Hospital-acquired malnutrition: point prevalence, risk identifiers and utility of a digital Dashboard to identify high-risk, long-stay patients in five Australian facilities.

BACKGROUND: There are limited hospital-acquired malnutrition (HAM) studies among the plethora of malnutrition literature, and a few studies utilise electronic medical records to assist with malnutrition care. This study therefore aimed to determine the point prevalence of HAM in long-stay adult patients across five facilities, whether any descriptors could assist in identifying these patients and whether a digital Dashboard accurately reflected 'real-time' patient nutritional status. METHODS: HAM was defined as malnutrition first diagnosed >14 days after hospital admission. Eligible patients were consenting adult (≥18 years) inpatients with a length of stay (LOS) >14 days. Palliative, mental health and intensive care patients were excluded. Descriptive, clinical and nutritional data were collected, including nutritional status, and whether a patient had hospital-acquired malnutrition to determine point prevalence. Descriptive Fisher's exact and analysis of variance (ANOVA) tests were used. RESULTS: Eligible patients (n = 134) were aged 68 ± 16 years, 52% were female and 92% were acute admissions. HAM and malnutrition point prevalence were 4.5% (n = 6/134) and 19% (n = 26/134), respectively. Patients with HAM had 72 days greater LOS than those with malnutrition present on admission (p < 0.001). A high proportion of HAM patients were inpatients at a tertiary facility and longer-stay wards. The Dashboard correctly reflected recent ward dietitian assessments in 94% of patients at one facility (n = 29/31). CONCLUSIONS: HAM point prevalence was 4.5% among adult long-stay patients. Several descriptors may be suitable to screen for at-risk patients in future studies. Digital Dashboards have the potential to explore factors related to HAM.

Effects of COVID-19 on Irish general practice activity from 2019 to 2021: a retrospective analysis of 500,000 consultations using electronic medical record data.

BACKGROUND: General practice (GP) is crucial to primary care delivery in the Republic of Ireland and is almost fully computerised. General practice teams were the first point of contact for much COVID-19-related care and there were concerns routine healthcare activities could be disrupted due to COVID-19 and related restrictions. AIMS: The study aimed to assess effects of the pandemic on GP activity through analysis of electronic medical record data from general practice clinics in the Irish Midwest. METHODS: A retrospective, descriptive study of electronic medical record data relating to patient record updates, appointments and medications prescribed across 10 GP clinics over the period 2019-2021 inclusive. RESULTS: Data relating to 1.18 million record transactions for 32 k patients were analysed. Over 500 k appointments were examined, and demographic trends presented. Overall appointment and prescribing activity increased over the study period, while a dip was observed immediately after the pandemic's arrival in March 2020. Delivery of non-childhood immunisations increased sixfold as a result of COVID-19, childhood immunisation activity was maintained, while cervical smears decreased in 2020 as the screening programme was halted. A quarter of consultations in 2020 and 2021 were teleconsultations, and these were more commonplace for younger patients. CONCLUSIONS: General practice responded robustly to the pandemic by taking on additional activities while maintaining routine services where possible. The shift to teleconsulting was a significant change in workflow. Analysing routinely collected electronic medical record data can provide valuable insights for service planning, and access to these insights would be beneficial for future pandemic responses.

Analyzing Comorbidity Patterns in Patients With Thyroid Disease Using Large-Scale Electronic Medical Records: Network-Based Retrospective Observational Study.

