RESUMEN
Introduction: The use of remote patient monitoring (RPM) services for neurological disorders remains understudied, particularly in the context of newer billing codes introduced before the COVID-19 pandemic. Methods: This retrospective cohort study utilized data from commercial and Medicare employer-sponsored administrative claims between January 1, 2019, to December 31, 2021. The study population included all patients with at least one qualifying RPM-related Current Procedural Terminology (CPT) code for a neurological disorder, separated into first-generation (CPT 99091) codes and second-generation (CPT 99453, 99454, 99457, 99458) code cohorts. We compared patient and encounter characteristics between both cohorts. Results: We identified 27,756 encounters attributable to 11,326 patients who received RPM services for neurological disorders, of whom 5,785 (51.1%) received RPM via second-generation billing codes, 3,941 (34.8%) were female, 6,712 (59.3%) were between 45 and 64 years old, and 10,488 (92.6%) had a primary diagnosis of sleep-wake disorder. The second-generation cohort was significantly more likely to be female (41.5% vs. 27.8%, p < 0.001), be of age 65 or older (15.7% vs. 7.1%, p < 0.001), and reside in urban areas (93.4% vs. 87.6%, p < 0.001) than the first-generation cohort. Patients in the second-generation cohort were more likely to receive RPM in office settings (86.3% vs. 62.5%, p < 0.001), by physicians (77.0% vs. 40.3%, p < 0.001), and less likely for sleep-wake disorders (87.9% vs. 97.5%, p < 0.001) than the first-generation cohort. Patients who received RPM from physicians were most often evaluated by pulmonologists (31.4%). Discussion: In this commercially insured patient population receiving RPM for neurological disorders, we found that sleep-wake disorders and non-neurologists were over-represented.
RESUMEN
BACKGROUND: Medication reconciliation is essential for optimizing medication use. In part to promote effective medication reconciliation, the Department of Veterans Affairs (VA) invested substantial resources in health information exchange (HIE) technologies. The objectives of this qualitative study were to characterize VA clinicians' use of HIE tools for medication reconciliation in their clinical practice and to identify facilitators and barriers. METHODS: We recruited inpatient and outpatient prescribers (physicians, nurse practitioners, physician assistants) and pharmacists at four geographically distinct VA medical centers for observations and interviews. Participants were observed as they interacted with HIE or medication reconciliation tools during routine work. Participants were interviewed about clinical decision-making pertaining to medication reconciliation and use of HIE tools, and about barriers and facilitators to use of the tools. Qualitative data were analyzed via inductive and deductive approaches using a priori codes. RESULTS: A total of 63 clinicians participated. Over half (58%) were female, and the mean duration of VA clinical experience was 7 (range 0-32) years. Underlying motivators for clinicians seeking data external to their VA medical center were having new patients, current patients receiving care from an external institution, and clinicians' concerns about possible medication discrepancies among institutions. Facilitators for using HIE software were clinicians' familiarity with the HIE software, clinicians' belief that medication information would be available within HIE, and their confidence in the ability to find HIE medication-related data of interest quickly. Six overarching barriers to HIE software use for medication coordination included visual clutter and information overload within the HIE display; challenges with HIE interface navigation; lack of integration between HIE and other electronic health record interfaces, necessitating multiple logins and application switching; concerns with the dependability of HIE medication information; unfamiliarity with HIE tools; and a lack of HIE data from non-VA facilities. CONCLUSIONS: This study is believed to be the first to qualitatively characterize clinicians' HIE use with respect to medication reconciliation. Results inform recommendations to optimize HIE use for medication management activities. We expect that healthcare organizations and software vendors will be able to apply the findings to develop more effective and usable HIE information displays.
