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In modern healthcare, providers increasingly use cloud services to store and share electronic medical records. However, traditional cloud hosting, which depends on intermediaries, poses risks to privacy and security, including inadequate control over access, data auditing, and tracking data origins. Additionally, current schemes face significant limitations such as scalability concerns, high computational overhead, practical implementation challenges, and issues with interoperability and data standardization. Unauthorized data access by cloud providers further exacerbates these concerns. Blockchain technology, known for its secure and decentralized nature, offers a solution by enabling secure data auditing in sharing systems. This research integrates blockchain into healthcare for efficient record management. We proposed a blockchain-based method for secure EHR management and integrated Ciphertext-Policy Attribute-Based Encryption (CP-ABE) for fine-grained access control. The proposed algorithm combines blockchain and smart contracts with a cloud-based healthcare Service Management System (SMS) to ensure secure and accessible EHRs. Smart contracts automate key management, encryption, and decryption processes, enhancing data security and integrity. The blockchain ledger authenticates data transactions, while the cloud provides scalability. The SMS manages access requests, enhancing resource allocation and response times. A dual authentication system confirms patient keys before granting data access, with failed attempts leading to access revocation and incident logging. Our analyses show that this algorithm significantly improves the security and efficiency of health data exchanges. By combining blockchain's decentralized structure with the cloud's scalability, this approach significantly improves EHR security protocols in modern healthcare setting. [ABSTRACT FROM AUTHOR]

Intoduction: Common outcome measures for chronic neck pain are the Patient‐Specific Functional Scale (PSFS) and the neck disability index (NDI). The primary aim was to categorize the top‐rated, patient‐selected functional activity limitations of the PSFS to determine if there were consistent limited functional activities for individuals with chronic neck pain and how these compared to the constructs of activities on the NDI. The secondary aim was to determine the relationship between scores for individuals who completed both the NDI and PSFS. Design: A retrospective review of data extracted from the electronic medical record, EPIC, within two hospital‐based outpatient physical therapy clinics within a health care system. Methods: Retrospective analysis was performed on individual's characteristics, self‐selected functional activity limitations, and total scores of the PSFS and NDI. Most common categories of self‐selected functional activity limitations were developed by practicing physical therapists. These functional activity limitation categories of the PSFS were compared to the activities of the NDI. Mean PSFS total scores were correlated with the NDI total scores with Spearman's test. Results: Participants were individuals with chronic neck pain from January 2013–September 2018 (n = 2283). Movement‐based activities accounted for 60.8% of the functional activity limitations of the PSFS with the top functional activity limitations being cervical motion and exercise (32%). The PSFS total score moderately correlated with NDI (r = −0.50, p = <0.01) which may relate to the differences in constructs of the NDI and the top patient‐selected PSFS functional activity limitations found in this analysis. Conclusion: The results suggest that individuals with chronic neck pain present with similar categories of self‐selected functional activity limitations that differ from activities of the NDI. Additional research is needed to improve outcome measures to capture patient‐selected functional activity limitations and an individual's pain experience. [ABSTRACT FROM AUTHOR]

Background: Methemoglobinemia requires early identification and treatment, but limited knowledge exists regarding the current therapeutic approach taken by clinicians as well as the outcomes that occur in children. Objectives: To determine the current prevalence of this rare disease in the pediatric population, evaluate the impact of methemoglobin and functional hemoglobin levels, and assess how this disease is approached by clinicians. We hypothesize that methemoglobinemia prevalence is low and more methylene blue use would be observed in subjects with functional hemoglobin levels less than 7 g/dL. Design: This was a retrospective observational cohort study utilizing deidentified TriNetX® electronic health record (EHR) data. Methods: Using a multicenter EHR database, we evaluated subjective characteristics, diagnostic, laboratory results, medication, and procedural codes. Results: Ninety-eight children (mean age 5.3 ± 5.3 years) from 53 healthcare organizations were included. Methemoglobinemia prevalence was 0.0015% with an overall 30-day mortality of 6.1%. Subjects with methemoglobin percentages greater than 20% had a higher frequency of methylene blue administration (70.6% versus 24.7%, P =.0005). Critical care service requirements and methylene blue administration were similar in the subjects with functional hemoglobin less than 7 g/dL and more than 7 g/dL groups. Overall, 13 (13.2%) subjects underwent glucose-6-phosphate dehydrogenase deficiency (G6PD) testing. Conclusion: In our study, we found methemoglobinemia prevalence in children is low, there is a low frequency of G6PD testing despite methylene blue hemolysis risk, and subjects appeared to be treated similarly despite a low functional hemoglobin. These findings highlight the continued critical nature of this disease and may highlight opportunities for education aimed at improving care in children diagnosed with methemoglobinemia, particularly related to G6PD testing. [ABSTRACT FROM AUTHOR]

Background: Crohn's disease (CD) exhibits variability in colorectal cancer (CRC) incidence and prognostic factors due to diverse clinical and behavioral characteristics, presenting inconsistencies between Western and Eastern patients. Objectives: This study compared clinical characteristics between CD patients with CRC from the US and Korean tertiary referral centers and defined the prognostic factors related to mortality. Design: Retrospective study. Methods: We reviewed the electronic medical records of 236 adult CD patients with colorectal adenocarcinoma evaluated at Mayo Clinic Rochester, Florida, or Arizona (N = 200) and Asan Medical Center in Korea (N = 36) between January 1989 and August 2022. Results: Asan patients had a younger age, shorter CD duration, more colonic involvement (L2 plus L3), penetrating behavior, perianal fistula, and shorter biological treatment duration before CRC diagnosis than Mayo patients. Furthermore, despite significant differences in body mass index, smoking status, primary sclerosing cholangitis, immunomodulators, CRC diagnosis period, clinical presentation, CRC location, surgery, and some histopathological details between the two groups, overall survival was not statistically different (p value, 0.29, log-rank test). Advanced age (adjusted hazard ratio (aHR), 1.03 per year; 95% confidence interval (CI), 1.01–1.04; p value, <0.01), unresectable CRC (aHR, 5.02; 95% CI, 2.49–10.12; p value, <0.01), and advanced CRC stage (aHR, 1.45 per stage; 95% CI, 1.07–1.97; p value, 0.02) were significantly associated with increased risk of death. CD remission at CRC diagnosis (aHR, 0.26; 95% CI, 0.08–0.91; p value, 0.04), CRC diagnosis period of 2011–2022 (aHR relative to 1989–2000, 0.46; 95% CI, 0.25–0.87; p value, 0.02), and CRC diagnosis by surveillance (aHR, 0.56; 95% CI, 0.32–0.98; p value, 0.04) were significantly associated with decreased risk of death. Conclusion: Notably, some clinical features of CD with CRC differed between Asan and Mayo patients; however, overall survival was not different. CD remission, CRC surveillance, and more recent diagnosis of CRC were associated with a reduced risk of death. [ABSTRACT FROM AUTHOR]

Background: Recognizing the limitations of pre-market clinical data, regulatory authorities have embraced total product lifecycle management with post-market surveillance (PMS) data to assess medical device safety and performance. One method of proactive PMS involves the analysis of real-world data (RWD) through retrospective review of electronic health records (EHR). Because EHRs are patient-centered and focused on providing tools that clinicians use to determine care rather than collecting information on individual medical products, the process of transforming RWD into real-world evidence (RWE) can be laborious, particularly for medical devices with broad clinical use and extended clinical follow-up. This study describes a method to extract RWD from EHR to generate RWE on the safety and performance of embolization coils. Methods: Through a partnership between a non-profit data institute and a medical device manufacturer, information on implantable embolization coils' use was extracted, linked, and analyzed from clinical data housed in an electronic data warehouse from the state of Indiana's largest health system. To evaluate the performance and safety of the embolization coils, technical success and safety were defined as per the Society of Interventional Radiology guidelines. A multi-prong strategy including electronic and manual review of unstructured (clinical chart notes) and structured data (International Classification of Disease codes), was developed to identify patients with relevant devices and extract data related to the endpoints. Results: A total of 323 patients were identified as treated using Cook Medical Tornado, Nester, or MReye embolization coils between 1 January 2014 and 31 December 2018. Available clinical follow-up for these patients was 1127 ± 719 days. Indications for use, adverse events, and procedural success rates were identified via automated extraction of structured data along with review of available unstructured data. The overall technical success rate was 96.7%, and the safety events rate was 5.3% with 18 major adverse events in 17 patients. The calculated technical success and safety rates met pre-established performance goals (≥ 85% for technical success and ≤ 12% for safety), highlighting the relevance of this surveillance method. Conclusions: Generating RWE from RWD requires careful planning and execution. The process described herein provided valuable longitudinal data for PMS of real-world device safety and performance. This cost-effective approach can be translated to other medical devices and similar RWD database systems. [ABSTRACT FROM AUTHOR]