BACKGROUND: Thyroid disease (TD) is a prominent endocrine disorder that raises global health concerns; however, its comorbidity patterns remain unclear. OBJECTIVE: This study aims to apply a network-based method to comprehensively analyze the comorbidity patterns of TD using large-scale real-world health data. METHODS: In this retrospective observational study, we extracted the comorbidities of adult patients with TD from both private and public data sets. All comorbidities were identified using ICD-10 (International Classification of Diseases, 10th Revision) codes at the 3-digit level, and those with a prevalence greater than 2% were analyzed. Patients were categorized into several subgroups based on sex, age, and disease type. A phenotypic comorbidity network (PCN) was constructed, where comorbidities served as nodes and their significant correlations were represented as edges, encompassing all patients with TD and various subgroups. The associations and differences in comorbidities within the PCN of each subgroup were analyzed and compared. The PageRank algorithm was used to identify key comorbidities. RESULTS: The final cohorts included 18,311 and 50,242 patients with TD in the private and public data sets, respectively. Patients with TD demonstrated complex comorbidity patterns, with coexistence relationships differing by sex, age, and type of TD. The number of comorbidities increased with age. The most prevalent TDs were nontoxic goiter, hypothyroidism, hyperthyroidism, and thyroid cancer, while hypertension, diabetes, and lipoprotein metabolism disorders had the highest prevalence and PageRank values among comorbidities. Males and patients with benign TD exhibited a greater number of comorbidities, increased disease diversity, and stronger comorbidity associations compared with females and patients with thyroid cancer. CONCLUSIONS: Patients with TD exhibited complex comorbidity patterns, particularly with cardiocerebrovascular diseases and diabetes. The associations among comorbidities varied across different TD subgroups. This study aims to enhance the understanding of comorbidity patterns in patients with TD and improve the integrated management of these individuals.

Disambiguating Clinical Abbreviations by One-to-All Classification: Algorithm Development and Validation Study.

Background: Electronic medical records store extensive patient data and serve as a comprehensive repository, including textual medical records like surgical and imaging reports. Their utility in clinical decision support systems is substantial, but the widespread use of ambiguous and unstandardized abbreviations in clinical documents poses challenges for natural language processing in clinical decision support systems. Efficient abbreviation disambiguation methods are needed for effective information extraction. Objective: This study aims to enhance the one-to-all (OTA) framework for clinical abbreviation expansion, which uses a single model to predict multiple abbreviation meanings. The objective is to improve OTA by developing context-candidate pairs and optimizing word embeddings in Bidirectional Encoder Representations From Transformers (BERT), evaluating the model's efficacy in expanding clinical abbreviations using real data. Methods: Three datasets were used: Medical Subject Headings Word Sense Disambiguation, University of Minnesota, and Chia-Yi Christian Hospital from Ditmanson Medical Foundation Chia-Yi Christian Hospital. Texts containing polysemous abbreviations were preprocessed and formatted for BERT. The study involved fine-tuning pretrained models, ClinicalBERT and BlueBERT, generating dataset pairs for training and testing based on Huang et al's method. Results: BlueBERT achieved macro- and microaccuracies of 95.41% and 95.16%, respectively, on the Medical Subject Headings Word Sense Disambiguation dataset. It improved macroaccuracy by 0.54%-1.53% compared to two baselines, long short-term memory and deepBioWSD with random embedding. On the University of Minnesota dataset, BlueBERT recorded macro- and microaccuracies of 98.40% and 98.22%, respectively. Against the baselines of Word2Vec + support vector machine and BioWordVec + support vector machine, BlueBERT demonstrated a macroaccuracy improvement of 2.61%-4.13%. Conclusions: This research preliminarily validated the effectiveness of the OTA method for abbreviation disambiguation in medical texts, demonstrating the potential to enhance both clinical staff efficiency and research effectiveness.

Patient comfort with sexual orientation and gender identity questions in adult congenital cardiology clinics.

BACKGROUND: Lack of sexual orientation and gender identity (SOGI) data creates barriers for lesbian, gay, bisexual, transgender, and queer (LGBTQ+) people in health care. Barriers to SOGI data collection include physician misperception that patients do not want to answer these questions and discomfort asking SOGI questions. This study aimed to assess patient comfort towards SOGI questions across five quaternary care adult congenital heart disease (ACHD) centres. METHODS: A survey administered to ACHD patients (≥18 years) asked (1) two-step gender identity and birth sex, (2) acceptance of SOGI data, and (3) the importance for ACHD physicians to know SOGI data. Chi-square tests were used to analyse differences among demographic groups and logistic regression modelled agreement with statement of patient disclosure of SOGI improving patient-physician communication. RESULTS: Among 322 ACHD patients, 82% identified as heterosexual and 16% identified as LGBTQ+, across the age ranges 18-29 years (39.4%), 30-49 years (47.8%), 50-64 years (8.7%), and > 65 years (4.0%). Respondents (90.4%) felt comfortable answering SOGI questions. Respondents with bachelor's/higher education were more likely to "agree" that disclosure of SOGI improves patient-physician communication compared to those with less than bachelor's education (OR = 2.45; 95% CI 1.41, 4.25; p = .0015). CONCLUSION: These findings suggest that in this largely heterosexual population, SOGI data collection is unlikely to cause patient discomfort. Respondents with higher education were twice as likely to agree that SOGI disclosure improves patient-physician communication. The inclusion of SOGI data in future studies will provide larger samples of underrepresented minorities (e.g. LGBTQ+ population), thereby reducing healthcare disparities within the field of cardiovascular research.

