PLOS digital health最新文献

{"title":"The value of diabetes technology enabled coaching (DTEC) to support remission evaluation of medical interventions in T2D: Patient and health coach perspectives.","authors":"Madison Taylor, Denise Ng, Kaylen J Pfisterer, Joseph A Cafazzo, Diana Sherifali","doi":"10.1371/journal.pdig.0000701","DOIUrl":"10.1371/journal.pdig.0000701","url":null,"abstract":"<p><p>The multicomponent Remission Evaluation of Medical Interventions in T2D (REMIT) program has shown reduction of hazard of diabetes relapse by 34-43%, but could benefit from improved ability to scale, spread, and sustain it. This study explored, at the conceptualization phase, patient and health coach perspectives on the acceptability, adoption, feasibility, and appropriateness of a digital REMIT adaptation (diabetes technology enabled coaching (DTEC)). Twelve semi-structured interviews were conducted with patients (n = 6) and health coaches (n = 6) to explore their experiences with the REMIT study, opportunities for virtualisation, and a cognitive walkthrough of solution concepts. Transcripts were analyzed both inductively and deductively to allow for organic themes to emerge and to position these themes around the constructs of acceptability, adoption, feasibility, and appropriateness while allowing new codes to emerge for discussion. Participants saw value in DTEC as: an opportunity to facilitate and extend REMIT support; a convenient, efficient, and scalable concept (acceptability); having potential to motivate through connecting behaviours to outcomes (adoption); an opportunity for lower-effort demands for sustained use (feasibility). Participants also highlighted important considerations to ensure DTEC could provide compassionate insights and support automated data entry (appropriateness). Several considerations regarding equitable access were raised and warrant further consideration including: provision of technology, training to support technology literacy, and the opportunity for DTEC to support and improve health literacy. As such, DTEC may act as a moderator that can enhance or diminish access which affects who can benefit. Provided equity considerations are addressed, DTEC has the potential to address previous shortcomings of the conventional REMIT program.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 1","pages":"e0000701"},"PeriodicalIF":0.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11717255/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142960067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A data management system for precision medicine.","authors":"John J L Jacobs, Inés Beekers, Inge Verkouter, Levi B Richards, Alexandra Vegelien, Lizan D Bloemsma, Vera A M C Bongaerts, Jacqueline Cloos, Frederik Erkens, Patrycja Gradowska, Simon Hort, Michael Hudecek, Manel Juan, Anke H Maitland-van der Zee, Sergio Navarro-Velázquez, Lok Lam Ngai, Qasim A Rafiq, Carmen Sanges, Jesse Tettero, Hendrikus J A van Os, Rimke C Vos, Yolanda de Wit, Steven van Dijk","doi":"10.1371/journal.pdig.0000464","DOIUrl":"10.1371/journal.pdig.0000464","url":null,"abstract":"<p><p>Precision, or personalised medicine has advanced requirements for medical data management systems (MedDMSs). MedDMS for precision medicine should be able to process hundreds of parameters from multiple sites, be adaptable while remaining in sync at multiple locations, real-time syncing to analytics and be compliant with international privacy legislation. This paper describes the LogiqSuite software solution, aimed to support a precision medicine solution at the patient care (LogiqCare), research (LogiqScience) and data science (LogiqAnalytics) level. LogiqSuite is certified and compliant with international medical data and privacy legislations. This paper evaluates a MedDMS in five types of use cases for precision medicine, ranging from data collection to algorithm development and from implementation to integration with real-world data. The MedDMS is evaluated in seven precision medicine data science projects in prehospital triage, cardiovascular disease, pulmonology, and oncology. The P4O2 consortium uses the MedDMS as an electronic case report form (eCRF) that allows real-time data management and analytics in long covid and pulmonary diseases. In an acute myeloid leukaemia, study data from different sources were integrated to facilitate easy descriptive analytics for various research questions. In the AIDPATH project, LogiqCare is used to process patient data, while LogiqScience is used for pseudonymous CAR-T cell production for cancer treatment. In both these oncological projects the data in LogiqAnalytics is also used to facilitate machine learning to develop new prediction models for clinical-decision support (CDS). The MedDMS is also evaluated for real-time recording of CDS data from U-Prevent for cardiovascular risk management and from the Stroke Triage App for prehospital triage. The MedDMS is discussed in relation to other solutions for privacy-by-design, integrated data stewardship and real-time data analytics in precision medicine. LogiqSuite is used for multi-centre research study data registrations and monitoring, data analytics in interdisciplinary consortia, design of new machine learning / artificial intelligence (AI) algorithms, development of new or updated prediction models, integration of care with advanced therapy production, and real-world data monitoring in using CDS tools. The integrated MedDMS application supports data management for care and research in precision medicine.