Methods of Information in Medicine最新文献

{"title":"The Acceptance of Interruptive Medication Alerts in an Electronic Decision Support System Differs between Different Alert Types.","authors":"Janina A Bittmann,&nbsp;Elisabeth K Rein,&nbsp;Michael Metzner,&nbsp;Walter E Haefeli,&nbsp;Hanna M Seidling","doi":"10.1055/s-0041-1735169","DOIUrl":"https://doi.org/10.1055/s-0041-1735169","url":null,"abstract":"<p><strong>Background: </strong>Through targeted medication alerts, clinical decision support systems (CDSS) help users to identify medication errors such as disregarded drug-drug interactions (DDIs). Override rates of such alerts are high; however, they can be mitigated by alert tailoring or workflow-interrupting display of severe alerts that need active user acceptance or overriding. Yet, the extent to which the displayed alert interferes with the prescribers' workflow showed inconclusive impact on alert acceptance.</p><p><strong>Objectives: </strong>We aimed to assess whether and how often prescriptions were changed as a potential result of interruptive alerts on different (contraindicated) prescription constellations with particularly high risks for adverse drug events (ADEs).</p><p><strong>Methods: </strong>We retrospectively collected data of all interruptive alerts issued between March 2016 and August 2020 in the local CDSS (AiD<i>Klinik</i>) at Heidelberg University Hospital. The alert battery consisted of 31 distinct alerts for contraindicated DDI with simvastatin, potentially inappropriate medication for patients > 65 years (PIM, <i>N</i> = 14 drugs and 36 drug combinations), and contraindicated drugs in hyperkalemia (<i>N</i> = 5) that could be accepted or overridden giving a reason in free-text form.</p><p><strong>Results: </strong>In 935 prescribing sessions of 500 274 total sessions, at least one interruptive alert was fired. Of all interruptive alerts, about half of the sessions were evaluable whereof in total 57.5% (269 of 468 sessions) were accepted while 42.5% were overridden. The acceptance rate of interruptive alerts differed significantly depending on the alert type (<i>p</i> <0.0001), reaching 85.7% for DDI alerts (<i>N</i> = 185), 65.3% for contraindicated drugs in hyperkalemia (<i>N</i> = 98), and 25.1% for PIM alerts (<i>N</i> = 185).</p><p><strong>Conclusion: </strong>A total of 57.5% of the interruptive medication alerts with particularly high risks for ADE in our setting were accepted while the acceptance rate differed according to the alert type with contraindicated simvastatin DDI alerts being accepted most frequently.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"60 5-06","pages":"180-184"},"PeriodicalIF":1.7,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39358833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation Metrics for Health Chatbots: A Delphi Study.","authors":"Kerstin Denecke,&nbsp;Alaa Abd-Alrazaq,&nbsp;Mowafa Househ,&nbsp;Jim Warren","doi":"10.1055/s-0041-1736664","DOIUrl":"https://doi.org/10.1055/s-0041-1736664","url":null,"abstract":"<p><strong>Background: </strong>In recent years, an increasing number of health chatbots has been published in app stores and described in research literature. Given the sensitive data they are processing and the care settings for which they are developed, evaluation is essential to avoid harm to users. However, evaluations of those systems are reported inconsistently and without using a standardized set of evaluation metrics. Missing standards in health chatbot evaluation prevent comparisons of systems, and this may hamper acceptability since their reliability is unclear.</p><p><strong>Objectives: </strong>The objective of this paper is to make an important step toward developing a health-specific chatbot evaluation framework by finding consensus on relevant metrics.</p><p><strong>Methods: </strong>We used an adapted Delphi study design to verify and select potential metrics that we retrieved initially from a scoping review. We invited researchers, health professionals, and health informaticians to score each metric for inclusion in the final evaluation framework, over three survey rounds. We distinguished metrics scored relevant with high, moderate, and low consensus. The initial set of metrics comprised 26 metrics (categorized as global metrics, metrics related to response generation, response understanding and aesthetics).</p><p><strong>Results: </strong>Twenty-eight experts joined the first round and 22 (75%) persisted to the third round. Twenty-four metrics achieved high consensus and three metrics achieved moderate consensus. The core set for our framework comprises mainly global metrics (e.g., ease of use, security content accuracy), metrics related to response generation (e.g., appropriateness of responses), and related to response understanding. Metrics on aesthetics (font type and size, color) are less well agreed upon-only moderate or low consensus was achieved for those metrics.</p><p><strong>Conclusion: </strong>The results indicate that experts largely agree on metrics and that the consensus set is broad. This implies that health chatbot evaluation must be multifaceted to ensure acceptability.