Journal of Digital Health最新文献

{"title":"An audit on problem lists transfers in general practice in Leeds, United Kingdom","authors":"Pablo Millares Martin","doi":"10.55976/jdh.22023119882-87","DOIUrl":"https://doi.org/10.55976/jdh.22023119882-87","url":null,"abstract":"Background: Problem-oriented medical records are the standard among electronic health records (EHR) but after 50 years of use, problem lists (PL) do not seem to be the solution to clinicians' information needs. Objectives: To perform a quality improvement evaluation of PL content, considering available guidelines on its characteristics (accuracy, clarity, concision, currency) when transferring patients from one primary care organisation in England to another in Leeds. The standard should simply be the need to confirm currency. PL should be ready to be used safely after a brief check-up. Methods: During six months, all patients registering at a primary care setting in Leeds had their PL updated when they were transferred with an existing English electronic medical record. The content of the PL was later analysed by looking for the number of items in both lists (active and inactive), for the presence of duplicates and synonyms, and for items that needed to be added. It is normal practice to review the records at the time of transfer, usually by a nurse or healthcare assistant, but it was done by a general practitioner (GP) aiming to maximise the quality of the final PL. Results: Of the 175 newly registered patients studied, 3077 PL items were collected. Active PL included an average of 5.7 entries per patient, while inactive PL had an average of 11.8 entries. The number of duplicates per patient was about 1.8, while the number of synonyms was around 1.2. Unnecessary items were common. When records were reconciled, there was a 66.7% reduction in active PL entries and an 86.4% reduction in inactive entries. Discussion: Handover of PL among family physicians fails to transfer high-quality data. Different organisations follow distinct patterns in the use of PL. Major changes may be required to improve the flow of accurate, concise and up-to-date information. It could be argued that without further training, the use of clear guidelines or better support from health informatics, the PL will not provide the important summary information that clinicians need, which will affect clinicians' decision-making and to the detriment of patients.","PeriodicalId":131334,"journal":{"name":"Journal of Digital Health","volume":"109 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135138273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Designing NLP applications to support ICD coding: an impact analysis and guidelines to enhance baseline performance when processing patient discharge notes","authors":"Jessica Jha, Mario Almagro, Hegler Tissot","doi":"10.55976/jdh.22023119463-81","DOIUrl":"https://doi.org/10.55976/jdh.22023119463-81","url":null,"abstract":"Financial costs are a major concern in the healthcare system, with medical billing and coding playing a key role in facilitating transactions and financing procedures. Billing involves filing claims with insurance companies and requires scrutiny of clinical summaries and electronic health records to correctly match diagnoses, prescriptions, and procedures to standardized codes. Accuracy in assigning International Classification of Diseases (ICD) codes is critical to proper reimbursement of care. Incorrect codes waste time and resources, and cause administrative and financial problems for hospitals, insurance companies and patients. Manual medical coding is a labor-intensive and error-prone process that creates additional administrative burden and inconvenience for hospitals, insurance companies, and patients. To simplify the process, clinical records are often processed to automatically identify and extract clinical concepts and corresponding ICD codes. Deep learning and natural language processing techniques have shown promise in a variety of tasks but applying them to medical coding has been challenging. Accurate coding requires a deep understanding of medical terminology, context, and guidelines that may be difficult to capture with traditional deep learning methods. Although deep learning shows promise in healthcare, its specific impact on ICD coding is not fully understood, and translating scalable deep learning methods into practical improvements in ICD coding remains a challenge. Evaluating deep learning models under the scenarios of real-world coding and comparing them to established practice is critical to determining their true effectiveness. In this work, we address the automation of ICD coding by highlighting pitfalls and contrasting different perspectives. We investigated automatic ICD coding using baseline machine learning models, with a focus on identifying ICD-9 codes in discharge notes from Medical Information Mart for Intensive Care (MIMIC) database. A thorough evaluation of different models and approaches is crucial to avoid over-reliance on any method. Our findings show that simpler methods can achieve comparable results to deep learning models while still requiring fewer computational resources.","PeriodicalId":131334,"journal":{"name":"Journal of Digital Health","volume":"25 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136068125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The