Methods of Information in Medicine最新文献

{"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}

{"title":"Proposing an International Standard Accident Number for Interconnecting Information and Communication Technology Systems of the Rescue Chain.","authors":"Nicolai Spicher,&nbsp;Ramon Barakat,&nbsp;Ju Wang,&nbsp;Mostafa Haghi,&nbsp;Justin Jagieniak,&nbsp;Gamze Söylev Öktem,&nbsp;Siegfried Hackel,&nbsp;Thomas Martin Deserno","doi":"10.1055/s-0041-1728676","DOIUrl":"https://doi.org/10.1055/s-0041-1728676","url":null,"abstract":"<p><strong>Background: </strong>The rapid dissemination of smart devices within the internet of things (IoT) is developing toward automatic emergency alerts which are transmitted from machine to machine without human interaction. However, apart from individual projects concentrating on single types of accidents, there is no general methodology of connecting the standalone information and communication technology (ICT) systems involved in an accident: systems for alerting (e.g., smart home/car/wearable), systems in the responding stage (e.g., ambulance), and in the curing stage (e.g., hospital).</p><p><strong>Objectives: </strong>We define the International Standard Accident Number (ISAN) as a unique token for interconnecting these ICT systems and to provide embedded data describing the circumstances of an accident (time, position, and identifier of the alerting system).</p><p><strong>Materials and methods: </strong>Based on the characteristics of processes and ICT systems in emergency care, we derive technological, syntactic, and semantic requirements for the ISAN, and we analyze existing standards to be incorporated in the ISAN specification.</p><p><strong>Results: </strong>We choose a set of formats for describing the embedded data and give rules for their combination to generate an ISAN. It is a compact alphanumeric representation that is generated easily by the alerting system. We demonstrate generation, conversion, analysis, and visualization via representational state transfer (REST) services. Although ISAN targets machine-to-machine communication, we give examples of graphical user interfaces.</p><p><strong>Conclusion: </strong>Created either locally by the alerting IoT system or remotely using our RESTful service, the ISAN is a simple and flexible token that enables technological, syntactic, and semantic interoperability between all ICT systems in emergency care.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"60 S 01","pages":"e20-e31"},"PeriodicalIF":1.7,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1055/s-0041-1728676","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38975040","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":"Recent Trends in Patient Registries for Health Services Research.","authors":"Jürgen Stausberg,&nbsp;Sonja Harkener,&nbsp;Sebastian C Semler","doi":"10.1055/s-0041-1724104","DOIUrl":"https://doi.org/10.1055/s-0041-1724104","url":null,"abstract":"<p><strong>Background: </strong>Patient registries are an established methodology in health services research. Since more than 150 years, registries collect information concerning groups of similar patients to answer research questions. Elaborated recommendations about an appropriate development and an efficient operation of registries are available. However, the scene changes rapidly.</p><p><strong>Objectives: </strong>The aim of the study is to describe current trends in registry research for health services research.</p><p><strong>Methods: </strong>Registries developed within a German funding scheme for model registries in health services research were analyzed. The observations were compared with recent recommendations of the Agency for Healthcare Research and Quality (AHRQ) on registries in the 21st century.</p><p><strong>Results: </strong>Analyzing six registries from the funding scheme revealed the following trends: recruiting healthy individuals, representing familial or other interpersonal relationships, recording of patient-reported experiences or outcomes, accepting participants as study sites, active informing of participants, integrating the registry with other data collections, and transferring data from the registry to electronic patient records. This list partly complies with the issues discussed by the AHRQ. The AHRQ structured its ideas in five chapters, increasing focus on the patient, engaging patients as partners, digital health and patient registries, direct-to-patient registry, and registry networks.</p><p><strong>Conclusion: </strong>For the near future, it can be said that the concept and the design of a registry should place the patient in the center. Registries will be increasingly linked together and interconnected with other data collections. New challenges arise regarding the management of data quality and the interpretation of results from less controlled settings. Here, further research related to the methodology of registries is needed.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"60 S 01","pages":"e1-e8"},"PeriodicalIF":1.7,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1055/s-0041-1724104","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38879945","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":"Semantic Textual Similarity in Japanese Clinical Domain Texts Using BERT.","authors":"Faith Wavinya Mutinda, Shuntaro Yada, Shoko Wakamiya, Eiji Aramaki","doi":"10.1055/s-0041-1731390","DOIUrl":"10.1055/s-0041-1731390","url":null,"abstract":"<p><strong>Background: </strong>Semantic textual similarity (STS) captures the degree of semantic similarity between texts. It plays an important role in many natural language processing applications such as text summarization, question answering, machine translation, information retrieval, dialog systems, plagiarism detection, and query ranking. STS has been widely studied in the general English domain. However, there exists few resources for STS tasks in the clinical domain and in languages other than English, such as Japanese.