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Objective: Electronic medical records (EMRs) contain patients' medical and health information. The Utilization of EMRs for assisted diagnosis is of significant importance for the rehabilitation of spinal cord injury (SCI) patients. Therefore, this study proposes a decision-making model for rehabilitation programs of SCI patients based on EMRs. Methods: First, an Electronic Medical Records (EMR) dataset comprising 1252 Spinal Cord Injury (SCI) patients was constructed, and data preprocessing was completed. Second, the Random Forest (RF) feature extraction algorithm was utilized to select case features with high contribution levels. Then, to address the imbalance issue in EMRs, a multi-label learning framework based on the improved MLSMOTE was adopted. Finally, seven multi-label classification models were employed to predict patients' physical therapy (PT) prescriptions. Results: The proposed improved MLSMOTE multi-label learning framework can solve the problem of class imbalance. Compared with the other six models, the CC model has improved significantly in many metrics. Its hamming loss and ranking loss were 0.1388 and 0.2467, and precision, recall, and F1-score were 83.33 %, 81.20 %, and 79.82 % respectively. Conclusions: The improved MLSMOTE multi-label learning framework proposed in this study can make full use of the information in EMRs and effectively improve the decision-making accuracy of rehabilitation treatment programs.

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INTRODUCTION: Medication nonadherence is common in systemic lupus erythematosus (SLE) and associated with morbidity and mortality. We explored the reliability of pharmacy data within the electronic medical record (EMR) to examine factors associated with nonadherence to SLE medications. METHODS: We included patients with SLE who were prescribed ≥1 SLE medication for ≥90 days. We compared two datasets of pharmacy fill data, one within the EMR and another from the vendor who obtained this information from pharmacies and prescription benefit managers. Adherence was defined by medication possession ratio (MPR) ≥80%. In addition to MPR for each SLE medication, we evaluated the weighted-average MPR and the proportion of patients adherent to ≥1 SLE medication and to all SLE medications. We used logistic regression to examine factors associated with adherence. RESULTS: Among 181 patients (median age 36, 96% female, 58% Black), 98% were prescribed hydroxychloroquine, 34% azathioprine, 33% mycophenolate, 18% methotrexate, and 7% belimumab. Among 1276 pharmacy records, 74% overlapped between linked EMR-pharmacy data and data obtained directly from the vendor. Only 9% were available from the vendor but not through linked EMR-pharmacy data. The weighted-average MPR was 57%; 45% were adherent to hydroxychloroquine, 46% to ≥1 SLE medication, and 32% to all SLE medications. Older age was associated with adherence in univariable and multivariable analyses. DISCUSSION: Our study showed that obtaining linked EMR-pharmacy data is feasible with minimal missing data and can be leveraged in future adherence research. Younger patients were more likely to be nonadherent and may benefit from targeted intervention.

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OBJECTIVE: Electronic medical records (EMRs) have streamlined workflows for health care professionals, yet their full potential is not always actualized. Modern EMRs are often capable of generating automated prepopulated inpatient lists, however if these capabilities are not made available to inpatient teams or not designed with the end user in mind, resident physicians may be left to create alternative, manual solutions to ensure reliable and efficient care. The purpose of the current study was to longitudinally compare the impact of both manual and automated inpatient lists on resident education, wellness, and patient safety. DESIGN: Retrospective standardized surveys were administered to resident physicians in the orthopedic surgery department at a level I trauma center over a 3-year period to evaluate the impact of various automated and manual list iterations coinciding with changes to the EMR. Data collected included post graduate year (PGY) status, arrival time to work, daily time spent preparing and updating the list, perceived impact on patient safety, resident workload, education, and sleep. We compared manual versus automated list data with unpaired t-tests and chi-squared tests. SETTING: The study was conducted at Brooke Army Medical Center, a level 1 trauma center in San Antonio, Texas. It is an Academic Medical Center and the Department of Defense's largest medical facility. PARTICIPANTS: A total of 71 surveys were collected from 33 orthopedic surgery residents in all levels of training. RESULTS: Intern list prep time in the morning was 27 ± 16 minutes for the automated list (n = 17) vs 72 ± 21 minutes for the manual lists (n = 23) (p < 0.0001). Total time spent by interns updating the list for the entire day was on average 83 minutes for the automated list (n = 17) vs 196 minutes for the manual lists (n = 23) (p < 0.0001). In addition, 86% of interns felt the time spent on the manual list impacted their education, with 96% stating that it directly impacted the amount of time they had to study and 100% agreed that it negatively impacted their sleep (n = 23). Only 48% of interns (n = 23) were satisfied with the performance of the manual lists compared to 94% satisfaction (n = 17) with the automated list. Further, 87% of interns felt the manual list negatively impacted patient care and negatively affected their job satisfaction. In comparison, 59% of interns felt the automated list improved their job satisfaction. Ultimately, for an intern an automated list versus a manual list affords them an extra 106 minutes per day for education, sleep, or other activities. PGY2 residents and above noted similar trends regarding their experience with the lists. CONCLUSIONS: The benefits of utilizing automated inpatient lists as determined by our study are improved efficiency in the morning with less preparation and maintenance throughout the day. Additionally, with automated lists there was more perceived time for education and sleep, with improved job satisfaction. Most importantly, respondents felt that automated lists were safer for patient care when compared to manual lists. This should compel further research and efforts into improving automated EMR tracking lists in hospitals. In summary, as compared to the automated electronic medical record inpatient list, manual lists resulted in substantially more preparation time and maintenance throughout the day thereby negatively impacting resident education and quality of life, while raising concerns for patient safety.

