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Background: Although the prescription of physical activity in clinical care has been advocated worldwide, in the Netherlands, "Exercise is Medicine" (E = M) is not yet routinely implemented in clinical care. Methods: A set of implementation strategies was pilot implemented to test its feasibility for use in routine care by clinicians in 2 departments of a university medical center. An extensive learning process evaluation was performed, using structured mixed methods methodology, in accordance with the Reach, Effect, Adoption, Implementation, and Maintenance framework. Results: From 5 implementation strategies employed (education, E = M tool embedded in the electronic medical records, lifestyle coach situated within the department, overviews of referral options, and project support), the presence of adequate project support was a strong facilitator of the implementation of E = M. Also, the presence of the lifestyle coach within the department seemed essential for referral rate. Although clinicians appreciated the E = M tool, barriers hampered its use in practice. Conclusions: Specific implementation strategies, tailored to the setting, are effective in facilitating the implementation of E = M with specific regard to education for clinicians on E = M, deployment of a lifestyle coach within a department, and project coordination. Care providers do see a future for lifestyle coaches who are structurally embedded in the hospital, to whom they can easily refer. [ABSTRACT FROM AUTHOR]

This paper explains how value co-creation is affected by the introduction of tools to digitalise healthcare. In particular, this study examines how pregnant women in France collect, utilise, and share their health records after the launch of a new Electronic Health Record (EHR). The paper combines methods from the design field (cultural probes) and micro-phenomenological interviews to capture value co-creation and value co-destruction dynamics. Through analysing nine cases, we highlight the role of patients' expectations in potential value co-destruction with an EHR. We describe why it is important to know by whom and how co-creation is carried out to improve the launch of digital health applications. Finally, the paper emphasises that value co-creation is a long-term process that needs to be studied longitudinally. [ABSTRACT FROM AUTHOR]

The article shares views on opportunities and challenges posed by big data (BD), mobile health and artificial intelligence to dentistry and craniofacial research. Topics discussed include concerns about privacy, security and data sharing in relation to the use of BD analysis, the Dental, Oral, Medical Epidemiological (DOME) electronic records-based BD study, and the role of combining electronic health record with mobile health tools in improving data sets and in advancing BD research.

Objective: The evidence base for the use of space maintainers is relatively sparce despite being used for decades after the premature loss of primary molars. This study aims to increase the dental evidence base via investigating retrospectively the success rates of prefabricated fixed and removable space maintainers inserted from 2019 to 2021 and followed up until February 2023 at a specialized university clinic and to identify reasons for any reported minor and major failure. The authors hypothesized that there is no significant difference in failure rates between fixed and removable space maintainers inserted after the premature loss of a single primary molar per quadrant. Method and materials: Patients' digital records were searched yielding 645 space maintainers. After the application of inclusion criteria, 157 (67%) fixed prefabricated space maintainers in 112 children and 77 (33%) removable space maintainers in 61 children were analyzed for an average of 18.4 ± 9.5 months. Results: Kaplan-Meier survival analysis with Mantel-Cox statistics showed an overall cumulative survival time of 31.6 months (SE = 1.15, 95% CI = 29.4 to 33.9). Major failure occurred significantly more in removable maintainers (n = 40/67, 59.7%), mostly due to loss of the appliance, compared to fixed space maintainers (n = 27/67, 40.3%; P < .001). The present study indicates that space maintainers were mainly placed in young children with high caries experience, where treatment was mostly possible using advanced behavior management. Conclusions: Fixed space maintainers had a significantly lower failure rate than their removable counterpart. However, both require continual repairs, preservation, or even replacement till the eruption of the permanent tooth. [ABSTRACT FROM AUTHOR]

Clinical prediction models have been widely acknowledged as informative tools providing evidence‐based support for clinical decision making. However, prediction models are often underused in clinical practice due to many reasons including missing information upon real‐time risk calculation in electronic health records (EHR) system. Existing literature to address this challenge focuses on statistical comparison of various approaches while overlooking the feasibility of their implementation in EHR. In this article, we propose a novel and feasible submodel approach to address this challenge for prediction models developed using the model approximation (also termed "preconditioning") method. The proposed submodel coefficients are equivalent to the corresponding original prediction model coefficients plus a correction factor. Comprehensive simulations were conducted to assess the performance of the proposed method and compared with the existing "one‐step‐sweep" approach as well as the imputation approach. In general, the simulation results show the preconditioning‐based submodel approach is robust to various heterogeneity scenarios and is comparable to the imputation‐based approach, while the "one‐step‐sweep" approach is less robust under certain heterogeneity scenarios. The proposed method was applied to facilitate real‐time implementation of a prediction model to identify emergency department patients with acute heart failure who can be safely discharged home. [ABSTRACT FROM AUTHOR]

Healthcare data is crucial and sensitive, as it contains absolute information about a patient's medical history, treatments, and actions; this information gets shared among the stakeholders on a routine basis. Patients' information is vital and should be kept accurate, up-to-date, and secret; it should be available only to authorized users. Most of the existing systems are centralized and may breach data privacy. This study mainly focuses on protecting the privacy and security of sensitive healthcare data while sharing with multiple stakeholders. This work presents a privacy-preserving and access-control blockchain-based framework that uses consensus-driven decentralized data management on top of peer-to-peer distributed computing platforms to ensure the privacy, security, accessibility, and integrity of healthcare data. Blockchain technology helps to protect transactions from manipulation due to its features of irreversibility and immutability. Additionally, we thoroughly examine the security requirements afforded by blockchain-enabled systems by incorporating stakeholders like patients, doctors, chemists, and pathology labs as system entities; they can only share information through a proper channel. The proposed framework has been implemented and evaluated using Hyperledger Fabric. We observe that the proposed framework reveals promising benefits in security, regulation compliance, reliability, flexibility, and accuracy. [ABSTRACT FROM AUTHOR]

Background: The success of an electronic health records (EHR) system is dependent on the effectiveness, satisfaction, and efficient use of these systems by health professionals. This paper explores the interdependency among effectiveness, satisfaction, and efficient use of Lightwave Health Information Management Systems (LHIMS) which is an EHR by health professionals. Methods: A non-interventional descriptive cross-sectional study design was employed for this research. The study used stratified random sampling for the selection of participants. The population for the study was all the health professionals in the Central Region of Ghana. The number of health professionals who responded to the questions was 1126. Results: The findings suggest that understanding the interdependency of efficiency, effectiveness and satisfaction is crucial for the successful implementation and adoption of LHIMS in healthcare organisations. The study investigated the interdependence among effectiveness, satisfaction, and efficient use of the LHIMS by health professionals, and found a positive but weak significant relationship between these factors. Conclusion: In conclusion, this study aimed to assess the interdependency among effectiveness, satisfaction, and efficient use of LHIMS by health professionals. The. results support a positive but weak significant relationship between satisfaction, effectiveness, and efficient use of the LHIMS. These findings have implications for policymakers and hospital management in their efforts to improve the use of LHIMS. Policymakers can focus on any one of the three usability areas to enhance LHIMS use, with visible effects across the other two domains. [ABSTRACT FROM AUTHOR]

Background and aims Design and setting Participants and Measurements Findings Conclusions Amphetamine‐type stimulants are the second‐most used illicit drugs globally, yet there are no US Food and Drug Administration (FDA)‐approved treatments for amphetamine‐type stimulant use disorders (ATSUD). The aim of this study was to utilize a drug discovery framework that integrates artificial intelligence (AI)‐based drug prediction, clinical corroboration and mechanism of action analysis to identify FDA‐approved drugs that can be repurposed for treating ATSUD.An AI‐based knowledge graph model was first utilized to prioritize FDA‐approved drugs in their potential efficacy for treating ATSUD. Among the top 10 ranked candidate drugs, ketamine represented a novel candidate with few studies examining its effects on ATSUD. We therefore conducted a retrospective cohort study to assess the association between ketamine and ATSUD remission using US electronic health record (EHR) data. Finally, we analyzed the potential mechanisms of action of ketamine in the context of ATSUD.ATSUD patients who received anesthesia (n = 3663) or were diagnosed with depression (n = 4328) between January 2019 and June 2022. The outcome measure was the diagnosis of ATSUD remission within one year of the drug prescription.Ketamine for anesthesia in ATSUD patients was associated with greater ATSUD remission compared with other anesthetics: hazard ratio (HR) = 1.58, 95% confidence interval (CI) = 1.15–2.17. Similar results were found for ATSUD patients with depression when comparing ketamine with antidepressants and bupropion/mirtazapine with HRs of 1.51 (95% CI = 1.14–2.01) and 1.68 (95% CI = 1.18–2.38), respectively. Functional analyses demonstrated that ketamine targets several ATSUD‐associated pathways including neuroactive ligand‐receptor interaction and amphetamine addiction.There appears to be an association between clinician‐prescribed ketamine and higher remission rates in patients with amphetamine‐type stimulant use disorders. [ABSTRACT FROM AUTHOR]

