Compass Rose™ Implementation in a Large Academic Medical Center.

Background: Compass Rose™, a case management tool developed by Epic®, was designed to track various patient coordination tasks, outreaches, and outcomes. This report describes the implementation of Compass Rose™ within an internal health-system specialty pharmacy (HSSP) and changes in care coordination metrics before and after implementation. To the best of our knowledge, this is the first study of its kind to discuss the implementation of Compass Rose™. Objectives: The goals of this study were to describe the implementation process of Compass Rose™ at an internal HSSP and compare staff satisfaction before and after Compass Rose™ as the primary outcome. Methods: This was an Institutional Review Board exempt, retrospective cohort study conducted between June 2022 to December 2022 that assessed staff satisfaction, refill documentation time, prescription turnaround time, and patient satisfaction pre- and post- Compass Rose™ implementation through survey administration, observed time studies, and internal data reports. The process of Compass Rose™ implementation was also described and discussed. Results: 24 specialty pharmacy staff members participated in the Compass Rose™ implementation survey. No statistically significant differences were observed in either staff satisfaction (3.96 ± 0.95 versus 3.70 ± 0.69, p = 0.29) or predicted versus actual challenge of implementation (3.67 ± 1.17 versus 3.09 ± 0.96, p = 0.064). There was no significant difference in refill documentation time pre- versus post- Compass Rose™ implementation (4.22 ± 3.15 minutes versus 4.10 ± 2.36 minutes, p = 0.82); however, there was a statistically significant increase in prescription turnaround time post implementation (2.59 ± 2.85 days versus 2.69 ± 2.35 days, p = 0.002). Conclusion: Compass Rose™ implementation had no significant impact on staff satisfaction, patient satisfaction, or overall refill documentation time. Prescription turnaround time increased, which could be due to significant workflow changes with Compass Rose™ or several other contributing factors such as increased prescription volume and training new staff during this period.Benefits of Compass Rose™ included standardization of workflow, ability to quantify staff performance and clinical impact, and increased transparency regarding care provided by the specialty pharmacy team.

Development and Optimization of Machine Learning Algorithms for Predicting In-hospital Patient Charges for Congestive Heart Failure Exacerbations, Chronic Obstructive Pulmonary Disease Exacerbations and Diabetic Ketoacidosis.

Background: Hospitalizations for exacerbations of congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD) and diabetic ketoacidosis (DKA) are costly in the United States. The purpose of this study was to predict in-hospital charges for each condition using machine learning (ML) models. Results: We conducted a retrospective cohort study on national discharge records of hospitalized adult patients from January 1st, 2016, to December 31st, 2019. We used numerous ML techniques to predict in-hospital total cost. We found that linear regression (LM), gradient boosting (GBM) and extreme gradient boosting (XGB) models had good predictive performance and were statistically equivalent, with training R-square values ranging from 0.49-0.95 for CHF, 0.56-0.95 for COPD, and 0.32-0.99 for DKA. We identified important key features driving costs, including patient age, length of stay, number of procedures. and elective/nonelective admission. Conclusions: ML methods may be used to accurately predict costs and identify drivers of high cost for COPD exacerbations, CHF exacerbations and DKA. Overall, our findings may inform future studies that seek to decrease the underlying high patient costs for these conditions.

The use of natural language processing for the identification of ageing syndromes including sarcopenia, frailty and falls in electronic healthcare records: a systematic review.

BACKGROUND: Recording and coding of ageing syndromes in hospital records is known to be suboptimal. Natural Language Processing algorithms may be useful to identify diagnoses in electronic healthcare records to improve the recording and coding of these ageing syndromes, but the feasibility and diagnostic accuracy of such algorithms are unclear. METHODS: We conducted a systematic review according to a predefined protocol and in line with Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. Searches were run from the inception of each database to the end of September 2023 in PubMed, Medline, Embase, CINAHL, ACM digital library, IEEE Xplore and Scopus. Eligible studies were identified via independent review of search results by two coauthors and data extracted from each study to identify the computational method, source of text, testing strategy and performance metrics. Data were synthesised narratively by ageing syndrome and computational method in line with the Studies Without Meta-analysis guidelines. RESULTS: From 1030 titles screened, 22 studies were eligible for inclusion. One study focussed on identifying sarcopenia, one frailty, twelve falls, five delirium, five dementia and four incontinence. Sensitivity (57.1%-100%) of algorithms compared with a reference standard was reported in 20 studies, and specificity (84.0%-100%) was reported in only 12 studies. Study design quality was variable with results relevant to diagnostic accuracy not always reported, and few studies undertaking external validation of algorithms. CONCLUSIONS: Current evidence suggests that Natural Language Processing algorithms can identify ageing syndromes in electronic health records. However, algorithms require testing in rigorously designed diagnostic accuracy studies with appropriate metrics reported.