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In medical research, the accuracy of data from electronic medical records (EMRs) is critical, particularly when analyzing dense functional data, where anomalies can severely compromise research integrity. Anomalies in EMRs often arise from human errors in data measurement and entry, and increase in frequency with the volume of data. Despite the established methods in computer science, anomaly detection in medical applications remains underdeveloped. We address this deficiency by introducing a novel tool for identifying and correcting anomalies specifically in dense functional EMR data. Our approach utilizes studentized residuals from a mean-shift model, and therefore assumes that the data adheres to a smooth functional trajectory. Additionally, our method is tailored to be conservative, focusing on anomalies that signify actual errors in the data collection process while controlling for false discovery rates and type II errors. To support widespread implementation, we provide a comprehensive R package, ensuring that our methods can be applied in diverse settings. Our methodology's efficacy has been validated through rigorous simulation studies and real-world applications, confirming its ability to accurately identify and correct errors, thus enhancing the reliability and quality of medical data analysis.

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PURPOSE: The European health data space promises an efficient environment for research and policy-making. However, this data space is dependent on high data quality. The implementation of electronic medical record systems has a positive impact on data quality, but improvements are not consistent across empirical studies. This study aims to analyze differences in the changes of data quality and to discuss these against distinct stages of the electronic medical record's adoption process. METHODS: Paper-based and electronic medical records from three surgical departments were compared, assessing changes in data quality after the implementation of an electronic medical record system. Data quality was operationalized as completeness of documentation. Ten information that must be documented in both record types (e.g. vital signs) were coded as 1 if they were documented, otherwise as 0. Chi-Square-Tests were used to compare percentage completeness of these ten information and t-tests to compare mean completeness per record type. RESULTS: A total of N = 659 records were analyzed. Overall, the average completeness improved in the electronic medical record, with a change from 6.02 (SD = 1.88) to 7.2 (SD = 1.77). At the information level, eight information improved, one deteriorated and one remained unchanged. At the level of departments, changes in data quality show expected differences. CONCLUSION: The study provides evidence that improvements in data quality could depend on the process how the electronic medical record is adopted in the affected department. Research is needed to further improve data quality through implementing new electronical medical record systems or updating existing ones.

