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Hospitals have increased the adoption of Hospital Information Systems to optimize processes for the efficient and effective delivery of services to customers (i.e., patients). However, there are still challenges in adopting information systems in healthcare, despite technological advancements and the enormous business benefits. These challenges have led to resistance of some healthcare providers to use these systems. Moreover, investigative and diagnostic measures are not exhaustively carried out to improve the processes of healthcare centers and the implemented information systems used in executing the processes. Fortunately, process mining techniques help optimize business processes, but they are primarily utilized post-implementation/post-go-live. This paper demonstrates the role process mining can play in adopting an EMR/EHR by improving the existing EHR Implementation Lifecycle by proposing an Enhanced Model. We suggest using process mining at suitable phases of the EHR Implementation lifecycle, not post-implementation/post-go-live only. Therefore, we propose an Enhanced EHR Implementation Lifecycle that supports process mining as part of a testing protocol adopted for EMRs/EHRs usability in conformance to the organization’s workflow in other implementation phases. An experiment is performed with event logs from an open-source EHR for the feasibility of the proposed Enhanced EHR/EMR implementation model.

Purpose: There have been mixed findings of clinicians’ perceptions of Electronic Health Record (EHR). This study aims to explore the lived experiences of clinicians, to assess the role of EHR in improving the quality and safety of healthcare. Basic Procedures: A qualitative study design was used. We collected the opinions from different groups of clinicians (physicians, hospitalists, nurse practitioners, nurses, and patient safety officers) using semi-structured interviews. Organizations represented were trauma hospitals, academic medical centers, medical clinics, home health centers, and small hospitals. Main findings: Our study found clinicians’ ambivalent assessments toward EHR, which confirms extant literature. We compared the responses by job roles and found that nurses were positive about improving efficiency with EHR while others regarded EHR as time-consuming. While many underscored the importance of EHR in avoiding medical errors by improving data accessibility, nurses had concerns regarding data accuracy. Interoperability appeared to be a concern given limited system integration. Principal conclusions: Lived experiences of clinicians further tease out the mixed views about the effectiveness of EHR and highlight the challenges in EHR implementation. Redesigning the EHR and improving its implementation process may be potential solutions to increase its effectiveness.

Although the Office of The National Coordinator for Health Information Technology’s (ONC) Information Blocking Provision in the Cures Act Final Rule is an important step forward in providing patients free and unfettered access to their electronic health information (EHI), in the contexts of multiuser electronic health record (EHR) access and proxy access, concerns on the potential for harm in adolescent care contexts exist. We describe how the provision could erode patients’ (both adolescent and older patients alike) trust and willingness to seek care. The rule’s preventing harm exception does not apply to situations where the patient is a minor and the health care provider wishes to restrict a parent’s or guardian’s access to the minor’s EHI to avoid violating the minor’s confidentiality and potentially harming patient-clinician trust. This may violate previously developed government principles in the design and implementation of EHRs for pediatric care. Creating legally acceptable workarounds by means such as duplicate “shadow charting” will be burdensome (and prohibitive) for health care providers. Under the privacy exception, patients have the opportunity to request information to not be shared; however, depending on institutional practices, providers and patients may have limited awareness of this exception. Notably, the privacy exception states that providers cannot “improperly encourage or induce a patient’s request to block information.” Fearing being found in violation of the information blocking provisions, providers may feel that they are unable to guide patients navigating the release of their EHI in the multiuser or proxy access setting. ONC should provide more detailed guidance on their website and targeted outreach to providers and their specialty organizations that care for adolescents and other individuals affected by the Cures Act, and researchers should carefully monitor charting habits in these multiuser or proxy access situations.

Background Electronic health record (EHR) transitions are inherently disruptive to healthcare workers who must rapidly learn a new EHR and adapt to altered clinical workflows. Healthcare workers’ perceptions of EHR usability and their EHR use patterns following transitions are poorly understood. The Department of Veterans Affairs (VA) is currently replacing its homegrown EHR with a commercial Cerner EHR, presenting a unique opportunity to examine EHR use trends and usability perceptions. Objective To assess EHR usability and uptake up to 1-year post-transition at the first VA EHR transition site using a novel longitudinal, mixed methods approach. Design A concurrent mixed methods strategy using EHR use metrics and qualitative interview data. Participants 141 clinicians with data from select EHR use metrics in Cerner Lights On Network®. Interviews with 25 healthcare workers in various clinical and administrative roles. Approach We assessed changes in total EHR time, documentation time, and order time per patient post-transition. Interview transcripts (n = 90) were coded and analyzed for content specific to EHR usability. Key Results Total EHR time, documentation time, and order time all decreased precipitously within the first four months after go-live and demonstrated gradual improvements over 12 months. Interview participants expressed ongoing concerns with the EHR’s usability and functionality up to a year after go-live such as tasks taking longer than the old system and inefficiencies related to inadequate training and inherent features of the new system. These sentiments did not seem to reflect the observed improvements in EHR use metrics. Conclusions The integration of quantitative and qualitative data yielded a complex picture of EHR usability. Participants described persistent challenges with EHR usability 1 year after go-live contrasting with observed improvements in EHR use metrics. Combining findings across methods can provide a clearer, contextualized understanding of EHR adoption and use patterns during EHR transitions.