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 1","pages":"e0000464"},"PeriodicalIF":0.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11717228/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142960062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Goldilocks Zone: Finding the right balance of user and institutional risk for suicide-related generative AI queries.","authors":"Anna R Van Meter, Michael G Wheaton, Victoria E Cosgrove, Katerina Andreadis, Ronald E Robertson","doi":"10.1371/journal.pdig.0000711","DOIUrl":"10.1371/journal.pdig.0000711","url":null,"abstract":"<p><p>Generative artificial intelligence (genAI) has potential to improve healthcare by reducing clinician burden and expanding services, among other uses. There is a significant gap between the need for mental health care and available clinicians in the United States-this makes it an attractive target for improved efficiency through genAI. Among the most sensitive mental health topics is suicide, and demand for crisis intervention has grown in recent years. We aimed to evaluate the quality of genAI tool responses to suicide-related queries. We entered 10 suicide-related queries into five genAI tools-ChatGPT 3.5, GPT-4, a version of GPT-4 safe for protected health information, Gemini, and Bing Copilot. The response to each query was coded on seven metrics including presence of a suicide hotline number, content related to evidence-based suicide interventions, supportive content, harmful content. Pooling across tools, most of the responses (79%) were supportive. Only 24% of responses included a crisis hotline number and only 4% included content consistent with evidence-based suicide prevention interventions. Harmful content was rare (5%); all such instances were delivered by Bing Copilot. Our results suggest that genAI developers have taken a very conservative approach to suicide-related content and constrained their models' responses to suggest support-seeking, but little else. Finding balance between providing much needed evidence-based mental health information without introducing excessive risk is within the capabilities of genAI developers. At this nascent stage of integrating genAI tools into healthcare systems, ensuring mental health parity should be the goal of genAI developers and healthcare organizations.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 1","pages":"e0000711"},"PeriodicalIF":0.0,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11709298/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142960066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A mobile intervention to reduce anxiety among university students, faculty, and staff: Mixed methods study on users' experiences.","authors":"Sarah Livermon, Audrey Michel, Yiyang Zhang, Kaitlyn Petz, Emma Toner, Mark Rucker, Mehdi Boukhechba, Laura E Barnes, Bethany A Teachman","doi":"10.1371/journal.pdig.0000601","DOIUrl":"10.1371/journal.pdig.0000601","url":null,"abstract":"<p><p>Anxiety is highly prevalent among college communities, with significant numbers of students, faculty, and staff experiencing severe anxiety symptoms. Digital mental health interventions (DMHIs), including Cognitive Bias Modification for Interpretation (CBM-I), offer promising solutions to enhance access to mental health care, yet there is a critical need to evaluate user experience and acceptability of DMHIs. CBM-I training targets cognitive biases in threat perception, aiming to increase cognitive flexibility by reducing rigid negative thought patterns and encouraging more benign interpretations of ambiguous situations. This study used questionnaire and interview data to gather feedback from users of a mobile application called \"Hoos Think Calmly\" (HTC), which offers brief CBM-I training doses in response to stressors commonly experienced by students, faculty, and staff at a large public university. Mixed methods were used for triangulation to enhance the validity of the findings. Qualitative data was collected through semi-structured interviews from a subset of participants (n = 22) and analyzed thematically using an inductive framework, revealing five main themes: Effectiveness of the Training Program; Feedback on Training Sessions; Barriers to Using the App; Use Patterns; and Suggestions for Improvement. Additionally, biweekly user experience questionnaires sent to all participants in the active treatment condition (n = 134) during the parent trial showed the most commonly endorsed response (by 43.30% of participants) was that the program was somewhat helpful in reducing or managing their anxiety or stress. There was overall agreement between the quantitative and qualitative findings, indicating that graduate students found it the most effective and relatable, with results being moderately positive but somewhat more mixed for undergraduate students and staff, and least positive for faculty. Findings point to clear avenues to enhance the relatability and acceptability of DMHIs across diverse demographics through increased customization and personalization, which may help guide development of future DMHIs.