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BACKGROUND: International Classification of Diseases codes are widely used to describe diagnosis information, but manual coding relies heavily on human interpretation, which can be expensive, time consuming, and prone to errors. With the transition from the International Classification of Diseases, Ninth Revision, to the International Classification of Diseases, Tenth Revision (ICD-10), the coding process has become more complex, highlighting the need for automated approaches to enhance coding efficiency and accuracy. Inaccurate coding can result in substantial financial losses for hospitals, and a precise assessment of outcomes generated by a natural language processing (NLP)-driven autocoding system thus assumes a critical role in safeguarding the accuracy of the Taiwan diagnosis related groups (Tw-DRGs). OBJECTIVE: This study aims to evaluate the feasibility of applying an International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM), autocoding system that can automatically determine diagnoses and codes based on free-text discharge summaries to facilitate the assessment of Tw-DRGs, specifically principal diagnosis and major diagnostic categories (MDCs). METHODS: By using the patient discharge summaries from Kaohsiung Medical University Chung-Ho Memorial Hospital (KMUCHH) from April 2019 to December 2020 as a reference data set we developed artificial intelligence (AI)-assisted ICD-10-CM coding systems based on deep learning models. We constructed a web-based user interface for the AI-assisted coding system and deployed the system to the workflow of the certified coding specialists (CCSs) of KMUCHH. The data used for the assessment of Tw-DRGs were manually curated by a CCS with the principal diagnosis and MDC was determined from discharge summaries collected at KMUCHH from February 2023 to April 2023. RESULTS: Both the reference data set and real hospital data were used to assess performance in determining ICD-10-CM coding, principal diagnosis, and MDC for Tw-DRGs. Among all methods, the GPT-2 (OpenAI)-based model achieved the highest F1-score, 0.667 (F1-score 0.851 for the top 50 codes), on the KMUCHH test set and a slightly lower F1-score, 0.621, in real hospital data. Cohen κ evaluation for the agreement of MDC between the models and the CCS revealed that the overall average κ value for GPT-2 (κ=0.714) was approximately 12.2 percentage points higher than that of the hierarchy attention network (κ=0.592). GPT-2 demonstrated superior agreement with the CCS across 6 categories of MDC, with an average κ value of approximately 0.869 (SD 0.033), underscoring the effectiveness of the developed AI-assisted coding system in supporting the work of CCSs. CONCLUSIONS: An NLP-driven AI-assisted coding system can assist CCSs in ICD-10-CM coding by offering coding references via a user interface, demonstrating the potential to reduce the manual workload and expedite Tw-DRG assessment. Consistency in performance affirmed the effectiveness of the system in supporting CCSs in ICD-10-CM coding and the judgment of Tw-DRGs.

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BACKGROUND: Electronic medical record (EMR) systems have the potential to improve the quality of care and clinical outcomes for individuals with chronic and complex diseases. However, studies on the development and use of EMR systems for type 1 (T1) diabetes management in sub-Saharan Africa are few. OBJECTIVE: The aim of this study is to analyze the need for improvements in the care processes that can be facilitated by an EMR system and to develop an EMR system for increasing quality of care and clinical outcomes for individuals with T1 diabetes in Rwanda. METHODS: A qualitative, cocreative, and multidisciplinary approach involving local stakeholders, guided by the framework for complex public health interventions, was applied. Participant observation and the patient's personal experiences were used as case studies to understand the clinical care context. A focus group discussion and workshops were conducted to define the features and content of an EMR. The data were analyzed using thematic analysis. RESULTS: The identified themes related to feature requirements were (1) ease of use, (2) automatic report preparation, (3) clinical decision support tool, (4) data validity, (5) patient follow-up, (6) data protection, and (7) training. The identified themes related to content requirements were (1) treatment regimen, (2) mental health, and (3) socioeconomic and demographic conditions. A theory of change was developed based on the defined feature and content requirements to demonstrate how these requirements could strengthen the quality of care and improve clinical outcomes for people with T1 diabetes. CONCLUSIONS: The EMR system, including its functionalities and content, can be developed through an inclusive and cocreative process, which improves the design phase of the EMR. The development process of the EMR system is replicable, but the solution needs to be customized to the local context.