Asunto(s)
Intercambio de Información en Salud , Conciliación de Medicamentos , Investigación Cualitativa , United States Department of Veterans Affairs , Humanos , Conciliación de Medicamentos/métodos , Estados Unidos , Femenino , Masculino , Persona de Mediana Edad , Registros Electrónicos de Salud , Entrevistas como Asunto , Adulto , Actitud del Personal de SaludRESUMEN
Gastroesophageal reflux disease (GERD) is a global chronic disease. Short video platforms make it easy for patients with GERD to obtain medical information. However, the quality of information from these videos remains uncertain. This study aimed to systematically assess videos related to GERD on TikTok and Bilibili. We conducted a search and gathered 241 Chinese videos related to GERD and recorded the essential information. Two independent evaluators assessed each video based on the completeness of six components of the GERD guidelines, and assessed the quality and reliability of the information in the videos using recognition tools. Finally, videos from different sources were compared. The uploaders of most videos were medical professionals (86.7%, n = 209). The content was mainly about symptoms and treatment. The quality of the videos information varied depending on the sources. Among videos posted on Bilibili, those posted by medical professionals had a lower content score for definition (P < 0.001). Videos produced by non-gastroenterologists had the highest mean modified DISCERN. (The DISCERN scoring tool was initially created for assessing written publications, but nowadays, it is frequently leveraged for appraising various health-related videos. Details can be found in the text) quality of the videos information was acceptable; however, the content varied significantly depending on the type of source used. Videos with broad content should be carefully screened to meet more needs.
RESUMEN
OBJECTIVES: Automated dispensing cabinets (ADCs) offer improved medication safety, greater efficiency and return on investment. However, integrating ADCs into medication dispensing processes can be challenging in complex hospital environments. This study aimed to draft suggestions to help hospitals adopt ADCs. METHODS: Two-day visits were organised in seven European hospitals operating ADCs. Investigators used an observational grid, a questionnaire and interviews, each divided into the themes of medication processes before and after the introduction of ADCs, the major steps followed and the resources involved, ergonomics and staff perceptions. RESULTS: ADCs were integrated into four global hospital medication dispensing systems (packs of drugs are distributed from the central pharmacy to wards for dispensing) and three nominative systems-that is, patient-specific ones (drug doses prescribed for individuals are distributed from the central pharmacy to wards with ADC as supplementary stock). A general ADC project implementation timeline was shaped: main drivers of automation to initiate the project, visit of other sites, pilot test (with IT integration and staff training), and evaluation phase (satisfaction, safety, efficiency) to justify a possible expansion. Users (7 pharmacists, 21 nurses, 7 data engineers) identified facilitators (such as a dedicated project manager, a pilot phase, an intuitive device), barriers and any improvements needed (training for incoming staff, reorganisation of ward workflow, dynamic inventories). CONCLUSIONS: Despite their diverse pharmacy organisations, each hospital raised similar challenges and reported analogous major steps in project implementation. Although integration processes are complex, ADCs rapidly provide users with benefits. By following the practical advice and recommendations from these hospitals, new adopters might reduce the risks of failed ADC projects and accelerate their integration.
RESUMEN
BACKGROUND: The mechanism for recording International Classification of Diseases (ICD) and diagnosis related groups (DRG) codes in a patient's chart is through a certified medical coder who manually reviews the medical record at the completion of an admission. High-acuity ICD codes justify DRG modifiers, indicating the need for escalated hospital resources. In this manuscript, we demonstrate that value of rules-based computer algorithms that audit for omission of administrative codes and quantifying the downstream effects with regard to financial impacts and demographic findings did not indicate significant disparities. METHODS: All study data were acquired via the UCLA Department of Anesthesiology and Perioperative Medicine's Perioperative Data Warehouse. The DataMart is a structured reporting schema that contains all the relevant clinical data entered into the EPIC (EPIC Systems, Verona, WI) electronic health record. Computer algorithms were created for eighteen disease states that met criteria for DRG modifiers. Each algorithm was run against all hospital admissions with completed billing from 2019. The algorithms scanned for the existence of disease, appropriate ICD coding, and DRG modifier appropriateness. Secondarily, the potential financial impact of ICD omissions was estimated by payor class and an analysis of ICD miscoding was done by ethnicity, sex, age, and financial class. RESULTS: Data from 34,104 hospital admissions were analyzed from January 1, 2019, to December 31, 2019. 11,520 (32.9%) hospital admissions were algorithm positive for a disease state with no corresponding ICD code. 1,990 (5.8%) admissions were potentially eligible for DRG modification/upgrade with an estimated lost revenue of $22,680,584.50. ICD code omission rates compared against reference groups (private payors, Caucasians, middle-aged patients) demonstrated significant p-values < 0.05; similarly significant p-value where demonstrated when comparing patients of opposite sexes. CONCLUSIONS: We successfully used rules-based algorithms and raw structured EHR data to identify omitted ICD codes from inpatient medical record claims. These missing ICD codes often had downstream effects such as inaccurate DRG modifiers and missed reimbursement. Embedding augmented intelligence into this problematic workflow has the potential for improvements in administrative data, but more importantly, improvements in administrative data accuracy and financial outcomes.