internet of medical things in healthcare management: a review","authors":"C. Ejiyi, Zhen Qin, M. B. Ejiyi, G. Nneji, H. Monday, Favour Amarachi Agu, Thomas Ugochukwu Ejiyi, Chidinma N. Diokpo, C. Orakwue","doi":"10.55976/jdh.22023116330-62","DOIUrl":"https://doi.org/10.55976/jdh.22023116330-62","url":null,"abstract":"The widespread adoption of Internet of Things (IoT) technologies across various domains has given rise to the Internet of Medical Things (IoMT), which has significantly enhanced the accuracy and capabilities of electronic devices in producing reliable results applicable to the healthcare industry. To leverage the potential of IoMT in healthcare, a series of interconnected events must take place, starting with edge devices collecting data, followed by data aggregation, processing, and informed decision-making based on data analysis. This review article stems from a collaborative and innovative project conducted by participants in the digital economy, organized by the Department of Software Engineering at Tsinghua University in 2021. The project focused on implementing technologies in various fields, with specific teams dedicated to healthcare. During this project, several gaps were identified, and solutions centered around the IoT were proposed. In this comprehensive review, we extensively investigated IoMT services and applications and emphasized how these applications can be optimally implemented to unlock their potentials. Our survey encompassed over 300 research papers, that examined the implementation of IoMT in domains such as Pharmacy Management and Health Insurance Management. Additionally, we analyzed the key enablers and barriers to the successful implementation of IoMT in recent times. To provide a practical perspective, we presented a feasible case study that applied deep learning to IoMT, considering the security concerns associated with its implementation. Furthermore, we identified future research directions and potential areas of improvement based on the gaps identified from the reviewed literatures. By undertaking this review, we aim to contribute to a deeper understanding of IoMT services and applications, shedding light on their optimal utilization within the healthcare industry. Ultimately, our goal is to facilitate advancements in IoMT implementation and to pave the way for enhanced healthcare delivery and improved patient outcomes.","PeriodicalId":131334,"journal":{"name":"Journal of Digital Health","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131928038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of open access COVID-19 related mobile applications in India: An application store-based quantitative analysis","authors":"Anish Patial, Aravind Gandhi Periyasamy, S. Rajavel, S. Kathirvel","doi":"10.55976/jdh.22023114022-29","DOIUrl":"https://doi.org/10.55976/jdh.22023114022-29","url":null,"abstract":"Purpose: To assess the features/functionalities and quality of the (open access) COVID-19 specific mobile application for India using the Mobile Application Rating Scale (MARS) and the quality of the reported COVID-19 data using the COVID-19 Data Reporting System (CDRS).\u0000Methods: We used an analytical, cross-sectional study in which we reviewed all open access (free) mobile phone-based applications across the application stores, namely Google Android Play Store, iTunes and Google search engine. We used MARS and CDRS to assess the mobile applications applicable to India.\u0000Results: We found a total of 247 applications through the iTunes store (n=176), android store (n=70) and Google search (n=1). Out of 247, 70 applications matched the inclusion criteria, and only 42 applications were accessible for detailed evaluation using MARS. The overall mean (SD) MARS score was 3.27 (0.59). The mean (SD) score for application mean quality, app subjective quality and app-specific quality domains were 3.43 (0.43), 2.95 (0.71), and 3.44 (0.82), respectively. Of the 20 applications evaluated using CDRS, Aarogya (Agra) Sarvam Setu and Odisha COVID had the highest normalized score (0.9), whereas Madhya Pradesh COVID response app and WHO Academy COVID-19 had the lowest (0.1).\u0000Conclusion: Though the overall quality of the mobile applications is good, the engagement aspect of the mobile application quality needs improvement. Applications providing comprehensive COVID-19 related services are still lacking. The necessity of the hour is to assess the user’s perspective and the impact of application features on COVID-19 prevention and control, either individually or in groups.","PeriodicalId":131334,"journal":{"name":"Journal of Digital Health","volume":"39 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120906063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI image-based diagnosis systems: how to implement them?","authors":"Ablameyko Maria, S. Ablameyko","doi":"10.55976/jdh.22023113912-21","DOIUrl":"https://doi.org/10.55976/jdh.22023113912-21","url":null,"abstract":"Artificial intelligence (AI) is starting to be widely used in the medical field and has great potential benefits to help doctors and patients. However, it also raises new challenges and problems. This paper analyzed the existing capacities of AI to make a diagnosis and assessed the legal consequences. We present AI medical image analysis systems developed in Belarus. International practice on how