BACKGROUND: Racism and implicit bias underlie disparities in health care access, treatment, and outcomes. An emerging area of study in examining health disparities is the use of stigmatizing language in the electronic health record (EHR).
OBJECTIVE: We sought to summarize the existing literature related to stigmatizing language documented in the EHR. To this end, we conducted a scoping review to identify, describe, and evaluate the current body of literature related to stigmatizing language and clinician notes.
METHODS: We searched PubMed, Cumulative Index of Nursing and Allied Health Literature (CINAHL), and Embase databases in May 2022, and also conducted a hand search of IEEE to identify studies investigating stigmatizing language in clinical documentation. We included all studies published through April 2022. The results for each search were uploaded into EndNote X9 software, de-duplicated using the Bramer method, and then exported to Covidence software for title and abstract screening.
RESULTS: Studies (N = 9) used cross-sectional (n = 3), qualitative (n = 3), mixed methods (n = 2), and retrospective cohort (n = 1) designs. Stigmatizing language was defined via content analysis of clinical documentation (n = 4), literature review (n = 2), interviews with clinicians (n = 3) and patients (n = 1), expert panel consultation, and task force guidelines (n = 1). Natural language processing was used in four studies to identify and extract stigmatizing words from clinical notes. All of the studies reviewed concluded that negative clinician attitudes and the use of stigmatizing language in documentation could negatively impact patient perception of care or health outcomes.
CONCLUSIONS: The current literature indicates that NLP is an emerging approach to identifying stigmatizing language documented in the EHR. NLP-based solutions can be developed and integrated into routine documentation systems to screen for stigmatizing language and alert clinicians or their supervisors. Potential interventions resulting from this research could generate awareness about how implicit biases affect communication patterns and work to achieve equitable health care for diverse populations.

OBJECTIVE: Risk stratification tools that predict healthcare utilisation are extensively integrated into primary care systems worldwide, forming a key component of anticipatory care pathways, where high-risk individuals are targeted by preventative interventions. Existing work broadly focuses on comparing model performance in retrospective cohorts with little attention paid to efficacy in reducing morbidity when deployed in different global contexts. We review the evidence supporting the use of such tools in real-world settings, from retrospective dataset performance to pathway evaluation.
METHODS: A systematic search was undertaken to identify studies reporting the development, validation and deployment of models that predict healthcare utilisation in unselected primary care cohorts, comparable to their current real-world application.
RESULTS: Among 3897 articles screened, 51 studies were identified evaluating 28 risk prediction models. Half underwent external validation yet only two were validated internationally. No association between validation context and model discrimination was observed. The majority of real-world evaluation studies reported no change, or indeed significant increases, in healthcare utilisation within targeted groups, with only one-third of reports demonstrating some benefit.
CONCLUSIONS: While model discrimination appears satisfactorily robust to application context there is little evidence to suggest that accurate identification of high-risk individuals can be reliably translated to improvements in service delivery or morbidity.
CONCLUSIONS: The evidence does not support further integration of care pathways with costly population-level interventions based on risk prediction in unselected primary care cohorts. There is an urgent need to independently appraise the safety, efficacy and cost-effectiveness of risk prediction systems that are already widely deployed within primary care.

With Artificial Intelligence (AI) increasingly permeating various aspects of society, including healthcare, the adoption of the Transformers neural network architecture is rapidly changing many applications. Transformer is a type of deep learning architecture initially developed to solve general-purpose Natural Language Processing (NLP) tasks and has subsequently been adapted in many fields, including healthcare. In this survey paper, we provide an overview of how this architecture has been adopted to analyze various forms of healthcare data, including clinical NLP, medical imaging, structured Electronic Health Records (EHR), social media, bio-physiological signals, biomolecular sequences. Furthermore, which have also include the articles that used the transformer architecture for generating surgical instructions and predicting adverse outcomes after surgeries under the umbrella of critical care. Under diverse settings, these models have been used for clinical diagnosis, report generation, data reconstruction, and drug/protein synthesis. Finally, we also discuss the benefits and limitations of using transformers in healthcare and examine issues such as computational cost, model interpretability, fairness, alignment with human values, ethical implications, and environmental impact.