Herpes Zoster Risk After Total Knee Replacement: a multicenter, propensity-score-matched cohort study in the United States.

Background: Total knee replacement (TKR) is a common surgical procedure for osteoarthritis (OA) patients. TKR may increase susceptibility to herpes zoster (HZ) by inducing immunosuppression, surgical stress, and nerve injury. However, limited data exist on the relationship between TKR and HZ. This study examined the risk of HZ over time among OA patients who underwent TKR and those who did not, using a large population-based cohort. Method: Utilizing the TriNetX research network, people with OA and underwent TKR were recruited as case group. After 1:1 propensity score matching, OA patients who never experienced TKR were included as control group. Covariates, including demographics, comorbidities, and laboratory data, were balanced using propensity score matching. A 5-year follow-up assessed the hazard ratio of incident HZ and related complications. Results: Compared to the control group, a significantly elevated risk of HZ was observed in the TKR cohort across 5-year follow-up period, with the hazard ratio of 1.223 (95% CI: 1.089-1.373). Zoster without complications presented 1.173-fold risk in TKR patients while comparing with non-TKR controls. However, most other secondary outcomes related to HZ complications-such as encephalitis, neurological involvement, ocular disease, and disseminated zoster-did not show a significant increase in risk. The risk of HZ was statistically significant for females and older adults in the TKR cohort than in the control cohort. Conclusions: OA patients who underwent TKR had an increased risk of HZ compared to those who did not receive the procedure, especially females and older adults. These findings highlight the need for HZ monitoring/prevention protocols and further research on mitigating viral reactivation after major joint surgery.

A Bedside Electronic Whiteboard System for Patient Care in Isolation Rooms: A Scenario-Based Preliminary Study.

AIM: To assess a commercially available electronic whiteboard's usability and acceptability in isolation rooms, focusing on improving nurse-patient communication and supporting data input. DESIGN: A cross-sectional study with quantitative and qualitative mixed methods. METHODS: We evaluated the usability and acceptability of electronic whiteboards among nurses using scenarios in a virtual isolation room environment. RESULTS: Nurses recognised the electronic whiteboard as a valuable tool for communication and error reductions in record-keeping but noted a learning curve for less tech-savvy users. Positive correlations were found between perceived usefulness, ease of use and adoption intent. Despite challenges, electronic whiteboards show promise for enhancing patient care, requiring comprehensive training and management systems. Time allocation in patient wards and nurse-patient interactions are crucial considerations. CONCLUSION: Electronic whiteboards have usability and acceptability as a tool to improve nurse-patient communication. However, considering technical issues and staff resistance, a management system and user training are necessary. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: Nurses perceive electronic whiteboards as user-friendly and as facilitating data input. REPORTING METHOD: TREND (Nonrandomised evaluations of behavioural and public health interventions). PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.

Evaluating applied security controls for safeguarding medical device-integrated electronic medical records.