A Novel Radiology Communication Tool to Reduce Workflow Interruptions: Clinical Evaluation of RadConnect.

Despite the importance of communication, radiology departments often depend on communication tools that were not created for the unique needs of imaging workflows, leading to frequent radiologist interruptions. The objective of this study was test the hypothesis that a novel asynchronous communication tool for the imaging workflow (RadConnect) reduces the daily average number of synchronous (in-person, telephone) communication requests for radiologists. We conducted a before-after study. Before adoption of RadConnect, technologists used three conventional communication methods to consult radiologists (in-person, telephone, general-purpose enterprise chat (GPEC)). After adoption, participants used RadConnect as a fourth method. Technologists manually recorded every radiologist consult request related to neuro and thorax CT scans in the 40 days before and 40 days after RadConnect adoption. Telephone traffic volume to section beepers was obtained from the hospital telephone system for the same period. The value and usability experiences were collected through an electronic survey and structured interviews. RadConnect adoption resulted in 53% reduction of synchronous (in-person, telephone) consult requests: from 6.1 ± 4.2 per day to 2.9 ± 2.9 (P < 0.001). There was 77% decrease (P < 0.001) in telephone volume to the neuro and thorax beepers, while no significant volume change was noted to the abdomen beeper (control group). Survey responses (46% response rate) and interviews confirmed the positive impact of RadConnect on interruptions. RadConnect significantly reduced radiologists' telephone interruptions. Study participants valued the role-based interaction and prioritized worklist overview in the survey and interviews. Findings from this study will contribute to a more focused work environment.

An exploratory deep learning approach to investigate tuberculosis pathogenesis in nonhuman primate model: Combining automated radiological analysis with clinical and biomarkers data.

BACKGROUND: Tuberculosis (TB) kills approximately 1.6 million people yearly despite the fact anti-TB drugs are generally curative. Therefore, TB-case detection and monitoring of therapy, need a comprehensive approach. Automated radiological analysis, combined with clinical, microbiological, and immunological data, by machine learning (ML), can help achieve it. METHODS: Six rhesus macaques were experimentally inoculated with pathogenic Mycobacterium tuberculosis in the lung. Data, including Computed Tomography (CT), were collected at 0, 2, 4, 8, 12, 16, and 20 weeks. RESULTS: Our ML-based CT analysis (TB-Net) efficiently and accurately analyzed disease progression, performing better than standard deep learning model (LLM OpenAI's CLIP Vi4). TB-Net based results were more consistent than, and confirmed independently by, blinded manual disease scoring by two radiologists and exhibited strong correlations with blood biomarkers, TB-lesion volumes, and disease-signs during disease pathogenesis. CONCLUSION: The proposed approach is valuable in early disease detection, monitoring efficacy of therapy, and clinical decision making.

Building a Hospital Pharmacist Workforce by a Diversified and Position-Oriented Learning System.

