JAMIA Open最新文献

{"title":"Black American women's attitudes toward seeking mental health services and use of mobile technology to support the management of anxiety.","authors":"Terika McCall, Meagan Foster, Holly R Tomlin, Todd A Schwartz","doi":"10.1093/jamiaopen/ooad088","DOIUrl":"10.1093/jamiaopen/ooad088","url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to understand Black American women's attitudes toward seeking mental health services and using mobile technology to receive support for managing anxiety.</p><p><strong>Methods: </strong>A self-administered web-based questionnaire was launched in October 2019 and closed in January 2020. Women who identified as Black/African American were eligible to participate. The survey consisted of approximately 70 questions and covered topics such as, attitudes toward seeking professional psychological help, acceptability of using a mobile phone to receive mental health care, and screening for anxiety.</p><p><strong>Results: </strong>The findings of the study (<i>N</i> = 395) showed that younger Black women were more likely to have greater severity of anxiety than their older counterparts. Respondents were most comfortable with the use of a voice call or video call to communicate with a professional to receive support to manage anxiety in comparison to text messaging or mobile app. Younger age, higher income, and greater scores for psychological openness and help-seeking propensity increased odds of indicating agreement with using mobile technology to communicate with a professional. Black women in the Southern region of the United States had twice the odds of agreeing to the use of mobile apps than women in the Midwest and Northeast regions.</p><p><strong>Discussion: </strong>Black American women, in general, have favorable views toward the use of mobile technology to receive support to manage anxiety.</p><p><strong>Conclusion: </strong>Preferences and cultural appropriateness of resources should be assessed on an individual basis to increase likelihood of adoption and engagement with digital mental health interventions for management of anxiety.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"6 4","pages":"ooad088"},"PeriodicalIF":2.1,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10582519/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49683052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Medical Informatics Operating Room Vitals and Events Repository (MOVER): a public-access operating room database.","authors":"Muntaha Samad, Mirana Angel, Joseph Rinehart, Yuzo Kanomata, Pierre Baldi, Maxime Cannesson","doi":"10.1093/jamiaopen/ooad084","DOIUrl":"10.1093/jamiaopen/ooad084","url":null,"abstract":"<p><strong>Objectives: </strong>Artificial intelligence (AI) holds great promise for transforming the healthcare industry. However, despite its potential, AI is yet to see widespread deployment in clinical settings in significant part due to the lack of publicly available clinical data and the lack of transparency in the published AI algorithms. There are few clinical data repositories publicly accessible to researchers to train and test AI algorithms, and even fewer that contain specialized data from the perioperative setting. To address this gap, we present and release the Medical Informatics Operating Room Vitals and Events Repository (MOVER).</p><p><strong>Materials and methods: </strong>This first release of MOVER includes adult patients who underwent surgery at the University of California, Irvine Medical Center from 2015 to 2022. Data for patients who underwent surgery were captured from 2 different sources: High-fidelity physiological waveforms from all of the operating rooms were captured in real time and matched with electronic medical record data.</p><p><strong>Results: </strong>MOVER includes data from 58 799 unique patients and 83 468 surgeries. MOVER is available for download at https://doi.org/10.24432/C5VS5G, it can be downloaded by anyone who signs a data usage agreement (DUA), to restrict traffic to legitimate researchers.</p><p><strong>Discussion: </strong>To the best of our knowledge MOVER is the only freely available public data repository that contains electronic health record and high-fidelity physiological waveforms data for patients undergoing surgery.</p><p><strong>Conclusion: </strong>MOVER is freely available to all researchers who sign a DUA, and we hope that it will accelerate the integration of AI into healthcare settings, ultimately leading to improved patient outcomes.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"6 4","pages":"ooad084"},"PeriodicalIF":2.1,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10582520/pdf/ooad084.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49683054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sequential autoencoders for feature engineering and pretraining in major depressive disorder risk prediction.","authors":"Barrett W Jones, Warren D Taylor, Colin G Walsh","doi":"10.1093/jamiaopen/ooad086","DOIUrl":"10.1093/jamiaopen/ooad086","url":null,"abstract":"<p><strong>Objectives: </strong>We evaluated autoencoders as a feature engineering and pretraining technique to improve major depressive disorder (MDD) prognostic risk prediction. Autoencoders can represent temporal feature relationships not identified by aggregate features. The predictive performance of autoencoders of multiple sequential structures was evaluated as feature engineering and pretraining strategies on an array of prediction tasks and compared to a restricted Boltzmann machine (RBM) and random forests as a benchmark.</p><p><strong>Materials and methods: </strong>We study MDD patients from Vanderbilt University Medical Center. Autoencoder models with Attention and long-short-term memory (LSTM) layers were trained to create latent representations of the input data. Predictive performance was evaluated temporally by fitting random forest models to predict future outcomes with engineered features as input and using autoencoder weights to initialize neural network layers. We evaluated area under the precision-recall curve (AUPRC) trends and variation over the study population's treatment course.