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INTRODUCTION: Lung cancer survival is significantly improved with early detection. However, lung cancer screening (LCS) uptake remains low despite national recommendations. Our aim was to determine whether implementation of an electronic medical record (EMR) alert and order set would increase LCS uptake. STUDY DESIGN: A query of current and former smokers identified 62,630 patients aged 50 and above in the primary care setting between January 1, 2021 and May 5, 2022. We randomly reviewed 3704 charts for LCS eligibility and recorded who received LCS in the form of low-dose computed tomography amongst the eligible patients. We collected demographic information including gender, race, primary language, ethnicity, zip code, and insurance. Data analysis was performed utilizing 2-proportional z tests. RESULTS: We identified 461 patients who were LCS eligible. Our overall LCS uptake was 19.9% (92/461). Three-time frames were analyzed: (1) prior to EMR alert implementation, (2) after implementation of EMR alert (January 7, 2021), and (3) after implementation of EMR alert and order set (March 3, 2021). Screening uptake was significantly improved with initiation of EMR alert (1/46 [2.2%] to 23/109 [21.1%]; P = .003). LCS uptake remained similarly high after subsequent order set implementation (23/109 [21.1%] and 68/306 [22.2%]; P = .72). Amongst the different demographics, age was significantly associated with screening uptake, with age ≥65 demonstrating statistically significant increased rates of screening (15.6% [41/263] for <65 vs 25.8% [51/198] for ≥65; P = .007). CONCLUSION: Implementation of EMR alerts significantly improves LCS uptake in the primary care setting. Such efforts should be considered in other hospital settings to improve LCS uptake.

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Introduction: To facilitate partnerships between nurses and their patients with psychiatric illness, it is important to provide a safe narrative space for both parties where patients can voice their opinions. Purpose: A case study shows how the Patient-Authored Medical Record (PAMR) can contribute to health practice reform. Methods: A patient who visited an outpatient psychiatric clinic was asked to describe his life events. The researchers created the patient's PAMR, a first-person account of how he thought his illness could be cured, which was used when conducting follow-up meetings. The contents of the PAMR and that of subsequent meetings were used to evaluate the tool's usefulness. Results: The narrative content of the PAMR and the follow-up meetings reflected a reduction in the patient's symptoms and a change in his perception of his illness. Conclusions: Patient-authored medical records could be a step toward health-care reform. Allyships created with patients can form new cooperative two-way relationships that are more equal than authoritative one-way relationships.

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Introduction: Dementia is a common co-morbidity in older people who require urgent or emergency ambulance attendance and influences clinical decisions and care pathways. Following an initial audit of dementia data and consultation with staff, a specific section (tab) to record dementia was introduced on an ambulance service electronic patient record (ePR). This includes a dementia diagnosis button and a free-text section. We aimed to assess whether and how this improved recording. Methods: To re-audit the proportion of ambulance ePRs where dementia is recorded for patients aged ≥65 years, and to describe the frequency of recording in patients aged <65; to analyse discrepancies in the place of recording dementia on the ePR by comparing data from the new dementia tab and other sections of the ePR. Results: We included 112,193 ePRs of patients aged ≥65 with ambulance attendance from a six-month period. The proportion with dementia recorded in patients aged ≥65 was 16.5%, increasing to 19.9% in patients aged ≥75, as compared to 13.5% (≥65) and 16.5% (≥75) in our previous audit. In this audit, of the 16.5% (n = 18,515) of records with dementia recorded, 69.9% (n = 12,939) used the dementia button and 25.4% (n = 4704) recorded text in the dementia tab. Dementia was recorded in ePR free-text fields (but not the dementia tab) in 29.7% of records. Eighteen other free-text fields were used in addition to, or instead of, the dementia tab, including the patient's social history, previous medical history and mental health. Dementia was present on the ePR of 0.4% (n = 461) of patients aged <65. Conclusions: An ePR dementia tab enabled ambulance clinicians to standardise the location of recording dementia and may have facilitated increased recording. We would recommend other ambulance trusts capture this information in a specific section to improve information sharing and to inform care planning for this patient group.

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Although the principle of recording and transmitting patient data is not new, the computerized medical record used in today's practice of care still does not meet the needs. We can easily - and often rightly - cast shame on the designers of those medical records and on administrators of our care institutions, but we as caregivers do need to share responsibility. If we really intend to use multiple sources of data wisely, in order to increase global health status at the individual level or for a population, we need to understand clearly the multiple dimensions of data and therefore acquire a real data culture. The revival of the medical record, for too long a source of disillusionment and burnout, is within reach especially as technical solutions appear to automate and facilitate our work of recording data in the field.