</p><p><strong>Objective: </strong>The objective of this study is to capture semantic similarity between Japanese clinical texts (Japanese clinical STS) by creating a Japanese dataset that is publicly available.</p><p><strong>Materials: </strong>We created two datasets for Japanese clinical STS: (1) Japanese case reports (CR dataset) and (2) Japanese electronic medical records (EMR dataset). The CR dataset was created from publicly available case reports extracted from the CiNii database. The EMR dataset was created from Japanese electronic medical records.</p><p><strong>Methods: </strong>We used an approach based on bidirectional encoder representations from transformers (BERT) to capture the semantic similarity between the clinical domain texts. BERT is a popular approach for transfer learning and has been proven to be effective in achieving high accuracy for small datasets. We implemented two Japanese pretrained BERT models: a general Japanese BERT and a clinical Japanese BERT. The general Japanese BERT is pretrained on Japanese Wikipedia texts while the clinical Japanese BERT is pretrained on Japanese clinical texts.</p><p><strong>Results: </strong>The BERT models performed well in capturing semantic similarity in our datasets. The general Japanese BERT outperformed the clinical Japanese BERT and achieved a high correlation with human score (0.904 in the CR dataset and 0.875 in the EMR dataset). It was unexpected that the general Japanese BERT outperformed the clinical Japanese BERT on clinical domain dataset. This could be due to the fact that the general Japanese BERT is pretrained on a wide range of texts compared with the clinical Japanese BERT.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"60 S 01","pages":"e56-e64"},"PeriodicalIF":1.7,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/79/46/10-1055-s-0041-1731390.PMC8294940.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39164921","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":"Leveraging Artificial Intelligence to Improve Chronic Disease Care: Methods and Application to Pharmacotherapy Decision Support for Type-2 Diabetes Mellitus.","authors":"Shinji Tarumi, Wataru Takeuchi, George Chalkidis, Salvador Rodriguez-Loya, Junichi Kuwata, Michael Flynn, Kyle M Turner, Farrant H Sakaguchi, Charlene Weir, Heidi Kramer, David E Shields, Phillip B Warner, Polina Kukhareva, Hideyuki Ban, Kensaku Kawamoto","doi":"10.1055/s-0041-1728757","DOIUrl":"10.1055/s-0041-1728757","url":null,"abstract":"<p><strong>Objectives: </strong>Artificial intelligence (AI), including predictive analytics, has great potential to improve the care of common chronic conditions with high morbidity and mortality. However, there are still many challenges to achieving this vision. The goal of this project was to develop and apply methods for enhancing chronic disease care using AI.</p><p><strong>Methods: </strong>Using a dataset of 27,904 patients with diabetes, an analytical method was developed and validated for generating a treatment pathway graph which consists of models that predict the likelihood of alternate treatment strategies achieving care goals. An AI-driven clinical decision support system (CDSS) integrated with the electronic health record (EHR) was developed by encapsulating the prediction models in an OpenCDS Web service module and delivering the model outputs through a SMART on FHIR (Substitutable Medical Applications and Reusable Technologies on Fast Healthcare Interoperability Resources) web-based dashboard. This CDSS enables clinicians and patients to review relevant patient parameters, select treatment goals, and review alternate treatment strategies based on prediction results.</p><p><strong>Results: </strong>The proposed analytical method outperformed previous machine-learning algorithms on prediction accuracy. The CDSS was successfully integrated with the Epic EHR at the University of Utah.</p><p><strong>Conclusion: </strong>A predictive analytics-based CDSS was developed and successfully integrated with the EHR through standards-based interoperability frameworks. The approach used could potentially be applied to many other chronic conditions to bring AI-driven CDSS to the point of care.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"60 S 01","pages":"e32-e43"},"PeriodicalIF":1.3,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/31/6d/10-1055-s-0041-1728757.PMC8294941.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38901351","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":"InspirerMundi-Remote Monitoring of Inhaled Medication Adherence through Objective Verification Based on Combined Image Processing Techniques.","authors":"Pedro Vieira-Marques,&nbsp;Rute Almeida,&nbsp;João F Teixeira,&nbsp;José Valente,&nbsp;Cristina Jácome,&nbsp;Afonso Cachim,&nbsp;Rui Guedes,&nbsp;Ana Pereira,&nbsp;Tiago Jacinto,&nbsp;João A Fonseca","doi":"10.1055/s-0041-1726277","DOIUrl":"https://doi.org/10.1055/s-0041-1726277","url":null,"abstract":"<p><strong>Background: </strong>The adherence to inhaled controller medications is of critical importance for achieving good clinical results in patients with chronic respiratory diseases. Self-management strategies can result in improved health outcomes and reduce unscheduled care and improve disease control. However, adherence assessment suffers from difficulties on attaining a high grade of trustworthiness given that patient self-reports of high-adherence rates are known to be unreliable.