</p><p><strong>Results: </strong>The pretrained LSTM model improved predictive performance over pretrained Attention models and benchmarks in 3 of 4 outcomes including self-harm/suicide attempt (AUPRCs, LSTM pretrained = 0.012, Attention pretrained = 0.010, RBM = 0.009, random forest = 0.005). The use of autoencoders for feature engineering had varied results, with benchmarks outperforming LSTM and Attention encodings on the self-harm/suicide attempt outcome (AUPRCs, LSTM encodings = 0.003, Attention encodings = 0.004, RBM = 0.009, random forest = 0.005).</p><p><strong>Discussion: </strong>Improvement in prediction resulting from pretraining has the potential for increased clinical impact of MDD risk models. We did not find evidence that the use of temporal feature encodings was additive to predictive performance in the study population. This suggests that predictive information retained by model weights may be lost during encoding. LSTM pretrained model predictive performance is shown to be clinically useful and improves over state-of-the-art predictors in the MDD phenotype. LSTM model performance warrants consideration of use in future related studies.</p><p><strong>Conclusion: </strong>LSTM models with pretrained weights from autoencoders were able to outperform the benchmark and a pretrained Attention model. Future researchers developing risk models in MDD may benefit from the use of LSTM autoencoder pretrained weights.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"6 4","pages":"ooad086"},"PeriodicalIF":2.1,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10561992/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41214963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of natural language processing to identify social needs from patient medical notes: development and assessment of a scalable, performant, and rule-based model in an integrated healthcare delivery system.","authors":"Geoffrey M Gray, Ayah Zirikly, Luis M Ahumada, Masoud Rouhizadeh, Thomas Richards, Christopher Kitchen, Iman Foroughmand, Elham Hatef","doi":"10.1093/jamiaopen/ooad085","DOIUrl":"10.1093/jamiaopen/ooad085","url":null,"abstract":"<p><strong>Objectives: </strong>To develop and test a scalable, performant, and rule-based model for identifying 3 major domains of social needs (residential instability, food insecurity, and transportation issues) from the unstructured data in electronic health records (EHRs).</p><p><strong>Materials and methods: </strong>We included patients aged 18 years or older who received care at the Johns Hopkins Health System (JHHS) between July 2016 and June 2021 and had at least 1 unstructured (free-text) note in their EHR during the study period. We used a combination of manual lexicon curation and semiautomated lexicon creation for feature development. We developed an initial rules-based pipeline (Match Pipeline) using 2 keyword sets for each social needs domain. We performed rule-based keyword matching for distinct lexicons and tested the algorithm using an annotated dataset comprising 192 patients. Starting with a set of expert-identified keywords, we tested the adjustments by evaluating false positives and negatives identified in the labeled dataset. We assessed the performance of the algorithm using measures of precision, recall, and <i>F</i>1 score.</p><p><strong>Results: </strong>The algorithm for identifying residential instability had the best overall performance, with a weighted average for precision, recall, and <i>F</i>1 score of 0.92, 0.84, and 0.92 for identifying patients with homelessness and 0.84, 0.82, and 0.79 for identifying patients with housing insecurity. Metrics for the food insecurity algorithm were high but the transportation issues algorithm was the lowest overall performing metric.</p><p><strong>Discussion: </strong>The NLP algorithm in identifying social needs at JHHS performed relatively well and would provide the opportunity for implementation in a healthcare system.</p><p><strong>Conclusion: </strong>The NLP approach developed in this project could be adapted and potentially operationalized in the routine data processes of a healthcare system.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"6 4","pages":"ooad085"},"PeriodicalIF":2.1,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/2e/eb/ooad085.PMC10550267.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41168703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the impact of missingness on racial disparities in predictive performance of a machine learning model for emergency department triage","authors":"Stephanie Teeple, Aria G. Smith, Matthew F. Toerper, Scott Levin, Scott Halpern, Oluwakemi Badaki‐Makun, J. Hinson","doi":"10.1093/jamiaopen/ooad107","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooad107","url":null,"abstract":"To investigate how missing data in the patient problem list may impact racial disparities in the predictive performance of a machine learning (ML) model for emergency department (ED) triage. Racial disparities may exist in the missingness of EHR data (eg, systematic differences in access, testing, and/or treatment) that can impact model predictions across racialized patient groups. We use an ML model that predicts patients’ risk for adverse events to produce triage-level recommendations, patterned after a clinical decision support tool deployed at multiple EDs. We compared the model’s predictive performance on sets of observed (problem list data at the point of triage) versus manipulated (updated to the more complete problem list at the end of the encounter) test data. These differences were compared between Black and non-Hispanic White patient groups using multiple performance measures relevant to health equity. There