Secondary Purposes Research Articles

The role of routine and structured social needs data collection in improving care in US hospitals.

To understand how health-related social needs (HRSN) data are collected at US hospitals and implications for use. Using 2023 nationally representative survey data on US hospitals (N = 2775), we described hospitals' routine and structured collection and use of HRSN data and examined the relationship between methods of data collection and specific uses. Multivariate logistic regression was used to identify characteristics associated with data collection and use and understand how methods of data collection relate to use. In 2023, 88% of hospitals collected HRSN data (64% routinely, 72% structured). While hospitals commonly used data for internal purposes (eg, discharge planning, 79%), those that collected data routinely and in a structured format (58%) used data for purposes involving coordination or exchange with other organizations (eg, making referrals, 74%) at higher rates than hospitals that collected data but not routinely or in a non-structured format (eg, 93% vs 67% for referrals, P< .05). In multivariate regression, routine and structured data collection was positively associated with all uses of data examined. Hospital location, ownership, system-affiliation, value-based care participation, and critical access designation were associated with HRSN data collection, but only system-affiliation was consistently (positively) associated with use. While most hospitals screen for social needs, fewer collect data routinely and in a structured format that would facilitate downstream use. Routine and structured data collection was associated with greater use, particularly for secondary purposes. Routine and structured screening may result in more actionable data that facilitates use for various purposes that support patient care and improve community and population health, indicating the importance of continuing efforts to increase routine screening and standardize HRSN data collection.

Enhancing real world data interoperability in healthcare: A methodological approach to laboratory unit harmonization

ObjectiveThe primary aim of this study is to address the critical issue of non-standardized units in clinical laboratory data, which poses significant challenges to data interoperability and secondary usage. Despite UCUM (Unified Code for Units of Measure) offering a unique representation for laboratory test units, nearly 60% of laboratory codes in healthcare organizations use non-standard units. We sought to design, implement and test a methodology for the harmonization of units to the UCUM standards across a large research network. MethodsUsing dimensional analysis and a curated equivalence table, the proposed methodology harmonizes disparate units to UCUM standards. The process focused on identifying and converting non-UCUM conforming units, with the goal of enhancing data comparability and interoperability across different systems. ResultsThe methodology successfully achieved over 90% coverage of laboratory data with units in UCUM standards across the TriNetX research network, a significant improvement from baseline measurements. This enhancement in unit standardization directly contributed to increased interoperability of laboratory data, facilitating more reliable and comparable data analysis across various healthcare organizations. ConclusionThe successful harmonization of laboratory data units to UCUM standards represents a significant advancement in the field of biomedical informatics. By demonstrating a practical and effective approach to overcoming the challenges of non-standardized units, our study contributes to the broader efforts to improve data interoperability and usability for secondary purposes such as research and observational studies. Future work will focus on addressing the remaining gaps in unit standardization and exploring the implications of this methodology on clinical outcomes and research capabilities.

One-Stage Tibiotalocalcaneal Arthrodesis for Severe Septic Destruction of the Ankle Joint Using a Retrograde Intramedullary Nail: A Retrospective Cross-sectional Study.

Severe septic destruction of the ankle joint poses a therapeutic challenge but lacks a consensus optimal treatment. Tibiotalocalcaneal arthrodesis (TTCA) is considered as a valuable salvage procedure, but the literature remains scarce. Conventional treatment approaches, including 2-stage procedures, have been associated with prolonged recovery times and varying success rates. Many authors prefer classical external fixation in these scenarios, citing concerns that internal implants might pose a risk for recurrent infection. To date, no study has investigated the outcomes of 1-stage surgery using a retrograde intramedullary (IM) nail. The main purpose was to assess the rate of recurrent infection at 2 years following 1-stage TTCA using a retrograde IM nail in severe septic destruction of the ankle joint. Fusion rates and functional outcomes were evaluated as secondary purposes. The clinical and radiologic data of patients who underwent 1-stage TTCA with retrograde IM nail following severe septic destruction of the ankle joint with a minimal follow-up of 2 years were retrospectively analyzed. Reinfection rate, fusion rate, functional outcomes, and complications were evaluated. A total of 25 patients were included with a mean follow-up of 42 months (24-92 months). The mean age was 55 ± 18 years old. At the last follow-up, reinfection occurred in 6 patients (24%) and fusion was obtained in 19 patients (83%). Eight patients (32%) required revision surgery. The mean postoperative modified American Orthopaedic Foot & Ankle Society (AOFAS) score, 12-Item Short Form Health Survey physical and mental component summary scores were respectively 53 ± 19.5, 35.5 ± 11.4, and 46.7 ± 13.5 points. One-stage TTCA with retrograde IM nail appears to be an acceptable alternative in severe septic destruction of the ankle joint, with a high eradication rate of infection and ankle fusion.