Background: In recent years, eHealth has received much attention as an opportunity to increase efficiency within healthcare organizations. Adoption of eHealth might consequently help to solve perceived health workforce challenges, including labor shortages and increasing workloads among primary care professionals, who serve as the first point of contact for healthcare in many countries. The purpose of this systematic review was to investigate the impact of general eHealth use and specific eHealth services use on general practice workload in the pre-COVID-19 era. Methods: The databases of CINAHL, Cochrane, Embase, IEEE Xplore, Medline ALL, PsycINFO, Web of Science, and Google Scholar were searched, using combinations of keywords including 'eHealth', 'workload', and 'general practice'. Data extraction and quality assessment of the included studies were independently performed by at least two reviewers. Publications were included for the period 2010 – 2020, before the start of the COVID-19 pandemic. Results: In total, 208 studies describing the impact of eHealth services use on general practice workload were identified. We found that two eHealth services were mainly investigated within this context, namely electronic health records and digital communication services, and that the largest share of the included studies used a qualitative study design. Overall, a small majority of the studies found that eHealth led to an increase in general practice workload. However, results differed between the various types of eHealth services, as a large share of the studies also reported a reduction or no change in workload. Conclusions: The impact of eHealth services use on general practice workload is ambiguous. While a small majority of the effects indicated that eHealth increased workload in general practice, a large share of the effects also showed that eHealth use reduced workload or had no impact. These results do not imply a definitive conclusion, which underscores the need for further explanatory research. Various factors, including the study setting, system design, and the phase of implementation, may influence this impact and should be taken into account when general practices adopt new eHealth services. Study registration number: PROSPERO (International Prospective Register of Systematic Reviews) CRD42020199897; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=199897. [ABSTRACT FROM AUTHOR]

Background: The Brazilian Ministry of Health has developed and provided the Citizen's Electronic Health Record (PEC e-SUS APS), a health information system freely available for utilization by all municipalities. Given the substantial financial investment being made to enhance the quality of health services in the country, it is crucial to understand how users evaluate this product. Consequently, this scoping review aims to map studies that have evaluated the PEC e-SUS APS. Methods: This scoping review is guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) framework, as well as by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Checklist extension for scoping reviews (PRISMA-ScR). The research question was framed based on the "CoCoPop" mnemonic (Condition, Context, Population). The final question posed is, "How has the Citizen's Electronic Health Record (PEC e-SUS APS) been evaluated?" The search strategy will be executed across various databases (LILACS, PubMed/MEDLINE, Scopus, Web of Science, ACM Digital Library, and IEEE Digital Library), along with gray literature from ProQuest Dissertation and Theses Global and Google Scholar, with assistance from a professional healthcare librarian skilled in supporting systematic reviews. The database search will encompass the period from 2013 to 2024. Articles included will be selected by three independent reviewers in two stages, and the findings will undergo a descriptive analysis and synthesis following a "narrative review" approach. Independent reviewers will chart the data as outlined in the literature. Discussion: The implementation process for the PEC e-SUS APS can be influenced by the varying characteristics of the over 5500 Brazilian municipalities. These factors and other challenges encountered by health professionals and managers may prove pivotal for a municipality's adoption of the PEC e-SUS APS system. With the literature mapping to be obtained from this review, vital insights into how users have evaluated the PEC will be obtained. Systematic review registration: The protocol has been registered prospectively at the Open Science Framework platform under the number 10.17605/OSF.IO/NPKRU. [ABSTRACT FROM AUTHOR]

The recent global outbreaks of infectious diseases such as COVID-19, yellow fever, and Ebola have highlighted the critical need for robust health data management systems that can rapidly adapt to and mitigate public health emergencies. In contrast to traditional systems, this study introduces an innovative blockchain-based Electronic Health Record (EHR) access control mechanism that effectively safeguards patient data integrity and privacy. The proposed approach uniquely integrates granular data access control mechanism within a blockchain framework, ensuring that patient data is only accessible to explicitly authorized users and thereby enhancing patient consent and privacy. This system addresses key challenges in healthcare data management, including preventing unauthorized access and overcoming the inefficiencies inherent in traditional access mechanisms. Since the latency is a sensitive factor in healthcare data management, the simulations of the proposed model reveal substantial improvements over existing benchmarks in terms of reduced computing overhead, increased throughput, minimized latency, and strengthened overall security. By demonstrating these advantages, the study contributes significantly to the evolution of health data management, offering a scalable, secure solution that prioritizes patient autonomy and privacy in an increasingly digital healthcare landscape. [ABSTRACT FROM AUTHOR]

Objective Methods Results Significance The incidence of late‐onset epilepsy (LOE) is rising, and these patients may use an excess of health care resources. This study aimed to measure pre‐/post‐diagnostic health care use (HCU) for patients with LOE compared to controls.This was an observational open cohort study covering years 1998–2019 using UK population‐based linked primary care (Clinical Practice Research Datalink [CPRD]) and hospital (HES) electronic health records. The participants included patients with incident LOE enrolled in CPRD and 1:10 age‐, sex‐, and general practice–matched controls. The exposure was incident LOE (diagnosed at age ≥65) using a 5‐year washout. The main outcome was all HCU (primary care [PC], accident and emergency [A&E], admitted patient and outpatient care) using inverse proportional weighting to PC use and HCU by setting. An interrupted time‐series analysis was used to examine pre‐/post‐diagnostic HCU between patients with LOE and controls over 4 years either side of diagnosis/matching date. An adjusted mixed‐effects negative binomial regression was used for post‐diagnosis HCU interactions.Of 2 569 874 people ≥65 years of age, 1048 (4%) developed incident LOE. Mean weighted total HCU increased by 32 visits per patient‐year (95% confidence interval [95% CI]: 13–50, p = .003) until LOE diagnosis, and then dropped by a mean of 60 visits per patient‐year (95% CI: −81 to −40). There was an acute rise and fall over the 1–2 years immediately pre‐/post‐diagnosis. Incident HCU remained higher for LOE compared to controls post‐diagnosis (adjusted incidence rate ratio: 1.72; 95% CI: 1.65–1.70; p < .001), including A&E, outpatient, and admitted care.Health care use demonstrates an acute on chronic rise over the 4 years before diagnosis of LOE. To what extent the partial reversal of the acute pre‐diagnosis rise, and the mediators of the accelerated increase compared to controls are attributed to epilepsy, comorbid and bidirectional disease states, or a combination of both warrants further exploration. [ABSTRACT FROM AUTHOR]

Objective: To analyze changes in renal function and associated risk factors in patients with bladder cancer undergoing robot-assisted radical cystectomy (RARC) with intracorporeal or extracorporeal urinary diversion (ICUD or ECUD). Methods: Clinical-pathological data was extracted from electronic medical records of 266 patients with bladder cancer who underwent RARC at our institution between August 2015 and August 2022. Postoperative renal function was assessed using the estimated glomerular filtration rate (eGFR). Result: Patients were classified into ECUD and ICUD groups based on the surgical approach. Significant differences in eGFR were observed between the two groups at 1, 2, and 3 years postoperatively. Moreover, 112 patients (42.1%) experienced long-term renal function injury. Independent risk factors for long-term renal function injury included the type of surgical approach, ureteroenteric anastomotic strictures, and pathological stage T3 or above. In terms of short-term renal function, 30 cases of acute kidney injury (AKI) were observed, with an incidence rate of 11.3%. No difference in AKI incidence was found between the groups. Conclusions: Postoperative AKI and chronic kidney injury are prevalent complications following RC. This study highlights that pathological stage, ureteroenteric anastomotic strictures, and ECUD significantly impact long-term renal function, but the type of urinary diversion (ileal conduit or orthotopic neobladder) had no effect on renal function, and ICUD was superior in terms of long-term renal injury rate. Therefore, precise preoperative assessment and the selection of appropriate surgical approach are crucial for preserving renal function in patients with bladder cancer. [ABSTRACT FROM AUTHOR]

Background: As the population ages, the occurrence of cognitive decline and dementia is continuously increasing. Frailty is a prevalent problem among older adults. Epidemiologic studies have shown a comorbidity between frailty and cognitive impairment. However, their relationship remains unclear. The frailty index is an important indicator for measuring frailty. This study aims to investigate the relationship between frailty index and cognitive dysfunction in older adults aged 60 years and older in the United States from the 2011-2014 National Health and Nutrition Examination Survey (NHANES). Methods: Community-dwelling older adults aged 60 years or older from 2011 to 2014 were extracted from the NHANES database. The frailty index was calculated using the formula: frailty index = total number of deficits present/total number of deficits measured. The Animal Fluency (AF), the Digit Symbol Substitution Test (DSST), the Consortium to Establish a Registry for Alzheimer's disease Delayed Recall (CERAD-DR), and Word Learning (CERADWL) were used to evaluate cognitive dysfunction. Firstly, weighted logistic regression analysis was used to explore the relationship between frailty index and cognitive dysfunction. Secondly, the influence of covariates on the frailty index was evaluated by subgroup analysis and interaction. Finally, the non-linear relationship is discussed by using the restricted cubic spline regression model. Results: Our study included a total of 2,574 patients, weighted logistic regression analysis, after adjusting for all covariates, showed that the frailty index was associated with every test score. The interaction showed that covariates had no significant effect on this association in AF. The association between the frailty index and AF in the restricted cubic spline regression model is non-linear. As the frailty index increased, the risk of AF reduction increased, suggesting a higher risk of cognitive dysfunction. Conclusion: In general, a high frailty index appears to be associated with an increased risk of cognitive dysfunction in the elderly. Consequently, protecting against cognitive decline necessitates making geriatric frailty prevention and treatment top priorities. [ABSTRACT FROM AUTHOR]

