INTRODUCTION: Mixed methods are used to holistically understand the "what", "how", and "why" questions within a single study by integrating quantitative and qualitative methods. Although this approach has demonstrated value in other disciplines, their use in applied clinical informatics research remains largely unexplored. This methodological review characterized the types of informatics intervention studies that used mixed methods designs and the specific methods of evaluation and reporting. MATERIALS AND METHODS: On December 13, 2024, we searched Embase, MEDLINE, PubMed, CINAHL and PsycINFO for studies that reportedly used a mixed methods design to study the development or evaluation of clinical informatics interventions. We developed themes in topics studied, designs used, and reporting issues. Quality assessment was conducted using the Mixed Methods Appraisal Tool. RESULTS: We included 54 studies. Despite being described as mixed methods, 14 studies (25.9%) lacked the integration of quantitative and qualitative data. Of the remaining studies, convergent study designs were commonly used. Mixed methods were predominantly used during pre-implementation and post-implementation evaluations. Quality issues included non-representative samples and non-reporting of qualitative approaches and paradigms. DISCUSSION AND CONCLUSIONS: The use of mixed methods to study clinical informatics development and implementation is uncommon. This review confirmed that reporting and design problems found in other disciplines extend to informatics. Calls to action include a need to disseminate mixed methods guidance within the informatics community and offer mixed methods training in graduate programs. We also synthesized an initial reporting checklist from the literature. Our findings offer baseline levels to assess educational efforts.

OBJECTIVES: Successful implementation of machine learning-augmented clinical decision support systems (ML-CDSS) in perioperative care requires the prioritization of patient-centric approaches to ensure alignment with societal expectations. We assessed general public and surgical patient attitudes and perspectives on ML-CDSS use in perioperative care. MATERIALS AND METHODS: A sequential explanatory study was conducted. Stage 1 collected public opinions through a survey. Stage 2 ascertained surgical patients' experiences and attitudes via focus groups and interviews. RESULTS: For Stage 1, a total of 281 respondents' (140 males [49.8%]) data were considered. Among participants without ML awareness, males were almost three times more likely than females to report more acceptance (OR = 2.97; 95% CI, 1.36-6.49) and embrace (OR = 2.74; 95% CI, 1.23-6.09) of ML-CDSS use by perioperative teams. Males were almost twice as likely as females to report more acceptance across all perioperative phases with ORs ranging from 1.71 to 2.07. In Stage 2, insights from 10 surgical patients revealed unanimous agreement that ML-CDSS should primarily serve a supportive function. The pre- and post-operative phases were identified explicitly as forums where ML-CDSS can enhance care delivery. Patients requested for education on ML-CDSS's role in their care to be disseminated by surgeons across multiple platforms. DISCUSSION AND CONCLUSION: The general public and surgical patients are receptive to ML-CDSS use throughout their perioperative care provided its role is auxiliary to perioperative teams. However, the integration of ML-CDSS into perioperative workflows presents unique challenges for healthcare settings. Insights from this study can inform strategies to support large-scale implementation and adoption of ML-CDSS by patients in all perioperative phases. Key strategies to promote the feasibility and acceptability of ML-CDSS include clinician-led discussions about ML-CDSS's role in perioperative care, established metrics to evaluate the clinical utility of ML-CDSS, and patient education.