AI-systems are implemented in medicine is analyzed. Russian experience in developing standards to test and use AI systems in hospitals is described. Finally, the paper put forward some suggestions on how to improve the legal framework of AI systems using in medicine.","PeriodicalId":131334,"journal":{"name":"Journal of Digital Health","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117129862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence assisted diagnoses of fine-needle aspiration of breast diseases: a single-center experience","authors":"P. Fritz, R. Raoufi, P. Dalquen, A. Sediqi, S. Müller, J. Mollin, S. Goletz, J. Dippon, M. Hubler, T. Aeppel, B. Soudah, H. Firooz, M. Weinhara, I. Fabian de Barreto, C. Aichmüller, G. Stauch","doi":"10.55976/jdh.2202311501-11","DOIUrl":"https://doi.org/10.55976/jdh.2202311501-11","url":null,"abstract":"Abstract: Purpose: Since 2010, physicians from Afghanistan have been uploading images of histological and cytological specimens to a telemedicine internet platform (iPath network) for expert evaluation. From this collective work, all cases with fine-needle aspirations (FNA) of mammary gland diseases were extracted and analyzed. The aim of the present retrospective feasibility study is to investigate the utility of artificial intelligence assisted diagnoses in fine-needle aspiration (FNA) of breast diseases.Material and Methods: A total of 3304 microphotographic images from 438 patients of smears from FNA of the mammary gland were available for this study. Telemedical expert diagnoses from 4 experienced cytopathologists were available in all 438 cases. Their diagnosis (malignant tumor of the mammary gland or benign mammary gland disease) was set as the gold standard. AI analysis was performed using i) clinical context data and ii) two different image recognition methods to determine the probability values for the presence of malignant breast tumor. Youden index and AUC (area under the curve) were used to evaluate test performance. Results: A score for invasive breast cancer (IBC) calculated from contextual variables agreed with the expert diagnosis (accuracy) in 85.2% and with the two image recognition systems in 78.4% and 65.2%. This simplifies health healthcare management of breast diseases in low income countries as in many patients the less expensive and less time-consuming technique of FNA may replace a histological examination.Conclusion: Image classification and analysis of context variables can be used to test the validity and plausibility of cytologic diagnoses, especially when cytologic interpretation has to be performed by people who are inexperienced in cytopathology.","PeriodicalId":131334,"journal":{"name":"Journal of Digital Health","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122270551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of artificial intelligence in respiratory medicine","authors":"Chunxi Zhang, Weijin Wu, Jia Yang, Jiayuan Sun","doi":"10.55976/jdh.20221153","DOIUrl":"https://doi.org/10.55976/jdh.20221153","url":null,"abstract":"In recent years, thanks to the dawn of big data, the substantial improvement in computing power, and breakthroughs in algorithm research, artificial intelligence has developed rapidly, and remarkable progress has also been made in its application in medicine. In the field of respiratory medicine, the auxiliary diagnosis of lung cancer is currently the topic on which medical artificial intelligence is the most studied. Therefore, this paper mainly focuses on the diagnosis procedure of lung cancer, and comprehensively summarizes the application of artificial intelligence in the segmentation and detection of pulmonary nodules, classification of benign/malignant pulmonary nodules and intrathoracic lymph nodes, classification of lung cancer pathological images, and lung cancer prognosis analysis.\u0000In addition, the application of artificial intelligence in other respiratory diseases such as COVID-19, pneumothorax and pleural effusion is briefly introduced. In summary, artificial intelligence is widely used in the auxiliary diagnosis of respiratory diseases, and has a great potential to become a valuable assistant to respiratory physicians in the near future.","PeriodicalId":131334,"journal":{"name":"Journal of Digital Health","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114828844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An artificial intelligence-based system assisted endoscopists to detect early gastric cancer: a case report","authors":"Jiejun Lin, Xiao Tao, Jie Pan","doi":"10.55976/jdh.20221145","DOIUrl":"https://doi.org/10.55976/jdh.20221145","url":null,"abstract":"Background: The mucosal changes of early gastric cancers (EGC) are slight and difficult to be recognized, leading to a high miss rate. Artificial intelligence (AI) systems have the potential to improve the detection rate of EGC. Here, we reported a case of EGC discovered by an endoscopist with the assistance of an AI system.