Background: Postoperative complications of pancreaticoduodenectomy (PD) are still a thorny problem. This study aims to verify the preventative impact of T-tube on them. Methods: The electronic medical records and follow-up data of patients who received pancreaticoduodenectomy in our center from July 2016 to June 2020 were reviewed. According to whether T tube was placed during the operation, the patients were divided into T-tube group and not-T-tube group. Propensity score matching analysis was performed to minimize selection bias. Results: A total of 330 patients underwent PD (Not-T-tube group =226, T-tube group=104). Propensity score matching resulted in 222 patients for further analysis (Not-T-tube group =134, T-tube group=88). Patients' demographics were comparable in the matched cohorts. Significantly higher incidences of clinically relevant postoperative pancreatic fistula (CR-POPF) ((14 (10.45%) VS 20 (22.73%)), P=0.013) were observed in the T-tube group. The total incidence of biliary anastomotic stricture (BAS) was 3.15%. The incidence was slightly lower in the T-tube group, but there was no statistically significant differentiation (6 (4.48%) VS 1 (1.14%), P=0.317). Conclusions: It is not feasible to prevent postoperative complications with the application of a T-tube in PD. [ABSTRACT FROM AUTHOR]

In certain cloud Electronic Medical Records (EMRs) applications, the data ownership may need to be transferred. In practice, not only the data but also the auditing ability should be transferred securely and efficiently. However, we investigate and find that most of the existing data ownership transfer protocols only work well between two individuals, and they become inefficient when dealing between two communities. The proposals for transferring tags between communities are problematic as well since, they require all members get involved or a fully trusted aggregator facilitates ownership transfer, which are unrealistic in certain scenarios. To alleviate these problems, in this paper we develop a secure auditing protocol with decentralized (t, n)-threshold ownership transfer for cloud EMRs. This protocol is designed to operate efficiently without requiring the mandatory participation of every user or the involvement of any trusted third-party. It is achieved by employing the threshold signature. Rigorous security analyses and comprehensive performance evaluations illustrate the security and practicality of our protocol. Specifically, according to the evaluations and comparisons, the communication and computational consumption is independent of the file size, i.e., it is constant in our protocol for both communities. [ABSTRACT FROM AUTHOR]

Background: The objectives of this study were to examine the association of psychiatric comorbidities and patient characteristics with treatment change and response as well as to assess the association between treatment change and healthcare resource utilization (HCRU) among adult patients with attention-deficit/hyperactivity disorder (ADHD) and psychiatric comorbidities. Methods: De-identified electronic health records from the NeuroBlu Database (2002–2021) were used to select patients ≥ 18 years with ADHD who were prescribed ADHD-specific medication. The index date was set as the first prescription of ADHD medication. The outcomes were treatment change (discontinuation, switch, add-on, or drop) and HCRU (inpatient, outpatient, composite) within 12 months of follow-up. Cox proportional-hazard model was used to assess the association between clinical and demographic patient characteristics and treatment change, while generalized linear model with negative binomial distribution and log link function was used to assess the association between key risk factors linked to treatment change and HCRU rates. Results: A total of 3,387 patients with ADHD were included (ADHD only: 1,261; ADHD + major depressive disorder (MDD): 755; ADHD + anxiety disorder: 467; ADHD + mood disorder: 164). Nearly half (44.8%) of the study cohort experienced a treatment change within the 12-month follow-up period. Treatment switch and add-on were more common in patients with ADHD and comorbid MDD and anxiety disorder (switch: 18.9%; add-on: 20.5%) compared to other cohorts (range for switch: 8.5–13.6%; range for add-on: 8.9–12.1%) Survival analysis demonstrated that the probability of treatment change within 12 months from treatment initiation in the study cohort was estimated to be 42.4%. Outpatient visit rates statistically significantly increased from baseline (mean [SD] 1.03 [1.84] visits/month) to 3 months post-index (mean [SD] 1.62 [1.91] visits/month; p < 0.001), followed by a gradual decline up to 12 months post-index. Being prescribed both a stimulant and a non-stimulant at index date was statistically significantly associated with increased risk of treatment change (adjusted hazard ratio: 1.64; 95% CI: 1.13, 2.38; p = 0.01). Conclusions: This real-world study found that treatment change was common among patients with ADHD and psychiatric comorbidities. These findings support the need for future studies to examine the unmet medical and treatment needs of this complex patient population. [ABSTRACT FROM AUTHOR]

Background: The high complexity of systemic autoimmune diseases (SADs) has hindered precise management. This study aims to investigate heterogeneity in SADs. Methods: We applied a joint cluster analysis, which jointed multiple correspondence analysis and k-means, to immunomarkers and measured the heterogeneity of clusters by examining differences in immunomarkers and clinical manifestations. The electronic health records of patients who received an antinuclear antibody test and were diagnosed with SADs, namely systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), and Sjögren's syndrome (SS), were retrieved between 2001 and 2016 from hospitals in Taiwan. Results: With distinctive patterns of immunomarkers, a total of 11,923 patients with the three SADs were grouped into six clusters. None of the clusters was composed only of a single SAD, and these clusters demonstrated considerable differences in clinical manifestation. Both patients with SLE and SS had a more dispersed distribution in the six clusters. Among patients with SLE, the occurrence of renal compromise was higher in Clusters 3 and 6 (52% and 51%) than in the other clusters (p < 0.001). Cluster 3 also had a high proportion of patients with discoid lupus (60%) than did Cluster 6 (39%; p < 0.001). Patients with SS in Cluster 3 were the most distinctive because of the high occurrence of immunity disorders (63%) and other and unspecified benign neoplasm (58%) with statistical significance compared with the other clusters (all p < 0.05). Conclusions: The immunomarker-driven clustering method could recognise more clinically relevant subgroups of the SADs and would provide a more precise diagnosis basis. [ABSTRACT FROM AUTHOR]

Background: Electronic Health Record systems (EHRs) offer significant benefits and have transformed healthcare in developed countries. However, their implementation and adoption in low- and middle-income countries (LMICs) remains low due to challenges and competing interests. Health professionals' perception of EHRs can influence their adoption and continued use. The objectives of this study are to explore the perception of health professionals regarding implemented EHR systems in three hospitals in Ghana and identify factors influencing their perception and satisfaction. Methods: In this study, we employed a concurrent mixed method design to collect data from study participants from May to June 2023. The quantitative part employed a descriptive-survey and the qualitative (in-depth interview) techniques was applied. After obtaining written informed consent from each respondent, a structured survey questionnaire was filled out by the health professionals from three hospitals. An a priori power calculation was used to determine the sample size for the quantitative component. Two hundred and sixty-three (263) health professionals completed the questionnaire from the three facilities. A purposive sampling technique was used to select fifteen [1] participants for the interviews. A semi-structured interview guide was used for the in-depth interviews. The interviews were audio recorded, transcribed, and coded into themes using QSR Nvivo 12 software before thematic content analysis. Results: Our findings revealed that 213 (80.99%) health professionals perceived the EHRs as beneficial to patients and were generally satisfied. An overwhelming majority, 197 (74.90%) of the health professionals, were satisfied with its use and expressed interest in continuing to use the system. The majority of health professionals viewed the EHRs to have improved their work and workflow processes and provided the desired results. However, few other health professionals were dissatisfied with the system because they viewed the EHRs as frustrating due to unstable internet connectivity and power supply. Other concerns were related to the privacy and confidentiality of patient information. They believe access to patient information should be on a need-to-know basis, and patient information should not be accessible to all other clinicians except those involved directly in their care processes. Conclusion: The study revealed that health professionals have a positive perception of the implemented EHRs, are highly satisfied with them, and are interested in continuing to use them. However, health professionals' concerns about the unstable power supply, poor internet connectivity, security, and confidentiality of patient's information need attention, to mitigate their frustrations and boost their confidence in the system. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Breast cancer prediction and diagnosis are critical for timely and effective treatment, significantly impacting patient outcomes. Machine learning algorithms have become powerful tools for improving the prediction and diagnosis of breast cancer. The Breast Cancer Prediction and Diagnosis Model (BCPM), which utilises machine learning techniques to improve the precision and efficiency of breast cancer diagnosis and prediction, is presented in this paper. BCPM collects comprehensive and high-quality data from diverse sources, including electronic medical records, clinical trials, and public datasets. Through rigorous pre-processing, the data is cleaned, inconsistencies are addressed, and missing values are handled. Feature scaling techniques are applied to normalize the data, ensuring fair comparison and equal importance among different features. Furthermore, feature-selection algorithms are utilized to identify the most relevant features that contribute to breast cancer projection and diagnosis, optimizing the model's efficiency. The BCPM employs numerous machine learning methods, such as logistic regression, random forests, decision trees, support vector machines, and neural networks, to generate accurate models. Area under the curve (AUC), sensitivity, specificity, and accuracy are only some of the metrics used to assess a model's performance once it has been trained on a subset of data. The BCPM holds promise in improving breast cancer prediction and diagnosis, aiding in personalized treatment planning, and ultimately taming patient results. By leveraging machine learning algorithms, the BCPM contributes to ongoing efforts in combating breast cancer and saving lives. [ABSTRACT FROM AUTHOR]