</p><p><strong>Objective: </strong>Aiming to increase patient adherence to medication and allow for remote monitoring by health professionals, a mobile gamified application was developed where a therapeutic plan provides insight for creating a patient-oriented self-management system. To allow a reliable adherence measurement, the application includes a novel approach for objective verification of inhaler usage based on real-time video capture of the inhaler's dosage counters.</p><p><strong>Methods: </strong>This approach uses template matching image processing techniques, an off-the-shelf machine learning framework, and was developed to be reusable within other applications. The proposed approach was validated by 24 participants with a set of 12 inhalers models.</p><p><strong>Results: </strong>Performed tests resulted in the correct value identification for the dosage counter in 79% of the registration events with all inhalers and over 90% for the three most widely used inhalers in Portugal. These results show the potential of exploring mobile-embedded capabilities for acquiring additional evidence regarding inhaler adherence.</p><p><strong>Conclusion: </strong>This system helps to bridge the gap between the patient and the health professional. By empowering the first with a tool for disease self-management and medication adherence and providing the later with additional relevant data, it paves the way to a better-informed disease management decision.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"60 S 01","pages":"e9-e19"},"PeriodicalIF":1.7,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1055/s-0041-1726277","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38921889","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":"Development and Validation of a Useful Taxonomy of Patient Portals Based on Characteristics of Patient Engagement.","authors":"Michael Glöggler,&nbsp;Elske Ammenwerth","doi":"10.1055/s-0041-1730284","DOIUrl":"https://doi.org/10.1055/s-0041-1730284","url":null,"abstract":"<p><strong>Objective: </strong>Taxonomies are classification systems used to reduce complexity and better understand a domain. The present research aims to develop a useful taxonomy for health information managers to classify and compare patient portals based on characteristics appropriate to promote patient engagement. As a result, the taxonomy should contribute to understanding the differences and similarities of the portals. Further, the taxonomy shall support health information managers to more easily define which general type and functionalities of patient portals they need and to select the most suitable solution offered on the market.</p><p><strong>Methods: </strong>We followed the formal taxonomy-building method proposed by Nickerson et al. Based on a literature review, we created a preliminary taxonomy following the conceptional approach of the model. We then evaluated each taxa's appropriateness by analyzing and classifying 17 patient portals offered by software vendors and 11 patient portals offered by health care providers. After each iteration, we examined the achievement of the determined objective and subjective ending conditions.</p><p><strong>Results: </strong>After two conceptional approaches to create our taxonomy, and two empirical approaches to evaluate it, the final taxonomy consists of 20 dimensions and 49 characteristics. To make the taxonomy easy to comprehend, we assigned to the dimensions seven aspects related to patient engagement. These aspects are (1) portal design, (2) management, (3) communication, (4) instruction, (5) self-management, (6) self-determination, and (7) data management. The taxonomy is considered finished and useful after all ending conditions that defined beforehand have been fulfilled. We demonstrated that the taxonomy serves to understand the differences and similarities by comparing patient portals. We call our taxonomy \"Taxonomy of Patient Portals based on Characteristics of Patient Engagement (TOPCOP).\"</p><p><strong>Conclusion: </strong>We developed the first useful taxonomy for health information managers to classify and compare patient portals. The taxonomy is based on characteristics promoting patient engagement. With 20 dimensions and 49 characteristics, our taxonomy is particularly suitable to discriminate among patient portals and can easily be applied to compare portals. The TOPCOP taxonomy enables health information managers to better understand the differences and similarities of patient portals. Further, the taxonomy may help them to define the type and general functionalities needed. But it also supports them in searching and comparing patient portals offered on the market to select the most suitable solution.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"60 S 01","pages":"e44-e55"},"PeriodicalIF":1.7,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1055/s-0041-1730284","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39169402","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}