\u0000 \u0000Case presentation: A 67-year-old male patient came to our hospital for Esophagogastroduodenoscopy (EGD) due to a routine physical examination. He had previously been healthy but was treated for a Helicobacter pylori infection two years ago. In the process of EGD, the AI system flagged a tiny mucosal lesion that was far away and was not detected by the endoscopist, and this lesion attracted the endoscopist's additional attention. After the close observation of the lesion, the AI system immediately gave a red prompt box, suggesting that the endoscopist further observe it. Under magnifying endoscopy with narrow-band imaging (ME-NBI), the mucosal glands and blood vessels of the lesion were found to be irregular, and this patient was diagnosed with suspicious gastric carcinoma by AI. Biopsy pathology showed that it was high-grade intraepithelial neoplasia, and after endoscopic mucosal dissection (ESD), post-ESD histology confirmed that the lesion was a highly differentiated adenocarcinoma confined to the mucosa, with a lesion range of 1.1 cm × 1.0 cm. The patient was discharged from the hospital without any postoperative complications.\u0000 \u0000Conclusion: AI has been widely applied in the field of gastrointestinal endoscopy and has the potential to help improve the detection rate of early gastrointestinal cancers. We reported a case of early gastric cancer discovered by the endoscopist with the assistance of AI.\u0000 ","PeriodicalId":131334,"journal":{"name":"Journal of Digital Health","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128589194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The effect of data diversity on the performance of deep learning models for predicting early gastric cancer under endoscopy","authors":"Conghui Shi, Jia Li, Lianlian Wu","doi":"10.55976/jdh.1202214319-24","DOIUrl":"https://doi.org/10.55976/jdh.1202214319-24","url":null,"abstract":" \u0000Aims: This study aimed to explore the effect of training set diversity on the performance of deep learning models for predicting early gastric cancer (EGC) under endoscopy.\u0000Methods: Images of EGC and non-cancerous lesions under narrow-band imaging (ME-NBI) and magnifying blue laser imaging (ME-BLI) were retrospectively collected. Training set 1 was composed of 150 non-cancerous and 309 EGC ME-NBI images, training set 2 was composed of 1505 non-cancerous and 309 EGC ME-BLI images, and training set 3 was the combination of training set 1 and 2. Test set 1 was composed of 376 non-cancerous and 1052 EGC ME-NBI images, test set 2 consisted of 529 non-cancerous and 71 EGC ME-BLI images, and test set 3 was the combination of test set 1 and test set 2. Three deep learning models, convolutional neural network (CNN) 1, CNN 2 and CNN 3 (CNN 1, CNN 2 and CNN 3 were independently trained using training set 1, training set 2 and training set 3, respectively), were constructed, and their performances on each test set were respectively evaluated. One hundred and thirty-eight ME-NBI videos and 17 ME-BLI videos were further collected to evaluate and compare the performance of each model in real time.\u0000Results: On the whole, the performance of CNN 3 was the best. The accuracy (Acc), sensitivity (Sn), specificity (Sp) and area under the curve (AUC) of test set 1 in CNN 3 were 87.89% (1255/1428), 90.96% (342/376), 86.79% (913/1052) and 94.60%, respectively. The Acc, Sn, Sp and AUC of test set 2 in CNN 3 were 95% (570/600), 97.92% (518/529), 73.24% (52/71) and 90.93% respectively. The Acc, Sn, Sp and AUC of test set 3 in CNN 3 were 89.99% (1825/2028), 95.03% (860/905), 85.93% (965/1123) and 94.89%, respectively. The performance of CNN 3 was also the best in videos test set. The Acc, Sn and Sp of videos test set in CNN 3 were 91.03% (142/156), 90.58% (125/138) and 94.44% (17/18), respectively.\u0000Conclusions: The deep learning model with the most diverse training data has the best diagnostic effect.","PeriodicalId":131334,"journal":{"name":"Journal of Digital Health","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127197009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inauguration of a unique journal the Journal of Digital Health: a new beginning seeking innovative technology and research for digital health","authors":"Honggang Yu","doi":"10.55976/jdh.120221521-2","DOIUrl":"https://doi.org/10.55976/jdh.120221521-2","url":null,"abstract":"As the Editor-in-Chief of the newly founded journal the Journal of Digital Health (JDH), I am honored to introduce this journal to you on behalf of  Luminescience Press Ltd.\u0000JDH is an open access and peer-reviewed journal that publishes high-quality original articles from a wide variety of interdisciplinary perspectives concerning the theory and practice of artificial intelligence (AI), big data and informatics in the medical and healthcare industries to improve the quality and efficiency of healthcare. The journal publishes original articles, reviews, perspectives, research highlights and case reports that present the application of digital technologies in medical diagnostics and treatment, medical devices, machine learning-based decision support, medical record database and intelligent and process-aware information system in healthcare and medicine. We are committed to promoting the interdisciplinary research among clinicians, basic scientists and industrial experts and facilitating the clinical transformation of industrial technologies. Our ultimate mission is to enable the frontiers of science and technology better serve medicine and ultimately contribute to the healthcare of patients. ","PeriodicalId":131334,"journal":{"name":"Journal of Digital Health","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127961945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}