Background: Improving hypertension control is a public health priority. However, consistent identification of uncontrolled hypertension using computable definitions in electronic health records (EHR) across health systems remains uncertain. Methods: In this retrospective cohort study, we applied two computable definitions to the EHR data to identify patients with controlled and uncontrolled hypertension and to evaluate differences in characteristics, treatment, and clinical outcomes between these patient populations. We included adult patients (≥ 18 years) with hypertension (based on either ICD-10 codes of hypertension or two elevated blood pressure [BP] measurements) receiving ambulatory care within Yale-New Haven Health System (YNHHS; a large US health system) and OneFlorida Clinical Research Consortium (OneFlorida; a Clinical Research Network comprised of 16 health systems) between October 2015 and December 2018. We identified patients with controlled and uncontrolled hypertension based on either a single BP measurement from a randomly selected visit or all BP measurements recorded between hypertension identification and the randomly selected visit). Results: Overall, 253,207 and 182,827 adults at YNHHS and OneFlorida were identified as having hypertension. Of these patients, 83.1% at YNHHS and 76.8% at OneFlorida were identified using ICD-10-CM codes, whereas 16.9% and 23.2%, respectively, were identified using elevated BP measurements (≥ 140/90 mmHg). A total of 24.1% of patients at YNHHS and 21.6% at OneFlorida had both diagnosis code for hypertension and elevated blood pressure measurements. Uncontrolled hypertension was observed among 32.5% and 43.7% of patients at YNHHS and OneFlorida, respectively. Uncontrolled hypertension was disproportionately higher among Black patients when compared with White patients (38.9% versus 31.5% in YNHHS; p < 0.001; 49.7% versus 41.2% in OneFlorida; p < 0.001). Medication prescription for hypertension management was more common in patients with uncontrolled hypertension when compared with those with controlled hypertension (overall treatment rate: 39.3% versus 37.3% in YNHHS; p = 0.04; 42.2% versus 34.8% in OneFlorida; p < 0.001). Patients with controlled and uncontrolled hypertension had similar incidence rates of deaths, CVD events, and healthcare visits at 3, 6, 12, and 24 months. The two computable definitions generated consistent results. Conclusions: While the current EHR systems are not fully optimized for disease surveillance and stratification, our findings illustrate the potential of leveraging EHR data to conduct digital population surveillance in the realm of hypertension management. [ABSTRACT FROM AUTHOR]

Pompe disease (OMIM #232300), a rare genetic disorder, leads to glycogen buildup in the body due to an enzyme deficiency, particularly harming the heart and muscles. Infantile-onset Pompe disease (IOPD) requires urgent treatment to prevent mortality, but the unavailability of these methods often delays diagnosis. Our study aims to streamline IOPD diagnosis in the UAE using electronic health records (EHRs) for faster, more accurate detection and timely treatment initiation. This study utilized electronic health records from the Abu Dhabi Healthcare Company (SEHA) healthcare network in the UAE to develop an expert rule-based screening approach operationalized through a dashboard. The study encompassed six diagnosed IOPD patients and screened 93,365 subjects. Expert rules were formulated to identify potential high-risk IOPD patients based on their age, particular symptoms, and creatine kinase levels. The proposed approach was evaluated using accuracy, sensitivity, and specificity. The proposed approach accurately identified five true positives, one false negative, and four false positive IOPD cases. The false negative case involved a patient with both Pompe disease and congenital heart disease. The focus on CHD led to the overlooking of Pompe disease, exacerbated by no measurement of creatine kinase. The false positive cases were diagnosed with Mitochondrial DNA depletion syndrome 12-A (SLC25A4 gene), Immunodeficiency-71 (ARPC1B mutation), Niemann–Pick disease type C (NPC1 gene mutation leading to frameshift), and Group B Streptococcus meningitis. The proposed approach of integrating expert rules with a dashboard facilitated efficient data visualization and automated patient screening, which aids in the early detection of Pompe disease. Future studies are encouraged to investigate the application of machine learning methodologies to enhance further the precision and efficiency of identifying patients with IOPD. [ABSTRACT FROM AUTHOR]

Background: The aim of this study is to analyze the trend of acute onset of chronic cor pulmonale at Chenggong Hospital of Kunming Yan'an Hospital between January 2018 and December 2022.Additionally, the study will compare the application of the ARIMA model and Holt-Winters model in predicting the number of chronic cor pulmonale cases. Methods: The data on chronic cor pulmonale cases from 2018 to 2022 were collected from the electronic medical records system of Chenggong Hospital of Kunming Yan'an Hospital. The ARIMA and Holt-Winters models were constructed using monthly case numbers from January 2018 to December 2022 as training data. The performance of the model was tested using the monthly number of cases from January 2023 to December 2023 as the test set. Results: The number of acute onset of chronic cor pulmonale in Chenggong Hospital of Kunming Yan'an Hospital exhibited a downward trend overall from 2018 to 2022. There were more cases in winter and spring, with peaks observed in November to December and January of the following year. The optimal ARIMA model was determined to be ARIMA (0,1,1) (0,1,1)12, while for the Holt-Winters model, the optimal choice was the Holt-Winters multiplicative model. It was found that the Holt-Winters multiplicative model yielded the lowest error. Conclusion: The Holt-Winters multiplicative model predicts better accuracy. The diagnosis of acute onset of chronic cor pulmonale is related to many risk factors, therefore, when using temporal models to fit and predict the data, we must consider such factors' influence and try to incorporate them into the models. [ABSTRACT FROM AUTHOR]

Background: Evaluating outcome reliability is critical in real-world evidence studies. Overall survival is a common outcome in these studies; however, its capture in real-world data (RWD) sources is often incomplete and supplemented with linked mortality information from external sources. Conflicting recommendations exist for censoring overall survival in real-world evidence studies. This simulation study aimed to understand the impact of different censoring methods on estimating median survival and log hazard ratios when external mortality information is partially captured. Methods: We used Monte Carlo simulation to emulate a non-randomized comparative effectiveness study of two treatments with RWD from electronic health records and linked external mortality data. We simulated the time to death, the time to last database activity, and the time to data cutoff. Death events after the last database activity were attributed to linked external mortality data and randomly set to missing to reflect the sensitivity of contemporary real-world data sources. Two censoring schemes were evaluated: (1) censoring at the last activity date and (2) censoring at the end of data availability (data cutoff) without an observed death. We assessed the performance of each method in estimating median survival and log hazard ratios using bias, coverage, variance, and rejection rate under varying amounts of incomplete mortality information and varying treatment effects, length of follow-up, and sample size. Results: When mortality information was fully captured, median survival estimates were unbiased when censoring at data cutoff and underestimated when censoring at the last activity. When linked mortality information was missing, censoring at the last activity date underestimated the median survival, while censoring at the data cutoff overestimated it. As missing linked mortality information increased, bias decreased when censoring at the last activity date and increased when censoring at data cutoff. Conclusions: Researchers should consider the completeness of linked external mortality information when choosing how to censor the analysis of overall survival using RWD. Substantial bias in median survival estimates can occur if an inappropriate censoring scheme is selected. We advocate for RWD providers to perform validation studies of their mortality data and publish their findings to inform methodological decisions better. [ABSTRACT FROM AUTHOR]

Background. Sarcopenia is prevalent in older adults. It is essential for nurses to sustain updated information regarding the knowledge of sarcopenia, particularly in relation to approaches to diagnosing and addressing sarcopenia. However, there are limited studies examining the attitudes and awareness of nurses in relation to this condition. Objective. To assess the attitude and knowledge of nurses regarding sarcopenia and correlation between positive attitude and scores on knowledge. Materials and Methods. An electronic survey was carried out among the nurses of the Faculty of Medicine, Khon Kaen University, Thailand, during November 2022 and January 2024. This survey encompassed questionnaires aimed at evaluating the nurses' perspectives and understanding of sarcopenia. The participants were invited to complete the survey, and they were returned to the researchers for analysis. Results. A total of 231 nurses were recruited (response rate 46.2%). Only 58.4% of them were familiar with "sarcopenia," while only 16% had confidence in diagnosing it. For general knowledge, they achieved a total score of 19/30 (63.3%). They demonstrated proficiency in "etiology" (75%), while their performance was fair in "management and prevention" (62.5%) and low in "terminology & importance" (50%) and "diagnosis" (50%). The correlation between positive attitude and knowledge on sarcopenia was 0.22 (p < 0.05). Conclusion. Nurses exhibited a reduced awareness regarding "sarcopenia." Their comprehension about terminology, diagnosis, prevention, and management of this condition was limited. There was a weak correlation between positive attitude and knowledge. The findings emphasize the essentiality of augmenting the educational programs to enhance the recognition of sarcopenia among nurses. [ABSTRACT FROM AUTHOR]

Background: Nirmatrelvir/ritonavir (NM/r) is a safe and effective oral antiviral therapeutic used for treatment of mild-to-moderate COVID-19. Case reports described a clinical rebound syndrome whereby individuals experience a relapse of symptoms shortly after completing successful treatment. There is a lack of information on frequency of COVID-19 rebound after NM/r in routine clinical care, contributing factors, and clinical outcomes. Methods: We reviewed electronic medical records to verify COVID-19 diagnosis, symptoms, and treatment with NM/r from January-June 2022. We defined COVID-19 clinical rebound as clear improvement in symptoms followed by recurrence or worsening of symptoms within 30 days of a five-day course of NM/r. Results: We studied 268 adults with median age 57 (IQR 47, 68), 80% White race, 85% non-Hispanic ethnicity, 55% female, 80% vaccinated and boosted against SARS-CoV-2, and 68% with any co-morbidity. Sixteen (6.0%) of studied patients were determined to have COVID-19 clinical rebound. The median time from starting NM/r to rebound was 11 days (IQR 9, 13). Notable demographic and clinical factors with higher proportion (not statistically significant) among COVID-19 rebound patients were female sex (75% rebound vs. 54.5% no rebound), Black race (12.5% rebound vs. 4.9% no rebound), presence of at least one co-morbidity (81.3% rebound vs. 67.5% no rebound), and lack of prior SARS-CoV-2 infection (100% rebound vs. 92.9% no rebound). Only one patient (6.25%) was hospitalized after COVID-19 rebound. Conclusions: COVID-19 clinical rebound after treatment with NM/r is mild with favorable outcomes and more common than previously reported from real-world clinical care studies. [ABSTRACT FROM AUTHOR]