OBJECTIVE: Early detection of surgical complications allows for timely therapy and proactive risk mitigation. Machine learning (ML) can be leveraged to identify and predict patient risks for postoperative complications. We developed and validated the effectiveness of predicting postoperative complications using a novel surgical Variational Autoencoder (surgVAE) that uncovers intrinsic patterns via cross-task and cross-cohort presentation learning. MATERIALS AND METHODS: This retrospective cohort study used data from the electronic health records of adult surgical patients over 4 years (2018-2021). Six key postoperative complications for cardiac surgery were assessed: acute kidney injury, atrial fibrillation, cardiac arrest, deep vein thrombosis or pulmonary embolism, blood transfusion, and other intraoperative cardiac events. We compared surgVAE's prediction performance against widely-used ML models and advanced representation learning and generative models under 5-fold cross-validation. RESULTS: 89 246 surgeries (49% male, median [IQR] age: 57 [45-69]) were included, with 6502 in the targeted cardiac surgery cohort (61% male, median [IQR] age: 60 [53-70]). surgVAE demonstrated generally superior performance over existing ML solutions across postoperative complications of cardiac surgery patients, achieving macro-averaged AUPRC of 0.409 and macro-averaged AUROC of 0.831, which were 3.4% and 3.7% higher, respectively, than the best alternative method (by AUPRC scores). Model interpretation using Integrated Gradients highlighted key risk factors based on preoperative variable importance. DISCUSSION AND CONCLUSION: Our advanced representation learning framework surgVAE showed excellent discriminatory performance for predicting postoperative complications and addressing the challenges of data complexity, small cohort sizes, and low-frequency positive events. surgVAE enables data-driven predictions of patient risks and prognosis while enhancing the interpretability of patient risk profiles.

OBJECTIVES: We evaluated the effectiveness and implementability of a standardized EHR-integrated handoff report to support intraoperative handoffs. MATERIALS AND METHODS: A pre-post intervention study was used to compare the quality of intraoperative handoffs supported by unstructured notes (pre) to structured, standardized EHR-integrated handoff reports (post). Participants included anesthesia clinicians involved in intraoperative handoffs. A mixed-method approach was followed, supported by general observations, shadowing, surveys, and interviews. RESULTS: One hundred and fifty-one intraoperative permanent handoffs (78 pre, 73 post) were included. One hundred percent of participants in the post-intervention cohort utilized the report. Compared to unstructured, structured handoffs using the EHR-integrated handoff report led to: (1) significant increase in the transfer of information about airway management (55%-78%, P < .001), intraoperative course (63%-86%, P < .001), and potential concerns (64%-88%, P < .001); (2) significant improvement in clinician satisfaction scores, with regards to information clarity and succinctness (4.5-4.7, P = .002), information transfer (3.8-4.2, P = .011), and opportunities for fewer errors reported by senders (3.3-2.5, P < .001) and receivers (3.2-2.4, P < .001); and (3) significant decrease in handoff duration (326.2-262.3 s, P = .016). Clinicians found the report implementation highly acceptable, appropriate, and feasible but noted a few areas for improvement to enhance its usability and integration within the intraoperative workflow. DISCUSSION AND CONCLUSION: A standardized EHR-integrated handoff report ensures the effectiveness and efficiency of intraoperative handoffs with its structured, consistent format that-promotes up-to-date and pertinent intraoperative information transfer; reduces opportunities for errors; and streamlines verbal communication. Handoff standardization can promote safe and high-quality intraoperative care.

Major postoperative complications are devastating to surgical patients. Some of these complications are potentially preventable via early predictions based on intraoperative data. However, intraoperative data comprise long and fine-grained multivariate time series, prohibiting the effective learning of accurate models. The large gaps associated with clinical events and protocols are usually ignored. Moreover, deep models generally lack transparency. Nevertheless, the interpretability is crucial to assist clinicians in planning for and delivering postoperative care and timely interventions. Towards this end, we propose a hierarchical model combining the strength of both attention and recurrent models for intraoperative time series. We further develop an explanation module for the hierarchical model to interpret the predictions by providing contributions of intraoperative data in a fine-grained manner. Experiments on a large dataset of 111,888 surgeries with multiple outcomes and an external high-resolution ICU dataset show that our model can achieve strong predictive performance (i.e., high accuracy) and offer robust interpretations (i.e., high transparency) for predicted outcomes based on intraoperative time series.