Identifying patients' reasons for visiting is the central task at medical openings, the structure of which has been well studied in Western primary care, but much under-researched in China's mainland. Drawing on conversation analysis of 91 audio-recorded primary care consultations in China, this study explores interactional features of patients' problem presentation at medical openings in terms of sequential positions, forms, and contextual contingencies, which has implications for the model of medical service encounters in Chinese primary care openings. Although problem description is commonly solicited by doctors across cultures, Chinese patients' problem presentation often takes forms other than problem description. Nearly two thirds of problem presentation in our data are designed as a request-making action (57/91 cases), being more often self-initiated than solicited. This blurs the boundary between medical visits for new and non-new problems. The analysis of Chinese patients' problem presentation points to a high degree of patient agency in primary care in China, suggesting a strong orientation to the "provider-consumer" (vs. "professional-client") model of service encounters in the opening structure of doctor-patient interaction. [ABSTRACT FROM AUTHOR]

With the rapid development of e-health and telemedicine, previous studies have explored the relationship between physician-patient communication and patient satisfaction; however, there is a paucity of research on the influence of the characteristics of patient communication on the characteristics of physician feedback. Based on the communication accommodation theory, as well as the computer-mediated communication theory and media richness theory, this study aimed to explore how characteristics of patient communication influence characteristics of physician feedback in online health communities. We employed a crawler software to download the communication data between 1652 physicians and 105,325 patients from the Good Doctor platform, the biggest online health community in China. We built an empirical model using this data and employed a multilevel model to test our hypotheses using Stata and Python software. The results indicate that the amount of patients' rendered information positively influences the physicians' text (α = 0.123, t = 33.147, P <.001) and voice feedback (β = 0.201, t = 40.011, P <.001). Patients' hope for help signals and the provision of their electronic health records weaken the effect of the amount of patients' rendered information on physicians' text feedback (α = –0.040, t = –24.857, P <.001; α = –0.048, t = –15.784, P <.001), whereas, it strengthened the effect of the amount of patients' rendered information on physicians' voice feedback (β = 0.033, t = 14.789, P <.001; β = 0.017, t = 4.208, P <.001). Moreover, the occurrence of high-privacy diseases strengthened the effect of the amount of patients' presented information on physicians' text and voice feedback (α = 0.023, t = 4.870, P <.001; β = 0.028, t = 4.282, P <.001). This research contributes to the development of computer-mediated communication theories and sheds light on service delivery in the online health community. [ABSTRACT FROM AUTHOR]

Purpose Due to the low specificity of drug-drug interaction (DDI) warnings, hospitals and healthcare systems would benefit from the ability to customize alerts, thereby reducing the burden of alerts while simultaneously preventing harm. We developed a tool, called the Drug Interaction Customization Editor (DICE), as a prototype to identify features and functionality that could assist healthcare organizations in customizing DDI alerts. Methods A team of pharmacists, physicians, and DDI experts identified attributes expected to be useful for filtering DDI warnings. A survey was sent to pharmacists with informatics responsibilities and other medication safety committee members to obtain their opinions about the tool. The survey asked participants to evaluate the 4 sections of the DICE tool (General, Medication, Patient, and Visit) on a scale ranging from 0 (not useful) to 100 (very useful). The survey provided an opportunity for participants to express their opinions on the overall usefulness of the DICE tool and to provide other comments. Results The 50 survey respondents were mainly pharmacists (n = 47, 94%) with almost half (n = 23, 47%) having health information technology/informatics training. Most respondents (n = 33, 80%) were employed by organizations with over 350 beds. Respondents indicated the most useful features of the DICE tool were the ability to filter DDI warnings based on routes of administrations (mean [SD] rating scale score, 86.5 [21.6]), primary drug properties (85.7 [20.5]), patient attributes (85.6 [16.7]) and laboratory attributes (88.8 [18.0]). The overall impression of the DICE tool was rated at 82.8 (19.0), and when asked about the potential to reduce DDI alerts, respondents rated the tool at 83.7 (21.8). Conclusion The ability to customize DDI alerts using data elements currently within the electronic health records (EHRs) has the potential to decrease alert fatigue and override rates. This prototype DICE tool could be used by end users and vendors as a template for developing a more advanced DDI filtering tool within EHR systems. [ABSTRACT FROM AUTHOR]

Background: Alcohol use disorders (AUD) are prevalent and responsible for substantial morbidity and mortality; yet efficacious treatments are underused. Previous studies have identified demographic and clinical predictors of medication fills, yet these studies typically do not include patients who were prescribed a medication but did not fill it. Objectives: To examine rates of and factors associated with prescription order and prescription fill for medications for AUD (MAUD) among individuals diagnosed with AUD in outpatient settings. Design: In a cross-sectional analysis, we used multivariate logistic regression to identify factors associated with prescription order and fill. Patients: We used data from the Optum Labs Data Warehouse that linked 2016–2021 de-identified claims and electronic health record (EHR) data, allowing us to observe prescription orders and whether they were filled. We identified 14,674 patients aged ≥ 18 who had an index outpatient encounter with an AUD diagnosis in the EHR. Key Measures: We computed the proportion for whom a MAUD prescription was ordered within 1 year of index visit, and for whom one was filled within 30 days of the order. Key Results: 5.8% of the sample had a MAUD prescription order within 1 year of their index visit. Among those with an order, 87% filled their MAUD prescription within 30 days of receipt (i.e., 5.1% of full sample). After multivariable adjustment, receipt of a MAUD prescription order was more likely for patients who were female (adjusted odds ratio (aOR) [95%CI] = 1.44 [1.24–1.67]), or had moderate or severe AUD (1.74 [1.50–2.01]). Patients receiving an order were more likely to fill it if they had a comorbid mental disorder (1.64 [1.09–2.49]). Conclusions: The low rate of prescription orders was notable. Low use of MAUD appears to result chiefly from prescription order decisions, rather than from prescription fill decisions made by patients. [ABSTRACT FROM AUTHOR]

Background: The enactment of the Health Information Technology for Economic and Clinical Health Act and the wide adoption of electronic health record (EHR) systems have ushered in increasing documentation burden, frequently cited as a key factor affecting the work experience of healthcare professionals and a contributor to burnout. This systematic review aims to identify and characterize measures of documentation burden. Methods: We integrated discussions with Key Informants and a comprehensive search of the literature, including MEDLINE, Embase, Scopus, and gray literature published between 2010 and 2023. Data were narratively and thematically synthesized. Results: We identified 135 articles about measuring documentation burden. We classified measures into 11 categories: overall time spent in EHR, activities related to clinical documentation, inbox management, time spent in clinical review, time spent in orders, work outside work/after hours, administrative tasks (billing and insurance related), fragmentation of workflow, measures of efficiency, EHR activity rate, and usability. The most common source of data for most measures was EHR usage logs. Direct tracking such as through time–motion analysis was fairly uncommon. Measures were developed and applied across various settings and populations, with physicians and nurses in the USA being the most frequently represented healthcare professionals. Evidence of validity of these measures was limited and incomplete. Data on the appropriateness of measures in terms of scalability, feasibility, or equity across various contexts were limited. The physician perspective was the most robustly captured and prominently focused on increased stress and burnout. Discussion: Numerous measures for documentation burden are available and have been tested in a variety of settings and contexts. However, most are one-dimensional, do not capture various domains of this construct, and lack robust validity evidence. This report serves as a call to action highlighting an urgent need for measure development that represents diverse clinical contexts and support future interventions. [ABSTRACT FROM AUTHOR]

Background: While enrolled in Hospital at Home (HaH) programs, patients rely on their social network to provide supportive behaviors that are routinely provided by hospital staff in the inpatient setting. Objective: This study investigated how social connectedness is associated with patient outcomes in a HaH program. Design: The explanatory iterative sequential mixed methods design included an electronic health record review to collect quantitative measures to describe the severity of patient illness and healthcare utilization and then qualitative interviews to explain quantitative findings. Participants: The quantitative phase included 100 patients (18 years or older) admitted to the hospital who were subsequently enrolled in the HaH program. In the qualitative phase, 33 of the 100 patients participated in semi-structured interviews. Analysis: Qualitative data was analyzed using the Sort & Sift, Think & Shift method. Integrated analysis included merged data displays of healthcare utilization data and patient descriptions of their care and genogram-type illustrations to enable variable-oriented analysis of structural support. We then examined patient narratives by two variables: life course and care elevation, to understand differences in the trajectories of six subsets of patients as identified by the quantitative data. Key Results: Three factors prompted patients to enroll in HaH: low attention from hospital staff during hospital stay; loneliness and isolation during hospital stay; and family encouragement to enroll. After discharge, social support within the home structure facilitated recovery during HaH. Conversely, HaH patients with limited support within the home were more likely to be readmitted. Conclusions: Structural social connectedness facilitates patient recovery in HaH. Before enrolling patients in HaH, clinicians should take an in-depth social history, including questions about social/familial roles, household responsibilities, and technology acceptance. Clinicians should engage formal and informal caregivers in these conversations early and communicate a clear picture of what caregivers should do to support the patient through recovery. [ABSTRACT FROM AUTHOR]