Real-world data (RWD) in the medical field, such as electronic health records (EHRs) and medication orders, are receiving increasing attention from researchers and practitioners. While structured data have played a vital role thus far, unstructured data represented by text (e.g., discharge summaries) are not effectively utilized because of the difficulty in extracting medical information. We evaluated the information gained by supplementing structured data with clinical concepts extracted from unstructured text by leveraging natural language processing techniques. Using a machine learning-based pretrained named entity recognition tool, we extracted disease and medication names from real discharge summaries in a Japanese hospital and linked them to medical concepts using medical term dictionaries. By comparing the diseases and medications mentioned in the text with medical codes in tabular diagnosis records, we found that: (1) the text data contained richer information on patient symptoms than tabular diagnosis records, whereas the medication-order table stored more injection data than text. In addition, (2) extractable information regarding specific diseases showed surprisingly small intersections among text, diagnosis records, and medication orders. Text data can thus be a useful supplement for RWD mining, which is further demonstrated by (3) our practical application system for drug safety evaluation, which exhaustively visualizes suspicious adverse drug effects caused by the simultaneous use of anticancer drug pairs. We conclude that proper use of textual information extraction can lead to better outcomes in medical RWD mining. [ABSTRACT FROM AUTHOR]

Objective Materials and Methods Results Conclusion This project demonstrates the feasibility of connecting medical imaging data and features, SARS‐CoV‐2 genome variants, with clinical data in the National Clinical Cohort Collaborative (N3C) repository to accelerate integrative research on detection, diagnosis, and treatment of COVID‐19‐related morbidities. The N3C curated a rich collection of aggregated and de‐identified electronic health records (EHR) data of over 18 million patients, including 7.5 million COVID‐positive patients, seen at hospitals across the United States. Medical imaging data and variant samples are important data modalities used in the study of COVID‐19.Imaging data and features are hosted on the Cancer Imaging Archive (TCIA), and sequenced variant samples are analyzed and stored at the NIH GenBank. The University of Arkansas for Medical Sciences (UAMS) published the first COVID‐19 data set of 105 patients on TCIA and 37 patients on GenBank. We developed a process to link imaging and genomic variants and N3C EHR data through Privacy Preserving Record Linkage (PPRL) using de‐identified cryptographic hashes to match records associated with the same individuals without using patient identifiers.The PPRL techniques were piloted using clinical and imaging data sets provided by UAMS. Developed software components and processes executed properly, and linked data were returned and processed for visualization.Linking across clinical data sources at the patient level provides opportunities to gain insights from data that may not be known otherwise. The PPRL prototype and the pilot serve as a model to link disparate and diverse data repositories to enhance clinical research. [ABSTRACT FROM AUTHOR]

Aims Methods Results Conclusion Computerized decision support systems (CDSSs) aim to prevent adverse drug events. However, these systems generate an overload of alerts that are not always clinically relevant. Anticoagulants are frequently involved in these alerts. The aim of this study was to investigate the efficiency of CDSS alerts on anticoagulants in Dutch hospital pharmacies.A multicentre, single‐day, cross‐sectional study was conducted using a flashmob design in Dutch hospital pharmacies, which have CDSSs that operate on both a national medication surveillance database and on self‐developed clinical rules. Hospital pharmacists and pharmacy technicians collected data on the number and type of alerts and time needed for assessing these alerts. The primary outcome was the CDSS efficiency on anticoagulants, defined as the percentage of alerts on anticoagulants that led to an intervention. Secondary outcomes where among other CDSSs efficiency related to any medications and the time expenditure. Descriptive data‐analysis was used.Of the 69 hospital pharmacies invited, 42 (61%) participated. The efficiency of CDSS alerts on anticoagulants was 4.0% (interquartile range [IQR] 14.0%) for the national medication surveillance database alerts and 14.3% (IQR 40.0%) for alerts from clinical rules. For any medication, the efficiency was lower: 1.8% (IQR 7.5%) and 13.4% (IQR 21.5%) respectively. The median time for assessing the relevance of all alerts was 2 (IQR 1:21) h/day for pharmacists and 6 (IQR 5:01) h/day for pharmacy technicians.CDSS efficiency is generally low, both for anticoagulants and any medication, while the time investment is high. Optimization of CDSSs is needed. [ABSTRACT FROM AUTHOR]

Background: Cancer is a complex disease, caused and impacted by a combination of genetic, demographic, clinical, environmental and lifestyle factors. Analysis of cancer characteristics, risk factors, treatment options and the heterogeneity across cancer types has been the focus of medical research for years. The aim of this study is to describe and summarise genetic, clinicopathological, behavioural and demographic characteristics and their differences across ten common cancer types and evaluate their impact on overall survival outcomes. Methods: This study included data from 9977 patients with bladder, breast, colorectal, endometrial, glioma, leukaemia, lung, ovarian, prostate, and renal cancers. Genetic data collected through the 100,000 Genomes Project was linked with clinical and demographic data provided by the National Cancer Registration and Analysis Service (NCRAS), Hospital Episode Statistics (HES) and Office for National Statistics (ONS). Descriptive and Kaplan Meier survival analyses were performed to visualise similarities and differences across cancer types. Cox proportional hazards regression models were applied to identify statistically significant prognostic factor associations with overall survival. Results: 161 clinical and 124 genetic factors were evaluated for prognostic association with overall survival. Of these, 116 unique factors were found to have significant prognostic effect for overall survival across ten cancer types when adjusted for age, sex and stage. The findings confirmed prognostic associations with overall survival identified in previous studies in factors such as multimorbidity, tumour mutational burden, and mutations in genes BRAF, CDH1, NF1, NRAS, PIK3CA, PTEN, TP53. The results also identified new prognostic associations with overall survival in factors such as mental health conditions, female health-related conditions, previous hospital encounters and mutations in genes FANCE, FBXW7, GATA3, MSH6, PTPN11, RB1, RNF43. Conclusion: This study provides a comprehensive view of clinicopathological and genetic prognostic factors across different cancer types and draws attention to less commonly known factors which might help produce more precise prognosis and survival estimates. The results from this study contribute to the understanding of cancer disease and could be used by researchers to develop complex prognostic models, which in turn could help predict cancer prognosis more accurately and improve patient outcomes. [ABSTRACT FROM AUTHOR]

Improving the understanding of the complex relationship between genetic predispositions, environmental influences, and sociocultural factors in the development and progression of mental illness is crucial for optimizing treatment efficacy and addressing longstanding health disparities. This paper discusses the ethical, legal, and social implications (ELSI) of recent advancements in biomedical research, particularly in genome-wide association studies (GWAS), phenome-wide association studies (PheWAS), and genome-wide environment interaction studies (GWEIS). Despite recent scientific progresses, challenges such as inadequate study methodology (e.g., correlational studies) and lack of diversity within study samples persist. Recent discoveries of several genetic variants of diseases, could augment and improve, or even challenge, existing understanding of the onset and management of mental illness. Leveraging real-world data (RWD), including electronic health record data (EHRs) focused on social determinant of health alongside biobank data, offers further opportunities to enhance the understanding of gene-environment interactions and inform efforts for reducing disparities in mental healthcare. Increased knowledge can support timely, holistic, evidence-based, and personalized care. Addressing ELSI considerations and maximizing the use of RWD is essential for advancing ethical and inclusive psychiatric genetics research, ultimately improving patient outcomes and promoting equitable access to evidence-based treatments. [ABSTRACT FROM AUTHOR]

Background: Pneumonia, a leading cause of morbidity and mortality worldwide, often necessitates Intensive Care Unit (ICU) admission. Accurate prediction of pneumonia mortality is crucial for tailored prevention and treatment plans. However, existing mortality prediction models face limited adoption in clinical practice due to their lack of interpretability. Objective: This study aimed to develop an interpretable model for predicting pneumonia mortality in ICUs. Leveraging the Shapley Additive Explanation (SHAP) method, we sought to elucidate the Extreme Gradient Boosting (XGBoost) model and identify prognostic factors for pneumonia. Methods: Conducted as a retrospective cohort study, we utilized electronic health records from the eICU-CRD (2014–2015) for all adult pneumonia patients. The first 24 h of each ICU admission records were considered, with 70% of the dataset allocated for model training and 30% for validation. The XGBoost model was employed, and performance was assessed using the area under the receiver operating characteristic curve (AUC). The SHAP method provided insights into the XGBoost model. Results: Among 10,962 pneumonia patients, in-hospital mortality was 16.33%. The XGBoost model demonstrated superior predictive performance (AUC: 0.778 ± 0.016)) compared to traditional scoring systems and other machine learning method, which achieved an improvement of 10% points. SHAP analysis identified Aspartate Aminotransferase (AST) as the most crucial predictor. Conclusions: Interpretable predictive models enhance mortality risk assessment for pneumonia patients in the ICU, fostering transparency. AST emerged as the foremost predictor, followed by patient age, albumin, BMI et al. These insights, rooted in strong correlations with mortality, facilitate improved clinical decision-making and resource allocation. [ABSTRACT FROM AUTHOR]

We compared the risks and benefits of COVID-19 vaccines using a causal pathway analysis to weigh up possible risk factors of thromboembolic events post-vaccination. The self-controlled case series (SCCS) method examined the association between thromboembolic events and vaccination while a case-control study assessed the association between thromboembolic events and COVID-19, addressing under-reported infection data issues. The net vaccine effect was estimated using results from SCCS and case-control studies. We used electronic health record data from Corewell Health (16,640 subjects in SCCS and 106,143 in case-control). We found increased risks of thromboembolic events post-vaccination (incidence rate ratio: 1.19, 95% CI: [1.08, 1.31] after the first dose; 1.22, 95% CI: [1.11, 1.34] after the second dose). Vaccination attenuated infection-associated thromboembolic risks (odds ratio: 4.65, 95% CI: [4.18, 5.17] in unvaccinated vs 2.77, 95% CI: [2.40, 3.24] in vaccinated). After accounting for vaccine efficacy and protection against infection-associated thromboembolic events, vaccination decreases thromboembolic event risk, especially during high infection rate periods. [ABSTRACT FROM AUTHOR]