Background: The role of hospital pharmacists has shifted from primarily ensuring drug supply to providing comprehensive pharmaceutical care. To accommodate this shift, new positions are needed. The traditional training model for hospital pharmacists is no longer sufficient for the evolving demands of pharmaceutical care and these new roles. This study aimed to describe the development of a position-oriented learning system explicitly tailored for hospital pharmacists and to assess its impact on workforce development and pharmacy service. Methods: The position-oriented learning system for hospital pharmacists, aimed at enhancing training and workforce development, was evaluated based on two critical criteria: the completion rate of learning modules and the subsequent improvement in pharmaceutical care at the hospital. The completion rate assessed the engagement and effectiveness of the training content. At the same time, the improvement in pharmaceutical care evaluated practical outcomes such as percentages of patients who received pharmaceutical care and percentages of inappropriate medication orders intercepted. Results: In 2021, 218 employees participated in the learning system. The pharmacy department has identified 22 pharmacists for various positions through this system. The quantity and quality of pharmaceutical care have improved significantly. Conclusion: The position-oriented diversified learning system achieves the perfect combination of department development direction and individual career planning of employees. The learning system can significantly improve the learning efficiency of pharmacists, enhance the quality of various pharmaceutical care, and promote the development of disciplines.

Advancing equity in breast cancer care: natural language processing for analysing treatment outcomes in under-represented populations.

OBJECTIVE: The study aimed to develop natural language processing (NLP) algorithms to automate extracting patient-centred breast cancer treatment outcomes from clinical notes in electronic health records (EHRs), particularly for women from under-represented populations. METHODS: The study used clinical notes from 2010 to 2021 from a tertiary hospital in the USA. The notes were processed through various NLP techniques, including vectorisation methods (term frequency-inverse document frequency (TF-IDF), Word2Vec, Doc2Vec) and classification models (support vector classification, K-nearest neighbours (KNN), random forest (RF)). Feature selection and optimisation through random search and fivefold cross-validation were also conducted. RESULTS: The study annotated 100 out of 1000 clinical notes, using 970 notes to build the text corpus. TF-IDF and Doc2Vec combined with RF showed the highest performance, while Word2Vec was less effective. RF classifier demonstrated the best performance, although with lower recall rates, suggesting more false negatives. KNN showed lower recall due to its sensitivity to data noise. DISCUSSION: The study highlights the significance of using NLP in analysing clinical notes to understand breast cancer treatment outcomes in under-represented populations. The TF-IDF and Doc2Vec models were more effective in capturing relevant information than Word2Vec. The study observed lower recall rates in RF models, attributed to the dataset's imbalanced nature and the complexity of clinical notes. CONCLUSION: The study developed high-performing NLP pipeline to capture treatment outcomes for breast cancer in under-represented populations, demonstrating the importance of document-level vectorisation and ensemble methods in clinical notes analysis. The findings provide insights for more equitable healthcare strategies and show the potential for broader NLP applications in clinical settings.

Adaptability of prognostic prediction models for patients with acute coronary syndrome during the COVID-19 pandemic.

BACKGROUND: The detrimental repercussions of the COVID-19 pandemic on the quality of care and clinical outcomes for patients with acute coronary syndrome (ACS) necessitate a rigorous re-evaluation of prognostic prediction models in the context of the pandemic environment. This study aimed to elucidate the adaptability of prediction models for 30-day mortality in patients with ACS during the pandemic periods. METHODS: A total of 2041 consecutive patients with ACS were included from 32 institutions between December 2020 and April 2023. The dataset comprised patients who were admitted for ACS and underwent coronary angiography for the diagnosis during hospitalisation. The prediction accuracy of the Global Registry of Acute Coronary Events (GRACE) and a machine learning model, KOTOMI, was evaluated for 30-day mortality in patients with ST-elevation acute myocardial infarction (STEMI) and non-ST-elevation acute coronary syndrome (NSTE-ACS). RESULTS: The area under the receiver operating characteristics curve (AUROC) was 0.85 (95% CI 0.81 to 0.89) in the GRACE and 0.87 (95% CI 0.82 to 0.91) in the KOTOMI for STEMI. The difference of 0.020 (95% CI -0.098-0.13) was not significant. For NSTE-ACS, the respective AUROCs were 0.82 (95% CI 0.73 to 0.91) in the GRACE and 0.83 (95% CI 0.74 to 0.91) in the KOTOMI, also demonstrating insignificant difference of 0.010 (95% CI -0.023 to 0.25). The prediction accuracy of both models had consistency in patients with STEMI and insignificant variation in patients with NSTE-ACS between the pandemic periods. CONCLUSIONS: The prediction models maintained high accuracy for 30-day mortality of patients with ACS even in the pandemic periods, despite marginal variation observed.