Given the risks and cost of hospitalization, there has been significant interest in exploiting machine learning models to improve perioperative care. However, due to the high dimensionality and noisiness of perioperative data, it remains a challenge to develop accurate and robust encoding for surgical predictions. Furthermore, it is important for the encoding to be interpretable by perioperative care practitioners to facilitate their decision making process. We proposeclinical variational autoencoder (cVAE), a deep latent variable model that addresses the challenges of surgical applications through two salient features. (1) To overcome performance limitations of traditional VAE, it isprediction-guided with explicit expression of predicted outcome in the latent representation. (2) Itdisentangles the latent space so that it can be interpreted in a clinically meaningful fashion. We apply cVAE to two real-world perioperative datasets to evaluate its efficacy and performance in predicting outcomes that are important to perioperative care, including postoperative complication and surgery duration. To demonstrate the generality and facilitate reproducibility, we also apply cVAE to the open MIMIC-III dataset for predicting ICU duration and mortality. Our results show that the latent representation provided by cVAE leads to superior performance in classification, regression and multi-task predictions. The two features of cVAE are mutually beneficial and eliminate the need of a predictor. We further demonstrate the interpretability of the disentangled representation and its capability to capture intrinsic characteristics of hospitalized patients. While this work is motivated by and evaluated in the context of clinical applications, the proposed approach may be generalized for other fields using high-dimensional and noisy data and valuing interpretable representations.

BACKGROUND: Cardiac surgery patients are at high risk for readmissions after hospital discharge- few of these readmissions are preventable by mitigating barriers underlying discharge care transitions. An in-depth evaluation of the nuances underpinning the discharge process and the use of tools to support the process, along with insights on patient and clinician experiences, can inform the design of evidence-based strategies to reduce preventable readmissions. OBJECTIVE: The study objectives are 3-fold: elucidate perceived factors affecting the postsurgical discharge care transitions of cardiac surgery patients going home; highlight differences among clinician and patient perceptions of the postsurgical discharge experiences, and ascertain the impact of these transitions on patient recovery at home. METHODS: We conducted a prospective multi-stakeholder study using mixed methods, including general observations, patient shadowing, chart reviews, clinician interviews, and follow-up telephone patient and caregiver surveys/interviews. We followed thematic and content analyses. FINDINGS: Participants included 49 patients, 6 caregivers, and 27 clinicians. We identified interdependencies between the predischarge preparation, discharge education, and postdischarge follow-up care phases that must be coordinated for effective discharge care transitions. We identified several factors that could lead to fragmented discharges, including limited preoperative preparation, ill-defined discharge education, and postoperative plans. To address these, clinicians often performed behind-the-scenes work, including offering informal preoperative preparation, tailoring discharge education, and personalizing postdischarge follow-up plans. As a result, majority of patients reported high satisfaction with care transitions and their positive impact on their home recovery. DISCUSSION AND CONCLUSIONS: Articulation work by clinicians (ie, behind the scenes work) is critical for ensuring safety, care continuity, and overall patient experience during care transitions. We discuss key evidence-based considerations for re-engineering postsurgical discharge workflows and re-designing discharge interventions.

OBJECTIVE: The Anesthesiology Control Tower (ACT) for operating rooms (ORs) remotely assesses the progress of surgeries and provides real-time perioperative risk alerts, communicating risk mitigation recommendations to bedside clinicians. We aim to identify and map ACT-OR nonroutine events (NREs)-risk-inducing or risk-mitigating workflow deviations-and ascertain ACT's impact on clinical workflow and patient safety. MATERIALS AND METHODS: We used ethnographic methods including shadowing ACT and OR clinicians during 83 surgeries, artifact collection, chart reviews for decision alerts sent to the OR, and 10 clinician interviews. We used hybrid thematic analysis informed by a human-factors systems-oriented approach to assess ACT's role and impact on safety, conducting content analysis to assess NREs. RESULTS: Across 83 cases, 469 risk alerts were triggered, and the ACT sent 280 care recommendations to the OR. 135 NREs were observed. Critical factors facilitating ACT's role in supporting patient safety included providing backup support and offering a fresh-eye perspective on OR decisions. Factors impeding ACT included message timing and ACT and OR clinician cognitive lapses. Suggestions for improvement included tailoring ACT message content (structure, timing, presentation) and incorporating predictive analytics for advanced planning. DISCUSSION: ACT served as a safety net with remote surveillance features and as a learning healthcare system with feedback/auditing features. Supporting strategies include adaptive coordination and harnessing clinician/patient support to improve ACT's sustainability. Study insights inform future intraoperative telemedicine design considerations to mitigate safety risks. CONCLUSION: Incorporating similar remote technology enhancement into routine perioperative care could markedly improve safety and quality for millions of surgical patients.