Background: Research on Qi Gong (QG) supports promising health benefits. Both interest and use of QG in U.S. adults has increased over the past decade. Shared Medical Appointments (SMAs) are a novel, cost-effective, and time efficient health care delivery approach associated with patient and clinician satisfaction. Objectives: A telehealth delivered QG SMA was pilot tested among a diverse demographic population within an integrative medicine practice at an academic medical center to assess feasibility and acceptability. Methods: This was a feasibility/acceptability pilot study conducted at a large New York City academic medical center's Integrative Health and Wellness center from January to July 2023. A QG instructor-acupuncturist and an integrative medicine physician-acupuncturist co-led 3 separate series (5 Element QG, Eight-Section Brocade, and Joint-Mobilizing/Sinew Strengthening exercises) of weekly 30-45-minute sessions of QG SMA on a weekday afternoon via telemedicine. The first session included an overview of QG and Traditional Chinese Medicine (TCM) research while follow-up sessions included a check-in, didactic demonstration followed by QG practice, and a debrief to answer questions. Video links were provided for home practice. Surveys assessing satisfaction were sent pre/post series. Results: 18 sessions of QG SMA were offered over a 6-month period. A total of 40 unique participants from diverse demographics (gender, race/ethnicity, primary residence) attended, for a total of 197 virtual visits. A total of 20 participants enrolled in Series 1 (8 weeks), 23 enrolled in Series 2 (7 weeks), and 16 enrolled in Series 3 (3cweeks). For each session, group attendance ranged from 8-16 with an average of 11 participants. Attendance was high with participants attending an average of 72% of the sessions. Participants attended 88% of the first 8-week series, 54% of the second 7-week series, and 60% of the third series. Participant interest persisted over time with 35% of the 40 participants attending more than 1 series, and 12.5% attending all 3 series. Participants' diagnoses and health symptoms included pain (62.5%), cancer (45%) anxiety/depression (40%), cardiovascular disease (CVD) or metabolic conditions (32.5%), gastrointestinal (GI) symptoms/diagnoses (27.5%), stress (22.5%), osteopenia/osteoporosis (17.5%), and insomnia (17.5%). Pre-series [n = 27] participants endorsed symptoms including sleep disturbances, fatigue, pain, stress, weakness, GI symptoms, psychological symptoms, hot flashes, and brain fog. Post-program survey results [n = 11] suggested QG program addressed common symptoms including fatigue, insomnia, anxiety, stress, pain, weakness, and gastrointestinal symptoms. Participants reported incorporating QG, breathing techniques, and meditation into their daily routine. All participants reported their goals were met and that they would recommend the program to others. Regarding delivery preferences, 73% preferred telehealth, 27% hybrid, and none preferred in-person. Participants appreciated the format, new skills, community, and instructors. Conclusion: While the Virtual QG SMA series provided to diverse demographic population with mixed diagnoses is feasible, acceptable, and shows promising positive impact in this pilot, caution in interpreting the data is advised due to the low response rate of the post-program survey. Robust studies with longer follow-ups are recommended. [ABSTRACT FROM AUTHOR]

Background: Atherosclerotic Cardiovascular Disease (ASCVD) is a global public health concern. This study aimed to estimate the healthcare resource utilization (HRU) and costs stratified by cardiovascular disease (CVD) risk categories using real-world evidence, in a regional population in Portugal. Methods: This is a retrospective observational study, using data from Electronic Health Records between 2017 and 2021. Patients aged ≥ 40 years, and with at least one general practitioner (GP) appointment in the 3 years before 31st of December 2019, were included. CVD risk categories were determined based on 2021 ESC prevention guidelines. HRU encompassed hospital data (hospitalizations, outpatient and emergency room visits) and GP appointments. Total direct costs per patient were calculated based on the reference cost of the Portuguese legislation for payment methodology on Diagnosis-Related Groups (DRGs). Results: Analysis of 3 122 695 episodes, revealed consistent HRU and costs across the five years. Very high-risk patients, showed higher HRU, particularly in hospital admissions. Costs tended to rise with higher CVD risk level. Very high-risk patients with ASCVD had higher costs for hospital admissions, while low-to-moderate risk patients had higher costs for GP visits. Despite a smaller proportion, very high-risk patients with prior ASCVD represent the highest costs per patient across healthcare settings (from 115€ in emergency visits to 2 673€ in hospitalizations), followed by very high-risk patients without prior ASCVD (ASCVD-risk equivalents). Conclusion: This study revealed a substantial HRU and costs by patients with very high CVD risk, particularly those with prior ASCVD. Moreover, ASCVD-risk equivalents emerge as notable consumers, emphasizing the importance of risk assessment and preventive measures in cost-effective management of these patients. [ABSTRACT FROM AUTHOR]

Background: In developing nations, myocardial infarction (MI) remains a significant contributor to deaths from sudden cardiac arrest, with diet playing a key role in its incidence through oxidative stress mechanisms. Although the connection between the Dietary Antioxidant Index (DAI) and cardiovascular diseases has been demonstrated in some studies, the relationship between DAI and MI has not been extensively explored. Therefore, this research aims to investigate this association. Methods: We conducted a nested case-control study involving 156 MI cases and 312 healthy controls, utilizing data from the Fasa Adults Cohort Study (FACS), a population-based study of individuals aged 35–70 residing in Fasa, Iran, with 11,097 participants included at baseline. The DAI was determined by normalizing the intake values of six dietary vitamins and minerals, adjusting by subtracting the global mean, and then dividing by the global standard deviation. MI diagnosis was established by an experienced cardiologist using electronic medical records. Conditional logistic regression was employed to examine the association between DAI and MI. Results: There were no significant differences between the case and control groups in terms of age (P = 0.96), gender distribution (P = 0.98), and education level (P = 0.38). In a multiple conditional logistic regression analysis, after adjusting for key variables—including body mass index (BMI), smoking status, education level, and serum levels of triglycerides (TG), low-density lipoprotein (LDL), high-density lipoprotein (HDL), total cholesterol (TC), fasting blood sugar (FBS), saturated fatty acids (SFA), and polyunsaturated fatty acids (PUFA)—an inverse association was found between DAI and the risk of myocardial infarction (MI) [adjusted Odds Ratio (Adj OR) = 0.88, 95% Confidence Interval (CI): 0.85–0.92; P < 0.001]. Conclusions: This study highlights the crucial role of the DAI in reducing the risk of myocardial infarction. Promoting diets rich in antioxidants presents a straightforward and effective strategy for MI prevention and the promotion of cardiovascular health, underscoring the novelty and significance of this research in dietary approaches to disease prevention. [ABSTRACT FROM AUTHOR]

Background: Despite calls for regionalizing pancreatic cancer (PC) care to high-volume centers (HVCs), many patients with PC elect to receive therapy closer to their home or at multiple institutions. In the context of cross-institutional PC care, the challenges associated with coordinating care are poorly understood. Methods: In this qualitative study we conducted semi-structured interviews with oncology clinicians from a HVC (n = 9) and community-based hospitals (n = 11) to assess their perspectives related to coordinating the care of and treating PC patients across their respective institutions. Interviews were transcribed, coded, and analyzed using deductive and inductive approaches to identify themes related to cross-institutional coordination challenges and to note improvement opportunities. Results: Clinicians identified challenges associated with closed-loop communication due, in part, to not having access to a shared electronic health record. Challenges with patient co-management were attributed to patients receiving inconsistent recommendations from different clinicians. To address these challenges, participants suggested several improvement opportunities such as building rapport with clinicians across institutions and updating tumor board processes. The opportunity to update tumor board processes was reportedly multi-dimensional and could involve: (1) designating a tumor board coordinator; (2) documenting and disseminating tumor board recommendations; and (3) using teleconferencing to facilitate community-based clinician engagement during tumor board meetings. Conclusions: In light of communication barriers and challenges associated with patient co-management, enabling the development of relationships among PC clinicians and improving the practices of multidisciplinary tumor boards could potentially foster cross-institutional coordination. Research examining how multidisciplinary tumor board coordinators and teleconferencing platforms could enhance cross-institutional communication and thereby improve patient outcomes is warranted. [ABSTRACT FROM AUTHOR]

Introduction: Adrenoleukodystrophy (ALD) patients exhibit three primary clinical phenotypes: primary adrenal insufficiency, adrenomyeloneuropathy, and cerebral demyelination due to the accumulation of saturated very long-chain fatty acids in the adrenal cortex and central nervous system white matter and axons. We investigated the diagnosis of adrenal insufficiency (AI) and the use of mineralocorticoid treatment in male ALD patients. Methods: A retrospective chart review of electronic medical records was conducted for all ALD patients at a single institution between January 1, 2011, and December 6, 2021. Results: Among the 437 ALD patients, 82% were male and 18% were female. Of the male ALD patients, 60% (213 out of 358) had a diagnosis of AI, and 39% (84 out of 213) of those with AI were prescribed mineralocorticoid replacement therapy. Conclusion: AI is highly prevalent among ALD patients, with approximately 40% of those with a diagnosis of AI undergoing mineralocorticoid replacement therapy. Further research is warranted to delineate the characteristics of patients predisposed to developing mineralocorticoid deficiency within the context of ALD and AI. [ABSTRACT FROM AUTHOR]