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 1","pages":"e0000601"},"PeriodicalIF":0.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11706487/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142960063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Post-concussion symptom burden and dynamics: Insights from a digital health intervention and machine learning.","authors":"Rebecca Blundell, Christine d'Offay, Charles Hand, Daniel Tadmor, Alan Carson, David Gillespie, Matthew Reed, Aimun A B Jamjoom","doi":"10.1371/journal.pdig.0000697","DOIUrl":"10.1371/journal.pdig.0000697","url":null,"abstract":"<p><p>Individuals who sustain a concussion can experience a range of symptoms which can significantly impact their quality of life and functional outcome. This study aims to understand the nature and recovery trajectories of post-concussion symptomatology by applying an unsupervised machine learning approach to data captured from a digital health intervention (HeadOn). As part of the 35-day program, patients complete a daily symptom diary which rates 8 post-concussion symptoms. Symptom data were analysed using K-means clustering to categorize patients based on their symptom profiles. During the study period, a total of 758 symptom diaries were completed by 84 patients, equating to 6064 individual symptom ratings. Fatigue, sleep disturbance and difficulty concentrating were the most prevalent symptoms reported. A decline in symptom burden was observed over the 35-day period, with physical and emotional symptoms showing early rates of recovery. In a correlation matrix, there were strong positive correlations between low mood and irritability (r = 0.84), and poor memory and difficulty concentrating (r = 0.83). K-means cluster analysis identified three distinct patient clusters based on symptom severity. Cluster 0 (n = 24) had a low symptom burden profile across all the post-concussion symptoms. Cluster 1 (n = 35) had moderate symptom burden but with pronounced fatigue. Cluster 2 (n = 25) had a high symptom burden profile across all the post-concussion symptoms. Reflecting the severity of the clusters, there was a significant relationship between the symptom clusters for both the Rivermead (p = 0.05) and PHQ-9 (p = 0.003) questionnaires at 6-weeks follow-up. By leveraging digital ecological momentary assessments, a rich dataset of daily symptom ratings was captured allowing for the identification of symptom severity clusters. These findings underscore the potential of digital technology and machine learning to enhance our understanding of post-concussion symptomatology and offer a scalable solution to support patients with their recovery.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 1","pages":"e0000697"},"PeriodicalIF":0.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11706386/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142960065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A scoping review of the electronic collection and capture of patient reported outcome measures for children and young people in the hospital setting.","authors":"Anne Alarilla, Neil J Sebire, Josh Keith, Mario Cortina-Borja, Jo Wray, Gwyneth Davies","doi":"10.1371/journal.pdig.0000704","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000704","url":null,"abstract":"<p><p>Patient reported outcome measures (PROMs) capture patients' views of their health status and the use of PROMs as part of standard care of children and young people has the potential to improve communication between patients/carers and clinicians and the quality of care. Electronic systems for the collection of or access to PROMs and integrating PROMs into electronic health records facilitates their implementation in routine care and could help maximise their value. Yet little is known about the technical aspects of implementation including the electronic systems available for collection and capture and how this may influence the value of PROMs in routine care which this scoping review aims to explore. The Joanna Briggs Institute review process was used. Seven databases were searched (Emcare, Embase MEDLINE, APA PsychInfo, Scopus and Web of Science), initially in February 2021 and updated in April 2023. Only studies that mentioned the use of electronic systems for the collection, storage and/or access of PROMs as part of standard care of children and young people in secondary (or tertiary) care settings were included. Data were analysed using frequency counts and thematically mapped using basic content analysis in relation to the research questions. From the 372 studies that were eligible for full text review, 85 studies met the inclusion criteria. The findings show that there is great variability in the electronic platforms used in the collection, storage and access of PROMs resulting in different configurations and fragmented approaches to implementation. There appears to be a lack of consideration on the technical aspects of the implementation such as the accessibility, useability and interoperability of the data collected. Electronic platforms for the collection and capture of PROMs in routine care of CYP is popular, yet, further understanding of the technical considerations in the use of electronic systems for implementation is needed to maximise the potential value and support the scalability of PROMs in routine care.