/NHGRI 1U01 HG008673–01 during the conduct of the study and also has a provisional patent related to the design of Ancillary Genomic Systems that is no longer being pursued. R.R.F. reports grants from NIH/NHGRI during the conduct of the study., (Thieme. All rights reserved.)

In order for enterprise imaging to be successful across a multitude of specialties, systems, and sites, standards are essential to categorize and classify imaging data. The HIMSS-SIIM Enterprise Imaging Community believes that the Digital Imaging Communications in Medicine (DICOM) Anatomic Region Sequence, or its equivalent in other data standards, is a vital data element for this role, when populated with standard coded values. We believe that labeling images with standard Anatomic Region Sequence codes will enhance the user's ability to consume data, facilitate interoperability, and allow greater control of privacy. Image consumption-when a user views a patient's images, he or she often wants to see relevant comparison images of the same lesion or anatomic region for the same patient automatically presented. Relevant comparison images may have been acquired from a variety of modalities and specialties. The Anatomic Region Sequence data element provides a basis to allow for efficient comparison in both instances. Interoperability-as patients move between health care systems, it is important to minimize friction for data transfer. Health care providers and facilities need to be able to consume and review the increasingly large and complex volume of data efficiently. The use of Anatomic Region Sequence, or its equivalent, populated with standard values enables seamless interoperability of imaging data regardless of whether images are used within a site or across different sites and systems. Privacy-as more visible light photographs are integrated into electronic systems, it becomes apparent that some images may need to be sequestered. Although additional work is needed to protect sensitive images, standard coded values in Anatomic Region Sequence support the identification of potentially sensitive images, enable facilities to create access control policies, and can be used as an interim surrogate for more sophisticated rule-based or attribute-based access control mechanisms. To satisfy such use cases, the HIMSS-SIIM Enterprise Imaging Community encourages the use of a pre-existing body part ontology. Through this white paper, we will identify potential challenges in employing this standard and provide potential solutions for these challenges.

Background: One of the main objectives of Electronic Health Records (EHRs) is to enhance collaboration among healthcare professionals. However, our knowledge of how EHRs actually affect collaborative practices is limited. This study examines how an EHR facilitates and constrains collaboration in five outpatient clinics., Methods: We conducted an embedded case study at five outpatient clinics of a Dutch hospital that had implemented an organization-wide EHR. Data were collected through interviews with representatives of medical specialties, administration, nursing, and management. Documents were analyzed to contextualize these data. We examined the following collaborative affordances of EHRs: (1) portability, (2) co-located access, (3) shared overviews, (4) mutual awareness, (5) messaging, and (6) orchestrating., Results: Our findings demonstrate how an EHR will both facilitate and constrain collaboration among specialties and disciplines. Affordances that were inscribed in the system for collaboration purposes were not fully actualized in the hospital because: (a) The EHR helps health professionals coordinate patient care on an informed basis at any time and in any place but only allows asynchronous patient record use. (b) The comprehensive patient file affords joint clinical decision-making based on shared data, but specialty- and discipline-specific user-interfaces constrain mutual understanding of that data. Moreover, not all relevant information can be easily shared across specialties and outside the hospital. (c) The reduced necessity for face-to-face communication saves time but is experienced as hindering collective responsibility for a smooth workflow. (d) The EHR affords registration at the source and registration of activities through orders, but the heightened administrative burden for physicians and the strict authorization rules on inputting data constrain the flexible, multidisciplinary collaboration. (e) While the EHR affords a complete overview, information overload occurs due to the parallel generation of individually owned notes and the high frequency of asynchronous communication through messages of varying clinical priority., Conclusions: For the optimal actualization of EHRs' collaborative affordances in hospitals, coordinated use of these affordances by health professionals is a prerequisite. Such coordinated use requires organizational, technical, and behavioral adaptations. Suggestions for hospital-wide policies to enhance trust in both the EHR and in its coordinated use for effective collaboration are offered.

Objective: This study aims to compare the management practices of a headache specialist with non-headache specialists in the treatment of children with migraine. The use of appropriate rescue medications and prophylactic agents, application of neuroimaging, and short-term outcomes are compared in children treated by the two groups of physicians., Methods: A retrospective cohort study was conducted by utilizing the electronic medical records of children 3-18 years of age with migraine, who were evaluated at a tertiary care children's hospital from 2016 to 2018., Results: Of the 849 patients who met the study criteria, 469 children were classified as having chronic migraine or high-frequency episodic migraine and were followed-up on at least 1 occasion by the neurologists. Imaging was obtained in 66.5% of all children with migraine. The headache specialist used 5-HT agonists ("triptans") for migraine management in 56.7% (76/135) of cases compared to non-headache specialists who prescribed them in 28.7% (96/334) of cases (P < .001). Of the children with chronic migraine, the headache specialist evaluated 135 patients while the non-headache specialists treated 334 children. Non-headache specialists prescribed prophylaxis in the form of natural supplements more frequently (63.8% of cases) compared to the headache specialist (38.5% of children) (P < .001). Moreover, prophylaxis with prescription drugs was utilized more often by headache specialist (66.7%) than non-headache specialists (37.4%) (P < .001)., Conclusions: Imaging appears to be commonly recommended by both headache specialists and non-headache specialists in children with migraine. The headache specialist was more likely to use triptans as rescue medications for pediatric migraine. Outcomes in the short-term were not statistically different whether children were being managed by the headache specialist or the non-headache specialists., (© 2019 American Headache Society.)