Artificial neural networks analysis predicts long-term fistula function in hemodialysis patients following percutaneous transluminal angioplasty.

Kidney failure is particularly common in the United States, where it affects over 700,000 individuals. It is typically treated through repeated sessions of hemodialysis to filter and clean the blood. Hemodialysis requires vascular access, in about 70% of cases through an arteriovenous fistula (AVF) surgically created by connecting an artery and vein. AVF take 6 weeks or more to mature. Mature fistulae often require intervention, most often percutaneous transluminal angioplasty (PTA), also known as fistulaplasty, to maintain the patency of the fistula. PTA is also the first-line intervention to restore blood flow and prolong the use of an AVF, and many patients undergo the procedure multiple times. Although PTA is important for AVF maturation and maintenance, research into predictive models of AVF function following PTA has been limited. Therefore, in this paper we hypothesize that based on patient-specific information collected during PTA, a predictive model can be created to help improve treatment planning. We test a set of rich, multimodal data from 28 patients that includes medical history, AVF blood flow, and interventional angiographic imaging (specifically excluding any post-PTA measurements) and build deep hybrid neural networks. A hybrid model combining a 3D convolutional neural network with a multi-layer perceptron to classify AVF was established. We found using this model that we were able to identify the association between different factors and evaluate whether the PTA procedure can maintain primary patency for more than 3 months. The testing accuracy achieved was 0.75 with a weighted F1-score of 0.75, and AUROC of 0.75. These results indicate that evaluating multimodal clinical data using artificial neural networks can predict the outcome of PTA. These initial findings suggest that the hybrid model combining clinical data, imaging and hemodynamic analysis can be useful to treatment planning for hemodialysis. Further study based on a large cohort is needed to refine the accuracy and model efficiency.

Creating a digital approach for promoting physical activity in pediatric pulmonary hypertension: A framework for future interventions.

Children with pulmonary hypertension (PH) often demonstrate limited exercise capacity. Data support exercise as an effective nonpharmacologic intervention among adults with PH. However, data on exercise training in children and adolescents are limited, and characteristics of the optimal exercise program in pediatric PH have not been identified. Exercise programs may have multiple targets, including muscle deficits which are associated with exercise limitations in both adult and pediatric PH. Wearable accelerometer sensors measure physical activity volume and intensity in the naturalistic setting and can facilitate near continuous data transfer and bidirectional communication between patients and the study team when paired with informatics tools during exercise interventions. To address the knowledge gaps in exercise training in pediatric PH, we designed a prospective, single arm, nonrandomized pilot study to determine feasibility and preliminary estimates of efficacy of a 16-week home exercise intervention, targeting lower extremity muscle mass and enriched by wearable mobile health technology. The exercIse Training in pulmONary hypertEnsion (iTONE) trial includes (1) semistructured exercise prescriptions tailored to the participant's baseline level of activity and access to resources; (2) interval goal setting fostering self-efficacy; (3) real time monitoring of activity via wearable devices; (4) a digital platform enabling communication and feedback between participant and study team; (5) multiple avenues to assess participant safety. This pilot intervention will provide information on the digital infrastructure needed to conduct home-based exercise interventions in PH and will generate important preliminary data on the effect of exercise interventions in youth with chronic cardiorespiratory conditions to power larger studies in the future.

Machine Learning-based Cerebral Venous Thrombosis Diagnosis with Clinical Data.

OBJECTIVES: Cerebral Venous Thrombosis (CVT) poses diagnostic challenges due to the variability in disease course and symptoms. The prognosis of CVT relies on early diagnosis. Our study focuses on developing a machine learning-based screening algorithm using clinical data from a large neurology referral center in southern Iran. METHODS: The Iran Cerebral Venous Thrombosis Registry (ICVTR code: 9001013381) provided data on 382 CVT cases from Namazi Hospital. The control group comprised of adult headache patients without CVT as confirmed by neuroimaging and was retrospectively selected from those admitted to the same hospital. We collected 60 clinical and demographic features for model development and validation. Our modeling pipeline involved imputing missing values and evaluating four machine learning algorithms: generalized linear model, random forest, support vector machine, and extreme gradient boosting. RESULTS: A total of 314 CVT cases and 575 controls were included. The highest AUROC was reached when imputation was used to estimate missing values for all the variables, combined with the support vector machine model (AUROC=0.910, Recall=0.73, Precision=0.88). The best recall was achieved also by the support vector machine model when only variables with less than 50% missing rate were included (AUROC=0.887, Recall=0.77, Precision=0.86). The random forest model yielded the best precision by using variables with less than 50% missing rate (AUROC=0.882, Recall=0.61, Precision=0.94). CONCLUSION: The application of machine learning techniques using clinical data showed promising results in accurately diagnosing CVT within our study population. This approach offers a valuable complementary assistive tool or an alternative to resource-intensive imaging methods.