Background Electronic health record (EHR) transitions are increasingly widespread and often highly disruptive. It is imperative we learn from past experiences to anticipate and mitigate such disruptions. Veterans Affairs (VA) is undergoing a large-scale transition from its homegrown EHR (CPRS/Vista) to a commercial EHR (Cerner), creating a unique opportunity of shedding light on large-scale EHR-to-EHR transition challenges. Objective To explore one facet of the organizational impact of VA’s EHR transition: its implications for employees’ roles and responsibilities at the first VA site to implement Cerner Millennium EHR. Design As part of a formative evaluation of frontline staff experiences with VA’s EHR transition, we conducted brief (~ 15 min) and full-length interviews (~ 60 min) with clinicians and staff at Mann-Grandstaff VA Medical Center in Spokane, WA, before, during, and after transition (July 2020-November 2021). Participants We conducted 111 interviews with 26 Spokane clinicians and staff, recruited via snowball sampling. Approach We conducted audio interviews using a semi-structured guide with grounded prompts. We coded interview transcripts using a priori and emergent codes, followed by qualitative content analysis. Key Results Unlike VA’s previous EHR, Cerner imposes additional restrictions on access to its EHR functionality based upon “roles” assigned to users. Participants described a mismatch between established institutional duties and their EHR permissions, unanticipated changes in scope of duties brought upon by the transition, as well as impediments to communication and collaboration due to different role-based views. Conclusions Health systems should anticipate substantive impacts on professional workflows when EHR role settings do not reflect prior workflows. Such changes may increase user error, dissatisfaction, and patient care disruptions. To mitigate employee dissatisfaction and safety risks, health systems should proactively plan for and communicate about expected modifications and monitor for unintended role-related consequences of EHR transitions, while vendors should ensure accurate role configuration and assignment.

Background Physicians are experiencing an increasing burden of messaging within the electronic health record (EHR) inbox. Studies have called for the implementation of tools and resources to mitigate this burden, but few studies have evaluated how these interventions impact time spent on inbox activities. Objective Explore the association between existing EHR efficiency tools and clinical resources on primary care physician (PCP) inbox time. Design Retrospective, cross-sectional study of inbox time among PCPs in network clinics affiliated with an academic health system. Participants One hundred fifteen community-based PCPs. Main Measures Inbox time, in hours, normalized to eight physician scheduled hours (IB-Time 8 ). Key Results Following adjustment for physician sex as well as panel size, age, and morbidity, we observed no significant differences in inbox time for physicians with and without message triage, custom inbox QuickActions, encounter specialists, and message pools. Moreover, IB-Time 8 increased by 0.01 inbox hours per eight scheduled hours for each additional staff member resource in a physician’s practice ( p = 0.03). Conclusions Physician inbox time was not associated with existing EHR efficiency tools evaluated in this study. Yet, there may be a slight increase in inbox time among physicians in practices with larger teams.

We contribute to the shifting discourse in the literature on information system use, towards context-specific (rather than general) theories and effective use (rather than just use). Organizations are under great pressure to use information systems effectively but they have few theories to turn to for insights. Motivated by this need, we propose an approach for developing context-specific theories of effective use. The approach suggests that effective use can be theorized by: (1) understanding how a network of affordances supports the achievement of organizational goals, (2) understanding how the affordances are actualized, and (3) using inductive theorizing to elaborate these principles in a given context. We demonstrate the approach in the context of a Canadian health authority’s use of a community-care electronic healthcare record (EHR). We discovered that effective use in this context can be viewed at a high level as the accuracy and consistency with which users work with the EHR, and how they engage in reflection-in-action across a network of nine affordances. The key, however, is understanding how those elements interact with the multiple levels of data needed to achieve the organization’s various goals. Overall, we contribute by offering an approach for developing context-specific theories of effective use, demonstrating its usefulness in an important context, and discovering the importance of understanding in a new way the multilevel nature of information systems. The online appendix is available at https://doi.org/10.1287/isre.2017.0702 .