ChatGPT as a tool for self-learning English among EFL learners: A multi-methods study

This study investigates the factors influencing ChatGPT acceptance in self-directed English language learning and explores its potential to enhance the learning process. A multi-methods approach was employed, comprising a systematic review of 40 empirical articles, a quantitative survey of 344 English as a Foreign Language (EFL) learners, and 19 semi-structured interviews. Results indicated that interactivity, enjoyment, trust, and subjective norms significantly affect continued use through perceived usefulness, while human-likeness, self-efficacy, and technology anxiety influence it through perceived ease of use. EFL learners utilize ChatGPT for both primary and secondary English learning purposes, supporting various language dimensions including reading, writing, vocabulary, and grammar. The study also identified both passive and active language learning engagement facilitated by ChatGPT. This research contributes to the field by proposing an extended Technology Acceptance Model and a conceptual framework for chatbot-assisted language learning. For chatbot developers, the findings underscore the need to enhance interactivity, human-likeness, enjoyment, and trust while minimizing technology anxiety to increase sustained use. Educators can utilize these insights to better integrate ChatGPT into their curricula, supporting diverse language skills and engaging learners more effectively. Together, these findings provide concrete guidance for enhancing the design and implementation of AI chatbots in language learning contexts.

A scoping review of large language model based approaches for information extraction from radiology reports

Radiological imaging is a globally prevalent diagnostic method, yet the free text contained in radiology reports is not frequently used for secondary purposes. Natural Language Processing can provide structured data retrieved from these reports. This paper provides a summary of the current state of research on Large Language Model (LLM) based approaches for information extraction (IE) from radiology reports. We conduct a scoping review that follows the PRISMA-ScR guideline. Queries of five databases were conducted on August 1st 2023. Among the 34 studies that met inclusion criteria, only pre-transformer and encoder-based models are described. External validation shows a general performance decrease, although LLMs might improve generalizability of IE approaches. Reports related to CT and MRI examinations, as well as thoracic reports, prevail. Most common challenges reported are missing validation on external data and augmentation of the described methods. Different reporting granularities affect the comparability and transparency of approaches.

Health data stewardship: achieving trust through accountability in health data sharing for research

ABSTRACT Attempts in the UK to regulate the sharing and use of health data for secondary purposes such as research have been dogged by controversy and failure. This article considers health data stewardship as a response to these challenges. It develops a normative understanding of the concept through an examination of stewardship in a variety of contexts. This leads to a definition of health data stewardship which encapsulates the necessary constituent elements that focus group participants viewed as crucial to an accountable health data sharing scheme in which they could place their trust. It examines two potential mechanisms for facilitating health data stewardship, the data trust and the data cooperative, arguing that whichever regulatory mechanism is adopted must incorporate mechanisms responsive to citizens’ requirements for accountable stewardship of their data. Without this, their wishes, and societal aims of improving health through research using comprehensive data sets may be unachievable.