Assessment of the information provided by ChatGPT regarding exercise for patients with type 2 diabetes: a pilot study.

OBJECTIVES: We assessed the feasibility of ChatGPT for patients with type 2 diabetes seeking information about exercise. METHODS: In this pilot study, two physicians with expertise in diabetes care and rehabilitative treatment in Republic of Korea discussed and determined the 14 most asked questions on exercise for managing type 2 diabetes by patients in clinical practice. Each question was inputted into ChatGPT (V.4.0), and the answers from ChatGPT were assessed. The Likert scale was calculated for each category of validity (1-4), safety (1-4) and utility (1-4) based on position statements of the American Diabetes Association and American College of Sports Medicine. RESULTS: Regarding validity, 4 of 14 ChatGPT (28.6%) responses were scored as 3, indicating accurate but incomplete information. The other 10 responses (71.4%) were scored as 4, indicating complete accuracy with complete information. Safety and utility scored 4 (no danger and completely useful) for all 14 ChatGPT responses. CONCLUSION: ChatGPT can be used as supplementary educational material for diabetic exercise. However, users should be aware that ChatGPT may provide incomplete answers to some questions on exercise for type 2 diabetes.

Artificial intelligence in medicine and healthcare: Opportunity and/or threat.

The aim of the article to present the development of artificial intelligence (AI) methods and their applications in medicine and health care. Current technological development contributes to generation of large volumes of data that cannot be evaluated only manually. We describe the process of patient care and its individual parts that can be supported by technology and data analysis methods. There are many successful applications that help in the decision support process, in processing complex multidimensional heterogeneous and/or long-term data. On the other side, failures appear in AI methods applications. In recent years, deep learning became very popular and to a certain extend it delivered promising results. However, it has certain flaws that might lead to misclassification. The correct methodological steps in design and implementation of selected methods to data processing are briefly presented.

Study protocol: Collaboration Oriented Approach to Controlling High blood pressure (COACH) in adults - a randomised controlled trial.

INTRODUCTION: Hypertension, the clinical condition of persistent high blood pressure (BP), is preventable yet remains a significant contributor to poor cardiovascular outcomes. Digital self-management support tools can increase patient self-care behaviours to improve BP. We created a patient-facing and provider-facing clinical decision support (CDS) application, called the Collaboration Oriented Approach to Controlling High BP (COACH), to integrate home BP data, guideline recommendations and patient-centred goals with primary care workflows. We leverage social cognitive theory principles to support enhanced engagement, shared decision-making and self-management support. This study aims to measure the effectiveness of the COACH intervention and evaluate its adoption as part of BP management. METHODS AND ANALYSIS: The study design is a multisite, two-arm hybrid type III implementation randomised controlled trial set within primary care practices across three health systems. Randomised participants are adults with high BP for whom home BP monitoring is indicated. The intervention arm will receive COACH, a digital web-based intervention with effectively enhanced alerts and displays intended to drive engagement with BP lowering; the control arm will receive COACH without the alerts and a simple display. Outcome measures include BP lowering (primary) and self-efficacy (secondary). Implementation preplanning and postevaluation use the Consolidated Framework for Implementation Research and Reach-Effectiveness-Adoption-Implementation-Maintenance metrics with iterative cycles for qualitative integration into the trial and its quantitative evaluation. The trial analysis includes logistic regression and constrained longitudinal data analysis. ETHICS AND DISSEMINATION: The trial is approved under a single IRB through the University of Missouri-Columbia, #2091483. Dissemination of the intervention specifications and results will be through open-source mechanisms. TRIAL REGISTRATION NUMBER: NCT06124716.