Si le principe de la récolte de données concernant les patients ne date pas d'hier, force est de constater que le dossier médical informatisé d'aujourd'hui ne répond toujours pas aux besoins. On peut, bien entendu, par facilité - et souvent à juste titre - jeter l'opprobre sur les concepteurs et les administratifs, mais le monde des soignants a aussi une part de responsabilité. Si nous avons vraiment l'intention d'utiliser à bon escient les multiples sources de données, que ce soit en matière de gestion de la santé individuelle et/ou populationnelle, il nous faut d'abord insister sur la meilleure compréhension par les soignants des multiples dimensions de ces données et donc acquérir une réelle culture en la matière. Le renouveau du dossier médical informatisé, trop longtemps source de désillusions et de burnout, est à portée de main, d'autant plus que des solutions techniques apparaissent, automatisant et facilitant le travail sur le terrain.

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BACKGROUND: Cardiac arrest (CA) is one of the leading causes of death among patients in the intensive care unit (ICU). Although many CA prediction models with high sensitivity have been developed to anticipate CA, their practical application has been challenging due to a lack of generalization and validation. Additionally, the heterogeneity among patients in different ICU subtypes has not been adequately addressed. OBJECTIVE: This study aims to propose a clinically interpretable ensemble approach for the timely and accurate prediction of CA within 24 hours, regardless of patient heterogeneity, including variations across different populations and ICU subtypes. Additionally, we conducted patient-independent evaluations to emphasize the model's generalization performance and analyzed interpretable results that can be readily adopted by clinicians in real-time. METHODS: Patients were retrospectively analyzed using data from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) and the eICU-Collaborative Research Database (eICU-CRD). To address the problem of underperformance, we constructed our framework using feature sets based on vital signs, multiresolution statistical analysis, and the Gini index, with a 12-hour window to capture the unique characteristics of CA. We extracted 3 types of features from each database to compare the performance of CA prediction between high-risk patient groups from MIMIC-IV and patients without CA from eICU-CRD. After feature extraction, we developed a tabular network (TabNet) model using feature screening with cost-sensitive learning. To assess real-time CA prediction performance, we used 10-fold leave-one-patient-out cross-validation and a cross-data set method. We evaluated MIMIC-IV and eICU-CRD across different cohort populations and subtypes of ICU within each database. Finally, external validation using the eICU-CRD and MIMIC-IV databases was conducted to assess the model's generalization ability. The decision mask of the proposed method was used to capture the interpretability of the model. RESULTS: The proposed method outperformed conventional approaches across different cohort populations in both MIMIC-IV and eICU-CRD. Additionally, it achieved higher accuracy than baseline models for various ICU subtypes within both databases. The interpretable prediction results can enhance clinicians' understanding of CA prediction by serving as a statistical comparison between non-CA and CA groups. Next, we tested the eICU-CRD and MIMIC-IV data sets using models trained on MIMIC-IV and eICU-CRD, respectively, to evaluate generalization ability. The results demonstrated superior performance compared with baseline models. CONCLUSIONS: Our novel framework for learning unique features provides stable predictive power across different ICU environments. Most of the interpretable global information reveals statistical differences between CA and non-CA groups, demonstrating its utility as an indicator for clinical decisions. Consequently, the proposed CA prediction system is a clinically validated algorithm that enables clinicians to intervene early based on CA prediction information and can be applied to clinical trials in digital health.

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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.

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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.

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BACKGROUND: Comorbidity is increasingly important in the medical literature, with ever-increasing implications for diagnosis, treatment, prognosis, management and health care. The objective of this study is to measure casual versus causal comorbidity in primary care in three family practice populations. METHODS: This is a longitudinal observational study using the Transition Project datasets. Transition Project family doctors in the Netherlands, Malta and Serbia recorded details of all patient contacts in an episode of care structure using electronic medical records and the International Classification of Primary Care, collecting data on all elements of the doctor-patient encounter, including diagnoses (1,178,178 in the Netherlands, 93,606 in Malta, 405,150 in Serbia), observing 158,370 patient years in the Netherlands, 43,577 in Malta, 72,673 in Serbia. Comorbidity was measured using the odds ratio of both conditions being incident or rest-prevalent in the same patient in one-year dataframes, as against not, corrected for the prior probability of such co-occurrence, between the 41 joint most prevalent (joint top 20) episode titles in the three populations. Specific associations were explored in different age groups to observe the changes in odds ratios with increasing age as a surrogate for a temporal or biological gradient. RESULTS: The high frequency of observed comorbidity with low consistency in both clinically and statistically significant odds ratios across populations indicates more casual than causal associations. A causal relationship would be expected to be manifest more consistently across populations. Even in the minority of cases where odds ratios were consistent between countries and numerically larger, those associations were observed to weaken with increasing patient age. CONCLUSION: After applying accepted criteria for testing the causality of associations, most observed primary care comorbidity is due to chance, likely as a result of increasing illness diversity. TRIAL REGISTRATION: This study was performed on electronic patient record datasets made publicly available by the University of Amsterdam Department of General Practice, and did not involve any patient intervention.