Undercounting diagnoses in Australian general practice: a data quality study with implications for population health reporting

BackgroundDiagnosis can often be recorded in electronic medical records (EMRs) as free-text or using a term with a diagnosis code. Researchers, governments, and agencies, including organisations that deliver incentivised primary care quality improvement programs, frequently utilise coded data only and often ignore free-text entries. Diagnosis data are reported for population healthcare planning including resource allocation for patient care. This study sought to determine if diagnosis counts based on coded diagnosis data only, led to under-reporting of disease prevalence and if so, to what extent for six common or important chronic diseases.MethodsThis cross-sectional data quality study used de-identified EMR data from 84 general practices in Victoria, Australia. Data represented 456,125 patients who attended one of the general practices three or more times in two years between January 2021 and December 2022. We reviewed the percentage and proportional difference between patient counts of coded diagnosis entries alone and patient counts of clinically validated free-text entries for asthma, chronic kidney disease, chronic obstructive pulmonary disease, dementia, type 1 diabetes and type 2 diabetes.ResultsUndercounts were evident in all six diagnoses when using coded diagnoses alone (2.57–36.72% undercount), of these, five were statistically significant. Overall, 26.4% of all patient diagnoses had not been coded. There was high variation between practices in recording of coded diagnoses, but coding for type 2 diabetes was well captured by most practices.ConclusionIn Australia clinical decision support and the reporting of aggregated patient diagnosis data to government that relies on coded diagnoses can lead to significant underreporting of diagnoses compared to counts that also incorporate clinically validated free-text diagnoses. Diagnosis underreporting can impact on population health, healthcare planning, resource allocation, and patient care. We propose the use of phenotypes derived from clinically validated text entries to enhance the accuracy of diagnosis and disease reporting. There are existing technologies and collaborations from which to build trusted mechanisms to provide greater reliability of general practice EMR data used for secondary purposes.

The benefit of chart curation of electronic health record (EHR) data: Comparing data completeness between curated and structured data.

11165 Background: The main objective of an EHR is to effectively standardize clinical workflow. However, EHR data is also utilized extensively as secondary data for clinical research and quality improvement. Healthcare data exists in different forms such as paper, digital images, and notes, only some of which are recorded as structured data in the EHR. It is therefore essential to curate detailed information from unstructured data so that the patient’s journey can be understood in depth for research and quality studies. Methods: Patients diagnosed with metastatic breast cancer (mBC) between 01-Jan-2020 and 31-Dec-2022 were identified from the Integra Connect PrecisionQ de-identified database of 3 million cancer patients across 500 sites of care across ~80% community oncology and ~20% academic practices. Manual curation was conducted for a sample of these patients and the fill rates of crucial elements were captured as part of this study. Results: A total of 13,763 mBC patients were identified during this study period. The availability of data for the different variables was assessed from structured data in 13,120 patients. Additional information was obtained by manual curation for 643 patients. Information on staging and grade were available for 62.8% in the structured data, while curation increased the availability of these details to 99.5% among those 643 patients. The fill rates for patients’ tumor size and nodal status, as defined by the T and N values, were found to be 99% in the curated data compared to 60% in the structured data. Similarly, the fill rates were more than 98% for estrogen, progesterone, and HER2 receptor by curation, while they were only approximately 65% in the structured data. HER2 low, HER2-ve, and HER2+ve status was identified in 8%, 45.2%, and 10.1% by structured data and in 57.1%, 84.1%, and 14.0%, respectively, by curated data. Furthermore, the fill rates via curation were 97.1% for HER2 immunohistochemistry tests compared to 49.0% for structured data, while the fill rates for in-situ hybridization (ISH) or fluorescent in-situ hybridization (FISH) tests were 61.3% for curated data compared to 28.4% for structured data. This discrepancy is caused by the fact that IHC and FISH/ISH tests are primarily found only in PDF format. Conclusions: This study highlights the need for curation in order to maximize the utilization of EHR data for secondary research purposes and quality studies. Natural language processing and augmented curation methods can further enhance the quality of EHR data for secondary research.

A Web-Based Intervention for Insufficiently Active College Students: Feasibility and Preliminary Efficacy.