OBJECTIVE: To systematically synthesize and appraise the evidence on the effectiveness of health information technology (HIT)-based discharge care transition interventions (CTIs) on readmissions and emergency room visits. MATERIALS AND METHODS: We conducted a systematic search on multiple databases (MEDLINE, CINAHL, EMBASE, and CENTRAL) on June 29, 2020, targeting readmissions and emergency room visits. Prospective studies evaluating HIT-based CTIs published as original research articles in English language peer-reviewed journals were eligible for inclusion. Outcomes were pooled for narrative analysis. RESULTS: Eleven studies were included for review. Most studies (n = 6) were non-RCTs. Several studies (n = 9) assessed bridging interventions comprised of at least 1 pre- and 1 post-discharge component. The narrative analysis found improvements in patient experience and perceptions of discharge care. DISCUSSION: Given the statistical and clinical heterogeneity among studies, we could not ascertain the cumulative effect of CTIs on clinical outcomes. Nevertheless, we found gaps in current research and its implications for future work, including the need for a HIT-based care transition model for guiding theory-driven design and evaluation of HIT-based discharge CTIs. CONCLUSIONS: We appraised and aggregated empirical evidence on the cumulative effectiveness of HIT-based interventions to support discharge transitions from hospital to home, and we highlighted the implications for evidence-based practice and informatics research.

OBJECTIVE: To determine factors that influence the adoption and use of patient-reported outcomes (PROs) in the electronic health record (EHR) among users. MATERIALS AND METHODS: Q methodology, supported by focus groups, semistructured interviews, and a review of the literature was used for data collection about opinions on PROs in the EHR. An iterative thematic analysis resulted in 49 statements that study participants sorted, from most unimportant to most important, under the following condition of instruction: "What issues are most important or most unimportant to you when you think about the adoption and use of patient-reported outcomes within the electronic health record in routine clinical care?" Using purposive sampling, 50 participants were recruited to rank and sort the 49 statements online, using HTMLQ software. Principal component analysis and Varimax rotation were used for data analysis using the PQMethod software. RESULTS: Participants were mostly physicians (24%) or physician/researchers (20%). Eight factors were identified. Factors included the ability of PROs in the EHR to enable: efficient and reliable use; care process improvement and accountability; effective and better symptom assessment; patient involvement for care quality; actionable and practical clinical decisions; graphical review and interpretation of results; use for holistic care planning to reflect patients' needs; and seamless use for all users. DISCUSSION: The success of PROs in the EHR in clinical settings is not dependent on a "one size fits all" strategy, demonstrated by the diversity of viewpoints identified in this study. A sociotechnical approach for implementing PROs in the EHR may help improve its success and sustainability. CONCLUSIONS: PROs in the EHR are most important to users when the technology is used to improve patient outcomes. Future research must focus on the impact of embedding this EHR functionality on care processes.

OBJECTIVE: We sought to ascertain perceived factors affecting women's career development efforts in the American Medical Informatics Association (AMIA) and to provide recommendations for improvements. MATERIALS AND METHODS: Data were collected using a 27-item survey administered via the AMIA newsletter and other social channels. Survey questions comprised 3 demographics, 15 Likert-scale, and 9 open-ended items. Likert-scale responses were summarized across respondent ages, career stages, and career domains, and open-ended responses were thematically analyzed. RESULTS: We received survey responses from 109 AMIA women members. Our findings demonstrate that AMIA had made strides in promoting career development, and the most effective AMIA efforts included social events (83%), panel discussions (80%), and scientific sessions (79%). However, despite these efforts, women members perceived that gender-specific challenges persisted within AMIA, and recognized the need for increased networking opportunities (96%), raising awareness of gender-specific challenges (95%), and encouraging gender proportional representation in leadership (92%). DISCUSSION: International and national biomedical informatics professional communities have put forth efforts to address gender-specific issues in career development. Yet, our study identified that some of these, including the deep-rooted gender power hierarchy and bias, are still perceived as profound in AMIA. CONCLUSION: Even though existing career development efforts for women are highly effective, important perceived gender-specific career development issues require further attention and investigation to improve existing AMIA activities.