Asunto(s)
Algoritmos , Comorbilidad , Grupos Diagnósticos Relacionados , Clasificación Internacional de Enfermedades , Humanos , Estudios Retrospectivos , Programas Informáticos , Registros Electrónicos de Salud/normas , Masculino , Femenino , Persona de Mediana Edad , AdultoRESUMEN
BACKGROUND: The biomedical and health informatics (BMHI) fields have been advancing rapidly, a trend particularly emphasised during the recent COVID-19 pandemic, introducing innovations in BMHI. Over nearly 50 years since its establishment as a scientific discipline, BMHI has encountered several challenges, such as mishaps, delays, failures, and moments of enthusiastic expectations and notable successes. This paper focuses on reviewing the progress made in the BMHI discipline, evaluating key milestones, and discussing future challenges. METHODS: To, Structured, step-by-step qualitative methodology was developed and applied, centred on gathering expert opinions and analysing trends from the literature to provide a comprehensive assessment. Experts and pioneers in the BMHI field were assigned thematic tasks based on the research question, providing critical inputs for the thematic analysis. This led to the identification of five key dimensions used to present the findings in the paper: informatics in biomedicine and healthcare, health data in Informatics, nurses in informatics, education and accreditation in health informatics, and ethical, legal, social, and security issues. RESULTS: Each dimension is examined through recently emerging innovations, linking them directly to the future of healthcare, like the role of artificial intelligence, innovative digital health tools, the expansion of telemedicine, and the use of mobile health apps and wearable devices. The new approach of BMHI covers newly introduced clinical needs and approaches like patient-centric, remote monitoring, and precision medicine clinical approaches. CONCLUSIONS: These insights offer clear recommendations for improving education and developing experts to advance future innovations. Notably, this narrative review presents a body of knowledge essential for a deep understanding of the BMHI field from a human-centric perspective and, as such, could serve as a reference point for prospective analysis and innovation development.
RESUMEN
Two decades into the era of Electronic Health Records (EHRs), the promise of streamlining clinical care, reducing burden, and improving patient outcomes has yet to be realized. A cross-sectional family physician census conducted by the American Board of Family Medicine in 2022 and 2023 included self-reported physician EHR satisfaction. Of the nearly 10,000 responding family physicians, only one-in-four (26.2%) report being very satisfied and one-in-three (33.8%) were not satisfied. These low levels of satisfaction point to the need for greater transparency in the marketplace and pressure to increase user-centric EHR design.
Asunto(s)
Registros Electrónicos de Salud , Médicos de Familia , Registros Electrónicos de Salud/estadística & datos numéricos , Humanos , Médicos de Familia/estadística & datos numéricos , Estudios Transversales , Estados Unidos , Femenino , Masculino , Medicina Familiar y Comunitaria , Actitud del Personal de Salud , Persona de Mediana Edad , AdultoRESUMEN
Artificial Intelligence (AI) is poised to revolutionize family medicine, offering a transformative approach to achieving the Quintuple Aim. This article examines the imperative for family medicine to adapt to the rapidly evolving field of AI, with an emphasis on its integration in clinical practice. AI's recent advancements have the potential to significantly transform health care. We argue for the proactive engagement of family medicine in directing AI technologies toward enhancing the "Quintuple Aim."The article highlights potential benefits of AI, such as improved patient outcomes through enhanced diagnostic tools, clinician well-being through reduced administrative burdens, and the promotion of health equity by analyzing diverse data sets. However, we also acknowledge the risks associated with AI, including the potential for automation to diverge from patient-centered care and exacerbate health care disparities. Our recommendations stress the need for family medicine education to incorporate AI literacy, the development of a collaborative for AI integration, and the establishment of guidelines and standards through interdisciplinary cooperation. We conclude that although AI poses challenges, its responsible and ethical implementation can revolutionize family medicine, optimizing patient care and enhancing the role of clinicians in a technology-driven future.