Food is Medicine (FIM) programs to improve the accessibility of fruits and vegetables (FVs) or other healthy foods among patients with low income and diet-related chronic diseases are promising to improve food and nutrition security in the United States (US). However, FIM programs are relatively new and implementation guidance for healthcare settings using an implementation science lens is lacking. We used a narrative review to describe the evidence base on barriers and facilitators to FIM program integration in US healthcare settings following the Exploration, Preparation, Implementation, and Sustainment (EPIS) Framework. Evidence surrounding the EPIS Inner Context was a focus, including constructs Leadership, Organizational Characteristics, Quality and Fidelity Monitoring and Support, Organizational Staffing Processes, and Individual Characteristics. Peer-reviewed and grey literature about barriers and facilitators to FIM programs were of interest, defined as programs that screen and refer eligible patients with diet-related chronic disease experiencing food insecurity to healthy, unprepared foods. Thirty-one sources were included in the narrative review, including 22 peer-reviewed articles, four reports, four toolkits, and one thesis. Twenty-eight sources (90%) described EPIS Inner Context facilitators and 26 sources (84%) described FIM program barriers. The most common barriers and facilitators to FIM programs were regarding Quality and Fidelity Monitoring and Support (e.g., use of electronic medical records for tracking and evaluation, strategies to support implementation) and Organizational Staffing Processes (e.g., clear delineation of staff roles and capacity); although, barriers and facilitators to FIM programs were identified among all EPIS Inner Context constructs. We synthesized barriers and facilitators to create an EPIS-informed implementation checklist for healthcare settings for use among healthcare organizations/providers, partner organizations, and technical assistance personnel. We discuss future directions to align FIM efforts with implementation science terminology and theories, models, and frameworks to improve the implementation evidence base and support FIM researchers and practitioners. [ABSTRACT FROM AUTHOR]

While PTSD is commonly associated with multiple comorbidities, studies have yet to quantify the impact of these comorbidities on key clinical outcomes and HCRU. This study explored risks of emergency room (ER) visits, inpatient admissions (IA), suicidal ideation (SI), and treatment follow-up duration (FU), amongst PTSD patients with comorbid MDD and/or SUD. Using real-world data (RWD) generated by electronic health records accessed from the NeuroBlu database, a cohort of adolescent patients (12-17 yrs) was examined over a one-year study period following PTSD diagnosis. 5794 patients were included in the cohort. Compared to patients with only PTSD (n = 3061), those with comorbid MDD (n = 1820) had greater odds of ER (4.5 times), IA (1.6 times), and FU (4.3 times). Those with comorbid SUD (n = 653) had greater odds of IA (4.5 times), shorter FU (34 days), and lower odds of ER (0.5 times). Both comorbidities (n = 260) had greater odds of ER (3.8 times), IA (2.6 times), SI (3.6 times), and shorter FU (12 days). These RWD had a high proportion of missingness. Health records of patients who changed service providers could not be accounted for in this study. Both MDD and SUD substantially elevated the risk of HCRU and suicidal ideation for PTSD patients. • Limited literature studying the cumulative effects of major depressive disorder (MDD) and substance use disorder (SUD) on adolescents diagnosed with PTSD. • Comorbid MDD and SUD was respectively present in 31 % and 11 % of the 5794 adolescents diagnosed with PTSD. • Minimally, the odds of inpatient admission is at least 40 % higher in patients with comorbid MDD or SUD present, with the odds of patients with comorbid SUD being admitted to the hospital being 4.5 times higher than those with PTSD only. • Patients with both MDD and SUD were not observed to develop worse outcomes. • Screening for comorbid MDD and SUD may substantially improve prognosis of PTSD in adolescents. [ABSTRACT FROM AUTHOR]

AbstractElectronic health records offer abundant data on various diseases and health conditions, enabling researchers to explore the relationship between disease onset age and underlying risk factors. Unlike mortality data, the event of interest is non-terminal, hence individuals can retrospectively report their disease-onset-age upon recruitment to the study. These individuals, diagnosed with the disease before entering the study, are termed “prevalent”. The ascertainment imposes a left truncation condition, also known as a “delayed entry”, because individuals had to survive a certain period before being eligible for enrollment. The standard method to accommodate delayed entry conditions on the entire history up to recruitment, hence the retrospective prevalent failure times are conditioned upon and cannot participate in estimating the disease-onset-age distribution. Other methods that condition on less information and allow the incorporation of the prevalent observations either bring about numerical and computational difficulties or require statistical assumptions that are violated by most biobanks. This work presents a novel estimator of the coefficients in a regression model for the age-at-onset, successfully utilizing the prevalent data. Asymptotic results are provided, and simulations are conducted to showcase the substantial efficiency gain. In particular, the method is highly useful in leveraging large-scale repositories for replication analysis of genetic variants. Indeed, analysis of urinary bladder cancer data reveals that the proposed approach yields about twice as many replicated discoveries compared to the popular approach. [ABSTRACT FROM AUTHOR]

Background: About 80% of pregnant women use at least one medication during their pregnancy period. Many drugs that are not allowed to be used during pregnancy (from FDA Pregnancy Categories D and X) were used. Irrational use of these drugs during pregnancy may result in different birth defects, as explained by thalidomide and diethylstilbestrol's tragedy. Knowledge of drug utilization and associated factors that affect the pattern is important to enhance rational prescribing. But information about prescription patterns and associated factors among pregnant women is scarce in the Debre Tabor area and generally in Ethiopia. Objective: This study was aimed at assessing drug prescription patterns, potential teratogenicity, and associated factors among pregnant women attending the antenatal care unit at Debre Tabor Comprehensive Specialized Hospital, Debre Tabor, Northwest Ethiopia. Methods: A retrospective cross‐sectional study design was performed on 359 pregnant women attending antenatal care units from June 01, 2022, to August 30, 2022, in the hospital. Necessary data were obtained through a questionnaire by reviewing the medical charts of the women. Analysis of the data was performed using SPSS Version 23. The association of the independent variables to medication use was assessed using multivariate logistic regression. A p value of less than 0.05 was considered significant. Results: Most of the study participants (325/359) were married (90.5%). From a total of 359 participants, 350 (97.5%) were prescribed with drugs. About 64% (385/602) of the prescribed medications were iron and vitamins. The most commonly prescribed medications are iron and folic acid combination (340/602, 56.5%), albendazole (48/602, 8%), mebendazole (37/602, 6.1%), omeprazole (33/602, 5.5%), followed by amoxicillin (32/602, 5.3%). The majority (79.3%) of the drugs were from FDA Pregnancy Categories A and B. Prescribed drug utilization was more probable in women who first visited the facility at their second (AOR = 2.91, 95% CI [1.12–6.64]) and third trimesters (AOR = 4.32, 95% CI [1.37–6.81]), had chronic illness (AOR = 7.54, 95% CI [2.34–14.68]), and live in rural areas (AOR = 2.47, 95% CI [1.56–8.43]). Conclusion: The study revealed that the prescription pattern in the hospital is in line with the WHO reference. Age, gravidity, number of ANC visits, first visit to the facility, presence of chronic illness, educational status, and residency were significantly associated with prescription drug use in pregnant mothers. But still, some pregnant women received drugs that may have teratogenicity risk (FDA Category C). [ABSTRACT FROM AUTHOR]

Background: Patients with traumatic pelvic fracture (TPF) are at high risk for developing deep vein thrombosis (DVT). However, there is still no unified standard on how to distinguish high-risk groups for DVT in patients with TPF and how to accurately use anticoagulants at present. Objectives: This observational study aimed to establish a DVT risk nomogram score (DRNS) model for TPF patients, and to explore the value of the DRNS model as a clinical guideline in the prevention of DVT with low molecular weight heparin (LMWH). Methods: Independent risk factors of lower extremity DVT were screened through Lasso regression and logistic regression. A DRNS model was established per this. Results: The independent risk factors of DVT included combined femoral fractures, age ≥ 40 years old, BMI (body mass index) ≥ 24 kg/m2, ISS score, fibrinogen concentration, and the minimum concentration of ionized calcium within 48 h after admission. The optimal cutoff value for DRNS was 78.5. In the low-risk population of DVT (DRNS < 78.5), there was no statistical significance of variation about the incidence of DVT progression between the LMWH once a day (qd) group and the LMWH once every 12 h (q12h) group, with P = 0.323. In the high-risk population of DVT (DRNS ≥ 78.5), the incidence of DVT progression in the LMWH qd group was significantly higher than that in the LMWH q12h group, with P = 0.002. Conclusions: The DRNS model based on independent risk factors of DVT could stratify the risk of DVT for TPF patients, and it was able to provide more precise DVT drug prevention plans for clinicians. [ABSTRACT FROM AUTHOR]

Objective Methods Results Conclusion To conduct a systematic literature review of real‐world data (RWD) studies to summarise treatment patterns among men with metastatic hormone‐sensitive prostate cancer (mHSPC). While androgen‐deprivation therapy (ADT) is a primary treatment strategy for mHSPC, ADT intensification with androgen receptor pathway inhibitors (ARPIs) and/or chemotherapy is recommended by current guidelines and has improved clinical outcomes in the last decade.We searched electronic databases (PubMed; Excerpta Medica dataBASE [EMBASE]) for eligible studies (retrospective or prospective observational RWD studies examining mHSPC treatment patterns) between database inception and July 2023, and manually screened the past 2 years of relevant conference proceedings.Of 2336 retrieved citations, 29 studies met the inclusion criteria, covering North America (United States, n = 21; Canada, n = 2), Europe (n = 8), and Asia (n = 6). Most studies utilised retrospective cohorts (n = 26) and included men with a median age of ≥70 years (n = 20). ADT monotherapy was predominantly used across geographies, followed by ADT + ARPI and ADT + docetaxel in the United States and Europe but not in Asia, where use of each combination remained low. Studies with recent electronic medical record data from cancer centres/registries showed >40% use of ADT + ARPI in the United States and Europe. Abiraterone was the most frequently used ARPI, followed by enzalutamide. Quantitative factors associated with ADT intensification were high disease burden, younger age, Eastern Cooperative Oncology Group performance status score of 0 to 1, fewer comorbidities, and oncologist physician specialty; qualitative factors were patient preference, unsatisfactory response to ADT, ability to tolerate adverse events, and absence of cost barriers.While there was an increasing trend in ADT intensification for mHSPC over the study period across geographies, use remained suboptimal considering the high proportion of patients who were still receiving ADT monotherapy only. These findings highlight the need for interventions to further optimise current mHSPC therapies with high guideline concordance. [ABSTRACT FROM AUTHOR]