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"60 5-06","pages":"171-179"},"PeriodicalIF":1.7,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39577534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating Topic Model and Network Embedding for Thread Recommendation.","authors":"Wei Wei,&nbsp;Rui Wang","doi":"10.1055/s-0041-1736462","DOIUrl":"https://doi.org/10.1055/s-0041-1736462","url":null,"abstract":"<p><strong>Objectives: </strong>A thread is the most common information aggregation unit in a health forum, so effective thread recommendation is critical for improving the user experience in an online health community (OHC). This paper proposes an OHC thread recommendation method based on topic model and network embedding, which recommends threads to users by training a classifier and predicting user reply behavior.</p><p><strong>Methods: </strong>The proposed model uses the network structure to describe valid information in OHCs and treats a recommendation as the task of predicting links between users and threads in the network. Topic nodes are added to the information network to better represent the features of users and threads. The results of the latent Dirichlet allocation (LDA) model describe thread topics and user interests from the perspectives of consumer health vocabulary in OHCs and social support types. The large-scale information network embedding technology LINE is used to mine the node's contextual information from the network structure to obtain the low-dimensional vectors of nodes. We optimize the representation method and similarity calculation of network nodes and enrich the network structure information contained in the recommended features to improve the recommendation effect.</p><p><strong>Results: </strong>To verify the proposed model, we collected data from the diabetes forum \"Sweet Home.\" The experimental results show that the proposed model can effectively extract user interests in threads from the information network and optimize thread recommendation in OHCs.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"60 5-06","pages":"133-146"},"PeriodicalIF":1.7,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39550416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing an Analytical Pipeline to Classify Patient Safety Event Reports Using Optimized Predictive Algorithms.","authors":"Asa Adadey,&nbsp;Robert Giannini,&nbsp;Lorraine B Possanza","doi":"10.1055/s-0041-1735620","DOIUrl":"https://doi.org/10.1055/s-0041-1735620","url":null,"abstract":"<p><strong>Background: </strong>Patient safety event reports provide valuable insight into systemic safety issues but deriving insights from these reports requires computational tools to efficiently parse through large volumes of qualitative data. Natural language processing (NLP) combined with predictive learning provides an automated approach to evaluating these data and supporting the work of patient safety analysts.</p><p><strong>Objectives: </strong>The objective of this study was to use NLP and machine learning techniques to develop a generalizable, scalable, and reliable approach to classifying event reports for the purpose of driving improvements in the safety and quality of patient care.</p><p><strong>Methods: </strong>Datasets for 14 different labels (themes) were vectorized using a bag-of-words, <i>tf-idf</i>, or document embeddings approach and then applied to a series of classification algorithms via a hyperparameter grid search to derive an optimized model. Reports were also analyzed for terms strongly associated with each theme using an adjusted F-score calculation.</p><p><strong>Results: </strong>F₁ score for each optimized model ranged from 0.951 (\"Fall\") to 0.544 (\"Environment\"). The bag-of-words approach proved optimal for 12 of 14 labels, and the naïve Bayes algorithm performed best for nine labels. Linear support vector machine was demonstrated as optimal for three labels and XGBoost for four of the 14 labels. Labels with more distinctly associated terms performed better than less distinct themes, as shown by a Pearson's correlation coefficient of 0.634.</p><p><strong>Conclusions: </strong>We were able to demonstrate an analytical pipeline that broadly applies NLP and predictive modeling to categorize patient safety reports from multiple facilities. This pipeline allows analysts to more rapidly identify and structure information contained in patient safety data, which can enhance the evaluation and the use of this information over time.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"60 5-06","pages":"147-161"},"PeriodicalIF":1.7,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39577533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Pipeline for Standardizing Russian Unstructured Allergy Anamnesis Using FHIR AllergyIntolerance Resource.","authors":"Iuliia D Lenivtceva,&nbsp;Georgy Kopanitsa","doi":"10.1055/s-0041-1733945","DOIUrl":"https://doi.org/10.1055/s-0041-1733945","url":null,"abstract":"<p><strong>Background: </strong>The larger part of essential medical knowledge is stored as free text which is complicated to process. Standardization of medical narratives is an important task for data exchange, integration, and semantic interoperability.