OBJECTIVE: We utilized a computerized order entry system-integrated function referred to as "void" to identify erroneous orders (ie, a "void" order). Using voided orders, we aimed to (1) identify the nature and characteristics of medication ordering errors, (2) investigate the risk factors associated with medication ordering errors, and (3) explore potential strategies to mitigate these risk factors. MATERIALS AND METHODS: We collected data on voided orders using clinician interviews and surveys within 24 hours of the voided order and using chart reviews. Interviews were informed by the human factors-based SEIPS (Systems Engineering Initiative for Patient Safety) model to characterize the work systems-based risk factors contributing to ordering errors; chart reviews were used to establish whether a voided order was a true medication ordering error and ascertain its impact on patient safety. RESULTS: During the 16-month study period (August 25, 2017, to December 31, 2018), 1074 medication orders were voided; 842 voided orders were true medication errors (positive predictive value = 78.3 ± 1.2%). A total of 22% (n = 190) of the medication ordering errors reached the patient, with at least a single administration, without causing patient harm. Interviews were conducted on 355 voided orders (33% response). Errors were not uniquely associated with a single risk factor, but the causal contributors of medication ordering errors were multifactorial, arising from a combination of technological-, cognitive-, environmental-, social-, and organizational-level factors. CONCLUSIONS: The void function offers a practical, standardized method to create a rich database of medication ordering errors. We highlight implications for utilizing the void function for future research, practice and learning opportunities.

Electronic health records (EHR) use is often considered a significant contributor to clinician burnout. Informatics researchers often measure clinical workload using EHR-derived audit logs and use it for quantifying the contribution of EHR use to clinician burnout. However, translating clinician workload measured using EHR-based audit logs into a meaningful burnout metric requires an alignment with the conceptual and theoretical principles of burnout. In this perspective, we describe a systems-oriented conceptual framework to achieve such an alignment and describe the pragmatic realization of this conceptual framework using 3 key dimensions: standardizing the measurement of EHR-based clinical work activities, implementing complementary measurements, and using appropriate instruments to assess burnout and its downstream outcomes. We discuss how careful considerations of such dimensions can help in augmenting EHR-based audit logs to measure factors that contribute to burnout and for meaningfully assessing downstream patient safety outcomes.