One third of college students do not achieve aerobic activity levels recommended for physical and mental health. The web-based "I Can Be Active!" intervention was designed to help college students increase their physical activity. The intervention was grounded in the Multi-Process Action Control (M-PAC) framework which emphasizes translating intention into sustainable action. The primary purpose was to evaluate the feasibility of the intervention with insufficiently active young adult college students. The secondary purposes were to describe the preliminary effects of the intervention on: (1) the M-PAC constructs and (2) physical activity. Twenty-one college students, ages 18 to 24, were enrolled in the pre-post quasi-experimental study to test the 8-week intervention during Spring 2021. Data were collected via self-report questionnaires, web-analytics, and interviews. Feasibility outcomes included recruitment, retention, acceptability, practicality, and implementation. Preliminary efficacy outcomes were self-report M-PAC constructs and physical activity. Data analyses included descriptive statistics, t tests, Wilcoxon signed rank tests, Hedge's g, and thematic analysis. Recruitment and retention rates were 70% and 71%, respectively. Participants reacted positively to the program, content, and features, except the manual entry step tracker and private social media group. Positive trends and significant increases were found in the regulatory and reflexive M-PAC constructs (self-regulation, habit, and identity) and physical activity. Findings support the feasibility and preliminary effects of the intervention for insufficiently active college students and highlight implications for intervention refinement. Future research will test intervention effectiveness using a randomized controlled trial with a larger diverse sample of college students.

A compromise programming approach for cross efficiency measurement in basic two-stage network system

Data envelopment analysis (DEA) is a technique that allows each decision-making unit (DMU) to calculate efficiency using the most favorable weights for inputs and outputs. The resulting efficiency scores are incomparable and difficult to discriminate. Cross-efficiency is a concept for solving the problem of incomparability among the efficiencies of a set of DMUs that are calculated from different weights and is helpful for ranking. In basic DEA, several cross-efficiency evaluation methods with various secondary purposes have been presented, however, the internal mechanism of the DMUs in cross-efficiency evaluation is often neglected. In this study, we use the composition approach for a basic two-stage structure, so that the efficiencies of the divisions are estimated first, and then the overall efficiency of the system is obtained. To solve the linear bi-objective model in the combination approach, we consider a compromise programming problem to find the solution that is as close as possible to the ideal point. Finally, a secondary objective model is proposed to select a weight among the optimal weights of the compromise model, which also guarantees the efficiency of the optimal weight for the bi-objective problem. We use the optimal solution obtained from this model to evaluate cross-efficiency and rank DMUs for a series two-stage system. The proposed approach enables us to rank the DMUs by assessing the performance of each DMU and each of its separate divisions. The validity of the suggested network cross-efficiency evaluation is demonstrated by a numerical example from the literature.

Six-month pain and function outcome expectations were established for total knee arthroplasty using the smallest worthwhile effect.

Interpretations of patient-reported outcome measures following knee arthroplasty lack context and typically do not account for costs, risks and benefits compared to an alternative treatment. The primary purpose of our paper is to estimate expectations patients have for pain and function destination outcome, six-months following surgery relative to the outcome expected if knee arthroplasty was not done. Secondary purposes were to determine if statistically significant changes in the smallest worthwhile six-month outcome occurred following an interactive discussion and to assess the construct validity of the expected six-month outcome obtained at baseline. This was a secondary analysis of a prospective longitudinal cohort study of 121 patients undergoing knee arthroplasty. Smallest worthwhile effect estimates were determined and expected six-month KOOS Pain and Function, daily activity measures were established during a pre-operative visit. The average six-month expected (the destination of interest) KOOS Pain score was 75 (IQR = 64 to 86) and the average KOOS Function, daily activity score was 74 (IQR = 59 to 86). The smallest worthwhile effect discussion led to significant changes in expected destination scores. For example, KOOS Pain expected outcome changed from 87.7 (9.8) to 75.0 (13.6), a statistically significant reduction in expected outcome (t(119) = 16.942, p < 0.001. Six-month expected KOOS outcomes following knee arthroplasty were established and approximate the average six-month outcomes reported in the literature. Validity of these estimates was established. These data can be used to aid shared decision-making discussions regarding patient expectations of knee arthroplasty outcomes during a patient encounter.