Background Aims Methods Results Conclusion Abdominal paracentesis is a frequently performed procedure in hospitalised patients with ascites. Concurrently, most hospitalised adult patients receive pharmacologic anticoagulation, either for therapeutic purposes or prophylactically to prevent venous thromboembolism. Despite this, minimal evidence exists to guide management of anticoagulant therapy pre‐ and post‐paracentesis.The authors aimed to investigate the safety of abdominal paracentesis in hospitalised patients receiving therapeutic or prophylactic anticoagulation, including in patients for whom these medications were withheld periprocedurally.TriNetX, an electronic health record data set, was queried to identify patients between the ages of 18 and 80 years who received an abdominal paracentesis while hospitalised at the authors' institution between September 2017 and June 2022. Patients receiving prophylactic anticoagulation (137), therapeutic anticoagulation (74) and no anticoagulation because of coagulopathy or thrombocytopenia (15) were compared. Rates of withholding anticoagulation, performing service, pre‐ and post‐paracentesis haemoglobin, bleeding complications, thrombotic complications and need for red blood cell transfusion were analysed.Procedure‐related bleeding complications occurred in two (1.4%) patients in the prophylactic group and 0 (0%) patients in the therapeutic group (P = 0.54). No thrombotic complications occurred. Rates of red blood cell transfusions post‐paracentesis were similar between groups. Analysis of secondary end‐points identified significant differences in rates of withholding anticoagulation and mean change in haemoglobin between performing services.Performance of abdominal paracentesis in patients receiving therapeutic or prophylactic anticoagulation appears to be safe regardless of whether anticoagulation was interrupted periprocedurally, with low rates of bleeding complications, thrombotic complications or need for red blood cell transfusions post‐paracentesis. [ABSTRACT FROM AUTHOR]

There are routine hospital workflows that are not addressed by certain institutional electronic medical records, including the detection of patients requiring haemodialysis who are admitted under non‐nephrology services. In this study, the feasibility and performance of a clinician‐developed automated haemodialysis patient finder was evaluated. The programme ran with zero downtime for 6 months and had zero false negatives or false positives. This work demonstrates the potential benefits that may be gained when clinicians can meaningfully alter electronic clinical workflows. [ABSTRACT FROM AUTHOR]

ABSTRACT Background and Aims Methods Results Conclusions Alcohol use is prevalent among hepatology clinic patients with chronic liver disease (CLD). We explored factors associated with the importance and confidence dimensions of motivation to reduce drinking.Participants (N = 121) with unhealthy alcohol use (i.e., over NIH guidelines) receiving care in hepatology clinics from a safety‐net hospital (SN, N = 54) and two Veterans Affairs Healthcare Systems (VA, N = 67) were enrolled in an alcohol intervention trial from March 2022 through October 2023. Baseline assessments included Generalised Anxiety Disorder (GAD‐7), Patient Health Questionnaire (PHQ‐8), Alcohol Use Disorders Identification Test (AUDIT), COVID‐19 stress; and measures of importance and confidence to decrease alcohol use (readiness rulers, scales of 1–10). Liver disease aetiology and severity were extracted from electronic health records. We performed multivariable linear regression models with forward selection to assess pre‐specified variables' associations with importance and confidence.The sample was 84% male, 40% Latino, 31% White, 18% Black and 11% other races; median age was 61 years. Median (Q1–Q3) AUDIT score was 16 (12–24), importance was 9 (6–10) and confidence was 8 (5–9). On multivariable analysis, VA site (vs. SN) participants had a 0.97‐point lower importance score (p = 0.02); higher symptoms of depression (PHQ‐8 score ≥ 10 vs. < 10) and AUDIT scores (for each point increase) were associated with higher importance score (estimates 1.2 and 0.08, p < 0.05, respectively). Liver disease aetiology and severity were not significantly associated with outcomes.Depression, alcohol problem severity and treatment site may influence motivation to reduce alcohol use and could inform future hepatology‐based interventions. [ABSTRACT FROM AUTHOR]

Background: Organizational turnover exacerbates the shortage of nurses in the global workforce. However, no study has yet explored how deep structure usage—nurses' integration of electronic patient records into nursing practice delivery—reduces their turnover intention and moderates the impact of affective, continuance, and normative professional commitment on their turnover intention. Aims: To ascertain (1) the linkage between the deep structure usage of electronic patient records and nurses' organizational turnover intention and (2) the moderating role of deep structure usage on the associations between elements of commitment (affective, continuance, and normative) and turnover intention. Methods: Using a cross‐sectional survey and proportionate random sampling by ward unit, we collected data from 417 full‐time nurses via a self‐administered questionnaire. We performed hierarchical regression analyses to test the study hypotheses. Results: Deep structure usage was not directly related to organizational turnover intention (β = −0.07, p = 0.06). However, the results suggested that deep structure usage may enhance the effect of high affective commitment on nurses' organizational turnover intention (β = −0.09, p = 0.04), while potentially mitigating the effect of low continuance commitment on organizational turnover intention (β = 0.10, p = 0.01). Conclusions: Deep structure usage of electronic patient records helps to ease nurses' workload and facilitates their retention, which is particularly due to their affective commitment (attachment) but not their continuance commitment (switching costs). Implications for Nursing Management: Nursing management may advise hospital management that medical records systems need to be improved and fully embedded for nursing care delivery, as a more in‐depth use of these systems can help to retain nurses. [ABSTRACT FROM AUTHOR]

This study aimed to compare the serum lipid profiles between tenofovir disoproxil fumarate (TDF) and tenofovir alafenamide (TAF) in the long-term treatment of chronic hepatitis B (CHB). We analyzed data from treatment-naïve CHB patients administered with TDF or TAF, collected from electronic medical records between May 2017 and September 2022. Serum lipid indices, including total cholesterol (TC), triglycerides (TG), low-density (LDL) and high-density lipoprotein (HDL), and their ratios (TC/HDL, LDL/HDL), were assessed at baseline, and at 48 and 96 weeks. Propensity score matching (PSM) adjusted for baseline differences between groups. From 2344 patients initially screened, 418 were included for the 48-week analysis (265 on TDF, 153 on TAF) and 292 for the 96-week analysis (238 on TDF, 54 on TAF). At 48 weeks, comparing the serum lipid indicators between the pre- and post-treatment, TDF significantly reduced TC and TC/HDL, whereas TAF induced widespread dyslipidemia, characterized by elevated levels of TC, TG, LDL, LDL/HDL, and TC/HDL, and reduced HDL (P < 0.05). After PSM grouping, TAF remained significantly associated with higher TC, TG, LDL, LDL/HDL, and TC/HDL compared to TDF (P < 0.05). Over 48 weeks, TAF treatment was associated with significant increases in TC, TG, and LDL, whereas TDF treatment led to decreases (P < 0.05). TC/HDL and LDL/HDL increased in both groups, but more significant in TAF (P < 0.05). At 96 weeks, the TAF group continued to exhibit significantly higher levels of TC, LDL, and LDL/HDL compared to the TDF group (P < 0.05). Notably, LDL levels were 115.65 ± 28.07 mg/dL in TAF versus 96.07 ± 23.97 mg/dL in TDF. The increase in TC/HDL ratio in the TAF group was higher than in the TDF group, though not statistically significant. Furthermore, TAF treatment was associated with significant increases in LDL (18.58 ± 24.35 mg/dL) and LDL/HDL ratio (0.41 ± 0.95) over 96 weeks, while TDF treatment showed reductions in TC (-8.13 ± 30.86 mg/dL). Between 48 and 96 weeks, most lipid changes in the TDF group were not statistically significant, except for increases in LDL and LDL/HDL. In the TAF group, an increasing trend of LDL and TC/HDL was noted, although LDL showed a slight turnover after 48 weeks. This real-world study provides new evidence that TAF can induce dyslipidemia, while TDF exhibits a lipid-lowering effect in CHB. Patients at high risk for hepatic steatosis and cardiovascular diseases should consider these effects when choosing between TAF and TDF. [ABSTRACT FROM AUTHOR]