Asunto(s)
Inteligencia Artificial , Medicina Familiar y Comunitaria , Humanos , Medicina Familiar y Comunitaria/métodos , Atención Dirigida al Paciente/organización & administraciónRESUMEN
Background: Over 200 health information exchanges (HIEs) are currently operational in Japan. The most common feature of HIEs is remote on-demand viewing or searching of aggregated patient health data from multiple institutions. However, the usage of this feature by individual users and institutions remains unknown. Objective: This study aims to understand usage of the on-demand patient data viewing feature of large-scale HIEs by individual health care workers and institutions in Japan. Methods: We conducted audit log analyses of large-scale HIEs. The research subjects were HIEs connected to over 100 institutions and with over 10,000 patients. Each health care worker's profile and audit log data for HIEs were collected. We conducted four types of analyses on the extracted audit log. First, we calculated the ratio of the number of days of active HIE use for each hospital-affiliated doctor account. Second, we calculated cumulative monthly usage days of HIEs by each institution in financial year (FY) 2021/22. Third, we calculated each facility type's monthly active institution ratio in FY2021/22. Fourth, we compared the monthly active institution ratio by medical institution for each HIE and the proportion of cumulative usage days by user type for each HIE. Results: We identified 24 HIEs as candidates for data collection and we analyzed data from 7 HIEs. Among hospital doctors, 93.5% (7326/7833) had never used HIEs during the available period in FY2021/22, while 19 doctors used them at least 30% of days. The median (IQR) monthly active institution ratios were 0.482 (0.470-0.487) for hospitals, 0.243 (0.230-0.247) for medical clinics, and 0.030 (0.024-0.048) for dental clinics. In 51.9% (1781/3434) of hospitals, the cumulative monthly usage days of HIEs was 0, while in 26.8% (921/3434) of hospitals, it was between 1 and 10, and in 3% (103/3434) of hospitals, it was 100 or more. The median (IQR) monthly active institution ratio in medical institutions was 0.511 (0.487-0.529) for the most used HIE and 0.109 (0.0927-0.117) for the least used. The proportion of cumulative usage days of HIE by user type was complex for each HIE, and no consistent trends could be discerned. Conclusions: In the large-scale HIEs surveyed in this study, the overall usage of the on-demand patient data viewing feature was low, consistent with past official reports. User-level analyses of audit logs revealed large disparities in the number of days of HIE use among health care workers and institutions. There were also large disparities in HIE use by facility type or HIE; the percentage of cumulative HIE usage days by user type also differed by HIE. This study indicates the need for further research into why there are large disparities in demand for HIEs in Japan as well as the need to design comprehensive audit logs that can be matched with other official datasets.
Asunto(s)
Intercambio de Información en Salud , Japón , Intercambio de Información en Salud/estadística & datos numéricos , Humanos , Personal de Salud/estadística & datos numéricosRESUMEN
Structured reporting (SR) has long been a goal in radiology to standardize and improve the quality of radiology reports. Despite evidence that SR reduces errors, enhances comprehensiveness, and increases adherence to guidelines, its widespread adoption has been limited. Recently, large language models (LLMs) have emerged as a promising solution to automate and facilitate SR. Therefore, this narrative review aims to provide an overview of LLMs for SR in radiology and beyond. We found that the current literature on LLMs for SR is limited, comprising ten studies on the generative pre-trained transformer (GPT)-3.5 (n = 5) and/or GPT-4 (n = 8), while two studies additionally examined the performance of Perplexity and Bing Chat or IT5. All studies reported promising results and acknowledged the potential of LLMs for SR, with six out of ten studies demonstrating the feasibility of multilingual applications. Building upon these findings, we discuss limitations, regulatory challenges, and further applications of LLMs in radiology report processing, encompassing four main areas: documentation, translation and summarization, clinical evaluation, and data mining. In conclusion, this review underscores the transformative potential of LLMs to improve efficiency and accuracy in SR and radiology report processing. KEY POINTS: Question How can LLMs help make SR in radiology more ubiquitous? Findings Current literature leveraging LLMs for SR is sparse but shows promising results, including the feasibility of multilingual applications. Clinical relevance LLMs have the potential to transform radiology report processing and enable the widespread adoption of SR. However, their future role in clinical practice depends on overcoming current limitations and regulatory challenges, including opaque algorithms and training data.