Background: An increase in the prevalence of HIV drug resistance (HIVDR) has been reported in recent years, especially in persons on non-nucleoside reverse transcriptase inhibitors (NNRTIs) due to their low genetic barrier to mutations. However, there is a paucity of epidemiological data quantifying HIVDR in the era of new drugs like dolutegravir (DTG) in sub-Saharan Africa. We, therefore, sought to determine the prevalence and correlates of viral load (VL) suppression in adult people with HIV (PWH) on a fixed-dose combination of tenofovir disoproxil fumarate/lamivudine/dolutegravir (TLD) or tenofovir alafenamide/emtricitabine/dolutegravir (TAFED) and describe patterns of mutations in individuals failing treatment. Methods: We conducted a cross-sectional study among 384 adults living with HIV aged ≥15 years between 5th June 2023 and 10th August 2023. Demographic, laboratory and clinical data were collected from electronic health records using a data collection form. Viral load suppression was defined as plasma HIV-1 RNA VL of <1000 copies/ml after being on ART for ≥ 6 months. SPSS version 22 to analyze the data. Descriptive statistics and logistic regression were the statistical methods used. Results: The median (interquartile range (IQR)) age was 22 (IQR 18, 38) years, and 66.1% (n = 254) were females. VL suppression was 90.4% (n = 347); (95% confidence interval (CI) 87.6%-93.6%) after switching to TLD/TAFED. Among the virally suppressed, the majority (67.1%, n = 233) were female. Those who missed ≥2 doses in the last 30 days prior to the most recent review were less likely to attain viral suppression compared to those who did not miss any dose (adjusted odds ratio (AOR) 0.047; 95% CI 0.016–0.136; p<0.001). Four participants had resistance mutations to lamivudine and tenofovir. The most common NRTI mutations were M184MV and K65R while K101E was the most common NNRTI mutation. Conclusion: Our findings show that viral suppression was high after switching to TLD/TAFED; but lower than the last 95% target of the UNAIDS. Adherence to antiretroviral therapy was a significant correlate of VL suppression. We, therefore, recommend prompt switching of PWH to TLD/TAFED regimen and close monitoring to enhance adherence to therapy. [ABSTRACT FROM AUTHOR]

Interest in artificial intelligence (AI) applications for ulcerative colitis (UC) has grown tremendously in recent years. In the past 5 years, there have been over 80 studies focused on machine learning (ML) tools to address a wide range of clinical problems in UC, including diagnosis, prognosis, identification of new UC biomarkers, monitoring of disease activity, and prediction of complications. AI classifiers such as random forest, support vector machines, neural networks, and logistic regression models have been used to model UC clinical outcomes using molecular (transcriptomic) and clinical (electronic health record and laboratory) datasets with relatively high performance (accuracy, sensitivity, and specificity). Application of ML algorithms such as computer vision, guided image filtering, and convolutional neural networks have also been utilized to analyze large and high-dimensional imaging datasets such as endoscopic, histologic, and radiological images for UC diagnosis and prediction of complications (post-surgical complications, colorectal cancer). Incorporation of these ML tools to guide and optimize UC clinical practice is promising but will require large, high-quality validation studies that overcome the risk of bias as well as consider cost-effectiveness compared to standard of care. Plain language summary: Artificial intelligence in ulcerative colitis Ulcerative colitis (UC) is a chronic inflammatory disorder of the colon. The clinical care of patients with UC and research efforts to better understand the disease has inevitably produced a significant quantity of diverse and complex datasets ranging from electronic health records, laboratory values, images (endoscopy, radiology, histology) to gene expression. The size and complexity of datasets derived from UC poses a significant challenge to accurately and effectively predict clinically meaningful endpoints in order to ultimately improve UC outcomes. Artificial intelligence through the application of machine learning tools has the potential to improve the analysis of large, complex, high-dimensional datasets and reveal novel, deeper insights compared to traditional analytical tools. Here, we provide an updated and comprehensive summary of AI applications in UC. [ABSTRACT FROM AUTHOR]

Objective: This study aims to examine the validity of the MFS by analyzing the electronic medical records on fall risk in obstetrics and gynecology wards and determine the optimal cut-off score of the Morse Fall Scale. Design: A retrospective survey. Methods: The research was conducted in an Obstetrics and Gynecology Hospital and a general hospital. The sample included 136 fall inpatients and 120 no-fall inpatients recruited from January 1st, 2020, to July 10th, 2022. The Morse Fall Scale was analyzed using the gold standard of patients who fell while hospitalized, assessing the area under the Receiver Operating Characteristic curve, sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and Kappa. Results: At cut-off scores of 40, 45,50, and 55, the area under the Receiver Operating Characteristic curve was 0.772, 0.761, 0.749, and 0.763, respectively. The Youden index was 0.543, 0.521, 0.498, and 0.525, while Kappa values were 0.540, 0.518, 0.490, and 0.515. Sensitivity was 0.735, 0.713, 0.640, and 0.625; specificity was 0.808, 0.808, 0.858, and 0.900. The positive predictive values were 0.813, 0.808, 0.837, and 0.876, and the negative predictive values were 0.729, 0.713, 0.678, and 0.679. Accuracy were 0.770, 0.758, 0.742, and 0.754. Conclusions: The Morse Fall Scale demonstrates good predictive performance for assessing fall risk in gynecology and obstetrics wards. The optimal cut-off score is 40. [ABSTRACT FROM AUTHOR]

Introduction: Deprescribing fall-risk increasing drugs (FRIDs) is promising for reducing the risk of falling in older adults. Applying appropriate deprescribing in practice can be difficult due to the outcome uncertainties associated with stopping FRIDs. The ADFICE_IT intervention addresses this complexity with a clinical decision support system (CDSS) that facilitates optimum deprescribing of FRIDs by using a fall-risk prediction model, aggregation of deprescribing guidelines, and joint medication management. Methods: The development process of the CDSS is described in this paper. Development followed a user-centered design approach in which users and experts were involved throughout each phase. In phase I, a prototype of the CDSS was developed which involved a literature and systematic review, European survey (n = 581), and semi-structured interviews with clinicians (n = 19), as well as the aggregation and testing of deprescribing guidelines and the development of the fall-risk prediction model. In phase II, the feasibility of the CDSS was tested by means of two usability testing rounds with users (n = 11). Results: The final CDSS consists of five web pages. A connection between the Electronic Health Record allows for the retrieval of patient data into the CDSS. Key design requirements for the CDSS include easy-to-use features for fast-paced clinical environments, actionable deprescribing recommendations, information transparency, and visualization of the patient's fall-risk estimation. Key elements for the software include a modular architecture, open source, and good security. Conclusion: The ADFICE_IT CDSS supports physicians in deprescribing FRIDs optimally to prevent falls in older patients. Due to continuous user and expert involvement, each new feedback round led to an improved version of the system. Currently, a cluster-randomized controlled trial with process evaluation at hospitals in the Netherlands is being conducted to test the effect of the CDSS on falls. The trial is registered with ClinicalTrials.gov (date; 7-7-2022, identifier: NCT05449470). [ABSTRACT FROM AUTHOR]

Background: The Covid Connected Care Center (C4), a low-barrier telephone nurse hotline, was developed at an academic medical center to increase access to healthcare information and services across the state of Oregon, including to those without a usual source of care. Other studies have demonstrated that telephone triage services can positively influence health behaviors, but it is not known how this effect is maintained across racial/ethnic groups. The objective of this study was to show that the C4 reached throughout the state of Oregon, was valuable to callers, and that recommendations given affected callers' subsequent health-related behaviors. Methods: This mixed-methods study, informed by the RE-AIM (Reach, Effectiveness, Addoption, Implementation and Maintenance) framework, assessed caller demographics and clinical care from March 30 2020 until September 8, 2021. Descriptive statistics, multivariable risk models and Zou's modified Poisson modeling were applied to electronic health record and call system data; An inductive approach was used for patient and staff experience surveys and semi-structured interviews. Approval was obtained from the OHSU Institutional Review Board (Study 00021413). Results: 145,537 telephone calls and 92,100 text-based contacts (61% and 39%, respectively) were included. Callers tended to not have a usual source of primary care and utilized recommended services. Emergency department utilization was minimal (1.5%). Racial or ethnic disparities were not detected in the recommendations, but Black (RR 0.92, CI 0.86–0.98) and Multiracial (RR 0.90 CI 0.81–0.99) callers were less likely than non-Hispanic white callers to receive a COVID-19 test. Participants in the post-call survey (n = 50) would recommend this service to friends or family. Interviews with callers (n = 9) revealed this was because they valued assistance translating general recommendations into a personalized care plan. C4 staff interviewed (n = 9) valued the opportunity to serve the public. The C4 was a trusted resource to the public and reached the intended audiences. However, disparities in access to COVID-19 testing persisted. Conclusions: Nursing triage hotlines can guide caller behavior and be an effective part of a robust public health information infrastructure. [ABSTRACT FROM AUTHOR]