Person-centred data sharing: Empirical studies in private individuals’ attitudes

Background Recognising the power of data analytics, researchers are anxious to gain access to personal data either directly from data subjects or via research data sets. This requires a secure environment, such as a trusted research environment (TRE). However, it is unclear how the data subjects themselves regard sharing their data with TREs, especially if research goals are difficult to specify upfront or data are used for secondary purposes, making informed consent difficult to manage. We review three empirical studies to throw some light on individual attitudes to sharing health data. Methods Three anonymous, online surveys were run. The first involving 800 UK residents aimed at understanding how participants view the health data security. The second involving 500 UK residents aimed at identifying private individual views on privacy. These two surveys used a crowdsourcing platform. The third involved 1086 students at a UK university reporting their engagement with a trial diagnostic method for SARS-CoV-2. Results The first survey demonstrated that private individuals could make security decisions though they usually assume the recipient of their personal data to be responsible for all aspects of keeping the data safe. The second highlighted that individuals were aware of privacy risks but are motivated to share their data based on different contextual assumptions. The third, involving the incidental sharing of sensitive data during the SARS-CoV-2 pilot highlighted that prosocial motivations override potential personal benefit of such testing. Conclusions The three, unconnected surveys make clear that there are tensions between private individual understanding of data security and privacy risk, on the one hand, and how they behave, on the other. Respondents rely on data stewards to keep their data safe, though are likely to share even sensitive data for prosocial benefit. These findings have implications for those offering TRE services for research.

Frequency, complications, and mortality of inhalation injury in burn patients: A systematic review and meta-analysis protocol.

Burns are tissue traumas caused by energy transfer and occur with a variable inflammatory response. The consequences of burns represent a public health problem worldwide. Inhalation injury (II) is a severity factor when associated with burn, leading to a worse prognosis. Its treatment is complex and often involves invasive mechanical ventilation (IMV). The primary purpose of this study will be to assess the evidence regarding the frequency and mortality of II in burn patients. The secondary purposes will be to assess the evidence regarding the association between IIs and respiratory complications (pneumonia, airway obstruction, acute respiratory failure, acute respiratory distress syndrome), need for IMV and complications in other organ systems, and highlight factors associated with IIs in burn patients and prognostic factors associated with acute respiratory failure, need for IMV and mortality of II in burn patients. This is a systematic literature review and meta-analysis, according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA). PubMed/MEDLINE, Embase, LILACS/VHL, Scopus, Web of Science, and CINAHL databases will be consulted without language restrictions and publication date. Studies presenting incomplete data and patients under 19 years of age will be excluded. Data will be synthesized through continuous (mean and standard deviation) and dichotomous (relative risk) variables and the total number of participants. The means, sample sizes, standard deviations from the mean, and relative risks will be entered into the Review Manager web analysis software (The Cochrane Collaboration). Despite the extensive experience managing IIs in burn patients, they still represent an important cause of morbidity and mortality. Diagnosis and accurate measurement of its damage are complex, and therapies are essentially based on supportive measures. Considering the challenge, their impact, and their potential severity, IIs represent a promising area for research, needing further studies to understand and contribute to its better evolution. The protocol of this review is registered on the International prospective register of systematic reviews platform of the Center for Revisions and Disclosure of the University of York, United Kingdom (https://www.crd.york.ac.uk/prospero), under number RD42022343944.

The OMOP common data model in Australian primary care data: Building a quality research ready harmonised dataset.