RATIONALE, AIMS, AND OBJECTIVES: Medical device-integrated electronic medical records (MDI-EMR) pose significant challenges in ensuring effective usage, data security and patient safety. The complexities of MDI-EMR necessitate applying various security mechanisms to safeguard against cyber threats. Therefore, we evaluated cyber threats to MDI-EMR and the effectiveness of applied security controls using a proposed framework from sociotechnical and risk assessment perspectives. METHOD: We conducted a qualitative case study evaluation in a general hospital in Saudi Arabia using interviews, observation, and document analysis from the perspectives of major MDI-EMR stakeholders, including healthcare providers, IT professionals and cybersecurity specialists. RESULTS: The results showed the interplay among physical, technical and administrative security controls that maintained a secure posture of MDI-EMR. The effectiveness of security controls is highly influenced by the staff's cybersecurity awareness and training. The perceived effectiveness of security controls varied among users, with some expressing satisfaction with the ease of use and reliability, while others highlighting challenges such as password complexity and access procedures. Understanding these diverse perspectives is crucial for tailoring security measures to meet the needs of different stakeholders effectively. CONCLUSION: Collaboration among the key stakeholders is crucial for implementing security controls for MDI-EMR. Balancing security measures with usability concerns is essential, as highlighted by challenges in implementing technical controls. A comprehensive approach encompassing physical, technical and administrative controls, continuous education and awareness initiatives are significant to empower staff in recognising and mitigating cyber threats effectively to safeguard medical data and ensure the integrity of healthcare systems.

Proxy endpoints - bridging clinical trials and real world data.

OBJECTIVE: Disease severity scores, or endpoints, are routinely measured during Randomized Controlled Trials (RCTs) to closely monitor the effect of treatment. In real-world clinical practice, although a larger set of patients is observed, the specific RCT endpoints are often not captured, which makes it hard to utilize real-world data (RWD) to evaluate drug efficacy in larger populations. METHODS: To overcome this challenge, we developed an ensemble technique which learns proxy models of disease endpoints in RWD. Using a multi-stage learning framework applied to RCT data, we first identify features considered significant drivers of disease available within RWD. To create endpoint proxy models, we use Explainable Boosting Machines (EBMs) which allow for both end-user interpretability and modeling of non-linear relationships. RESULTS: We demonstrate our approach on two diseases, rheumatoid arthritis (RA) and atopic dermatitis (AD). As we show, our combined feature selection and prediction method achieves good results for both disease areas, improving upon prior methods proposed for predictive disease severity scoring. CONCLUSION: Having disease severity over time for a patient is important to further disease understanding and management. Our results open the door to more use cases in the space of RA and AD such as treatment effect estimates or prognostic scoring on RWD. Our framework may be extended beyond RA and AD to other diseases where the severity score is not well measured in electronic health records.

Electronic Discharge Summaries: Perspectives on National Implementation from Clinicians in Hospital and Primary Care.

This paper describes clinicians' views on the structure and content of an electronic discharge summary (EDS). A sample EDS template was developed by building on existing Australian guidelines to illustrate some of the proposed elements required for a high-quality clinical document. Surveys were widely disseminated to gather feedback and perspectives of hospital and primary care clinicians. A pragmatic approach to this study was underpinned by a strong evidence base and informed by implementation science methods. Key themes were identified, including variability in workflow and clinical needs, digital maturity, and digital health literacy of the clinical workforce. Understanding different workflows and priorities between hospital and primary care clinicians was a significant barrier to implementing a high-quality EDS. The strong consensus for change from both hospital and primary care clinicians, however, signaled the workforce's readiness as a potential enabler of high-quality EDS documentation.

Assessing the implementation of a tertiary care comprehensive pediatric asthma education program using electronic medical records and decision support tools.