RESUMEN
Background: Seasonal influenza and novel H1N1 influenza from 2009 present worldwide difficulties for public health sectors. It is difficult to distinguish between significant research output due to the rising quantity of papers mentioning this infectious disease. We aimed to identify a scientometric analysis of influenza diseases. We aimed to highlight the progress made in the discipline by the researchers affiliated with most documents. Methods: The h-index was used to evaluate the publication performance of highly cited papers. We retrieved the scientometric data using the keywords "Influenza" OR "Flu" OR "Orthomyxoviridae" AND "Antiviral agents" OR "Antiviral drugs." In all, 59013 documents were retrieved from the Web of Science between 2011 and 2020. The exported data to Biblioshiny and Microsoft Excel tools included sources by year, active authors, active journals, and countries. Also, we made use of quantitative analysis with scientometric indicators and knowledge mapping through the VOSviewer visualization software for creating the network visualization maps. Results: We found most papers written in English and other languages were from 402027 authors and listed in 4443 core journals. The researchers found that Palese P produced 155 and received an h-index of 55. The author Li Y has the highest contributions, with 313 publications. In global influenza research, Europe and North America are the most productive and impactful continents. The influenza research has been published in very few journals. Conclusion: This study will help hospital librarians and other library professionals to understand the status of research on influenza at any given point in time.
RESUMEN
BACKGROUND: While bibliometric studies of individual journals have been conducted, to the best of our knowledge, bibliometric mapping has not yet been utilized to analyze the literature published by the Journal of Medical Internet Research (JMIR). OBJECTIVE: In celebration of the journal's 25th anniversary, this study aimed to review the entire collection of JMIR publications from 1999 to 2024 and provide a comprehensive overview of the main publication characteristics. METHODS: This study included papers published in JMIR during the 25-year period from 1999 to 2024. The data were analyzed using CiteSpace, VOSviewer, and the "Bibliometrix" package in R. Through descriptive bibliometrics, we examined the dynamics and trend patterns of JMIR literature production and identified the most prolific authors, papers, institutions, and countries. Bibliometric maps were used to visualize the content of published articles and to identify the most prominent research terms and topics, along with their evolution. A bibliometric network map was constructed to determine the hot research topics over the past 25 years. RESULTS: This study revealed positive trends in literature production, with both the total number of publications and the average number of citations increasing over the years. And the global COVID-19 pandemic induced an explosive rise in the number of publications in JMIR. The most productive institutions were predominantly from the United States, which ranked highest in successful publications within the journal. The editor-in-chief of JMIR was identified as a pioneer in this field. The thematic analysis indicated that the most prolific topics aligned with the primary aims and scope of the journal. Currently and in the foreseeable future, the main themes of JMIR include "artificial intelligence," "patient empowerment," and "victimization." CONCLUSIONS: This bibliometric study highlighted significant contributions to digital health by identifying key research trends, themes, influential authors, and collaborations. The findings underscore the necessity to enhance publications from developing countries, improve gender diversity among authors, and expand the range of research topics explored in the journal.