The use of routinely collected health data for secondary research purposes is increasingly recognised as a methodology that advances medical research, improves patient outcomes, and guides policy. This secondary data, as found in electronic medical records (EMRs), can be optimised through conversion into a uniform data structure to enable analysis alongside other comparable health metric datasets. This can be achieved with the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM), which employs a standardised vocabulary to facilitate systematic analysis across various observational databases. The concept behind the OMOP-CDM is the conversion of data into a common format through the harmonisation of terminologies, vocabularies, and coding schemes within a unique repository. The OMOP model enhances research capacity through the development of shared analytic and prediction techniques; pharmacovigilance for the active surveillance of drug safety; and 'validation' analyses across multiple institutions across Australia, the United States, Europe, and the Asia Pacific. In this research, we aim to investigate the use of the open-source OMOP-CDM in the PATRON primary care data repository. We used standard structured query language (SQL) to construct, extract, transform, and load scripts to convert the data to the OMOP-CDM. The process of mapping distinct free-text terms extracted from various EMRs presented a substantial challenge, as many terms could not be automatically matched to standard vocabularies through direct text comparison. This resulted in a number of terms that required manual assignment. To address this issue, we implemented a strategy where our clinical mappers were instructed to focus only on terms that appeared with sufficient frequency. We established a specific threshold value for each domain, ensuring that more than 95% of all records were linked to an approved vocabulary like SNOMED once appropriate mapping was completed. To assess the data quality of the resultant OMOP dataset we utilised the OHDSI Data Quality Dashboard (DQD) to evaluate the plausibility, conformity, and comprehensiveness of the data in the PATRON repository according to the Kahn framework. Across three primary care EMR systems we converted data on 2.03 million active patients to version 5.4 of the OMOP common data model. The DQD assessment involved a total of 3,570 individual evaluations. Each evaluation compared the outcome against a predefined threshold. A 'FAIL' occurred when the percentage of non-compliant rows exceeded the specified threshold value. In this assessment of the primary care OMOP database described here, we achieved an overall pass rate of 97%. The OMOP CDM's widespread international use, support, and training provides a well-established pathway for data standardisation in collaborative research. Its compatibility allows the sharing of analysis packages across local and international research groups, which facilitates rapid and reproducible data comparisons. A suite of open-source tools, including the OHDSI Data Quality Dashboard (Version 1.4.1), supports the model. Its simplicity and standards-based approach facilitates adoption and integration into existing data processes.

Health data sharing attitudes towards primary and secondary use of data: a systematic review

To receive the best care, people share their health data (HD) with their health practitioners (known as sharing HD for primary purposes). However, during the past two decades, sharing for other (i.e., secondary) purposes has become of great importance in numerous fields, including public health, personalized medicine, research, and development. We aimed to conduct the first comprehensive overview of all studies that investigated people's HD sharing attitudes-along with associated barriers/motivators and significant influencing factors-for all data types and across both primary and secondary uses. We searched PubMed, MEDLINE, PsycINFO, Web of Science, EMBASE, and CINAHL for relevant studies published in English between database inception and February 28, 2023, using a predefined set of keywords. Studies were included, regardless of their design, if they reported outcomes related to attitudes towards sharing HD. We extracted key data from the included studies, including the type of HD involved and findings related to: HD sharing attitudes (either in general or depending on type of data/user); barriers/motivators/benefits/concerns of the study participants; and sociodemographic and other variables that could impact HD sharing behaviour. The qualitative synthesis was conducted by dividing the studies according to the data type (resulting in five subgroups) as well as the purpose the data sharing was focused on (primary, secondary or both). The Newcastle-Ottawa Scale (NOS) was used to assess the quality of non-randomised studies. This work was registered with PROSPERO, CRD42023413822. Of 2109 studies identified through our search, 116 were included in the qualitative synthesis, yielding a total of 228,501 participants and various types of HD represented: person-generated HD (n=17 studies and 10,771 participants), personal HD in general (n=69 studies and 117,054 participants), Biobank data (n=7 studies and 27,073 participants), genomic data (n=13 studies and 54,716 participants), and miscellaneous data (n=10 studies and 18,887 participants). The majority of studies had a moderate level of quality (83 [71.6%] of 116 studies), but varying levels of quality were observed across the included studies. Overall, studies suggest that sharing intentions for primary purposes were observed to be high regardless of data type, and it was higher than sharing intentions for secondary purposes. Sharing for secondary purposes yielded variable findings, where both the highest and the lowest intention rates were observed in the case of studies that explored sharing biobank data (98% and 10%, respectively). Several influencing factors on sharing intentions were identified, such as the type of data recipient, data, consent. Further, concerns related to data sharing that were found to be mutual for all data types included privacy, security, and data access/control, while the perceived benefits included those related to improvements in healthcare. Findings regarding attitudes towards sharing varied significantly across sociodemographic factors and depended on data type and type of use. In most cases, these findings were derived from single studies and therefore warrant confirmations from additional studies. Sharing health data is a complex issue that is influenced by various factors (the type of health data, the intended use, the data recipient, among others) and these insights could be used to overcome barriers, address people's concerns, and focus on spreading awareness about the data sharing process and benefits. None.