BACKGROUND: Self-management education is integral for proper asthma management. However, there is an accessibility gap to self-management education following asthma hospitalizations. Most pediatric patients and their families receive suboptimal or no education. OBJECTIVE: To implement a comprehensive pediatric asthma education program and evaluate subsequent self-management knowledge in patients as well as behavior change outcomes reflected in the frequency of asthma related repeat emergency department visits and hospitalization. The program implementation was informed by the Knowledge to Translation Action Framework and the i-PARIHS model for quality improvement and involved several iterative stages. METHODS: We implemented a comprehensive asthma education program for the families of all children 0-18 years old who had been admitted for an asthma exacerbation to the Children's Hospital of Eastern Ontario (CHEO), beginning on April 1, 2018. The program was adapted to the stages of the Knowledge Translation to Action Framework including undertaking an environmental scan, expert stakeholder feedback, reviews, addressing barriers, and tailoring the intervention, along with evaluating knowledge and health outcomes. Education was delivered over 1-2 h in personalized individual or small group settings, within 4 wk of hospital discharge. All education was provided by registered nurses or respiratory therapists who were also certified asthma educators. The EPIC electronic medical record was used to facilitate referral and scheduling of asthma education sessions, and to track subsequent acute asthma visits. We compared the frequency of a repeat asthma emergency department (ED) visit or hospitalization within 1-year following an initial asthma hospitalization for children who would have received comprehensive asthma education, to a historical cohort of children who were hospitalized between April 9, 2017 - Apr 8, 2018, and did not receive asthma education. RESULTS: The program had a high enrollment, capturing nearly 75% of the target population. Most families found the program to be acceptable and reported increased knowledge of how to manage asthma. We identified a crude overall 54% reduction in repeat hospitalizations among children 1 year after implementation of the asthma education program (i.e. 10.2% (23/225) repeat hospitalization rate pre- implementation versus 4.8% (11/227) post-implementation). In adjusted time-to event analysis, this reduction was prominent at 3 months among those who received comprehensive asthma education, relative to those who did not, but this improvement was not sustained by 1 year (HR =1.1, 95% CI =0.55- 2.05; p-value = 0.6). DISCUSSION: Although we did not find long-term improvements in ED visits, or hospitalizations, in children of caregivers who participated in comprehensive asthma education, the asthma education program holds potential given that most patients found it to be acceptable and that it increased asthma management knowledge. A future asthma education program should include multiple sessions to ensure that the knowledge and behavior change will be sustained, leading ultimately to long-term reductions in repeat ED visits and hospitalizations.

Patients with hidradenitis suppurativa are associated with risk of new-onset glaucoma: a propensity-score-matched cohort study.

Background: Ocular comorbidities of hidradenitis suppurativa (HS) has been widely evaluated; however real-world evidence was scarce. Moreover, risk of glaucoma in HS patients remained unclear. This study aimed to evaluate the 5-year glaucoma risk in HS patients. Methods: This retrospective cohort study used the TriNetX database covering 2005-2017. In total, 53,281 HS patients were propensity score matched 1:1 to controls based on demographics, including comorbidities, medications, healthcare utilization, etc. Patients were followed for 5 years post-index date. Glaucoma risks were calculated based on hazard ratios and 95% confidence intervals (95% CI). Stratified analyses by sex and age were performed. Results: After matching, baseline characteristics were similar between groups. HS was associated with a 1.25 times higher 5-year glaucoma risk (95% CI, 1.10-1.42). The risk was significant within 1 year (HR=1.37; 95% CI, 1.03-1.82), 3 years (HR=1.31; 95% CI, 1.12-1.54), and 5 years post-index. In subgroup analysis, women had a 1.28 times higher risk (95% CI, 1.10-1.49). Patients aged 18-64 years (HR=1.33; 95% CI, 1.14-1.55) and ≥65 years (HR=1.33; 95% CI, 1.05-1.67) also presented elevated glaucoma risks. Conclusion: This real-world data analysis demonstrated a significantly increased 5-year glaucoma risk in HS patients versus matched controls. Ocular complications should be concerned while managing HS patients.

Association of a Novel Electronic Form for Preoperative Cardiac Risk Assessment With Reduction in Cardiac Consultations and Testing: Retrospective Cohort Study.