</p><p><strong>Objectives: </strong>The article aims to develop the end-to-end pipeline for structuring Russian free-text allergy anamnesis using international standards.</p><p><strong>Methods: </strong>The pipeline for free-text data standardization is based on FHIR (Fast Healthcare Interoperability Resources) and SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms) to ensure semantic interoperability. The pipeline solves common tasks such as data preprocessing, classification, categorization, entities extraction, and semantic codes assignment. Machine learning methods, rule-based, and dictionary-based approaches were used to compose the pipeline. The pipeline was evaluated on 166 randomly chosen medical records.</p><p><strong>Results: </strong>AllergyIntolerance resource was used to represent allergy anamnesis. The module for data preprocessing included the dictionary with over 90,000 words, including specific medication terms, and more than 20 regular expressions for errors correction, classification, and categorization modules resulted in four dictionaries with allergy terms (total 2,675 terms), which were mapped to SNOMED CT concepts. F-scores for different steps are: 0.945 for filtering, 0.90 to 0.96 for allergy categorization, 0.90 and 0.93 for allergens reactions extraction, respectively. The allergy terminology coverage is more than 95%.</p><p><strong>Conclusion: </strong>The proposed pipeline is a step to ensure semantic interoperability of Russian free-text medical records and could be effective in standardization systems for further data exchange and integration.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"60 3-04","pages":"95-103"},"PeriodicalIF":1.7,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39338364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing Identification of People Living with HIV from Electronic Medical Records: Computable Phenotype Development and Validation.","authors":"Yiyang Liu, Khairul A Siddiqi, Robert L Cook, Jiang Bian, Patrick J Squires, Elizabeth A Shenkman, Mattia Prosperi, Dushyantha T Jayaweera","doi":"10.1055/s-0041-1735619","DOIUrl":"10.1055/s-0041-1735619","url":null,"abstract":"<p><strong>Background: </strong>Electronic health record (EHR)-based computable phenotype algorithms allow researchers to efficiently identify a large virtual cohort of Human Immunodeficiency Virus (HIV) patients. Built upon existing algorithms, we refined, improved, and validated an HIV phenotype algorithm using data from the OneFlorida Data Trust, a repository of linked claims data and EHRs from its clinical partners, which provide care to over 15 million patients across all 67 counties in Florida.</p><p><strong>Methods: </strong>Our computable phenotype examined information from multiple EHR domains, including clinical encounters with diagnoses, prescription medications, and laboratory tests. To identify an HIV case, the algorithm requires the patient to have at least one diagnostic code for HIV and meet one of the following criteria: have 1+ positive HIV laboratory, have been prescribed with HIV medications, or have 3+ visits with HIV diagnostic codes. The computable phenotype was validated against a subset of clinical notes.</p><p><strong>Results: </strong>Among the 15+ million patients from OneFlorida, we identified 61,313 patients with confirmed HIV diagnosis. Among them, 8.05% met all four inclusion criteria, 69.7% met the 3+ HIV encounters criteria in addition to having HIV diagnostic code, and 8.1% met all criteria except for having positive laboratories. Our algorithm achieved higher sensitivity (98.9%) and comparable specificity (97.6%) relative to existing algorithms (77-83% sensitivity, 86-100% specificity). The mean age of the sample was 42.7 years, 58% male, and about half were Black African American. Patients' average follow-up period (the time between the first and last encounter in the EHRs) was approximately 4.6 years. The median number of all encounters and HIV-related encounters were 79 and 21, respectively.</p><p><strong>Conclusion: </strong>By leveraging EHR data from multiple clinical partners and domains, with a considerably diverse population, our algorithm allows more flexible criteria for identifying patients with incomplete laboratory test results and medication prescribing history compared with prior studies.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"60 3-04","pages":"84-94"},"PeriodicalIF":1.7,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8672443/pdf/nihms-1761917.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39473424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Natural Language Mapping of Electrocardiogram Interpretations to a Standardized Ontology.","authors":"Richard H Epstein, Yuel-Kai Jean, Roman Dudaryk, Robert E Freundlich, Jeremy P Walco, Dorothee A Mueller, Shawn E Banks","doi":"10.1055/s-0041-1736312","DOIUrl":"10.1055/s-0041-1736312","url":null,"abstract":"<p><strong>Background: </strong>Interpretations of the electrocardiogram (ECG) are often prepared using software outside the electronic health record (EHR) and imported via an interface as a narrative note. Thus, natural language processing is required to create a computable representation of the findings. Challenges include misspellings, nonstandard abbreviations, jargon, and equivocation in diagnostic interpretations.