In 2017, a US academic medical center switched to a commercial EHR system using the "specialist training the specialist" model, which combines peer-to-peer training, classroom based training, and web-based training. We conducted semi-structured interviews with physicians at multiple training levels to investigate the impact of this EHR switch and to explore the training experience of physicians and their perception of the training quality pre and post Go-Live. Our team used Grounded Theory methodology to classify the interview information. Themes that emerged from the interviews included stress and anxiety, the desire for more realistic training environments tailored to specialty needs, and concerns about the duration of time between training and implementation. In future implementations, we recommend more data-rich test patients and the demonstration of real-world workflows during training.

Methodological advancements in epidemiology, biostatistics, and data science have strengthened the research world's ability to use data captured from electronic health records (EHRs) to address pressing medical questions, but gaps remain. We describe methods investments that are needed to curate EHR data toward research quality and to integrate complementary data sources when EHR data alone are insufficient for research goals. We highlight new methods and directions for improving the integrity of medical evidence generated from pragmatic trials, observational studies, and predictive modeling. We also discuss needed methods contributions to further ease data sharing across multisite EHR data networks. Throughout, we identify opportunities for training and for bolstering collaboration among subject matter experts, methodologists, practicing clinicians, and health system leaders to help ensure that methods problems are identified and resulting advances are translated into mainstream research practice more quickly., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The 2018 International Conference on Intelligent Biology and Medicine (ICIBM 2018) was held on June 10-12, 2018, in Los Angeles, California, USA. The conference consisted of a total of eleven scientific sessions, four tutorials, one poster session, four keynote talks and four eminent scholar talks, which covered a wild range of aspects of bioinformatics, medical informatics, systems biology and intelligent computing. Here, we summarize nine research articles selected for publishing in BMC Bioinformatics.

Technological advances have played a key role over the last century in the development of humankind. Critical Care Medicine is one of the greatest examples of this revolution. Smartphones with multiple sensors constitute another step forward, and have led to the development of apps for use by both professionals and patients. We discuss their main medical applications in the field of Critical Care Medicine., (Copyright © 2017 Elsevier España, S.L.U. y SEMICYUC. All rights reserved.)

With the advent of digital cameras, there has been an explosion in the number of medical specialties using images to diagnose or document disease and guide interventions. In many specialties, these images are not added to the patient's electronic medical record and are not distributed so that other providers caring for the patient can view them. As hospitals begin to develop enterprise imaging strategies, they have found that there are multiple challenges preventing the implementation of systems to manage image capture, image upload, and image management. This HIMSS-SIIM white paper will describe the key workflow challenges related to enterprise imaging and offer suggestions for potential solutions to these challenges., Competing Interests: Compliance with ethical standards Disclosures Towbin: Grant Funding: Cystic Fibrosis Foundation; Siemens; Guerbet Author/Royalties: Elsevier. Mark Bronkalla: Employee of Merge Healthcare and IBM company which is in the business of providing Medical Imaging products including VNAs, PACS, and Enterprise viewers.

Objective: Patient portals are online applications that allow patients to interact with healthcare organizations. Portal adoption is increasing, and secure messaging between patients and healthcare providers is an emerging form of outpatient interaction. Research about portals and messaging has focused on medical specialties. We characterized adoption of secure messaging and the contribution of messaging to outpatient interactions across diverse clinical specialties after broad portal deployment., Methods: This retrospective cohort study at Vanderbilt University Medical Center examined use of patient-initiated secure messages and clinic visits in the three years following full deployment of a patient portal across adult and pediatric specialties. We measured the proportion of outpatient interactions (i.e., messages plus clinic visits) conducted through secure messaging by specialty over time. Generalized estimating equations measured the likelihood of message-based versus clinic outpatient interaction across clinical specialties., Results: Over the study period, 2,422,114 clinic visits occurred, and 82,159 unique portal users initiated 948,428 messages to 1,924 recipients. Medicine participated in the most message exchanges (742,454 messages; 78.3% of all messages sent), followed by surgery (84,001; 8.9%) and obstetrics/gynecology (53,424; 5.6%). The proportion of outpatient interaction through messaging increased from 12.9% in 2008 to 33.0% in 2009 and 39.8% in 2010 (p<0.001). Medicine had the highest proportion of outpatient interaction conducted through messaging in 2008 (23.3% of outpatient interactions in medicine). By 2010, this proportion was highest for obstetrics/gynecology (83.4%), dermatology (71.6%), and medicine (56.7%). Growth in likelihood of message-based interaction was greater for anesthesiology, dermatology, obstetrics/gynecology, pediatrics, and psychiatry than for medicine (p<0.001)., Conclusions: This study demonstrates rapid adoption of secure messaging across diverse clinical specialties, with messaging interactions exceeding face-to-face clinic visits for some specialties. As patient portal and secure messaging adoption increase beyond medicine and primary care, research is needed to understand the implications for provider workload and patient care.