Asunto(s)
Bibliometría , Publicaciones Periódicas como Asunto/estadística & datos numéricos , Humanos , Investigación Biomédica/tendencias , Investigación Biomédica/estadística & datos numéricos , Salud DigitalRESUMEN
BACKGROUND: Computerized clinical decision support systems (CDSSs) enhance patient care through real-time, evidence-based guidance for health care professionals. Despite this, the effective implementation of these systems for health services presents multifaceted challenges, leading to inappropriate use and abandonment over the course of time. Using the Non-Adoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) framework, this qualitative study examined CDSS adoption in a metropolitan health service, identifying determinants across implementation stages to optimize CDSS integration into health care practice. OBJECTIVE: This study aims to identify the theory-informed (NASSS) determinants, which included multiple CDSS interventions across a 2-year period, both at the health-service level and at the individual hospital setting, that either facilitate or hinder the application of CDSSs within a metropolitan health service. In addition, this study aimed to map these determinants onto specific stages of the implementation process, thereby developing a system-level understanding of CDSS application across implementation stages. METHODS: Participants involved in various stages of the implementation process were recruited (N=30). Participants took part in interviews and focus groups. We used a hybrid inductive-deductive qualitative content analysis and a framework mapping approach to categorize findings into barriers, enablers, or neutral determinants aligned to NASSS framework domains. These determinants were also mapped to implementation stages using the Active Implementation Framework stages approach. RESULTS: Participants comprised clinical adopters (14/30, 47%), organizational champions (5/30, 16%), and those with roles in organizational clinical informatics (5/30, 16%). Most determinants were mapped to the organization level, technology, and adopter subdomains. However, the study findings also demonstrated a relative lack of long-term implementation planning. Consequently, determinants were not uniformly distributed across the stages of implementation, with 61.1% (77/126) identified in the exploration stage, 30.9% (39/126) in the full implementation stage, and 4.7% (6/126) in the installation stages. Stakeholders engaged in more preimplementation and full-scale implementation activities, with fewer cycles of monitoring and iteration activities identified. CONCLUSIONS: These findings addressed a substantial knowledge gap in the literature using systems thinking principles to identify the interdependent dynamics of CDSS implementation. A lack of sustained implementation strategies (ie, training and longer-term, adopter-level championing) weakened the sociotechnical network between developers and adopters, leading to communication barriers. More rigorous implementation planning, encompassing all 4 implementation stages, may, in a way, help in addressing the barriers identified and enhancing enablers.
Asunto(s)
Sistemas de Apoyo a Decisiones Clínicas , Grupos Focales , Humanos , Investigación Cualitativa , Servicios Urbanos de Salud/organización & administración , Entrevistas como AsuntoRESUMEN
The complex transcriptional regulatory network leads to the poor prognosis of glioma. The role of orphan CpG islands (oCGIs) in the transcriptional regulatory network has been overlooked. We conducted a comprehensive exploration of the cis-regulatory roles of oCGIs and enhancers by integrating multi-omics data. Direct regulation of target genes by oCGIs or enhancers is of great importance in the cis-regulatory network. Furthermore, based on single-cell multi-omics data, we found that the highly activated cis-regulatory network in cluster 2 (C2) sustains the high proliferative potential of glioma cells. The upregulation of oCGIs and enhancers related genes in C2 results in glioma patients exhibiting resistance to radiotherapy and chemotherapy. These findings were further validated through glioma cell line related experiments. Our study offers insight into the pathogenesis of glioma and provides a strategy to treat this challenging disease.
RESUMEN
Primary care informatics (PCI) professionals address workflow and technology solutions in a wide spectrum of health, ranging from optimizing the experience of the individual patient in the clinic room to supporting the health of populations and augmenting the work of frontline primary care clinical teams. PCI overlaps uniquely with 2 disciplines with an impact on societal health-primary care and health informatics. Primary care is a gateway to health care access and aims to synthesize and coordinate numerous, complex elements of patients' health and medical care in a holistic manner. However, over the past 25 years, primary care has become a specialty in crisis: in a post-COVID-19 world, workforce shortages, clinician burnout, and continuing challenges in health care access all contribute to difficulties in sustaining primary care. Informatics professionals are poised to change this trajectory. In this viewpoint, we aim to inform readers of the discipline of PCI and its importance in the design, support, and maintenance of essential primary care services. Although this work focuses on primary care in the United States, which includes general internal medicine, family medicine, and pediatrics (and depending on definition, includes specialties such as obstetrics and gynecology), many of the principles outlined can also be applied to comparable health care services and settings in other countries. We highlight (1) common global challenges in primary care, (2) recent trends in the evolution of PCI (personalized medicine, population health, social drivers of health, and team-based care), and (3) opportunities to move forward PCI with current and emerging technologies using the 4Cs of primary care framework. In summary, PCI offers important contributions to health care and the informatics field, and there are many opportunities for informatics professionals to enhance the primary care experience for patients, families, and their care teams.