Improving Quality of ICD-10 (International Statistical Classification of Diseases, Tenth Revision) Coding Using AI: Protocol for a Crossover Randomized Controlled Trial.

Computer-assisted clinical coding (CAC) tools are designed to help clinical coders assign standardized codes, such as the ICD-10 (International Statistical Classification of Diseases, Tenth Revision), to clinical texts, such as discharge summaries. Maintaining the integrity of these standardized codes is important both for the functioning of health systems and for ensuring data used for secondary purposes are of high quality. Clinical coding is an error-prone cumbersome task, and the complexity of modern classification systems such as the ICD-11 (International Classification of Diseases, Eleventh Revision) presents significant barriers to implementation. To date, there have only been a few user studies; therefore, our understanding is still limited regarding the role CAC systems can play in reducing the burden of coding and improving the overall quality of coding. The objective of the user study is to generate both qualitative and quantitative data for measuring the usefulness of a CAC system, Easy-ICD, that was developed for recommending ICD-10 codes. Specifically, our goal is to assess whether our tool can reduce the burden on clinical coders and also improve coding quality. The user study is based on a crossover randomized controlled trial study design, where we measure the performance of clinical coders when they use our CAC tool versus when they do not. Performance is measured by the time it takes them to assign codes to both simple and complex clinical texts as well as the coding quality, that is, the accuracy of code assignment. We expect the study to provide us with a measurement of the effectiveness of the CAC system compared to manual coding processes, both in terms of time use and coding quality. Positive outcomes from this study will imply that CAC tools hold the potential to reduce the burden on health care staff and will have major implications for the adoption of artificial intelligence-based CAC innovations to improve coding practice. Expected results to be published summer 2024. The planned user study promises a greater understanding of the impact CAC systems might have on clinical coding in real-life settings, especially with regard to coding time and quality. Further, the study may add new insights on how to meaningfully exploit current clinical text mining capabilities, with a view to reducing the burden on clinical coders, thus lowering the barriers and paving a more sustainable path to the adoption of modern coding systems, such as the new ICD-11. clinicaltrials.gov NCT06286865; https://clinicaltrials.gov/study/NCT06286865. DERR1-10.2196/54593.

Play value of digital play spaces: Children's voices

Play for plays sake emphasises the significance of how children characterise play opposed to any secondary purposes or benefits it may generate. The concept of play value draws attention to how the space in which children play proffers the experiences that children want. Increasingly, digital play experiences are emerging as spaces for play. This inquiry aims to examine the play value of digital spaces, specifically how play is afforded by the digital space from the perspective of children. Eight children aged 11 years old participated in seven focus groups, creating comic strips which were used to elicit discussions. Focus group analysis generated three themes with associated sub-themes which were collectively explained by the overarching theme of endless possibilities in play in digital spaces. This study provides a new understanding of how digital spaces affords play value from the child's perspective.

Evaluation of Statin Indication and Dose Intensification Among Type 2 Diabetic Patients at a Tertiary Hospital.

Diabetes mellitus (DM) increases cardiovascular disease (CVD) incidence and mortality. While guidelines endorse statin use in type 2 DM (T2DM) to mitigate cardiovascular risks and mortality, challenges like statin initiation and prompt treatment adjustments affect patient outcomes. This study aimed to assess the appropriateness of indications for and dose intensification of statin therapy among T2DM patients at Tikur Anbessa Specialized Hospital (TASH). A hospital-based cross-sectional study was conducted from April 1 to June 30 2020. In total, 405 T2DM patients were selected using a systematic random sampling technique. The data were analyzed using SPSS version 26.0. An adjusted odds ratio (OR) was used and a 95% confidence interval (CI) and p-values of <0.05 were utilized to determine statistical significance. Of the total 405 participants, 346 (85.4%) started taking statins for primary or secondary prevention purposes. Indication for statin use was appropriate in 96.2% patients, while for 216 (62.4%) patients their doses were appropriately intensified. Predictors of the inappropriateness of statin use were an atherosclerotic cardiovascular disease (ASCVD) score of ≥7.5% (AOR=0.28; 95% CI: 0.102-0.738, p=0.01), the presence of dyslipidemia (AOR=4.48; 95% CI: 1.85-10.84; p=0.001), initiation of aspirin therapy (AOR=3.7; 95% CI: 1.522-9.144; p=0.004), and an LDL-cholesterol level of 70-189 mg/dL (AOR=0.124; 95% CI: 0.042-0.365; p=0.001). DM duration of ≥10 years (AOR=2.51; 95% CI: 1.35-4.66, p=0.004), male gender (AOR=2.04; 95% CI: 1.16-3.58, p=0.013), age ≥65 years (AOR=2.15; 95% CI: 1.23-3.75, p=0.007) and uncontrolled blood pressure (AOR=2.09; 95% CI: 1.07-4.08, p=0.031) were associated with inappropriate statin intensification. The study found that indication of statins was optimal and about two-thirds of patients had their doses appropriately intensified. Monitoring is needed to avoid inappropriate intensification of statin therapy, particularly in patients with longer diabetes duration, those of male gender and advanced age, and those with uncontrolled blood pressure.