Background: Tanzania is amongst the countries with high neonatal mortality in Sub-Saharan Africa (SSA), and estimates vary widely among regions. Various interventions are being implemented at Muhimbili National Hospital (MNH), a tertiary and teaching facility, to contribute towards the reduction of neonatal mortality. This study aimed to detail the magnitude, trends and factors associated with neonatal mortality at MNH. Methods and findings: A hospital-based retrospective cohort study was conducted from January 2018 to December 2020. Records of all neonates admitted during the study period were extracted from neonatal registers and the electronic medical record system and recorded in a pretested data collection form. Data cleaning and analysis were done using SPSS version 23. Poisson regression was used to determine adjusted relative risk (aRR) with a 95% confidence interval (CI) to test association. A p-value of <0.05 was considered significant. Of the 17,021 neonates admitted, 11,552 (67.9%) were inborn. During the three years reviewed, 1,814 (10.7%) neonates died. The mortality rates were 5.9% for inborn neonates, 24.1% for outborn neonates, and 4.8% for readmitted neonates. Mortality trends declined quarterly at an average of 0.15%. Factors associated with neonatal mortality included; birth weight < 2500g [{aRR = 1.14; 95% CI (1.01–1.29)}], GA < 37 weeks[{aRR = 1.17; 95% CI (1.03–1.33)}], fifth-minute Apgar score <7[{aRR = 2.53; 95% CI (2.32–2.77)}], vaginal delivery[{aRR = 1.39; 95% CI (1.24–1.55)}], out-born[{aRR = 2.85; 95% CI (2.58–3.14)}], positive maternal HIV status[{aRR = 1.18; 95% CI (1.02–1.36)}] and maternal age ≥35years [{aRR = 1.12; 95% CI (1.01–1.25)}]. Conclusion: Despite a declining trend, in-hospital neonatal mortality at MNH is still high. Ongoing quality improvement strategies should prioritize the identification of neonates with risk factors for poor outcomes. Specifically, the high proportion of mortality among out-born neonates requires more attention and further investigation. [ABSTRACT FROM AUTHOR]

ABSTRACT Introduction Methods Results Conclusions The subcutaneous implantable cardioverter defibrillator (S‐ICD) is an alternative to a transvenous ICD in patients who meet the criteria for ICD implantation without concurrent need for cardiac pacing. The objective of this study is to examine the rates of and indications for S‐ICD removal and extraction.A retrospective analysis of all patients who underwent S‐ICD implantation between 2010 and 2022 at a single multihospital healthcare system was performed. The primary endpoint was S‐ICD removal or extraction. Patient and device characteristics were abstracted from the electronic medical record. Univariate and multivariate analyses were completed to determine factors associated with S‐ICD extraction.A total of 372 patients (69.5% male; 48.6 ± 14.4 years old) underwent S‐ICD implantation during the study period. There were 22 (5.9%) patients (81.8% male; 52.1 ± 13.2 years old) who underwent S‐ICD extraction over a median follow‐up period of 4.4 [2.0−6.5] years. The median length of time between implantation and extraction was 39.6 [8.3−64.6] months. The most common indications for S‐ICD extraction were the need for bradycardia pacing (frequency among extractions, 18.2%), infection (22.7%), and inappropriate shocks due to oversensing (22.7%). A smoking history and higher body mass index were independently associated with S‐ICD extraction.The overall rate of S‐ICD extraction over 4.4 [2.0−6.5] years was 5.9%, with the most common indications for extraction being the need for bradycardia pacing, infection, and inappropriate shocks due to oversensing. A smoking history and high body mass index are associated with increased rates of S‐ICD extraction. With appropriate patient selection for the S‐ICD, the need to remove the device after implantation is low. [ABSTRACT FROM AUTHOR]

Acute pancreatitis (AP) can result in acute kidney injury (AKI), which is linked to poor outcomes. We aimed to assess the relationship between the hematocrit-to-albumin ratio (HAR) and AKI in this population. This retrospective cohort study included consecutive patients diagnosed with AP and admitted to hospital. Data were systematically extracted from electronic medical records, covering baseline demographic and clinical characteristics. Total 1514 AP patients were enrolled, with 17% (257/1514) developing AKI. Multivariable-adjusted regression analysis, curve fitting, threshold effects analyses, and subgroup analyses were conducted to evaluate the relationship between HAR and AKI incidence in AP patients. Compared to the reference tertile of HAR, the adjusted OR values for the lower and higher tertiles of HAR were 1.25 (95% CI, 0.82–1.91, P = 0.297) and 1.50 (95% CI, 1.03–2.20, P = 0.037), respectively, after adjusting for covariates. The curve fitting results showed a J-shaped relationship between HAR and AKI (non-linear, p = 0.001), with an inflection point of 8.969. Furthermore, validation using the Medical Information Mart for Intensive Care (MIMIC-IV) database AP population revealed a similar relationship with an inflection point at 10.257. Our findings suggest a J-shaped relationship between HAR and AKI in AP patients, indicating higher risk of AKI when HAR exceeds 8.969. [ABSTRACT FROM AUTHOR]

Background: Osteoporosis is a common condition that increases the risk of fracture and mortality. In Taiwan, clinical guidelines recommend pharmaceutical therapy for patients with a T-score of ≤ − 2.5; however, Taiwan's National Health Insurance (NHI) only covers these medications for those with a history of fragility fractures. This gap in coverage necessitates a discussion of out-of-pocket treatment options. To address this, we provided an integrated care program with patient education and shared decision-making by nurse case managers specializing in osteoporosis. We evaluated whether education by nurse case managers influences patients with a T-score ≤ − 2.5, who are not covered by the NHI, to choose out-of-pocket pharmaceutical therapy. Methods: We retrospectively reviewed medical records of patients who underwent bone density scanning at our hospital between January 2014 and December 2021. We identified 4,462 patients with a T-score of ≤ − 2.5 who were ineligible for NHI-covered anti-osteoporotic therapy and analyzed trends in out-of-pocket medication use. Since the integrated care program began in 2018, we evaluated whether education by nurse case managers between 2018 and 2021 influenced patients' decisions to pay out-of-pocket for therapy. After the implementation of the integrated care program, we identified 2,910 patients with a T-score ≤ -2.5 who were ineligible for NHI-covered anti-osteoporotic therapy. Of these, 640 opted for out-of-pocket treatment, while 2,270 chose conservative care. After a 1:1 propensity score match based on age and sex, logistic regression was used to analyze the impact of nurse case manager education on these decisions. Results: Between 2014 and 2021, 888 of the 4,462 patients chose out-of-pocket pharmaceutical therapy. Before the implementation of the integrated care program and patient education by nurse case managers (2014–2017), 16% of the patients opted to pay out-of-pocket for anti-osteoporotic therapy. After the program was implemented (2018–2021), the rate increased significantly to 22% (P < 0.001). A multivariate logistic regression model showed that a history of osteoarthritis (adjusted odds ratio = 1.576; P = 0.009) and education provided by the nurse case managers (adjusted odds ratio = 5.044; P < 0.001) were significantly associated with choosing out-of-pocket therapy. Conclusions: Education from nurse case managers significantly increased the likelihood of patients choosing out-of-pocket anti-osteoporotic therapy in our hospital, thereby bridging the gap between clinical guidelines and NHI reimbursement criteria. [ABSTRACT FROM AUTHOR]

The All of Us Research Program (AoU) is an initiative designed to gather a comprehensive and diverse dataset from at least one million individuals across the USA. This longitudinal cohort study aims to advance research by providing a rich resource of genetic and phenotypic information, enabling powerful studies on the epidemiology and genetics of human diseases. One critical challenge to maximizing its use is the development of accurate algorithms that can efficiently and accurately identify well-defined disease and disease-free participants for case-control studies. This study aimed to develop and validate type 1 (T1D) and type 2 diabetes (T2D) algorithms in the AoU cohort, using electronic health record (EHR) and survey data. Building on existing algorithms and using diagnosis codes, medications, laboratory results, and survey data, we developed and implemented algorithms for identifying prevalent cases of type 1 and type 2 diabetes. The first set of algorithms used only EHR data (EHR-only), and the second set used a combination of EHR and survey data (EHR+). A universal algorithm was also developed to identify individuals without diabetes. The performance of each algorithm was evaluated by testing its association with polygenic scores (PSs) for type 1 and type 2 diabetes. We demonstrated the feasibility and utility of using AoU EHR and survey data to employ diabetes algorithms. For T1D, the EHR-only algorithm showed a stronger association with T1D-PS compared to the EHR + algorithm (DeLong p-value = 3 × 10−5). For T2D, the EHR + algorithm outperformed both the EHR-only and the existing T2D definition provided in the AoU Phenotyping Library (DeLong p-values = 0.03 and 1 × 10−4, respectively), identifying 25.79% and 22.57% more cases, respectively, and providing an improved association with T2D PS. We provide a new validated type 1 diabetes definition and an improved type 2 diabetes definition in AoU, which are freely available for diabetes research in the AoU. These algorithms ensure consistency of diabetes definitions in the cohort, facilitating high-quality diabetes research. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusions Clinical Relevance The healthcare industry increasingly values high‐quality and personalized care. Patients with heart failure (HF) receiving home health care (HHC) often experience hospitalizations due to worsening symptoms and comorbidities. Therefore, close symptom monitoring and timely intervention based on risk prediction could help HHC clinicians prevent emergency department (ED) visits and hospitalizations. This study aims to (1) describe important variables associated with a higher risk of ED visits and hospitalizations in HF patients receiving HHC; (2) map data requirements of a clinical decision support (CDS) tool to the exchangeable data standard for integrating a CDS tool into the care of patients with HF; (3) outline a pipeline for developing a real‐time artificial intelligence (AI)‐based CDS tool.We used patient data from a large HHC organization in the Northeastern US to determine the factors that can predict ED visits and hospitalizations among patients with HF in HHC (9362 patients in 12,223 care episodes). We examined vital signs, HHC visit details (e.g., the purpose of the visit), and clinical note–derived variables. The study identified critical factors that can predict ED visits and hospitalizations and used these findings to suggest a practical CDS tool for nurses. The tool's proposed design includes a system that can analyze data quickly to offer timely advice to healthcare clinicians.Our research showed that the length of time since a patient was admitted to HHC and how recently they have shown symptoms of HF were significant factors predicting an adverse event. Additionally, we found this information from the last few HHC visits before the occurrence of an ED visit or hospitalization were particularly important in the prediction. One hundred percent of clinical demographic profiles from the Outcome and Assessment Information Set variables were mapped to the exchangeable data standard, while natural language processing–driven variables couldn't be mapped due to their nature, as they are generated from unstructured data. The suggested CDS tool alerts nurses about newly emerging or rising risks, helping them make informed decisions.This study discusses the creation of a time‐series risk prediction model and its potential CDS applications within HHC, aiming to enhance patient outcomes, streamline resource utilization, and improve the quality of care for individuals with HF.This study provides a detailed plan for a CDS tool that uses the latest AI technology designed to aid nurses in their day‐to‐day HHC service. Our proposed CDS tool includes an alert system that serves as a guard rail to prevent ED visits and hospitalizations. This tool can potentially improve how nurses make decisions and improve patient outcomes by providing early warnings about ED visits and hospitalizations. [ABSTRACT FROM AUTHOR]