BACKGROUND: Preoperative cardiac risk assessment is an integral part of preoperative evaluation; however, there is significant variation among providers, leading to inappropriate referrals for cardiology consultation or excessive low-value cardiac testing. We implemented a novel electronic medical record (EMR) form in our preoperative clinics to decrease variation. OBJECTIVE: This study aimed to investigate the impact of the EMR form on the preoperative utilization of cardiology consultation and cardiac diagnostic testing (echocardiograms, stress tests, and cardiac catheterization) and evaluate postoperative outcomes. METHODS: A retrospective cohort study was conducted. Patients who underwent outpatient preoperative evaluation prior to an elective surgery over 2 years were divided into 2 cohorts: from July 1, 2021, to June 30, 2022 (pre-EMR form implementation), and from July 1, 2022, to June 30, 2023 (post-EMR form implementation). Demographics, comorbidities, resource utilization, and surgical characteristics were analyzed. Propensity score matching was used to adjust for differences between the 2 cohorts. The primary outcomes were the utilization of preoperative cardiology consultation, cardiac testing, and 30-day postoperative major adverse cardiac events (MACE). RESULTS: A total of 25,484 patients met the inclusion criteria. Propensity score matching yielded 11,645 well-matched pairs. The post-EMR form, matched cohort had lower cardiology consultation (pre-EMR form: n=2698, 23.2% vs post-EMR form: n=2088, 17.9%; P<.001) and echocardiogram (pre-EMR form: n=808, 6.9% vs post-EMR form: n=591, 5.1%; P<.001) utilization. There were no significant differences in the 30-day postoperative outcomes, including MACE (all P>.05). While patients with "possible indications" for cardiology consultation had higher MACE rates, the consultations did not reduce MACE risk. Most algorithm end points, except for active cardiac conditions, had MACE rates <1%. CONCLUSIONS: In this cohort study, preoperative cardiac risk assessment using a novel EMR form was associated with a significant decrease in cardiology consultation and testing utilization, with no adverse impact on postoperative outcomes. Adopting this approach may assist perioperative medicine clinicians and anesthesiologists in efficiently decreasing unnecessary preoperative resource utilization without compromising patient safety or quality of care.

Early Diagnosis of Hereditary Angioedema in Japan Based on a US Medical Dataset: Algorithm Development and Validation.

BACKGROUND: Hereditary angioedema (HAE), a rare genetic disease, induces acute attacks of swelling in various regions of the body. Its prevalence is estimated to be 1 in 50,000 people, with no reported bias among different ethnic groups. However, considering the estimated prevalence, the number of patients in Japan diagnosed with HAE remains approximately 1 in 250,000, which means that only 20% of potential HAE cases are identified. OBJECTIVE: This study aimed to develop an artificial intelligence (AI) model that can detect patients with suspected HAE using medical history data (medical claims, prescriptions, and electronic medical records [EMRs]) in the United States. We also aimed to validate the detection performance of the model for HAE cases using the Japanese dataset. METHODS: The HAE patient and control groups were identified using the US claims and EMR datasets. We analyzed the characteristics of the diagnostic history of patients with HAE and developed an AI model to predict the probability of HAE based on a generalized linear model and bootstrap method. The model was then applied to the EMR data of the Kyoto University Hospital to verify its applicability to the Japanese dataset. RESULTS: Precision and sensitivity were measured to validate the model performance. Using the comprehensive US dataset, the precision score was 2% in the initial model development step. Our model can screen out suspected patients, where 1 in 50 of these patients have HAE. In addition, in the validation step with Japanese EMR data, the precision score was 23.6%, which exceeded our expectations. We achieved a sensitivity score of 61.5% for the US dataset and 37.6% for the validation exercise using data from a single Japanese hospital. Overall, our model could predict patients with typical HAE symptoms. CONCLUSIONS: This study indicates that our AI model can detect HAE in patients with typical symptoms and is effective in Japanese data. However, further prospective clinical studies are required to investigate whether this model can be used to diagnose HAE.

Modeling of joint extraction of entity relationships in clinical electronic medical records.

The advancement of medical informatization necessitates extracting entities and their relationships from electronic medical records. Presently, research on electronic medical records predominantly concentrates on single-entity relationship extraction. However, clinical electronic medical records frequently exhibit overlapping complex entity relationships, thereby heightening the challenge of information extraction. To rectify the absence of a clinical medical relationship extraction dataset, this study utilizes electronic medical records from 584 patients in a hospital to create a compact clinical medical relationship extraction dataset. To address the pipelined relationship extraction model's limitation in overlooking the one-to-many correlation problem between entities and relationships, this paper introduces a cascading relationship extraction model. This model integrates the MacBERT pre-training model, gated recurrent network, and multi-head self-attention mechanism to enhance the extraction of text features. Simultaneously, adversarial learning is incorporated to bolster the model's robustness. In scenarios involving one-to-many relationships between entities, a two-phase task is employed. Initially, the main entity is predicted, followed by predicting the associated object and their correspondences. Employing this cascade-structured approach enables the model to flexibly manage intricate entity relationships, thereby enhancing extraction accuracy. Experimental results demonstrate the model's efficiency, yielding F1-scores of 82.8%, 76.8%, and 88.2% for fulfilling relational extraction requirements and tasks on DuIE, CHIP-CDEE, and private datasets, respectively. These scores represent improvements over the benchmark model. The findings indicate the model's applicability in practical domains, particularly in tasks such as biomedical information extraction.