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 1","pages":"e0000704"},"PeriodicalIF":0.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11703060/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143070165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing a competency model for telerehabilitation therapists and patients: Results of a cross-sectional online survey.","authors":"Anna Lea Stark-Blomeier, Stephan Krayter, Christoph Dockweiler","doi":"10.1371/journal.pdig.0000710","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000710","url":null,"abstract":"<p><p>Telerehabilitation is a new form of care that provides digital access to rehabilitative services. However, it places many demands on the users-both patients and therapists. The aim of this study was to determine the requirements and competencies needed for successful usage, identify person- and context-specific differences and develop a competency model. We conducted two cross-sectional online surveys with telerehabilitation patients and therapists from Germany during June-August 2023. The adjusted dataset of 262 patients and 73 therapists was quantitatively analyzed including descriptive and bivariate statistics. Group differences were assessed using t-tests or U-tests. The development of two telerehabilitation competency models was guided by a competency modeling process. The surveys show that patients need to gather program information before program start, follow therapist's instructions, adapt therapy, deal with health problems, as well as motivate and remind oneself during the program. Therapists need to inform and instruct patients, adapt therapy, carry out technical set-up and support, give medical support, guide and monitor patients, give feedback, motivation and reminder, as well as documentation. The competency model for patients includes 23 and the model for therapists 24 core competencies, including various required areas of knowledge, skills, attitudes and experiences. The three most relevant competencies for patients are self-interest in the program, self-awareness and self-management. Also, disease severity, age, and language abilities can enable successful execution. Program type, technology affinity, and age significantly influence the rated relevance of competencies. The three most relevant competencies for therapists are therapeutic-professional skills, medical and telerehabilitation knowledge. The type of therapy practiced and language abilities can enable successful execution. Therapist's age, technology affinity, and job type significantly impact the rated relevance. The models should be applied to develop tailored training formats and support decisions on the selection of suitable therapists and patients for telerehabilitation.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 1","pages":"e0000710"},"PeriodicalIF":0.0,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11698311/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142928893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Naïve Bayes is an interpretable and predictive machine learning algorithm in predicting osteoporotic hip fracture in-hospital mortality compared to other machine learning algorithms.","authors":"Jo-Wai Douglas Wang","doi":"10.1371/journal.pdig.0000529","DOIUrl":"10.1371/journal.pdig.0000529","url":null,"abstract":"<p><p>Osteoporotic hip fractures (HFs) in the elderly are a pertinent issue in healthcare, particularly in developed countries such as Australia. Estimating prognosis following admission remains a key challenge. Current predictive tools require numerous patient input features including those unavailable early in admission. Moreover, attempts to explain machine learning [ML]-based predictions are lacking. Seven ML prognostication models were developed to predict in-hospital mortality following minimal trauma HF in those aged ≥ 65 years of age, requiring only sociodemographic and comorbidity data as input. Hyperparameter tuning was performed via fractional factorial design of experiments combined with grid search; models were evaluated with 5-fold cross-validation and area under the receiver operating characteristic curve (AUROC). For explainability, ML models were directly interpreted as well as analysed with SHAP values. Top performing models were random forests, naïve Bayes [NB], extreme gradient boosting, and logistic regression (AUROCs ranging 0.682-0.696, p>0.05). Interpretation of models found the most important features were chronic kidney disease, cardiovascular comorbidities and markers of bone metabolism; NB also offers direct intuitive interpretation. Overall, NB has much potential as an algorithm, due to its simplicity and interpretability whilst maintaining competitive predictive performance.