Electronic health records (EHR) represent a rich and relatively untapped resource for characterizing the true nature of clinical practice and for quantifying the degree of inter-relatedness of medical entities such as drugs, diseases, procedures and devices. We provide a unique set of co-occurrence matrices, quantifying the pairwise mentions of 3 million terms mapped onto 1 million clinical concepts, calculated from the raw text of 20 million clinical notes spanning 19 years of data. Co-frequencies were computed by means of a parallelized annotation, hashing, and counting pipeline that was applied over clinical notes from Stanford Hospitals and Clinics. The co-occurrence matrix quantifies the relatedness among medical concepts which can serve as the basis for many statistical tests, and can be used to directly compute Bayesian conditional probabilities, association rules, as well as a range of test statistics such as relative risks and odds ratios. This dataset can be leveraged to quantitatively assess comorbidity, drug-drug, and drug-disease patterns for a range of clinical, epidemiological, and financial applications.

Objective: Efforts to promote adoption of electronic health records (EHRs) have focused on primary care physicians, who are now expected to exchange data electronically with other providers, including specialists. However, the variation of EHR adoption among specialists is underexplored., Methods: We conducted a retrospective cross-sectional study to determine the association between physician specialty and the prevalence of EHR adoption, and a retrospective serial cross-sectional study to determine the association of physician specialty and the rate of EHR adoption over time. We used the 2005-2009 National Ambulatory Medical Care Survey. We considered fourteen specialties, and four definitions of EHR adoption (any EHR, basic EHR, full EHR, and a novel definition of EHR sophistication). We used multivariable logistic regression, and adjusted for several covariates (geography, practice characteristics, revenue characteristics, physician degree)., Results: Physician specialty was significantly associated with EHR adoption, regardless of the EHR definition, after adjusting for covariates. Psychiatrists, dermatologists, pediatricians, ophthalmologists, and general surgeons were significantly less likely to adopt EHRs, compared to the reference group of family medicine/general practitioners. After adjustment for covariates, these specialties were 44-94% less likely to adopt EHRs than the reference group. EHR adoption increased in all specialties, by approximately 40% per year. The rate of EHR adoption over time did not significantly vary by specialty., Conclusions: Although EHR adoption is increasing in all specialties, adoption varies widely by specialty. In order to insure each individual's network of providers can electronically share data, widespread adoption of EHRs is needed across all specialties.

BACKGROUND: Despite efforts to view electronic health records (EHR) data through an equity lens, crucial contextual information regarding patients social environments remains limited. Integrating EHR data and Geographic Information Systems (GIS) technology can give deeper insights into the relationships between patients social environments, health outcomes, and geographic factors. This study aims to identify regions with the fastest and slowest access to outpatient physical therapy services using bivariate choropleth maps to provide contextual insights that may contribute to health disparity in access. METHODS: This was a retrospective cohort study of patients access timelines for the first visit to outpatient physical therapy services (n = 10,363). The three timelines evaluated were (1) referral-to-scheduled appointment time, (2) scheduled appointment to first visit time, and (3) referral to first visit time. Hot and coldspot analyses (CI 95%) determined the fastest and slowest access times with patient-level characteristics and bivariate choropleth maps that were developed to visualize associations between access patterns and disadvantaged areas using Area Deprivation Index scores. Data were collected between January 1, 2016 and January 1, 2020. EHR data were geocoded via GIS technology to calculate geospatial statistics (Gi∗ statistic from ArcGIS Pro) in an urban area. RESULTS: Statistically significant differences were found for all three access timelines between coldspot (i.e., fast access group) and hotspot (i.e., slow access group) comparisons (p