ABSTRACT Purpose Methods Results Conclusions Infection after fracture fixation (IAFF) is a severe complication. There are few multicenter studies targeting IAFF. This paper identifies independent risk factors associated with IAFF by analyzing multicenter clinical data. Appropriate interventions should be implemented to reduce the risk of IAFF.This is a multicenter retrospective cohort study. This study screened medical records of patients who underwent internal fixation for fractures at participating medical institutions from January 1, 2011, to December 31, 2020. Data extraction included demographic characteristics, disease features, surgical variables, and laboratory indicators. Logistic regression analysis was employed to identify the relationship between relevant risk factors and IAFF. Research data were sourced from the hospital's electronic medical record system and self‐constructed databases.In our study, 202 patients who underwent internal fixation for fractures experienced postoperative infections, which corresponds to an overall incidence rate of approximately 1.7%. The predominant pathogen identified in these infections was Staphylococcus aureus. A multifactorial analysis indicated that several factors were independently associated with the occurrence of IAFF. These factors included BMI ranges of 24.0–27.9 and 28.0–31.9, smoking, a high ASA score, high‐energy trauma, diabetes, open fracture, seasonal timing of the surgery (summer), bone grafting, drainage duration, surgical duration ≥ 180 min, and A/G ratio < 1.2.We strongly recommend that orthopedic surgeons perform comprehensive preoperative assessments on fracture patients to identify factors that may increase the risk of infection. Through the implementation of targeted interventions and beneficial modifications to these modifiable risk factors, it is possible to lower the incidence of IAFF. Additionally, proactive screening, risk stratification, and thorough patient education should be prioritized for patients with high risk but nonmodifiable factors. [ABSTRACT FROM AUTHOR]

BackgroundObjectiveMethodsResultsConclusionsThe Academy of Neurologic Physical Therapy’s Intensity Matters campaign recommends the implementation of high-intensity locomotor training for all patients with neurologic dysfunction with goals to improve walking.Retrospectively determine the effectiveness of a clinician-initiated implementation project on the adoption, reach, and fidelity of high-intensity locomotor training for patients with stroke during inpatient rehabilitation and, determine whether the project led to changes in patient outcomes.Retrospective analysis of electronic medical records from 1 year before and after the project. Patients admitted with a stroke diagnosis were included. Demographic information, the number of high-intensity sessions, the percentage of time spent in the target heart rate zone, standardized assessment scores for motor function, functional mobility, balance, gait speed and endurance, and discharge destination were extracted for descriptive and linear mixed model analysis.Clinician reach was 75%, and adoption of high-intensity training varied between clinicians from 47.1% and 83.3%. Of eligible patients, 55% received the target intervention at least once, reflecting the patient reach. Implementation fidelity was 18.84%. Linear mixed effects modeling revealed a statistically significant effect of time (p < .001) but not group allocation or group × time interaction.Although statistically significant differences in patient outcomes compared to pre-implementation were not found, results highlight the limitations associated with adopting high-intensity locomotor training for patients post-stroke in this setting .It remains unclear whether the implementation fidelity achieved was sufficient to impact patient outcomes. Further research is needed to establish fidelity targets and identify barriers to successful implementation projects by clinicians. [ABSTRACT FROM AUTHOR]

Manual decisions regarding the timing of surveillance endoscopy for premalignant Barrett's oesophagus (BO) is error-prone. This leads to inefficient resource usage and safety risks. To automate decision-making, we fine-tuned Bidirectional Encoder Representations from Transformers (BERT) models to categorize BO length (EndoBERT) and worst histopathological grade (PathBERT) on 4,831 endoscopy and 4,581 pathology reports from Guy's and St Thomas' Hospital (GSTT). The accuracies for EndoBERT test sets from GSTT, King's College Hospital (KCH), and Sandwell and West Birmingham Hospitals (SWB) were 0.95, 0.86, and 0.99, respectively. Average accuracies for PathBERT were 0.93, 0.91, and 0.92, respectively. A retrospective analysis of 1640 GSTT reports revealed a 27% discrepancy between endoscopists' decisions and model recommendations. This study underscores the development and deployment of NLP-based software in BO surveillance, demonstrating high performance at multiple sites. The analysis emphasizes the potential efficiency of automation in enhancing precision and guideline adherence in clinical decision-making. [ABSTRACT FROM AUTHOR]

Blood-based tests for multi-cancer early detection (MCED) are being developed to facilitate the detection of various cancer types. The Detecting cancers Earlier Through Elective mutation-based blood Collection and Testing study (DETECT-A) study evaluated an MCED test in 9,911 women, age 65-75, without personal history of cancer. In a post-hoc analysis, we report on the detection of precancerous neoplasms consequent to MCED testing and follow-up. Participants with positive baseline and confirmatory MCED testing underwent 2-deoxy-2[fluorine-18] fluoro-D-glucose positron emission tomography-computed tomography (PET-CT) and diagnostic evaluation as indicated by PET-CT results. We reviewed the electronic health records of participants with a precancerous neoplasm and summarized their clinical course. MCED results were positive in 134 participants. Clinically significant pre-malignant conditions were identified in three of these participants: A 71-year-old with an ovarian mucinous cystadenoma, a 67-year-old with an appendiceal mucinous neoplasm, and a 70-year-old with colon adenomas displaying high-grade dysplasia. All three participants underwent surgical treatment and remain alive and cancer-free as of last follow up. The diagnostic evaluation of a positive MCED test may occasionally reveal clinically significant pre-cancerous conditions amenable to interventions. The frequency of such findings and their clinical impact warrants further study. [ABSTRACT FROM AUTHOR]

Objective: Artificial intelligence (AI) is revolutionizing healthcare, but less is known about how it may facilitate methodological innovations in research settings. In this manuscript, we describe a novel use of AI in summarizing and reporting qualitative data generated from an expert panel discussion about the role of electronic health records (EHRs) in implementation science. Materials and methods: 15 implementation scientists participated in an hour-long expert panel discussion addressing how EHRs can support implementation strategies, measure implementation outcomes, and influence implementation science. Notes from the discussion were synthesized by ChatGPT (a large language model—LLM) to generate a manuscript summarizing the discussion, which was later revised by participants. We also surveyed participants on their experience with the process. Results: Panelists identified implementation strategies and outcome measures that can be readily supported by EHRs and noted that implementation science will need to evolve to assess future EHR advancements. The ChatGPT-generated summary of the panel discussion was generally regarded as an efficient means to offer a high-level overview of the discussion, although participants felt it lacked nuance and context. Extensive editing was required to contextualize the LLM-generated text and situate it in relevant literature. Discussion and conclusions: Our qualitative findings highlight the central role EHRs can play in supporting implementation science, which may require additional informatics and implementation expertise and a different way to think about the combined fields. Our experience using ChatGPT as a research methods innovation was mixed and underscores the need for close supervision and attentive human involvement. [ABSTRACT FROM AUTHOR]

The accurate extraction of surgical data from electronic health records (EHRs), particularly operative notes through manual chart review (MCR), is complex, crucial, and time-intensive, limited by human error due to fatigue and the level of training. This study aimed to develop and validate a novel Natural Language Processing (NLP) algorithm integrated with a Large Language Model (LLM; GPT4-Turbo) to automate the extraction of spinal surgery data from EHRs. The algorithm employed a two-stage approach. Initially, a rule-based NLP framework reviewed and classified candidate segments from the text, preserving their reference segments. These segments were then verified in the second stage through the LLM. The primary outcomes of this study were the accurate extraction of surgical data, including the type of surgery, levels operated, number of disks removed, and presence of intraoperative incidental durotomies. Secondary objectives explored time efficiency, tokenization lengths, and costs. The performance of the algorithm was assessed across two validation databases, analyzing metrics such as accuracy, sensitivity, discrimination, F1-score, and precision, with 95% confidence intervals calculated using percentile-based bootstrapping. The NLP + LLM algorithm markedly outperformed all performance metrics, demonstrating significant improvements in time and cost efficiency. These results suggest the potential for widespread adoption of this technology. [ABSTRACT FROM AUTHOR]