Atopic dermatitis (AD) is a chronic skin condition that can manifest in childhood and persist into adulthood or can present de novo in adults. The clinical presentation of adults with AD may differ among those with pediatric-onset versus adult-onset disease and potential differences between both groups remain to be better characterized. These atypical features might not be encompassed as part of current diagnostic criteria for AD, such as the Hanifin-Rajka (H-R) and the U.K. Working Party (UKWP) criteria. We conducted a retrospective chart review of the electronic medical records of a large, single, academic center to compare the clinical characteristics between adult-onset and pediatric onset AD and examine the proportion of patients who meet the H-R and/or UKWP criteria. Our single-center retrospective chart review included adults (≥ 18 years of age) with any AD-related ICD-10 codes, ≥ 2 AD-related visits, and a recorded physician-confirmed AD diagnosis. Descriptive statistics were used to compare adults with pediatric-onset (< 18 years of age) and adult-onset (≥ 18 years of age) AD. Logistic regression and x2 test were used to compare groups. We found that, compared to pediatric-onset AD, adults with adult-onset AD had less flexural involvement, flexural lichenification and a personal and family history of other atopic diseases. Compared to adults with pediatric-onset AD, adults with adult-onset AD had greater involvement of the extensor surfaces and more nummular eczema compared to pediatric-onset AD. In our cohort, adults with adult-onset AD were less likely to meet H-R and UKWP criteria compared to pediatric-onset AD. Adults with adult-onset AD may present with a clinical presentation that is different from those with pediatric-onset AD, which may not be completely captured by current AD criteria such as the H-R and UWKP criteria. This can lead to possibly mis- or underdiagnosing AD in adults. Thus, understanding the differences and working towards modifying criteria for adult-onset AD has the potential to improve accurate diagnosis of adults with AD.

UNASSIGNED: Despite the incentives and provisions created for hospitals by the US Affordable Care Act related to value-based payment and community health needs assessments, concerns remain regarding the adequacy and distribution of hospital efforts to address SDOH. This scoping review of the peer-reviewed literature identifies the key characteristics of hospital/health system initiatives to address SDOH in the US, to gain insight into the progress and gaps.
UNASSIGNED: PRISMA-ScR criteria were used to inform a scoping review of the literature. The article search was guided by an integrated framework of Healthy People SDOH domains and industry recommended SDOH types for hospitals. Three academic databases were searched for eligible articles from 1 January 2018 to 30 June 2023. Database searches yielded 3,027 articles, of which 70 peer-reviewed articles met the eligibility criteria for the review.
UNASSIGNED: Most articles (73%) were published during or after 2020 and 37% were based in Northeast US. More initiatives were undertaken by academic health centers (34%) compared to safety-net facilities (16%). Most (79%) were research initiatives, including clinical trials (40%). Only 34% of all initiatives used the EHR to collect SDOH data. Most initiatives (73%) addressed two or more types of SDOH, e.g., food and housing. A majority (74%) were downstream initiatives to address individual health-related social needs (HRSNs). Only 9% were upstream efforts to address community-level structural SDOH, e.g., housing investments. Most initiatives (74%) involved hot spotting to target HRSNs of high-risk patients, while 26% relied on screening and referral. Most initiatives (60%) relied on internal capacity vs. community partnerships (4%). Health disparities received limited attention (11%). Challenges included implementation issues and limited evidence on the systemic impact and cost savings from interventions.
UNASSIGNED: Hospital/health system initiatives have predominantly taken the form of downstream initiatives to address HRSNs through hot-spotting or screening-and-referral. The emphasis on clinical trials coupled with lower use of EHR to collect SDOH data, limits transferability to safety-net facilities. Policymakers must create incentives for hospitals to invest in integrating SDOH data into EHR systems and harnessing community partnerships to address SDOH. Future research is needed on the systemic impact of hospital initiatives to address SDOH.

BACKGROUND: Rapid response team (RRT) and code activation events occur relatively commonly in inpatient settings. RRT systems have been the subject of a significant amount of analysis, although this has been largely focused on the impact of RRT system implementation and RRT events on patient outcomes. There is reason to believe that the structured assessment of RRT and code events may be an effective way to identify opportunities for system improvement, although no standardised approach to event analysis is widely accepted. We developed and refined a protocolised system of RRT and code event review, focused on sustainable, timely and high value event analysis meant to inform ongoing improvement activities.
METHODS: A group of clinicians with expertise in process and quality improvement created a protocolised analytic plan for rapid response event review, piloted and then iteratively optimised a systematic process which was applied to all subsequent cases to be reviewed.
RESULTS: Hospitalist reviewers were recruited and trained in a methodical approach. Each reviewer performed a chart review to summarise RRT events, and collect specific variables for each case (coding). Coding was then reviewed for concordance, at monthly interdisciplinary group meetings and \'Action Items\' were identified and considered for implementation. In any 12-month period starting in 2021, approximately 12-15 distinct cases per month were reviewed and coded, offering ample opportunities to identify trends and patterns.
CONCLUSIONS: We have developed an innovative process for ongoing review of RRT-Code events. The review process is easy to implement and has allowed for the timely identification of high value improvement opportunities.

Digital approaches to support advance care planning (ACP) documentation and sharing are increasingly being used, with a lack of research to characterise their design, content, and use. This study aimed to characterise how digital approaches are being used to support ACP documentation and sharing internationally. A scoping review was performed in accordance with the JBI (formerly Joanna Briggs Institute) guidelines and the PRISMA 2020 checklist, prospectively registered on Open Science Framework (https://osf.io/xnrg3). MEDLINE, EMBASE, PsycINFO, ACM Digital, IEEE Xplore and CINAHL were searched in February 2023. Only publications in English, published from 2008 onwards were considered. Eligibility criteria included a focus on ACP and electronic systems. Out of 2,393 records, 34 reports were included, predominantly from the USA (76.5%). ACP documentation is typically stored in electronic health records (EHRs) (67.6%), with a third (32.4%) enabling limited patient access. Non-standard approaches (n = 15;44.1%) were the commonest study design of included reports, with outcome measures focusing on the influence of systems on the documentation (i.e. creation, quantity, quality, frequency or timing) of ACP information (n = 23;67.6%). Digital approaches to support ACP are being implemented and researched internationally with an evidence base dominated by non-standard study designs. Future research is needed to extend outcome measurement to consider aspects of care quality and explore whether the content of existing systems aligns with aspects of care that are valued by patients.