Recognition of Chinese Electronic Medical Records for Rehabilitation Robots: Information Fusion Classification Strategy.

Named entity recognition is a critical task in the electronic medical record management system for rehabilitation robots. Handwritten documents often contain spelling errors and illegible handwriting, and healthcare professionals frequently use different terminologies. These issues adversely affect the robot's judgment and precise operations. Additionally, the same entity can have different meanings in various contexts, leading to category inconsistencies, which further increase the system's complexity. To address these challenges, a novel medical entity recognition algorithm for Chinese electronic medical records is developed to enhance the processing and understanding capabilities of rehabilitation robots for patient data. This algorithm is based on a fusion classification strategy. Specifically, a preprocessing strategy is proposed according to clinical medical knowledge, which includes redefining entities, removing outliers, and eliminating invalid characters. Subsequently, a medical entity recognition model is developed to identify Chinese electronic medical records, thereby enhancing the data analysis capabilities of rehabilitation robots. To extract semantic information, the ALBERT network is utilized, and BILSTM and MHA networks are combined to capture the dependency relationships between words, overcoming the problem of different meanings for the same entity in different contexts. The CRF network is employed to determine the boundaries of different entities. The research results indicate that the proposed model significantly enhances the recognition accuracy of electronic medical texts by rehabilitation robots, particularly in accurately identifying entities and handling terminology diversity and contextual differences. This model effectively addresses the key challenges faced by rehabilitation robots in processing Chinese electronic medical texts, and holds important theoretical and practical value.

lab2clean: a novel algorithm for automated cleaning of retrospective clinical laboratory results data for secondary uses.

BACKGROUND: The integrity of clinical research and machine learning models in healthcare heavily relies on the quality of underlying clinical laboratory data. However, the preprocessing of this data to ensure its reliability and accuracy remains a significant challenge due to variations in data recording and reporting standards. METHODS: We developed lab2clean, a novel algorithm aimed at automating and standardizing the cleaning of retrospective clinical laboratory results data. lab2clean was implemented as two R functions specifically designed to enhance data conformance and plausibility by standardizing result formats and validating result values. The functionality and performance of the algorithm were evaluated using two extensive electronic medical record (EMR) databases, encompassing various clinical settings. RESULTS: lab2clean effectively reduced the variability of laboratory results and identified potentially erroneous records. Upon deployment, it demonstrated effective and fast standardization and validation of substantial laboratory data records. The evaluation highlighted significant improvements in the conformance and plausibility of lab results, confirming the algorithm's efficacy in handling large-scale data sets. CONCLUSIONS: lab2clean addresses the challenge of preprocessing and cleaning clinical laboratory data, a critical step in ensuring high-quality data for research outcomes. It offers a straightforward, efficient tool for researchers, improving the quality of clinical laboratory data, a major portion of healthcare data. Thereby, enhancing the reliability and reproducibility of clinical research outcomes and clinical machine learning models. Future developments aim to broaden its functionality and accessibility, solidifying its vital role in healthcare data management.

Health Data Sciences and Cardiovascular Diseases in South Asia: Innovations and Challenges in Digital Health.