</p><p><strong>Objectives: </strong>Our objective was to develop an algorithm to reliably and efficiently extract such information and map it to the standardized ECG ontology developed jointly by the American Heart Association, the American College of Cardiology Foundation, and the Heart Rhythm Society. The algorithm was to be designed to be easily modifiable for use with EHRs and ECG reporting systems other than the ones studied.</p><p><strong>Methods: </strong>An algorithm using natural language processing techniques was developed in structured query language to extract and map quantitative and diagnostic information from ECG narrative reports to the cardiology societies' standardized ECG ontology. The algorithm was developed using a training dataset of 43,861 ECG reports and applied to a test dataset of 46,873 reports.</p><p><strong>Results: </strong>Accuracy, precision, recall, and the F1-measure were all 100% in the test dataset for the extraction of quantitative data (e.g., PR and QTc interval, atrial and ventricular heart rate). Performances for matches in each diagnostic category in the standardized ECG ontology were all above 99% in the test dataset. The processing speed was approximately 20,000 reports per minute. We externally validated the algorithm from another institution that used a different ECG reporting system and found similar performance.</p><p><strong>Conclusion: </strong>The developed algorithm had high performance for creating a computable representation of ECG interpretations. Software and lookup tables are provided that can easily be modified for local customization and for use with other EHR and ECG reporting systems. This algorithm has utility for research and in clinical decision-support where incorporation of ECG findings is desired.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"60 3-04","pages":"104-109"},"PeriodicalIF":1.3,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8595771/pdf/nihms-1752621.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39487085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Do Hospitals Need to Extend Telehealth Services? An Experimental Study of Different Telehealth Modalities during the COVID-19 Pandemic.","authors":"Pouyan Esmaeilzadeh,&nbsp;Tala Mirzaei","doi":"10.1055/s-0041-1735947","DOIUrl":"https://doi.org/10.1055/s-0041-1735947","url":null,"abstract":"<p><strong>Background: </strong>The COVID-19 pandemic has changed health care systems and clinical workflows in many countries, including the United States. This public health crisis has accelerated the transformation of health care delivery through the use of telehealth. Due to the coronavirus' severity and pathogenicity, telehealth services are considered the best platforms to meet suddenly increased patient care demands, reduce the transformation of the virus, and protect patients and health care workers. However, many hospitals, clinicians, and patients are not ready to switch to virtual care completely.</p><p><strong>Objectives: </strong>We designed six experiments to examine how people (as an actual beneficiary of telehealth) evaluate five telehealth encounters versus face-to-face visits.</p><p><strong>Methods: </strong>We used an online survey to collect data from 751 individuals (patients) in the United States.</p><p><strong>Results: </strong>Findings demonstrate that significant factors for evaluating five types of telehealth encounters are perceived convenience expected from telehealth encounters, perceived psychological risks associated with telehealth programs, and perceived attentive care services delivered by telehealth platforms. However, significant elements for comparing telehealth services with traditional face-to-face clinic visits are perceived cost-saving, perceived time-saving, perceived hygienic services, perceived technical errors, perceived information completeness, perceived communication barriers, perceived trust in medical care platforms' competency, and perceived privacy concerns.</p><p><strong>Conclusion: </strong>Although the in-person visit was reported as the most preferred care practice, there was no significant difference between people's willingness to use face-to-face visits versus virtual care. Nevertheless, before the widespread rollout of telehealth platforms, health care systems need to determine and address the challenges of implementing virtual care to improve patient engagement in telehealth services. This study also provides practical implications for health care providers to deploy telehealth effectively during the pandemic and postpandemic phases.