How much of the energy in the electrochromic energy storage window can be reused?

Electrochromic energy storage technology that can store energy electrochemically while controlling the optical transmittance, could be mainly used in the development of next-generation smart window systems for net-zero energy buildings. The resultant apparatus can first implement indoor optical and thermal modulation, then their involved residual electric energy would be reused to power small electronic products. However, there has been confusion as to how much of the electric energy can be used for secondary purposes. Herein, a hyperbranched polyamide with dual redox centra of triphenylamine and pentaaniline was synthesized with good processability and multi-band absorption. Coupled with a zinc frame electrode, an electrochromic energy storage window (EESW) was manufactured, which simultaneously exhibited approving electrochromic performance (high optical contrast of >48 % and good thermal insulation), as well as ideal zinc ions energy storage properties (wide voltage window of 2.4 V and large capacitance of 71.06 mF cm−2). Furthermore, the spectrochronoamperometry was first coupled with rate performance to investigate its energy reused properties. As implementing electrochromism upon 2.4 V for 50 s, the engaged EESW possessed an energy density of 20.21 μWh cm−2, due to the nonoptimal constant-voltage charging mode. After 1-hour transmittance maintenance without any applied voltage, an 80 % energy density of 16.54 μWh cm−2 was determined, which should be considered available electric energy that can be reused.

Pediatric acute hospitalization for anorexia nervosa: an economic evaluation.

Anorexia nervosa (AN) is a psychiatric disorders which may potentially led to a high risk of health medical complications, suicide and self-harming behaviour. Since Covid-19 pandemic onset in March 2020, evidence suggested an increase occurrence of AN. The main aim of the retrospective analysis is to define the cost of hospitalization in the acute phase (HAP) at IRCCS Bambino Gesù Children Hospital, Rome, Italy, over 2 years study. Secondary purposes are defining the main risk factors for a prolonged hospitalization (including age, sex and comorbidities) and the possible influence of Covid-19 pandemic on AN admission and hospital stay. for the purpose of the study, we included children and adolescents aged less than 18 years, admitted to IRCCS Bambino Gesù Children Hospital, Rome, Italy, with a diagnosis of AN. Medical costs were calculated consulting the Lazio Regional Health Service Tariffs. Basing on the date of hospital admission, patients were later divided into two subgroups: subgroup A included patients hospitalized prior than Covid-19 onset (from March 2019 to February 2020) and subgroup B those admitted after (from March 2020 to October 2022). a total of 260 patients has been included in the study with a median age of 15 years (range 6-18 years). The total health care cost of AN hospitalized patients was of EUR 3,352,333 with a median cost of EUR 11,124 for each admission (range EUR 930 - 45,739) and a median daily cost of EUR 593 (range EUR 557-930). Median cost was higher in case of comorbidities, guarded patients, enteral feeding. A prolonged hospitalization has been documented in subgroup A with a higher economic burden. the economic burden of eating disorders is of note. Adequate sanitary policies as well as health economic analyses are required to gain insight into the cost-effectiveness of AN management. 2526-OPBG-2021.