Aims Methods and results Conclusion Abnormalities in specific echocardiographic parameters and cardiac biomarkers have been reported among individuals with diabetes. However, a comprehensive characterization of diabetic cardiomyopathy (DbCM), a subclinical stage of myocardial abnormalities that precede the development of clinical heart failure (HF), is lacking. In this study, we developed and validated a machine learning‐based clustering approach to identify the high‐risk DbCM phenotype based on echocardiographic and cardiac biomarker parameters.Among individuals with diabetes from the Atherosclerosis Risk in Communities (ARIC) cohort who were free of cardiovascular disease and other potential aetiologies of cardiomyopathy (training, n = 1199), unsupervised hierarchical clustering was performed using echocardiographic parameters and cardiac biomarkers of neurohormonal stress and chronic myocardial injury (total 25 variables). The high‐risk DbCM phenotype was identified based on the incidence of HF on follow‐up. A deep neural network (DeepNN) classifier was developed to predict DbCM in the ARIC training cohort and validated in an external community‐based cohort (Cardiovascular Health Study [CHS]; n = 802) and an electronic health record (EHR) cohort (n = 5071). Clustering identified three phenogroups in the derivation cohort. Phenogroup‐3 (n = 324, 27% of the cohort) had significantly higher 5‐year HF incidence than other phenogroups (12.1% vs. 4.6% [phenogroup 2] vs. 3.1% [phenogroup 1]) and was identified as the high‐risk DbCM phenotype. The key echocardiographic predictors of high‐risk DbCM phenotype were higher NT‐proBNP levels, increased left ventricular mass and left atrial size, and worse diastolic function. In the CHS and University of Texas (UT) Southwestern EHR validation cohorts, the DeepNN classifier identified 16% and 29% of participants with DbCM, respectively. Participants with (vs. without) high‐risk DbCM phenotype in the external validation cohorts had a significantly higher incidence of HF (hazard ratio [95% confidence interval] 1.61 [1.18–2.19] in CHS and 1.34 [1.08–1.65] in the UT Southwestern EHR cohort).Machine learning‐based techniques may identify 16% to 29% of individuals with diabetes as having a high‐risk DbCM phenotype who may benefit from more aggressive implementation of HF preventive strategies. [ABSTRACT FROM AUTHOR]

Background: The utilization of genomic information to improve health outcomes is progressively becoming more common in clinical practice. Nonetheless, disparities persist in accessing genetic services among ethnic minorities, individuals with low socioeconomic status, and other vulnerable populations. The Rio Grande Valley (RGV) at the Texas-Mexico border is predominantly Hispanic/Latino with a high poverty rate and very limited access to genetic services. Funded by the National Center for Advancing Translational Sciences, Project GIVE (Genetic Inclusion by Virtual Evaluation) was launched in 2022 to reduce the time to diagnosis and increase provider knowledge of genomics in this region, with the goal of improving pediatric health outcomes. We describe our experience of establishing a virtual pediatric genomic service in this region to expeditiously identify, recruit, and evaluate pediatric patients with undiagnosed diseases. Methods: We have utilized an innovative electronic health record (EHR) agnostic virtual telehealth and educational platform called Consultagene to receive referrals from healthcare providers in the RGV. Using this portal, genetic services, including virtual evaluation and genome sequencing (GS), are being delivered to children with rare diseases. The study has also integrated effective methods to involve and educate community providers through in-person meetings and Continuing Professional Education (CPE) events. Results: The recruitment efforts have proven highly successful with the utilization of Consultagene in this medically underserved region. The project's ongoing engagement efforts with local healthcare providers have resulted in progressively more referrals to the study over time, thus improving inclusion and access to genomic care in the RGV. Additionally, the curated CPE content has been well received by healthcare providers in the region. Conclusions: Project GIVE study has allowed advanced genetic evaluation and delivery of GS through the virtual Consultagene portal, effectively circumventing the recognized socioeconomic and logistical barriers to accessing genetic services within this border community. [ABSTRACT FROM AUTHOR]

Transferring and replicating predictive algorithms across healthcare systems constitutes a unique yet crucial challenge that needs to be addressed to enable the widespread adoption of machine learning in healthcare. In this study, we explored the impact of important differences across healthcare systems and the associated Electronic Health Records (EHRs) on machine-learning algorithms to predict mental health crises, up to 28 days in advance. We evaluated both the transferability and replicability of such machine learning models, and for this purpose, we trained six models using features and methods developed on EHR data from the Birmingham and Solihull Mental Health NHS Foundation Trust in the UK. These machine learning models were then used to predict the mental health crises of 2907 patients seen at the Rush University System for Health in the US between 2018 and 2020. The best one was trained on a combination of US-specific structured features and frequency features from anonymized patient notes and achieved an AUROC of 0.837. A model with comparable performance, originally trained using UK structured data, was transferred and then tuned using US data, achieving an AUROC of 0.826. Our findings establish the feasibility of transferring and replicating machine learning models to predict mental health crises across diverse hospital systems. [ABSTRACT FROM AUTHOR]

Introduction/objectives: Primary health care visits post-discharge could potentially play an important role in efforts of reducing hospital readmission. Focusing on a single or a particular type of visit obscures nuances in types of primary care contacts over time and fails to quantify the intensity of primary health care visits during the follow-up period. The aim of this study was to explore associations between the number and type of primary health care visits post-discharge and the risk of hospital readmission within 30 days. Methods: A register-based closed cohort study. The study population of 6135 individuals were residents of Stockholm who were discharged home from any of the 3 geriatric inpatient departments, excluding those who were readmitted within the next 24 h. The dependent variable was hospital readmission within 30 days of discharge. The key independent variable was the number and type of primary health care visits in 30 days post-discharge. Cox-regression with time-varying covariates was employed for data analyses. Results: Approximately, 12% of the participants were readmitted to hospital within 30 days. There was no statistically significant association between number of primary care visits post-discharge and readmission (HR 1.00; 95% CI 1.00-1.01). Compared to no primary health care visit, no statistically significant association were found for administrative care related visits (HR 0.33, 95%CI 0.08-1.33), clinic visits (HR 0.93, 95%CI 0.71-1.21), home visits (HR 1.03, 95%CI 0.84-1.27), or team visits (HR 0.76, 95%CI 0.54-1.07). Conclusions: There were no associations between primary health care visits post-discharge and hospital readmission after geriatric inpatient care. Further studies using survey or qualitative approaches can provide insights into the factors that are relevant to post-discharge care but are unavailable in this type of register data studies. [ABSTRACT FROM AUTHOR]

Introduction: Beyond our population growing older and living longer, there is an increased risk of developing a cognitive disorder. Standardized screening during a routine visit in primary care may be ideal for early detection of mild cognitive impairment (MCI) and follow-up for cognitive changes. Aim: This quality improvement (QI) project aimed to determine the impact of implementing the Mini-Cog© quick screening for early dementia detection to identify and follow up on the cognitive impairment of older adults in a primary care clinic setting. Methods: Implementation started in February 2024 in a primary care clinic in Southern California. Data was collected for this project over a total of 16 weeks. This QI project implemented a routine cognitive screening using the Mini-Cog©. Cognitive impairment was identified, and if indicated by the Mini-Cog© scores, follow-up for a cognitive assessment and care plan services were initiated. Data were obtained from the project site's electronic medical record on a total sample size of 471 participants (n = 382 in the pre-implementation group and n = 89 in the post-implementation group). Results: Pearson's chi-square test indicated a statistically significant improvement in the identification rate of cognitive impairment, increasing from 11.8% (n = 45 out of 382) at pre-implementation to 34.8% (n = 31 out of 89) at post-implementation, and specifically, mild cognitive impairment increased from zero identified in pre-implementation to 12.4% (n = 11 out of 89) post-implementation. Lastly, follow-up rates improved from 91.1% (n = 41 out of 45) to 100% (n = 31 out of 31) in post-implementation, and clinical significance was evident based on the phi-coefficient (φ = 0.196), indicating a small effect size and a 100% follow-up rate. Conclusions: The findings of this project suggest older adults should receive cognitive screenings to help identify early cognitive impairment and increase follow-up for further evaluation, treatment, and advanced care planning. [ABSTRACT FROM AUTHOR]

Background: This study describes how New York City (NYC) Health + Hospitals implemented a large-scale Community Health Worker (CHW) program in adult primary care clinics between January 2022 and December 2023 and established metrics to monitor program implementation. This study is timely as healthcare systems consider how to scale high-quality CHW programs. Methods: We collected metrics in the following areas: (1) Workforce demographics, team structure, and training; (2) Enrolled patient demographics; (3) Patient-centered metrics, such as patient counts (e.g. patients outreached and enrolled) and engagement (e.g. median time in program, caseloads per CHW), and goals (e.g. median number of goals identified and completed). Metrics are based on standard data elements captured through CHW documentation in the electronic health record collected during program implementation. Data cleaning is completed using SQL queries and R scripts. Results: In June 2023, there were a total of 97 CHW and 22 CHW Supervisor staff lines in adult primary care across 17 healthcare sites. There were 4.6 CHWs to 1 CHW supervisor on average though this ranged by facility from 1:1 to 1:6. Compared to the population that receives primary care at NYC H + H, CHWs served more African American/Black patients (40% vs. 32%) and an older patient population (35% older than 65 vs. 21% older than 65). From January 2022 to December 2023, 13,812 patients were outreached by CHWs. Of these, 9,069 (66%) were referred by clinicians, 7,331 (53%) were enrolled, and 5,044 (37%) successfully graduated. The median number of goals identified by patients was four, and the median number of goals completed with a CHW per patient was three. The top three goals were primary care engagement (47%), specialty care engagement (46%), and food insecurity (45%). Conclusion: Establishing clear implementation and process metrics helps to ensure that CHWs embedded in health systems can meaningfully engage adult patients in health care, address patient-centered goals, and connect patients to community and government services. [ABSTRACT FROM AUTHOR]