D-dimer is a biomarker of coagulation and fibrinolytic system activation in response to the hypercoagulable state of the body. The research aimed to analyze the value of D-dimer in the prognosis of patients with dilated cardiomyopathy (DCM). Patients admitted to our center for the first time with DCM were enrolled consecutively. The clinical characteristics variables were obtained from the electronic medical record system, and the prognostic information was obtained using telephone return visits and a review of repeated hospitalization records. Univariate and multivariate Cox regression was used to explore the association of D-dimer with all-cause mortality. Smooth curve fitting, threshold saturation effect analysis, and subgroup analysis were performed. Ultimately, 534 patients were included. After a follow-up of the enrolled patients, 485 patients obtained prognostic information, of which 159 died from all causes, and the main cause of death was heart failure (89/159), the sudden death accounted for about 17%. The independent positive association between D-dimer and all-cause mortality remained unchanged in both unadjusted and adjusted Cox regression models. In the fully adjusted model, each standard deviation increase in D-dimer was associated with a 14% increase in all-cause mortality (HR = 1.14; 95% CI: 1.02 ~ 1.27; P < 0.05). Curve fitting and threshold effect analysis showed an inflection point in the relationship between D-dimer and all-cause mortality (non-linear test: P = 0.03). When D-dimer was equal to 362ng/ml, HR = 1; and as the value increased, the risk of all-cause mortality increased by 34.7% for every 2-fold increase in D-dimer gradually (HR = 1.347; 95% CI: 1.069 ~ 1.697; P = 0.012). In subgroup analysis, D-dimer and BMI had a significant interaction on all-cause mortality, with a significantly increased risk of all-cause mortality in subjects with BMI ≥ 25 kg/m2 (HR = 1.99; 95% CI: 1.34 ~ 2.97; P < 0.01). The ROC curve showed that D-dimer was a good predictor of all-cause mortality, and the areas under the curve at 1-, 3-, and 5-year were 0.71, 0.64, and 0.59, respectively. In addition, D-dimer improved the predictive performance of the MAGGIC heart failure score in patients with DCM. D-dimer is not only independently associated with all-cause mortality in DCM patients, but also has good predictive value, suggesting that D-dimer may be an early and useful marker for improving the management of DCM patients. [ABSTRACT FROM AUTHOR]

Background: Non-suicidal self-injury (NSSI) behaviors among adolescents with mood disorders represent a significant global public health concern. This study aimed to assess the prevalence and identify key predictors of NSSI behaviors in Chinese adolescents diagnosed with depressive or bipolar disorders, addressing a critical gap in the literature. Method: Data from 2343 participants in the Chinese Adolescent Depression Cohort (CADC) were analyzed. The study employed a comprehensive approach, utilizing medical records, clinical assessments, and validated psychometric instruments. Statistical analyses included chi-square tests, logistic regression, and ROC curve analyses, culminating in the development of a predictive nomogram. Results: The prevalence of NSSI behaviors was strikingly high at 73.32%. Significant predictors included female gender (AOR = 2.14, 95% CI [1.70, 2.68]), presence of hallucinations (AOR = 1.52, 95% CI [1.18, 1.97]), borderline personality features (AOR = 1.03, 95% CI [1.01, 1.08]), and childhood trauma (AOR = 1.02, 95% CI [1.01, 1.03]). The study's key contribution is a predictive nomogram with an AUC of 0.74, demonstrating good efficacy in predicting NSSI behaviors. Conclusion: This research reveals an alarmingly high prevalence of NSSI behaviors in Chinese adolescents with mood disorders and identifies critical predictors spanning demographic, clinical, and psychometric domains. The developed nomogram offers a novel approach for risk assessment, highlighting the importance of comprehensive evaluations in adolescent mental healthcare. These findings have significant implications for developing targeted interventions and improving risk assessment strategies in clinical practice. [ABSTRACT FROM AUTHOR]

To examine the impact of the COVID-19 pandemic on cervical cancer screening rates of Hispanic individuals compared to non-Hispanic White (NHW) individuals in the United States, whether a responsive surge in catch-up screenings occurred as society adapted to pandemic changes, and to investigate the sociodemographic characteristics between the study populations. Using cross-sectional data from the All of Us Research Program, which incorporates electronic health record data and survey data from a demographically, geographically, and medically diverse participant group, we assessed the annual cervical cancer screening rates during 2019–2021 by race/ethnicity among eligible individuals ages 21–64. Among 116,052 unique individuals (78,829 NHW and 37,223 Hispanic), Hispanic individuals had lower annual cervical cancer screening rates than NHWI across the three years studied. They experienced a more significant decrease in screening from 2019 to 2020 (39.27 %) compared to NHWIs (21.15 %) and less of a rebound increase in the following year, 2021 (10.33 % vs 13.83 %). Hispanic individuals aged 50–64 experienced the sharpest decline in screening rates (−43.01 % from 2019 to 2020). Hispanic individuals also experienced greater adverse social conditions, including lack of insurance or employment, lower educational attainment, and lower household income. Hispanic individuals experienced a more significant decrease in cervical cancer screening rates with the onset of the COVID-19 pandemic compared with NHW individuals and did not experience a robust rebound in cervical cancer screening rates in 2021. As a result, the disparity in cervical cancer screening rates between NHW and Hispanic individuals considerably worsened with the COVID-19 pandemic. [Display omitted] • Cervical cancer screening disparities dramatically worsened with the COVID-19 pandemic. • Hispanic individuals experienced a greater decrease in screening in 2020 than non-Hispanic White individuals. • The screening rates of Hispanic individuals did not rebound as much as non-Hispanic individuals in 2021. • Hispanic individuals aged 50‐64 experienced the sharpest decline in screening rates. • A higher proportion of Hispanics were unemployed, uninsured, and had lower household incomes. [ABSTRACT FROM AUTHOR]

Clinical decision support systems are software tools that help clinicians to make medical decisions. However, their acceptance by clinicians is usually rather low. A known problem is that they often require clinicians to manually enter a lot of patient data, which is long and tedious. Existing solutions, such as the automatic data extraction from electronic health record, are not fully satisfying, because of low data quality and availability. In practice, many systems still include long questionnaire for data entry. In this paper, we propose an original solution to simplify patient data entry, using an adaptive questionnaire, i.e. a questionnaire that evolves during user interaction, showing or hiding questions dynamically. Considering a rule-based decision support systems, we designed methods for determining the relationships between rules and translating the system's clinical rules into display rules that determine the items to show in the questionnaire, and methods for determining the optimal order of priority among the items in the questionnaire. We applied this approach to a decision support system implementing STOPP/START v2, a guideline for managing polypharmacy. We show that it permits reducing by about two thirds the number of clinical conditions displayed in the questionnaire, both on clinical cases and real patient data. Presented to clinicians during focus group sessions, the adaptive questionnaire was found "pretty easy to use". In the future, this approach could be applied to other guidelines, and adapted for data entry by patients. [ABSTRACT FROM AUTHOR]

Background: Left ventricular ejection fraction (EF) is used to categorize heart failure (HF) into phenotypes but this information is often missing in electronic health records or non-HF registries. Methods: We tested the applicability of a simplified version of a multivariable algorithm, that was developed on data of the Swedish Heart Failure Registry to predict EF in patients with HF. We used data from 4,868 patients with HF from the Cardiology Centers of the Netherlands database, an organization of 13 cardiac outpatient clinics that operate between the general practitioner and the hospital cardiologist. The algorithm included 17 demographical and clinical variables. We tested model discrimination, model performance and calculated model sensitivity, specificity, positive and negative predictive values for EF ≥ vs. <50% and EF ≥ vs. <40%. We additionally performed a multivariable multinomial analysis for all three separate HF phenotypes (with reduced, mildly reduced and preserved EF) HFrEF vs. HFmrEF vs. HFpEF. Finally, we internally validated the model by using temporal validation. Results: Mean age was 66 ±12 years, 44% of patients were women, 68% had HFpEF, 17% had HFrEF, and 15% had HFmrEF. The C-statistic was of 0.71 for EF ≥/< 50% (95% CI: 0.69–0.72) and of 0.74 (95% CI: 0.73–0.75) for EF ≥/< 40%. The model had the highest sensitivities for EF ≥50% (0.72, 95% CI: 0.63–0.75) and for EF ≥40% (0.70, 95% CI: 0.65–0.71). Similar results were achieved by the multinomial model, but the C-statistics for predicting HFpEF vs HFrEF was lower (0.61, 95% CI 0.58–0.63). The internal validation confirmed good discriminative ability. Conclusions: A simple algorithm based on routine clinical characteristics can help discern HF phenotypes in non-cardiology datasets and research settings such as research on primary care data, where measurements of EF is often not available. [ABSTRACT FROM AUTHOR]