Diana Forsythe, PhD, was a scholar of biomedical informatics, medical anthropology, artificial intelligence (AI), and feminism. With her upbringing by 2 renowned computer scientists, Drs. Alexandra Illmer Forsythe and George Forsythe, she was aware of the hard problems in computer science during her early years. Although she pursued a graduate degree in cultural anthropology and social demography, she went on to introduce methods, frameworks, and insights from the social sciences and the study of science and technology to the nascent fields of AI and biomedical informatics in the 1980s and1990s. The scope of her work was foundational in establishing people and organizational studies as a subdiscipline within biomedical informatics and as a working group with the American Medical Informatics Association (AMIA). Indeed, the current JAMIA editor-in-chief—a colleague of Diana’s at University of California, San Francisco—asked her to attend and reflect upon a 1997 AMIA workshop that brought together nursing vocabulary developers and other key stakeholders to address the topic of implementing nursing vocabularies in computer-based systems. Her reflections, published in JAMIA,1 pointed out the importance of culture and embedded practice in concept naming and questioned the need for a single nursing vocabulary. During her relatively brief career, Diana challenged researchers to understand that technology is never neutral and that attitudes and perspectives of researchers and technology developers profoundly influence fundamental aspects of technology design. Her work consistently challenged the field to pay attention to how people intended to use the technology, the social context within which the technology was implemented, and the potential broader and unintended impacts of technology. Through the rigorous application of qualitative methods through the lens of anthropology, her work identified how these factors influenced the intended users of technology in ways that could be detrimental. Throughout her publications and in the landmark posthumously published collection of essays, Studying Those Who Study Us: An Anthropologist in the World of Artificial Intelligence,2 Diana questioned inherent assumptions about the design and implementation of technology in medicine. In particular, her work reflected and advocated for the rigorous application of social science theory and methods in biomedical informatics research and practice. She also introduced feminist perspectives into analyses of social and technical contexts, emphasizing how the lived experiences of women intersected with technology development, use, and implementation. Toward this end, recent efforts such as the Women in AMIA Initiative and the AMIA First Look Program have sought to raise the visibility of the contributions of women and individuals from groups typically underrepresented in AMIA. As a fierce advocate for feminism and ethnography (ie, a subdiscipline of anthropology), Diana’s interactions with biomedical informatics, at the time a field largely dominated by men with computer science and information technology (IT) perspectives, were not always smooth and seamless. Her work, both as a researcher and an anthropologist, was deeply rooted in a sense of social justice and shed light on the needs and rights of disempowered communities. Additionally, Diana and her colleagues engaged in robust debates regarding rigor, reliability, and validity in qualitative research, including the importance of truly understanding and engaging with social sciences. Since her untimely death in 1997, Diana’s continued influence on the field lives on. As a foundational leader in establishing the important research space that sits at the intersection of social sciences and biomedical informatics, her work sheds light on the relevance of social sciences methods and theories, notably the importance of subjective experience and context, in informatics and computer science fields, which have a dominant perspective of objective reality. Those of us who work in people and organizational spaces in the biomedical informatics field have been profoundly influenced by Diana’s work and have also sought to build on the foundations that she established. The sustained impact of her body of research on the field can be witnessed through discussions at panels at the AMIA Symposium3 and through awards that bear her name including the annual AMIA Diana Forsythe Award honoring an outstanding publication at the intersection of the social sciences and biomedical informatics, the annual American Anthropological Association (AAA) Diana Forsythe Prize, and the Forsythe Dissertation Award for Social Studies of Science, Technology, and Health at the University of California, San Francisco or Stanford University. The definition of biomedical informatics has rapidly evolved over the last 25 years and is recognized today as a truly interdisciplinary field.4,5 This broader definition of the field recognizes the social sciences, human factors engineering, cognitive sciences, and multiple other disciplines as core contributors to continued progress in the field. However, too often, a gap still exists in the published biomedical informatics literature in capturing and representing these perspectives critical to the current healthcare context as new advancements and technologies (eg, mobile health, social media, health information exchange) emerge and as we strive to increase diversity of perspectives in the field. Over the last thirty years, researchers have continued to expand the rigorous application of qualitative methods in informatics and to include representation of patient voices and other stakeholder perspectives in technology design efforts. Thus, the time is ideal for a special issue focused on the continuing legacy of Diana Forsythe in biomedical informatics, identifying the current status of qualitative methods and ethnography in biomedical informatics, and charting a path toward the future. The purpose of our Special Issue was two-fold: to highlight the continued presence of people and organizational focused work in biomedical and health informatics; and to explore future directions for this critically important subdiscipline moving forward. In honoring and continuing Diana’s legacy, this special issue focuses on innovative and interdisciplinary scholarship at the intersection of biomedical informatics and the social sciences. In particular, it highlights the advances in knowledge about: (a) the methods and theories used to understand problems at the intersection of social sciences and informatics; (b) the impact of women and feminism in shaping the field of informatics; and (c) the role of human meaning in developing and implementing health IT and computational tools. Fourteen articles were accepted to this Special Issue from a total of 27 submissions. With the exception of a scoping review,6 all articles reported on original research studies at the intersection of informatics and the social sciences (Table 1). The majority of research studies were conducted in the United States,7–16 with the exception of 3 studies conducted in the United Kingdom,17 Netherlands,18 and Saudi Arabia.19 More than half of the studies were multisite investigations,7,13–18 while 5 studies were single-site8–12 and 1 study used an online context, LinkedIn.19 Most studies received some form of intramural or extramural funding,7,8,10,11,13–15,17 acknowledging the relevance and value of applying methods from social sciences to not only understand but also address research questions and clinical problems within biomedical informatics. In addition to the studies targeted at examining practices of patients and clinicians,8–12,14–17,19 there has been a recent shift toward broadening the scope of the stakeholder population under inquiry—for example, studies included healthcare researchers,13 women managers in the biomedical informatics field,19 and scribes.7 All but 1 study14 used multiple methods for collecting data. Six studies utilized qualitative methods to understand current workflows and gather user needs and design guidelines for health IT,7,8,12,14,16,17 while the remaining studies adopted mixed-methods approaches supported by observations, interviews, and to design and of health Most studies used a by research questions a of were used to the of the at the collection or the interdisciplinary of biomedical informatics, the reported in these studies were from other fields such as human computer computer supported work and social sciences. of studies that qualitative of JAMIA and were in publication over studies were conducted while studies were conducted Studies used interviews, observations, and have typically been the of but there has been an of on healthcare there has been a of qualitative in scope has been with the of qualitative methods used in other fields, such as and science and technology included for and of a broader of qualitative methods by and as as an of in to that to and 27 25 27 during a perspective of that some to included at the of the healthcare and with a or to work sought with the Most were to to the of to patient information of and of to work is in role for have human than in the of be new the role of needs to to be there could be an role for or as as of a new of healthcare and 3 patients and patient of patient The 3 of patient were identified in the patient the patient and and were were with in the shift on the factors can be identified that influence patient and and and future research by the include and in for and methods of critically the culture and of and and to such as and and Women managers in the field of biomedical informatics experiences and experiences meaning of biomedical informatics, meaning of health IT future and and meaning of Saudi in and in understanding the field of informatics not that was an feminist theory have The to be a women not to by attention to the understanding that women not how it to systems. Social have an impact on how women experience and on regarding of health IT and the biomedical informatics field. 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Social have an impact on how women experience and on regarding of health IT and the biomedical informatics field. Studies at and the lived experiences of women in science and technology to the understanding of how these experiences knowledge of these fields in the context of healthcare advancements and work to patient to of cognitive to patient and through practices and patients to could patient to information and a to and individuals into patient and to information and on the of and and interviews, information and and for and and 5 key that health and also identified 3 practice of with health and in and to is and approaches to on context including health, social and the use, and be that the patient and the is not a for or to that the needs of the of health, the of and the and social in which to and to about of and of experience to and include technology to or new of and such information to on and systems. and in perspectives on and collecting on and methods, typically in This the need for and with mixed-methods from to for for future and but not was to working on and health and were critical 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OBJECTIVE: Medication order voiding allows clinicians to indicate that an existing order was placed in error. We explored whether the order voiding function could be used to record and study medication ordering errors. MATERIALS AND METHODS: We examined medication orders from an academic medical center for a 6-year period (2006-2011; n = 5 804 150). We categorized orders based on status (void, not void) and clinician-provided reasons for voiding. We used multivariable logistic regression to investigate the association between order voiding and clinician, patient, and order characteristics. We conducted chart reviews on a random sample of voided orders ( n = 198) to investigate the rate of medication ordering errors among voided orders, and the accuracy of clinician-provided reasons for voiding. RESULTS: We found that 0.49% of all orders were voided. Order voiding was associated with clinician type (physician, pharmacist, nurse, student, other) and order type (inpatient, prescription, home medications by history). An estimated 70 ± 10% of voided orders were due to medication ordering errors. Clinician-provided reasons for voiding were reasonably predictive of the actual cause of error for duplicate orders (72%), but not for other reasons. DISCUSSION AND CONCLUSION: Medication safety initiatives require availability of error data to create repositories for learning and training. The voiding function is available in several electronic health record systems, so order voiding could provide a low-effort mechanism for self-reporting of medication ordering errors. Additional clinician training could help increase the quality of such reporting.