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"60 3-04","pages":"71-83"},"PeriodicalIF":1.7,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39478304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Machine Learning to Capture Quality Metrics from Natural Language: A Case Study of Diabetic Eye Exams.","authors":"Allan Fong,&nbsp;Nicholas Scoulios,&nbsp;H Joseph Blumenthal,&nbsp;Ryan E Anderson","doi":"10.1055/s-0041-1736311","DOIUrl":"https://doi.org/10.1055/s-0041-1736311","url":null,"abstract":"<p><strong>Background and objective: </strong>The prevalence of value-based payment models has led to an increased use of the electronic health record to capture quality measures, necessitating additional documentation requirements for providers.</p><p><strong>Methods: </strong>This case study uses text mining and natural language processing techniques to identify the timely completion of diabetic eye exams (DEEs) from 26,203 unique clinician notes for reporting as an electronic clinical quality measure (eCQM). Logistic regression and support vector machine (SVM) using unbalanced and balanced datasets, using the synthetic minority over-sampling technique (SMOTE) algorithm, were evaluated on precision, recall, sensitivity, and f1-score for classifying records positive for DEE. We then integrate a high precision DEE model to evaluate free-text clinical narratives from our clinical EHR system.</p><p><strong>Results: </strong>Logistic regression and SVM models had comparable f1-score and specificity metrics with models trained and validated with no oversampling favoring precision over recall. SVM with and without oversampling resulted in the best precision, 0.96, and recall, 0.85, respectively. These two SVM models were applied to the unannotated 31,585 text segments representing 24,823 unique records and 13,714 unique patients. The number of records classified as positive for DEE using the SVM models ranged from 667 to 8,935 (2.7-36% out of 24,823, respectively). Unique patients classified as positive for DEE ranged from 3.5 to 41.8% highlighting the potential utility of these models.</p><p><strong>Discussion: </strong>We believe the impact of oversampling on SVM model performance to be caused by the potential of overfitting of the SVM SMOTE model on the synthesized data and the data synthesis process. However, the specificities of SVM with and without SMOTE were comparable, suggesting both models were confident in their negative predictions. By prioritizing to implement the SVM model with higher precision over sensitivity or recall in the categorization of DEEs, we can provide a highly reliable pool of results that can be documented through automation, reducing the burden of secondary review. Although the focus of this work was on completed DEEs, this method could be applied to completing other necessary documentation by extracting information from natural language in clinician notes.</p><p><strong>Conclusion: </strong>By enabling the capture of data for eCQMs from documentation generated by usual clinical practice, this work represents a case study in how such techniques can be leveraged to drive quality without increasing clinician work.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"60 3-04","pages":"110-115"},"PeriodicalIF":1.7,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39478757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Modular Approach to Combine Postmarket Clinical Follow-Up Studies and Postmarket Surveillance Studies.","authors":"Andreas Ziegler,&nbsp;Kristin Forßmann,&nbsp;Sabine Konopka,&nbsp;Katja Krockenberger","doi":"10.1055/s-0041-1735165","DOIUrl":"https://doi.org/10.1055/s-0041-1735165","url":null,"abstract":"<p><strong>Background: </strong>The European Medical Device Regulation 2017/745 (MDR) has its date of application in May 2021. This new legislation has refined and expanded the need of manufacturers to have a postmarket surveillance (PMS) system. According to this legislation, a postmarket clinical follow-up (PMCF) plan is also required. Manufacturers of high-risk medical devices are obliged to conduct both PMCF and PMS studies. There is thus the need to generate evidence from clinical data.</p><p><strong>Objectives: </strong>The conduct of several studies for PMS and PMCF can be cumbersome. We therefore aim to present a modular approach to combine PMS and PMCF studies into a single study.</p><p><strong>Materials and methods: </strong>We extracted the topics listed in the MDR, especially Annex XV, Section 3, the Good Clinical Practice for medical devices (EN 14155:2020, Annex A). In addition, we added topics according to the SPIRIT and the SPIRIT-PRO statement and created a draft clinical investigation plan (CIP).</p><p><strong>Results: </strong>The CIP template is provided as part of the manuscript. The modular concept has passed the required regulatory and legal requirements for one specific study.</p><p><strong>Conclusion: </strong>A modular approach for combining PMCF and PMS studies in a single CIP has been developed and implemented, and it is ready for use. The provided CIP template should enable other researchers and groups to adopt this concept according to their needs.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"60 3-04","pages":"116-122"},"PeriodicalIF":1.7,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39358832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}