Asunto(s)
Informática Médica , Atención Primaria de Salud , Humanos , COVID-19/epidemiología , Estados Unidos , Atención a la SaludRESUMEN
BACKGROUND: Health care insurance fraud is on the rise in many ways, such as falsifying information and hiding third-party liability. This can result in significant losses for the medical health insurance industry. Consequently, fraud detection is crucial. Currently, companies employ auditors who manually evaluate records and pinpoint fraud. However, an automated and effective method is needed to detect fraud with the continually increasing number of patients seeking health insurance. Blockchain is an emerging technology and is constantly evolving to meet business needs. With its characteristics of immutability, transparency, traceability, and smart contracts, it demonstrates its potential in the health care domain. In particular, self-executable smart contracts are essential to reduce the costs associated with traditional paradigms, which are mostly manual, while preserving privacy and building trust among health care stakeholders, including the patient and the health insurance networks. However, with the proliferation of blockchain development platform options, selecting the right one for health care insurance can be difficult. This study addressed this void and developed an automated decision map recommender system to select the most effective blockchain platform for insurance fraud detection. OBJECTIVE: This study aims to develop smart contracts for detecting health care insurance fraud efficiently. Therefore, we provided a taxonomy of fraud scenarios and implemented their detection using a blockchain platform that was suitable for health care insurance fraud detection. To automatically and efficiently select the best platform, we proposed and implemented a decision map-based recommender system. For developing the decision-map, we proposed a taxonomy of 102 blockchain platforms. METHODS: We developed smart contracts for 12 fraud scenarios that we identified in the literature. We used the top 2 blockchain platforms selected by our proposed decision-making map-based recommender system, which is tailored for health care insurance fraud. The map used our taxonomy of 102 blockchain platforms classified according to their application domains. RESULTS: The recommender system demonstrated that Hyperledger Fabric was the best blockchain platform for identifying health care insurance fraud. We validated our recommender system by comparing the performance of the top 2 platforms selected by our system. The blockchain platform taxonomy that we created revealed that 59 blockchain platforms are suitable for all application domains, 25 are suitable for financial services, and 18 are suitable for various application domains. We implemented fraud detection based on smart contracts. CONCLUSIONS: Our decision map recommender system, which was based on our proposed taxonomy of 102 platforms, automatically selected the top 2 platforms, which were Hyperledger Fabric and Neo, for the implementation of health care insurance fraud detection. Our performance evaluation of the 2 platforms indicated that Fabric surpassed Neo in all performance metrics, as depicted by our recommender system. We provided an implementation of fraud detection based on smart contracts.
Asunto(s)
Fraude , Seguro de Salud , Fraude/prevención & control , Seguro de Salud/clasificación , Humanos , Cadena de Bloques , ContratosRESUMEN
INTRODUCTION: The Medical Informatics Initiative (MII) in Germany has pioneered platforms such as the National Portal for Medical Research Data (FDPG) to enhance the accessibility of data from clinical routine care for research across both university and non-university healthcare settings. This study explores the efficacy of the Medical Informatics Hub in Saxony (MiHUBx) services by integrating Klinikum Chemnitz gGmbH (KC) with the FDPG, leveraging the Fast Healthcare Interoperability Resources Core Data Set of the MII to standardize and harmonize data from disparate source systems. METHODS: The employed procedures include deploying installation packages to convert data into FHIR format and utilizing the Research Data Repository for structured data storage and exchange within the clinical infrastructure of KC. RESULT: Our results demonstrate successful integration, the development of a comprehensive deployment diagram, additionally, it was demonstrated that the non-university site can report clinical data to the FDPG. DISCUSSION: The discussion reflects on the practical application of this integration, highlighting its potential scalability to even smaller healthcare facilities and to pave the way to access to more medical data for research. This exemplary demonstration of the interplay of different tools provides valuable insights into technical and operational challenges, setting a precedent for future expansions and contributing to the democratization of medical data access.