Objective: To report on the state of the science of clinical decision support (CDS) for hospital bedside nurses. Materials and Methods: We performed an integrative review of qualitative and quantitative peer-reviewed original research studies using a structured search of PubMed, Embase, Cumulative Index to Nursing and Applied Health Literature (CINAHL), Scopus, Web of Science, and IEEE Xplore (Institute of Electrical and Electronics Engineers Xplore Digital Library). We included articles that reported on CDS targeting bedside nurses and excluded in stages based on rules for titles, abstracts, and full articles. We extracted research design and methods, CDS purpose, electronic health record integration, usability, and process and patient outcomes. Results: Our search yielded 3157 articles. After removing duplicates and applying exclusion rules, 28 articles met the inclusion criteria. The majority of studies were single-site, descriptive or qualitative (43%) or quasi-experimental (36%). There was only 1 randomized controlled trial. The purpose of most CDS was to support diagnostic decision-making (36%), guideline adherence (32%), medication management (29%), and situational awareness (25%). All the studies that included process outcomes (7) and usability outcomes (4) and also had analytic procedures to detect changes in outcomes demonstrated statistically significant improvements. Three of 4 studies that included patient outcomes and also had analytic procedures to detect change showed statistically significant improvements. No negative effects of CDS were found on process, usability, or patient outcomes. Discussion and Conclusions: Clinical support systems targeting bedside nurses have positive effects on outcomes and hold promise for improving care quality; however, this research is lagging behind studies of CDS targeting medical decision-making in both volume and level of evidence.