Nowadays Artificial Intelligence (AI) has become a fundamental component of healthcare applications, both clinical and remote, but the best performing AI systems are often too complex to be self-explaining. Explainable AI (XAI) techniques are defined to unveil the reasoning behind the system’s predictions and decisions, and they become even more critical when dealing with sensitive and personal health data. It is worth noting that XAI has not gathered the same attention across different research areas and data types, especially in healthcare. In particular, many clinical and remote health applications are based on tabular and time series data, respectively, and XAI is not commonly analysed on these data types, while computer vision and Natural Language Processing (NLP) are the reference applications. To provide an overview of XAI methods that are most suitable for tabular and time series data in the healthcare domain, this paper provides a review of the literature in the last 5 years, illustrating the type of generated explanations and the efforts provided to evaluate their relevance and quality. Specifically, we identify clinical validation, consistency assessment, objective and standardised quality evaluation, and human-centered quality assessment as key features to ensure effective explanations for the end users. Finally, we highlight the main research challenges in the field as well as the limitations of existing XAI methods.

The American Medical Association recommends that electronic health record (EHR) notes, often dense and written in nuanced language, be made readable for patients and laypeople, a practice we refer to as the simplification of discharge notes. Our approach to achieving the simplification of discharge notes involves a process of incremental simplification steps to achieve the ideal note. In this paper, we present the first step of this process. Large language models (LLMs) have demonstrated considerable success in text summarization. Such LLM summaries represent the content of EHR notes in an easier-to-read language. However, LLM summaries can also introduce inaccuracies. This study aims to test the hypothesis that summaries generated by LLMs from highlighted discharge notes will achieve increased accuracy compared to those generated from the original notes. For this purpose, we aim to prove a hypothesis that summaries generated by LLMs of discharge notes in which detailed information is highlighted are likely to be more accurate than summaries of the original notes. To test our hypothesis, we randomly sampled 15 discharge notes from the MIMIC III database and highlighted their detailed information using an interface terminology we previously developed with machine learning. This interface terminology was curated to encompass detailed information from the discharge notes. The highlighted discharge notes distinguished detailed information, specifically the concepts present in the aforementioned interface terminology, by applying a blue background. To calibrate the LLMs' summaries for our simplification goal, we chose GPT-4o and used prompt engineering to ensure high-quality prompts and address issues of output inconsistency and prompt sensitivity. We provided both highlighted and unhighlighted versions of each EHR note along with their corresponding prompts to GPT-4o. Each generated summary was manually evaluated to assess its quality using the following evaluation metrics: completeness, correctness, and structural integrity. We used the study sample of 15 discharge notes. On average, summaries from highlighted notes (H-summaries) achieved 96% completeness, 8% higher than the summaries from unhighlighted notes (U-summaries). H-summaries had higher completeness in 13 notes, and U-summaries had higher or equal completeness in 2 notes, resulting in P=.01, which implied statistical significance. Moreover, H-summaries demonstrated better correctness than U-summaries, with fewer instances of erroneous information (2 vs 3 errors, respectively). The number of improper headers was smaller for H-summaries for 11 notes and U-summaries for 4 notes (P=.03; implying statistical significance). Moreover, we identified 8 instances of misplaced information in the U-summaries and only 2 in the H-summaries. We showed that our findings supported the hypothesis that summarizing highlighted discharge notes improves the accuracy of the summaries. Feeding LLMs with highlighted discharge notes, combined with prompt engineering, results in higher-quality summaries in terms of correctness, completeness, and structural integrity compared to unhighlighted discharge notes.

The use of Electronic Health Records (EHR)/Electronic Medical Records (EMR) data is becoming more prevalent for research. However, analysis of this type of data has many unique complications due to how they are collected, processed and types of questions that can be answered. This book covers many important topics related to using EHR/EMR data for research including data extraction, cleaning, processing, analysis, inference, and predictions based on many years of practical experience of the authors. The book carefully evaluates and compares the standard statistical models and approaches with those of machine learning and deep learning methods and reports the unbiased comparison results for these methods in predicting clinical outcomes based on the EHR data. Key Features: Written based on hands-on experience of contributors from multidisciplinary EHR research projects, which include methods and approaches from statistics, computing, informatics, data science and clinical/epidemiological domains. Documents the detailed experience on EHR data extraction, cleaning and preparation Provides a broad view of statistical approaches and machine learning prediction models to deal with the challenges and limitations of EHR data. Considers the complete cycle of EHR data analysis. The use of EHR/EMR analysis requires close collaborations between statisticians, informaticians, data scientists and clinical/epidemiological investigators. This book reflects that multidisciplinary perspective.