Aims: To compare the likelihood of being prescribed an antidepressant in depressed individuals with and without type 2 diabetes. Methods: We performed a matched cohort study using primary care record data from the UK Clinical Practice Research Datalink. We used multivariable logistic regression to compare antidepressant prescribing during the first five years of starting oral antidiabetic medication to a comparison group without type 2 diabetes, matched based on GP practice, age and sex. We performed subgroup analyses stratified by sex, age and ethnicity. Results: People with type 2 diabetes and depression were 75% less likely to be prescribed an antidepressant compared to people with depression alone (odds ratio (OR) 0.25, 95% confidence interval (CI) 0.25 to 0.26). This difference was greater in males (OR 0.23, 95% CI, 0.22 to 0.24), people older than 56 years (OR 0.23, 95% CI, 0.22 to 0.24), or from a minoritised ethnic background (Asian OR 0.14, 95% CI 0.12–0.14; Black OR 0.12, 95% CI 0.09–0.14). Conclusions: There may be inequalities in access to antidepressant treatment for people with type 2 diabetes, particularly those who are male, older or from minoritised ethnic backgrounds. [ABSTRACT FROM AUTHOR]

Objective: The study was aimed at comparing intrapartum and postpartum outcomes between pregnant patients with and without antenatal COVID‐19 infection using aggregated, deidentified electronic health record (EHR) data. Design and Setting: This retrospective cohort study included data from over 80 health care organizations within the TriNetX Analytics Research Network. Population: Individuals admitted for delivery from Jan 2020 to May 2023 were studied. Methods: We studied individuals with ICD‐10 codes for delivery, COVID‐19 diagnosis, and primary outcomes. We compared the incidence of adverse intrapartum and 30‐day postpartum outcomes in those with and without antenatal COVID‐19. Main Outcome Measures: The main outcomes compared were obstetric, cardiovascular, neurovascular, and respiratory outcomes within 30 days postpartum. Results: Twenty‐six thousand nine hundred seventy‐four of 369,923 (7%) birthing parents with a delivery encounter had an antenatal COVID‐19 diagnosis. Compared to matched controls, having COVID‐19 was associated with an increased risk of postpartum hemorrhage (RR—1.24 (CI—1.16–1.33)), gestational hypertension (RR—1.27 (CI—1.27–1.34)), preeclampsia (RR—1.25 (CI—1.18–1.32)), eclampsia (RR—1.66 (CI—1.29–2.32)), preterm labor (RR—1.21 (CI—1.21–1.34)), cerebral infarction (RR—1.74 (CI—1.04–2.90)), cardiomyopathy (RR—2.08 (CI—1.30–3.32)), heart failure (RR—1.55 (CI—1.04–2.31)), sepsis (RR—2.21 (CI—1.54–3.19)), DVT (RR—2.32 (CI—1.45–3.71)), and pulmonary embolism (RR—2.68 (CI—1.74–2.90)). Conclusion: Individuals with antenatal COVID‐19 were more likely to have intrapartum and postpartum obstetric, cardiovascular, neurovascular, and respiratory complications. This data will inform risk stratification and screening for prenatal care providers. [ABSTRACT FROM AUTHOR]

Venous thromboembolism is responsible for a significant burden of disease worldwide. Despite the publication of multiple international guidelines, anecdotal evidence suggests significant clinical variation exists in the diagnostic and management pathways of pulmonary embolism (PE). We conducted a retrospective cohort study using electronic medical records to examine clinical variation in patients admitted to a tertiary referral center in Australia with a diagnosis of PE between November 2018 and January 2020. Three hundred cases met the inclusion criteria; we found variation in rates of compression ultrasonography, acute investigation of the right ventricle, and planning of repeat imaging at specialist follow‐up. Guidelines do not address the use of compression ultrasonography in already diagnosed PE, are conflicting in their recommendation for acute investigation of the right ventricle, and recommend repeat imaging only if there are persistent symptoms at the time of specialist follow‐up. The variations we found in this study may in part be due to physician preference or due to the paucity of evidence for some of these diagnostic practices. Robust future studies are required to guide the use of these investigations in PE. [ABSTRACT FROM AUTHOR]

This study was performed to investigate the diagnostic value of radiomics models constructed by fat suppressed T2-weighted imaging (T2WI-FS) and contrast-enhanced T1-weighted imaging (CET1) based on magnetic resonance imaging (MRI) for differentiation of osteosarcoma (OS) and chondrosarcoma (CS). In this retrospective cohort study, we included all inpatients with pathologically confirmed OS or CS from Second Xiangya Hospital of Central South University (Hunan, China) as of October 2020. Demographic and imaging variables were extracted from electronic medical records and compared between OS and CS group. Totals of 530 radiomics features were extracted from CET1 and T2WI-FS sequences based on MRI. The least absolute shrinkage and selection operator (LASSO) method was used for screening and dimensionality reduction of the radiomics model. Multivariate logistic regression analysis was performed to construct the radiomics model, and receiver operating characteristic curve (ROC) was generated to evaluate the diagnostic accuracy of the radiomics model. The training cohort and validation cohort included 87 and 29 patients, respectively. 8 CET1 features and 15 T2WI-FS features were screened based on the radiomics features. In the training group, the area under the receiver-operator characteristic curve (AUC) value for CET1 and T2WI-FS sequences in the radiomics model was 0.894 (95% CI 0.817–0.970) and 0.970 (95% CI 0.940–0.999), respectively. In the validation group, the AUC value for CET1 and T2WI-FS sequences in the radiomics model was 0.821 (95% CI 0.642–1.000) and 0.899 (95% CI 0.785–1.000), respectively. In this study, we developed a radiomics model based on T2WI-FS and CET1 sequences to differentiate between OS and CS. This model exhibits good performance and can help clinicians make decisions and optimize the use of healthcare resources. [ABSTRACT FROM AUTHOR]

Objective Study setting and design Data sources and analytic sample Principal findings Conclusions This study aimed to compare imputation approaches to identify the likely undocumented patient population in electronic health record (EHRs). EHR are a promising source of information on undocumented immigrants' medical needs and care utilization, but there is no verified way to identify immigration status in the data. Different approaches to approximating immigration status in EHR introduce unique biases, which in turn has major implications on our understanding of undocumented immigrant patients.We used a dataset of all emergency department (ED) visits from 2016 to 2019 in the Los Angeles Department of Health Services (LADHS) merged across patient medical records, demographic data, and claims data. We included all ED visits from our patient groups of interest and limited to patients at or over the age of 18 years at the time of their ED visit and excluded empty encounter records (n = 1,106,086 ED encounters).We created three patient groups: (1) US‐born, (2) foreign‐born documented, and (3) undocumented using two different imputation approaches: a logical approach versus statistical assignment. We compared predicted probabilities for two outcomes: an ED visit related to a behavioral health (BH) disorder and inpatient admission/transfer to another facility.Both approaches provide comparable estimates among the three patient groups for ED encounters for a BH disorder and inpatient admission/transfer to another facility. Undocumented immigrants are less likely to have a BH diagnosis in the ED and are less likely to be admitted or transferred compared to the US‐born.Researchers should consider expanding EHR with administrative data when studying the undocumented patient population and may prefer a logical approach to estimate immigration status. Researchers who rely on payer status alone (i.e., restricted Medicaid) as a proxy for undocumented immigrants in EHR should consider how this may bias their results. As Medicaid expands for undocumented immigrants, statistical assignment may become the preferred method. [ABSTRACT FROM AUTHOR]

Background and objectives: Cognitive impairment is a core feature of schizophrenia, and it is now clear that there is a link between nutritional indicators and cognitive functioning. This study aimed to investigate correlations between three nutritional indicators (prognostic nutritional index [PNI], geriatric nutritional risk index [GNRI], and controlling nutritional status score [CONUT]) and cognitive function in hospitalized patients with stable schizophrenia. Methods: A total of 235 patients who were hospitalized with stable schizophrenia were included. Patient demographic information was collected through self-reports or electronic medical records, and cognitive function was assessed using the Montreal Cognitive Assessment in China (MoCA-C). Information on serum albumin and total cholesterol levels, lymphocyte counts, and body mass index during the stable stage of schizophrenia was collected to calculate the PNI, GNRI, and CONUT scores, according to their respective calculation criteria. Covariate-adjusted linear regression model and ordered logistic regression model were constructed to determine the relationship between nutritional indicators and cognitive function. Results: Overall, 90.2% of the patients were under the age of 60 years, and males comprised 60% of all patients. The median scores for MoCA-C, PNI, GNRI, and CONUT in hospitalized patients with stable schizophrenia were 18 (12,23), 52.85 (50.25,55.90), 110.85 (105.80,116.21), and 3 (3,3), respectively. The results of the correlation analysis showed that only PNI was associated with MoCA-C scores (r = 0.15, P = 0.021). This relationship was further confirmed by covariate-adjusted linear regression modeling (β = 0.147, 95%CI:0.049–0.351, p = 0.01) and ordered logistic regression modeling (OR = 0.054, 95%CI:0.001–0.106, p = 0.046). Conclusions: The findings revealed a significant correlation between PNI scores and MoCA-C scores in hospitalized patients with stable schizophrenia. [ABSTRACT FROM AUTHOR]