OBJECTIVE: Electronic health record (EHR) systems are used extensively in healthcare; their design can influence clinicians\' behaviour. We conducted a systematic review of EHR-based interventions aimed at changing the clinical practice of general practitioners in the UK, assessed their effectiveness and applied behaviour change theory to identify lessons for other settings.
METHODS: Mixed methods systematic review.
METHODS: MEDLINE, EMBASE, CENTRAL and APA PsycINFO were searched up to March 2023.
METHODS: Quantitative and qualitative findings from randomised controlled trials (RCTs) controlled before-and-after studies and interrupted time series of EHR-based interventions in UK general practice were included.
METHODS: Quantitative synthesis was based on Cochrane\'s Synthesis without Meta-analysis. Interventions were categorised using the Behaviour Change Wheel and MINDSPACE frameworks and effectiveness determined by vote-counting using direction of effect. Inductive thematic synthesis was used for qualitative studies.
RESULTS: Database searching identified 3824 unique articles; 10 were included (from 2002 to 2021), comprising eight RCTs and two associated qualitative studies. Four of seven quantitative studies showed a positive effect on clinician behaviour and three on patient-level outcomes. Behaviour change techniques that may trigger emotions and required less cognitive engagement appeared to have positive effects. Qualitative findings indicated that interventions reassured clinicians of their decisions but were sometimes ignored.
CONCLUSIONS: Despite widespread use, there is little high quality, up-to-date experimental evidence evaluating the effectiveness of EHR-based interventions in UK general practice. The evidence suggested EHR-based interventions may be effective at changing behaviour. Persistent, simple action-oriented prompts appeared more effective than complex interventions requiring greater cognitive engagement. However, studies lacked detail in intervention design and theory behind design choices. Future research should seek to optimise EHR-based behaviour change intervention design and delineate limitations, providing theory-based justification for interventions. This will be of increasing importance with the growing use of EHRs to influence clinicians\' decisions.
UNASSIGNED: CRD42022341009.

Mental health conditions are among the highest disease burden on society, affecting approximately 20% of children and adolescents at any point in time, with depression and anxiety being the leading causes of disability globally. To improve treatment outcomes, healthcare organizations turned to clinical decision support systems (CDSSs) that offer patient-specific diagnoses and recommendations. However, the economic impact of CDSS is limited, especially in child and adolescent mental health. This systematic literature review examined the economic impacts of CDSS implemented in mental health services. We planned to follow PRISMA reporting guidelines and found only one paper to describe health and economic outcomes. A randomized, controlled trial of 336 participants found that 60% of the intervention group and 32% of the control group achieved symptom reduction, i.e. a 50% decrease as per the Symptom Checklist-90-Revised (SCL-90-R), a method to evaluate psychological problems and identify symptoms. Analysis of the incremental cost-effectiveness ratio found that for every 1% of patients with a successful treatment result, it added €57 per year. There are not enough studies to draw conclusions about the cost-effectiveness in a mental health context. More studies on economic evaluations of the viability of CDSS within mental healthcare have the potential to contribute to patients and the larger society.

UNASSIGNED: Natural language processing (NLP) can enhance research on activities of daily living (ADL) by extracting structured information from unstructured electronic health records (EHRs) notes. This review aims to give insight into the state-of-the-art, usability, and performance of NLP systems to extract information on ADL from EHRs.
UNASSIGNED: A systematic review was conducted based on searches in Pubmed, Embase, Cinahl, Web of Science, and Scopus. Studies published between 2017 and 2022 were selected based on predefined eligibility criteria.
UNASSIGNED: The review identified 22 studies. Most studies (65%) used NLP for classifying unstructured EHR data on 1 or 2 ADL. Deep learning, combined with a ruled-based method or machine learning, was the approach most commonly used. NLP systems varied widely in terms of the pre-processing and algorithms. Common performance evaluation methods were cross-validation and train/test datasets, with F1, precision, and sensitivity as the most frequently reported evaluation metrics. Most studies reported relativity high overall scores on the evaluation metrics.
UNASSIGNED: NLP systems are valuable for the extraction of unstructured EHR data on ADL. However, comparing the performance of NLP systems is difficult due to the diversity of the studies and challenges related to the dataset, including restricted access to EHR data, inadequate documentation, lack of granularity, and small datasets.
UNASSIGNED: This systematic review indicates that NLP is promising for deriving information on ADL from unstructured EHR notes. However, what the best-performing NLP system is, depends on characteristics of the dataset, research question, and type of ADL.