OBJECTIVE: Given the complexities of the healthcare environment, efforts to develop standardized handoff practices have led to widely varying manifestations of handoff tools. A systematic review of the literature on handoff evaluation studies was performed to investigate the nature, methodological, and theoretical foundations underlying the evaluation of handoff tools and their adequacy and appropriateness in achieving standardization goals. METHOD: We searched multiple databases for articles evaluating handoff tools published between 1 February 1983 and 15 June 2012. The selected articles were categorized along the following dimensions: handoff tool characteristics, standardization initiatives, methodological framework, and theoretical perspectives underlying the evaluation. RESULTS: Thirty-six articles met our inclusion criteria. Handoff evaluations were conducted primarily on electronic tools (64%), with a more recent focus on electronic medical record-integrated tools (36% since 2008). Most evaluations centered on intra-departmental tools (95%). Evaluation studies were quasi-experimental (42%) or observational (50%), with a major focus on handoff-related outcome measures (94%) using predominantly survey-based tools (70%) with user satisfaction metrics (53%). Most of the studies (81%) based their evaluation on aspects of standardization that included continuity of care and patient safety. CONCLUSIONS: The nature, methodological, and theoretical foundations of handoff tool evaluations varied significantly in terms of their quality and rigor, thereby limiting their ability to inform strategic standardization initiatives. Future research should utilize rigorous, multi-method qualitative and quantitative approaches that capture the contextual nuances of handoffs, and evaluate their effect on patient-related outcomes.

The coordination of distributed activities is central to organizational work. The effective functioning of organizations hinges on their ability to manage interdependencies both within (intra-) and between (inter-) various departments. However, more than just the management of these individual interdependencies is required for smooth work system coordination. Organizations must also manage the interactions between intra- and inter-departmental activities. To investigate how these interactions manifest in organizational work, we conducted a field study examining patient transfer process. We highlight the cross-boundary breakdowns that occur when intra-departmental and inter-departmental activities related to patient transfers negatively affect each other. We then describe the re-coordinating activities performed to mitigate the effects of cross-boundary breakdowns. We discuss a framework of inter-departmental coordination work that describes the relationship between cross-boundary breakdowns and re-coordinating activities. Furthermore, we classify the re-coordinating activities as a type of articulation work performed by hospital staff. This research can help us develop empirically-driven and theoretically-based design guidelines for coordination tools to maintain overall organizational workflow by balancing the goals and requirements of the various departments.

Effective coordination is central to work in organizations. We conducted a field study examining challenges to coordination between clinical and non-clinical staff in the patient transfer process of a major academic hospital. We present one major challenge: lack of information sharing between these staff members and discuss the reasons for and consequences of this challenge to the work in the hospital.