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Background: Adverse social determinants of health (SDoH) have been associated with cardiometabolic disease; however, disparities in cardiometabolic outcomes are rarely the result of a single risk factor. Objective: This study aimed to identify and characterize SDoH phenotypes based on patient-reported and neighborhood-level data from the institutional electronic medical record and evaluate the prevalence of diabetes, obesity, and other cardiometabolic diseases by phenotype status. Methods: Patient-reported SDoH were collected (January to December 2020) and neighborhood-level social vulnerability, neighborhood socioeconomic status, and rurality were linked via census tract to geocoded patient addresses. Diabetes status was coded in the electronic medical record using International Classification of Diseases codes; obesity was defined using measured BMI ≥30 kg/m2. Latent class analysis was used to identify clusters of SDoH (eg, phenotypes); we then examined differences in the prevalence of cardiometabolic conditions based on phenotype status using prevalence ratios (PRs). Results: Complete data were available for analysis for 2380 patients (mean age 53, SD 16 years; n=1405, 59% female; n=1198, 50% non-White). Roughly 8% (n=179) reported housing insecurity, 30% (n=710) reported resource needs (food, health care, or utilities), and 49% (n=1158) lived in a high-vulnerability census tract. We identified 3 patient SDoH phenotypes: (1) high social risk, defined largely by self-reported SDoH (n=217, 9%); (2) adverse neighborhood SDoH (n=1353, 56%), defined largely by adverse neighborhood-level measures; and (3) low social risk (n=810, 34%), defined as low individual- and neighborhood-level risks. Patients with an adverse neighborhood SDoH phenotype had higher prevalence of diagnosed type 2 diabetes (PR 1.19, 95% CI 1.06-1.33), hypertension (PR 1.14, 95% CI 1.02-1.27), peripheral vascular disease (PR 1.46, 95% CI 1.09-1.97), and heart failure (PR 1.46, 95% CI 1.20-1.79). Conclusions: Patients with the adverse neighborhood SDoH phenotype had higher prevalence of poor cardiometabolic conditions compared to phenotypes determined by individual-level characteristics, suggesting that neighborhood environment plays a role, even if individual measures of socioeconomic status are not suboptimal.

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BACKGROUND: Proper documentation is essential for patient care. The popularity of artificial intelligence (AI) offers the potential for improvements in neurosurgical note-writing. The study aimed to assess how AI can optimize documentation in neurosurgical procedures. METHODS: Thirty-six notes were included. All identifiable data were removed. Essential information, such as perioperative data and diagnosis, was sourced from these notes. ChatGPT 4.0 was trained to draft notes from surgical vignettes using each surgeon's note template. One hundred forty-four surveys, with a surgeon or AI note, were shared with three surgeons to evaluate accuracy, content, and organization using a five-point scale. Accuracy was the factual correctness. Content was the comprehensiveness. Organization was the arrangement of the note. Flesch-Kincaid Grade Level (FKGL) and Flesch Reading Ease (FRE) scores quantified each note's readability. RESULTS: The mean AI accuracy (4.44) was not different from the mean surgeon accuracy (4.33, p = 0.512). The mean AI content (3.73) was lower than the mean surgeon content (4.42, p < 0.001). The mean AI organization (4.54) was greater than the mean surgeon organization (4.24, p = 0.064). The mean AI note's FKGL (13.13) was greater than the mean surgeon FKGL (9.99, p <0.001). The mean AI FRE (21.42) was lower than the mean surgeon FRE (41.70, p <0.001). CONCLUSION: AI notes were on par with surgeon notes in accuracy and organization, but lacked in content. Additionally, AI notes utilized language at an advanced reading level. These findings underscore the potential for ChatGPT to enhance the efficiency of neurosurgery documentation.