Analysis of the impact of a university distance learning course on digitalization in medicine on students and healthcare professionals.

PURPOSE: The public medical universities in Austria (educating 11,000 students) developed a joint public distance learning series in which clinicians discussed current digital lighthouse projects in their specialty. This study aims to examine the changes in attitude and knowledge of the participants before and after the lecture series to gain insights for future curriculum developments. METHOD: The lecture series was announced via various channels at the universities, in health newsletters and in social media. Attitudes toward digitalization in medicine were surveyed before and after the lecture series, together with demographic data. The data were analyzed statistically and descriptively for four groups of interest: female medical students, male medical students, faculty members and members from industry and public agencies. RESULTS: Out of 351 subjects who attended at least 1 lecture, 117 took part in the survey before and 47 after the lectures. Most participants had a positive attitude towards digitalization (85.3%). They improved their self-assessment of their knowledge from 34.4% to 64.7% (p < 0.05). After the lecture series 55.8% of participants considered digital medical applications to be important or very important today and 68.6% in the future. CONCLUSION: The study shows that the presentation and discussion of lighthouse projects improves understanding of digitalization in medicine but does not trigger a strong desire for additional further training.

Implementation barriers and facilitators of remote monitoring, remote consultation and digital care platforms through the eyes of healthcare professionals: a review of reviews.

OBJECTIVES: Digital transformation in healthcare is a necessity considering the steady increase in healthcare costs, the growing ageing population and rising number of people living with chronic diseases. The implementation of digital health technologies in patient care is a potential solution to these issues, however, some challenges remain. In order to navigate such complexities, the perceptions of healthcare professionals (HCPs) must be considered. The objective of this umbrella review is to identify key barriers and facilitators involved in digital health technology implementation, from the perspective of HCPs. DESIGN: Systematic umbrella review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. DATA SOURCES: Embase.com, PubMed and Web of Science Core Collection were searched for existing reviews dated up to 17 June 2022. Search terms included digital health technology, combined with terms related to implementation, and variations in terms encompassing HCP, such as physician, doctor and the medical discipline. ELIGIBILITY CRITERIA: Quantitative and qualitative reviews evaluating digital technologies that included patient interaction were considered eligible. Three reviewers independently synthesised and assessed eligible reviews and conducted a critical appraisal. DATA EXTRACTION AND SYNTHESIS: Regarding the data collection, two reviewers independently synthesised and interpreted data on barriers and facilitators. RESULTS: Thirty-three reviews met the inclusion criteria. Barriers and facilitators were categorised into four levels: (1) the organisation, (2) the HCP, (3) the patient and (4) technical aspects. The main barriers and facilitators identified were (lack of) training (n=22/33), (un)familiarity with technology (n=17/33), (loss of) communication (n=13/33) and security and confidentiality issues (n=17/33). Barriers of key importance included increased workload (n=16/33), the technology undermining aspects of professional identity (n=11/33), HCP uncertainty about patients' aptitude with the technology (n=9/33), and technical issues (n=12/33). CONCLUSIONS: The implementation strategy should address the key barriers highlighted by HCPs, for instance, by providing adequate training to familiarise HCPs with the technology, adapting the technology to the patient preferences and addressing technical issues. Barriers on both HCP and patient levels can be overcome by investigating the needs of the end-users. As we shift from traditional face-to-face care models towards new modes of care delivery, further research is needed to better understand the role of digital technology in the HCP-patient relationship.

Statistical analysis of interatomic transfer integrals for exploring high-mobility organic semiconductors.

Charge transport in organic semiconductors occurs via overlapping molecular orbitals quantified by transfer integrals. However, no statistical study of transfer integrals for a wide variety of molecules has been reported. Here we present a statistical analysis of transfer integrals for more than 27,000 organic compounds in the Cambridge Structural Database. Interatomic transfer integrals were used to identify substructures with high transfer integrals. As a result, thione and amine groups as in thiourea were found to exhibit high transfer integrals. Such compounds are considered as potential non-aromatic, water-soluble organic semiconductors.