Aims Design Methods Results Conclusion Implications for Patient Care Impact Reporting Method Patient or Public Contribution To identify facilitators and barriers and tailor implementation strategies to optimize emergency clinician's use of adult and paediatric sepsis pathways.A qualitative descriptive study using focus group methodology.Twenty‐two emergency nurses and ten emergency medical officers from four Australian EDs participated in eight virtual focus groups. Participants were asked about their experiences using the New South Wales Clinical Excellence Commission adult and paediatric sepsis pathways using a semi‐structured interview template. Facilitators and barriers to use of the sepsis pathways were categorized using the Theoretical Domains Framework. Tailored interventions were selected to address facilitators and barriers, and a re‐implementation plan was devised guided by the Behaviour Change Wheel.Thirty‐two facilitators and 58 barriers were identified corresponding to 11 Theoretical Domains Framework domains. Tailored strategies were selected to optimize emergency clinicians' use of the sepsis pathways including refinement of existing education and training programmes, modifications to the electronic medical record system, introduction of an audit and feedback system, staffing strategies and additional resources.The implementation of sepsis pathways in the Emergency Department setting is complex, impacted by a multitude of factors requiring tailored strategies to address facilitators and barriers and optimize uptake.This study presents a theory‐informed systematic approach to successfully implement and embed adult and paediatric sepsis pathways into clinical practice in the Emergency Department.Optimizing uptake of sepsis pathways has the potential to improve sepsis recognition and management, subsequently improving the outcome of patients with sepsis.The Consolidated Criteria for REporting Qualitative research guided the preparation of this report.Nil. [ABSTRACT FROM AUTHOR]

Abstract The accurate extraction of surgical data from electronic health records (EHRs), particularly operative notes through manual chart review (MCR), is complex, crucial, and time-intensive, limited by human error due to fatigue and the level of training. This study aimed to develop and validate a novel Natural Language Processing (NLP) algorithm integrated with a Large Language Model (LLM; GPT4-Turbo) to automate the extraction of spinal surgery data from EHRs. The algorithm employed a two-stage approach. Initially, a rule-based NLP framework reviewed and classified candidate segments from the text, preserving their reference segments. These segments were then verified in the second stage through the LLM. The primary outcomes of this study were the accurate extraction of surgical data, including the type of surgery, levels operated, number of disks removed, and presence of intraoperative incidental durotomies. Secondary objectives explored time efficiency, tokenization lengths, and costs. The performance of the algorithm was assessed across two validation databases, analyzing metrics such as accuracy, sensitivity, discrimination, F1-score, and precision, with 95% confidence intervals calculated using percentile-based bootstrapping. The NLP + LLM algorithm markedly outperformed all performance metrics, demonstrating significant improvements in time and cost efficiency. These results suggest the potential for widespread adoption of this technology.

Introduction: Protecting the privacy of internet customers is crucial in the field of health. In this area, there is sensitive and personal information, and privacy can increase customers’ trust in companies and create a stronger relationship between them.Methods: The target sample was chosen using a criterion-oriented purposeful sampling method. The sampling procedure was continued until the theoretical saturation of data was reached. Accordingly, 12 professors and administrators participated in the study. The data collection tool was a semi-structured interview. Nvivo software was used for theme analysis.Results: Based on the theme analysis method, two constructive themes of level one and 14 themes of level two were identified. Constructive themes of level one were technological infrastructure and obligations of the seller to the consumer. The themes of technological infrastructure were personalization services, social interaction performance, access control, information technology security, security enforcement measures, safety algorithm and data-based planning, and decision making. The constructive themes of the seller’s obligations to the consumer were awareness, user and seller education, safety, maintenance and support of information, responsibility, framework and principles, and trust.Conclusion: The proposed model showed that privacy protection was essential. Medical device businesses should implement a robust privacy policy, closely monitoring access, training employees on privacy protection, and upgrading security systems.

Abstract The co-occurrence of multiple chronic conditions, termed multimorbidity, presents an expanding global health challenge, demanding effective diagnostics and treatment strategies. Chronic ailments such as obesity, diabetes, and cardiovascular diseases have been linked to metabolites interacting between the host and microbiota. In this study, we investigated the impact of co-existing conditions on risk estimations for 1375 plasma metabolites in 919 individuals from population-based Estonian Biobank cohort using liquid chromatography mass spectrometry (LC–MS) method. We leveraged annually linked national electronic health records (EHRs) data to delineate comorbidities in incident cases and controls for the 14 common chronic conditions. Among the 254 associations observed across 13 chronic conditions, we primarily identified disease-specific risk factors (92%, 217/235), with most predictors (93%, 219/235) found to be related to the gut microbiome upon cross-referencing recent literature data. Accounting for comorbidities led to a reduction of common metabolite predictors across various conditions. In conclusion, our study underscores the potential of utilizing biobank-linked retrospective and prospective EHRs for the disease-specific profiling of diverse multifactorial chronic conditions.