PURPOSE OF REVIEW: Health data sciences can help mitigate high burden of cardiovascular disease (CVD) management in South Asia by increasing availability and affordability of healthcare services. This review explores the current landscape, challenges, and strategies for leveraging digital health technologies to improve CVD outcomes in the region. RECENT FINDINGS: Several South Asian countries are implementing national digital health strategies that aim to provide unique health account numbers for patients, creating longitudinal digital health records while others aim to digitize healthcare services and improve health outcomes. Significant challenges impede progress, including lack of interoperability, inadequate training of healthcare workers, cultural barriers, and data privacy concerns. Leveraging digital health for CVD management involves using big data for early detection, employing artificial intelligence for diagnostics, and integrating multiomics data for health insights. Addressing these challenges through policy frameworks, capacity building, and international cooperation is crucial for improving CVD outcomes in region.

Standardizing visual display of gastrointestinal pathology results for improved clinical interpretation.

OBJECTIVES: Pathology is an essential component of disease diagnosis and management in pediatric gastroenterology. Pathology reports have not been standardized in some areas of pediatric gastrointestinal pathology and pathology reporting varies. Development of electronic medical record (EMR) pathology synoptic report templates (PSRT) enables pathology data collection in a specific format and can help standardize pathology reporting. We developed, implemented, and evaluated EMR PSRTs for eosinophilic esophagitis (EoE) and inflammatory bowel disease (IBD). METHODS: PSRTs were developed by a multidisciplinary team of pediatric experts of allergy, gastroenterology, and pathology for both EoE and IBD based on available literature and validated scales. Likert surveys (range 1 low acceptance to 5 high acceptance) based on the Technology Acceptance Model assessed user acceptance of the developed PSRTs. The use of PSRTs was monitored via control charts. RESULTS: Overall, evaluation questionnaires achieved >80% response rates. Clinicians and pathologists reported moderate-to-high levels of Perceived Usefulness (median (interquartile range) for EoE PSRT: clinicians 4.0 (4.0, 5.0) and pathologists 3.5 (3.5, 4.0); and IBD PSRT: clinicians 4.0 (3.0, 4.0) and pathologists 4.0 (4.0, 5.0)) and Perceived Ease of Use (EoE PSRT: clinicians 4.5 (4.0, 5.0) and pathologists 4.0 (4.0, 4.0); and IBD PSRT: clinicians 4.0 (4.0, 5.0) and pathologists 4.0 (4.0, 5.0)) of the developed PSRTs. Control charts demonstrated 100% utilization by 2-5 months from launch. CONCLUSIONS: We demonstrate successful implementation of synoptic reporting for both pediatric EoE and IBD pathology. EMR synoptic reporting provides standardization of pathology reporting and improved methods of pathology data presentation, which could potentially optimize provider efficiency, clinician interpretation of pathology results and disease trajectory, patient care, and clinician satisfaction.

Topical amitriptyline in burning mouth syndrome: A retrospective real-world evidence study.

OBJECTIVE: To evaluate the effectiveness, tolerability, and safety of topical amitriptyline as a potential route of administration for the management of burning mouth syndrome. BACKGROUND: Burning mouth syndrome is a complex, idiopathic, and debilitating orofacial pain disorder that impairs quality of life, with a prevalence of up to 18% in menopausal women. Available drugs to alleviate its burning sensation have inconsistent and limited efficacy. Given its physicochemical properties, excellent tolerability, and ability to target peripheral pathways, topical amitriptyline seems a promising mechanistically specific analgesic drug for burning mouth syndrome. METHODS: In this retrospective cross-sectional real-world evidence study, patients with burning mouth syndrome who were prescribed topical amitriptyline for 8 weeks were identified. Eligibility criteria stemmed from ICHD-3, ICOP, and consensus definitions. The primary outcome measure was mean daily pain intensity (on a 0-10 scale); secondary outcomes included adverse events and patient global impression of improvement. Data are given as the mean ± SD. RESULTS: A total of 15 patients fulfilling the eligibility criteria were included and analyzed. Mean daily pain was 6.7 ± 2.1 at baseline and 3.7 ± 2.3 after treatment, with a mean reduction of 3.1 ± 2.8 (p = 0.002). Half of the patients experienced a decrease in pain by at least 50% (p = 0.008). Several mild adverse events were reported, such as somnolence or dry mouth. CONCLUSIONS: Topical amitriptyline may be a safe and potent route of administration in the treatment of burning mouth syndrome, a hypothesis to be tested in further controlled trials.