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OBJECTIVE: This study aimed to leverage real-world electronic medical record (EMR) data to develop interpretable machine learning models for diagnosis of Kawasaki disease, while also exploring and prioritizing the significant risk factors. METHODS: A comprehensive study was conducted on 4,087 pediatric patients at the Children's Hospital of Chongqing, China. The study collected demographic data, physical examination results, and laboratory findings. Statistical analyses were performed using SPSS 26.0. The optimal feature subset was employed to develop intelligent diagnostic prediction models based on the Light Gradient Boosting Machine (LGBM), Explainable Boosting Machine (EBM), Gradient Boosting Classifier (GBC), Fast Interpretable Greedy-Tree Sums (FIGS), Decision Tree (DT), AdaBoost Classifier (AdaBoost), and Logistic Regression (LR). Model performance was evaluated in three dimensions: discriminative ability via Receiver Operating Characteristic curves, calibration accuracy using calibration curves, and interpretability through Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). RESULTS: In this study, Kawasaki disease was diagnosed in 2,971 participants. Analysis was conducted on 31 indicators, including red blood cell distribution width and erythrocyte sedimentation rate. The EBM model demonstrated superior performance compared to other models, with an Area Under the Curve (AUC) of 0.97, second only to the GBC model. Furthermore, the EBM model exhibited the highest calibration accuracy and maintained its interpretability without relying on external analytical tools like SHAP and LIME, thus reducing interpretation biases. Platelet distribution width, total protein, and erythrocyte sedimentation rate were identified by the model as significant predictors for the diagnosis of Kawasaki disease. CONCLUSIONS: This study employed diverse machine learning models for early diagnosis of Kawasaki disease. The findings demonstrated that interpretable models, like EBM, outperformed traditional machine learning models in terms of both interpretability and performance. Ensuring consistency between predictive models and clinical evidence is crucial for the successful integration of artificial intelligence into real-world clinical practice.

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BACKGROUND: Health Information Managers (HIMs) play a crucial role in the management and governance of health information ensuring the accuracy, confidentiality and accessibility of health data for clinical care and business operational purposes. This role also extends to education and training in the workplace. OBJECTIVE: The aim of this scoping review was to explore and elucidate the role played by HIMs when they undertake a health workplace-based (healthcare organisation or service) educational role and/or functions as evidenced in the existing body of literature. METHOD: A scoping review of the literature to investigated the importance of the educator role for HIM health workplace-based educators. A three-step search strategy was designed to ensure a comprehensive exploration of relevant research. RESULTS: Of 63 articles assess for eligibility, 14 were included in the final analysis. All included articles acknowledged the importance of the HIM-educator workplace-based role. Half of the included articles had been published within the last 7 years. Only 8 of the 14 articles provided some description of HIM-educator attributes, suggesting that these characteristics remain unexplored. DISCUSSION: Findings from this scoping review have shed light on the limitations within the current available literature concerning the attributes of HIM health workplace-based educators. The findings also highlight an important gap in knowledge concerning the qualities of these HIM-educators. CONCLUSION: This identified gap in the literature signals a need for further exploration and investigation into the specific attributes, skills, and characteristics that define effective HIM-educators undertaking a health workplace-based educational role.

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A Medical Treatment Decision Maker (MTDM), also referred to as surrogate decision maker, by law, is to be appointed to make medical treatment decisions on behalf of a person who cannot make such decisions for themselves. In the Emergency Department (ED) and acute healthcare services, the clinicians' (nurses and doctors) ability to contact MTDMs is essential for patient care, particularly in time-critical situations. Our primary objective was to review the verification process and assess the accuracy of MTDM contact numbers in the Health Information System (HIS) to assess compliance with legislation. We used a quantitative method with retrospective observational study design and follow-up phone interview transcript. One hundred and fifty-nine participants were randomly selected of whom 76 % had MTDM. Patient advancing age had statistically significant association with the number of call attempts made to reach the listed MTDM (P = 0.043; CI, -3.541 to -0.057) and the MTDM's consent to participate (p = 0.023).