The analysis of interatomic transfer integrals for 27,718 organic compounds revealed that thione (S=R)­amine (NR₃) and thione­thione interactions tend to increase transfer integrals and are suitable to high­mobility organic semiconductors.

Longitudinal evaluation of an electronic audit and feedback system for patient safety in a large tertiary hospital setting.

Objective: This study sought to assess the impact of a novel electronic audit and feedback (e-A&F) system on patient outcomes. Methods: The e-A&F intervention was implemented in a tertiary hospital and involved near real-time feedback via web-based dashboards. We used a segmented regression analysis of interrupted time series. We modelled the pre-post change in outcomes for the (1) announcement of this priority list, and (2) implementation of the e-A&F intervention to have affected patient outcomes. Results: Across the study period there were 222,792 episodes of inpatient care, of which 13,904 episodes were found to contain one or more HACs, a risk of 6.24%. From the point of the first intervention until the end of the study the overall risk of a HAC reduced from 8.57% to 4.12% - a 51.93% reduction. Of this reduction the proportion attributed to each of these interventions was found to be 29.99% for the announcement of the priority list and 21.93% for the implementation of the e-A&F intervention. Discussion: Our findings lend evidence to a mechanism that the announcement of a measurement framework, at a national level, can lead to local strategies, such as e-A&F, that lead to significant continued improvements over time.

Developing prediction algorithms for late-life depression using wearable devices: a cohort study protocol.

INTRODUCTION: Despite the high prevalence of major depressive disorder (MDD) among the elderly population, the rate of treatment is low due to stigmas and barriers to medical access. Wearable devices such as smartphones and smartwatches can help to screen MDD symptoms earlier in a natural setting while forgoing these concerns. However, previous research using wearable devices has mostly targeted the younger population. By collecting longitudinal data using wearable devices from the elderly population, this research aims to produce prediction algorithms for late-life depression and to develop strategies that strengthen medical access in community care systems. METHODS AND ANALYSIS: The current cohort study recruited a subsample of 685 elderly people from the Korean Genome and Epidemiology Study-Cardiovascular Disease Association Study, a national large cohort established in 2004. The current study has been conducted over a 3-year period to explore the development patterns of late-life depression. Participants have completed three annual face-to-face interviews (baseline, the first follow-up and the second follow-up) and 2 years of app-based surveys and passive sensing data collection. All the data collection will end at the second follow-up interview. The collected self-report, observational and passive sensing data will be primarily analysed by machine learning. ETHICS AND DISSEMINATION: This study protocol has been reviewed and approved by the Yonsei University Mirae Campus Institutional Review Board (1041849-2 02 111 SB-180-06) in South Korea. All participants provided written informed consent. The findings of this research will be disseminated by academic publications and conference presentations.

Management of refined and personalized newborn blood specimen collection.

Background: Iatrogenic blood loss is an important cause of neonatal anemia. In this study, a spreadsheet tool was developed to reduce blood collection, providing a new idea for the prevention of iatrogenic blood loss in newborns. Methods: Based on hematocrit, minimum test volume and dead volume, a new tool was to calculate the minimum blood collection volume and the number of containers required for the test portfolio. We collected data from October 2022 to October 2023 from Xiamen Maternal and Child Health Hospital for analysis and validation. Results: During this year, there were 16,434 patients and 13,696 plasma/serological samples in the neonatology department. Among them, there were 8 test combinations of greater than 1%, and 9490 samples in total. According to the hospital manual, the recommended amount of blood collection is 27,534 ml and 9490 containers. Through the analysis of this tool, total blood collection was 8864.77 ml, marked qnantity of upward containers (closest level to the calculated blood collection volume) was 10301 ml, and the amount of containers was 8835, which decreased by 67.8%, 62.58% and 6.9% respectively. Besides, if the hematocrit information cannot be obtained in advance and the high hematocrit is calculated as 0.8, the recommended amount of blood collection is 14334.3 ml, and the marked amount of the upward container markering is 17340 ml, decreasing by 47.9% and 37.02% respectively. Conclusion: We have developed an auxiliary tool that can manage neonatal blood specimen collection in a fine and personalized way and can be applied among different laboratory instruments by parameters modification.