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 1","pages":"e0000529"},"PeriodicalIF":0.0,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11694905/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142924243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated craniofacial biometry with 3D T2w fetal MRI.","authors":"Jacqueline Matthew, Alena Uus, Alexia Egloff Collado, Aysha Luis, Sophie Arulkumaran, Abi Fukami-Gartner, Vanessa Kyriakopoulou, Daniel Cromb, Robert Wright, Kathleen Colford, Maria Deprez, Jana Hutter, Jonathan O'Muircheartaigh, Christina Malamateniou, Reza Razavi, Lisa Story, Joseph V Hajnal, Mary A Rutherford","doi":"10.1371/journal.pdig.0000663","DOIUrl":"10.1371/journal.pdig.0000663","url":null,"abstract":"<p><strong>Objectives: </strong>Evaluating craniofacial phenotype-genotype correlations prenatally is increasingly important; however, it is subjective and challenging with 3D ultrasound. We developed an automated label propagation pipeline using 3D motion- corrected, slice-to-volume reconstructed (SVR) fetal MRI for craniofacial measurements.</p><p><strong>Methods: </strong>A literature review and expert consensus identified 31 craniofacial biometrics for fetal MRI. An MRI atlas with defined anatomical landmarks served as a template for subject registration, auto-labelling, and biometric calculation. We assessed 108 healthy controls and 24 fetuses with Down syndrome (T21) in the third trimester (29-36 weeks gestational age, GA) to identify meaningful biometrics in T21. Reliability and reproducibility were evaluated in 10 random datasets by four observers.</p><p><strong>Results: </strong>Automated labels were produced for all 132 subjects with a 0.3% placement error rate. Seven measurements, including anterior base of skull length and maxillary length, showed significant differences with large effect sizes between T21 and control groups (ANOVA, p<0.001). Manual measurements took 25-35 minutes per case, while automated extraction took approximately 5 minutes. Bland-Altman plots showed agreement within manual observer ranges except for mandibular width, which had higher variability. Extended GA growth charts (19-39 weeks), based on 280 control fetuses, were produced for future research.</p><p><strong>Conclusion: </strong>This is the first automated atlas-based protocol using 3D SVR MRI for fetal craniofacial biometrics, accurately revealing morphological craniofacial differences in a T21 cohort. Future work should focus on improving measurement reliability, larger clinical cohorts, and technical advancements, to enhance prenatal care and phenotypic characterisation.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"3 12","pages":"e0000663"},"PeriodicalIF":0.0,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11684610/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142960092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and evaluation of a low-cost database solution for the Community Paramedicine at Clinic (CP@clinic) database.","authors":"Ricardo Angeles, Krzysztof Adamczyk, Francine Marzanek, Melissa Pirrie, Mikayla Plishka, Gina Agarwal","doi":"10.1371/journal.pdig.0000689","DOIUrl":"10.1371/journal.pdig.0000689","url":null,"abstract":"<p><p>The Community Paramedicine at Clinic (CP@clinic) program is a community program that utilizes community paramedics to support older adults in assessing their risk factors, managing their chronic conditions, and linking them to community resources. The aim of this project is to design a low-cost, portable, secure, user-friendly database for CP@clinic sessions and pilot test the database with paramedics and older adult volunteers. The CP@clinic program database using the Microsoft Access software was first developed through consultation with the CP@clinic research team. Next, the database was pilot tested with two sets of older adults and one set of paramedics to assess user experience. Volunteers completed a survey regarding their perceptions of the level of difficulty when using the database. A computer-based database was the best option as it provided flexibility while reducing costs. The final database should perform calculations and summarize risk assessment data, provide recommended resources, generate automated reports, capture changes in medical and medication history, and ensure that the sensitive information is secure. During pilot testing, the older adult participants and the paramedics indicated that the database was easy to use. This low-cost, user-friendly and secure database captures initial and follow-up data, incorporates algorithms that guide the paramedics, and calculates risk factor scores for the participants. This solution to a healthcare database is translatable to other health research studies in which ongoing patient data is collected electronically and longitudinally.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"3 12","pages":"e0000689"},"PeriodicalIF":0.0,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11676497/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142901027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}