Asunto(s)
Registros Electrónicos de Salud , Alemania , Humanos , Informática Médica , Almacenamiento y Recuperación de la Información/métodos , Integración de Sistemas , Interoperabilidad de la Información en SaludRESUMEN
INTRODUCTION: To support research projects that require medical data from multiple sites is one of the goals of the German Medical Informatics Initiative (MII). The data integration centers (DIC) at university medical centers in Germany provide patient data via FHIR® in compliance with the MII core data set (CDS). Requirements for data protection and other legal bases for processing prefer decentralized processing of the relevant data in the DICs and the subsequent exchange of aggregated results for cross-site evaluation. METHODS: Requirements from clinical experts were obtained in the context of the MII use case INTERPOLAR. A software architecture was then developed, modeled using 3LGM2, finally implemented and published in a github repository. RESULTS: With the CDS tool chain, we have created software components for decentralized processing on the basis of the MII CDS. The CDS tool chain requires access to a local FHIR endpoint and then transfers the data to an SQL database. This is accessed by the DataProcessor component, which performs calculations with the help of rules (input repo) and writes the results back to the database. The CDS tool chain also has a frontend module (REDCap), which is used to display the output data and calculated results, and allows verification, evaluation, comments and other responses. This feedback is also persisted in the database and is available for further use, analysis or data sharing in the future. DISCUSSION: Other solutions are conceivable. Our solution utilizes the advantages of an SQL database. This enables flexible and direct processing of the stored data using established analysis methods. Due to the modularization, adjustments can be made so that it can be used in other projects. We are planning further developments to support pseudonymization and data sharing. Initial experience is being gathered. An evaluation is pending and planned.
Asunto(s)
Programas Informáticos , Alemania , Registros Electrónicos de Salud , Humanos , Informática Médica , Seguridad Computacional , Conjuntos de Datos como AsuntoRESUMEN
An asymmetric windswept posture is often seen in children with severe cerebral palsy (CP). However, it is still unclear how long children with CP remain in the windswept posture in daily life. Thus, we developed a triple-accelerometer system for detecting windswept posture. The aim of this study was to assess the validity of a system for classifying various body postures and movements. We assessed the accuracy of our system in nine healthy young adults (age range, 21-23 years). The participants wore acceleration monitors on the sternum and both thighs, then spent 3 min each in eight different positions and three physical activities. Once accuracy was confirmed, we assessed the posture and movements for 24 h in six healthy young adults (age range, 21-23 years) in their home environments. The body postures and activities were correctly detected: the agreement across the subjects were 100% compatible with the subjects' activity logs at least 68% of the time, and at least 96% of the time for recumbent positions. We concluded that the proposed monitoring system is a reliable and valid approach for assessing windswept hip posture in a free-living setting.
RESUMEN
BACKGROUND: The public launch of OpenAI's ChatGPT platform generated immediate interest in the use of large language models (LLMs). Health care institutions are now grappling with establishing policies and guidelines for the use of these technologies, yet little is known about how health care providers view LLMs in medical settings. Moreover, there are no studies assessing how pediatric providers are adopting these readily accessible tools. OBJECTIVE: The aim of this study was to determine how pediatric providers are currently using LLMs in their work as well as their interest in using a Health Insurance Portability and Accountability Act (HIPAA)-compliant version of ChatGPT in the future. METHODS: A survey instrument consisting of structured and unstructured questions was iteratively developed by a team of informaticians from various pediatric specialties. The survey was sent via Research Electronic Data Capture (REDCap) to all Boston Children's Hospital pediatric providers. Participation was voluntary and uncompensated, and all survey responses were anonymous. RESULTS: Surveys were completed by 390 pediatric providers. Approximately 50% (197/390) of respondents had used an LLM; of these, almost 75% (142/197) were already using an LLM for nonclinical work and 27% (52/195) for clinical work. Providers detailed the various ways they are currently using an LLM in their clinical and nonclinical work. Only 29% (n=105) of 362 respondents indicated that ChatGPT should be used for patient care in its present state; however, 73.8% (273/368) reported they would use a HIPAA-compliant version of ChatGPT if one were available. Providers' proposed future uses of LLMs in health care are described. CONCLUSIONS: Despite significant concerns and barriers to LLM use in health care, pediatric providers are already using LLMs at work. This study will give policy makers needed information about how providers are using LLMs clinically.