were modest, but significant, changes in predictive performance comparing the observed to manipulated models across both Black and non-Hispanic White patient groups; c-statistic improvement ranged between 0.027 and 0.058. The manipulation produced no between-group differences in c-statistic by race. However, there were small between-group differences in other performance measures, with greater change for non-Hispanic White patients. Problem list missingness impacted model performance for both patient groups, with marginal differences detected by race. Further exploration is needed to examine how missingness may contribute to racial disparities in clinical model predictions across settings. The novel manipulation method demonstrated may aid future research.","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"13 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139323583","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":"Using natural language processing to identify the status of homelessness and housing instability among serious illness patients from clinical notes in an integrated healthcare system.","authors":"Fagen Xie,&nbsp;Susan Wang,&nbsp;Lori Viveros,&nbsp;Allegra Rich,&nbsp;Huong Q Nguyen,&nbsp;Ariadna Padilla,&nbsp;Lindsey Lyons,&nbsp;Claudia L Nau","doi":"10.1093/jamiaopen/ooad082","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooad082","url":null,"abstract":"<p><strong>Background: </strong>Efficiently identifying the social risks of patients with serious illnesses (SIs) is the critical first step in providing patient-centered and value-driven care for this medically vulnerable population.</p><p><strong>Objective: </strong>To apply and further hone an existing natural language process (NLP) algorithm that identifies patients who are homeless/at risk of homeless to a SI population.</p><p><strong>Methods: </strong>Patients diagnosed with SI between 2019 and 2020 were identified using an adapted list of diagnosis codes from the Center for Advance Palliative Care from the Kaiser Permanente Southern California electronic health record. Clinical notes associated with medical encounters within 6 months before and after the diagnosis date were processed by a previously developed NLP algorithm to identify patients who were homeless/at risk of homelessness. To improve the generalizability to the SI population, the algorithm was refined by multiple iterations of chart review and adjudication. The updated algorithm was then applied to the SI population.</p><p><strong>Results: </strong>Among 206 993 patients with a SI diagnosis, 1737 (0.84%) were identified as homeless/at risk of homelessness. These patients were more likely to be male (51.1%), age among 45-64 years (44.7%), and have one or more emergency visit (65.8%) within a year of their diagnosis date. Validation of the updated algorithm yielded a sensitivity of 100.0% and a positive predictive value of 93.8%.</p><p><strong>Conclusions: </strong>The improved NLP algorithm effectively identified patients with SI who were homeless/at risk of homelessness and can be used to target interventions for this vulnerable group.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"6 3","pages":"ooad082"},"PeriodicalIF":2.1,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10517738/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41151029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing usability of a mobile health intervention for Spanish-speaking Latinx people with HIV through user-centered design: a post-implementation study.","authors":"Kristen Petros De Guex, Tabor E Flickinger, Lisa Mayevsky, Hannah Zaveri, Michael Goncalves, Helen Reed, Lazaro Pesina, Rebecca Dillingham","doi":"10.1093/jamiaopen/ooad083","DOIUrl":"10.1093/jamiaopen/ooad083","url":null,"abstract":"<p><strong>Objective: </strong>Latinx people comprise 30% of all new human immunodeficiency virus (HIV) infections in the United States and face many challenges to accessing and engaging with HIV care. To bridge these gaps in care, a Spanish-language mobile health (mHealth) intervention known as ConexionesPositivas (CP) was adapted from an established English-language platform called PositiveLinks (PL) to help improve engagement in care and reduce viral nonsuppression among its users. We aimed to determine how CP can address the challenges that Latinx people with HIV (PWH) in the United States face.</p><p><strong>Materials and methods: </strong>We conducted a post-implementation study of the CP mHealth platform, guided by principles of user-centered design. We enrolled 20 Spanish-speaking CP users in the study, who completed the previously validated System Usability Scale (SUS) and semistructured interviews. Interviews were transcribed and translated for analysis. We performed thematic coding of interview transcripts in Dedoose.</p><p><strong>Results: </strong>The SUS composite score was 75, which is within the range of good usability. Four categories of themes were identified in the interviews: client context, strengths of CP, barriers to use and dislikes, and suggestions to improve CP. Positive impacts included encouraging self-monitoring of medication adherence, mood and stress, connection to professional care, and development of a support system for PWH.</p><p><strong>Discussion: </strong>While CP is an effective and easy-to-use application, participants expressed a desire for improved personalization and interactivity, which will guide further iteration.</p><p><strong>Conclusion: </strong>This study highlights the importance of tailoring mHealth interventions to improve equity of access, especially for populations with limited English proficiency.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"6 3","pages":"ooad083"},"PeriodicalIF":2.1,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10508965/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41172420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial neural network approaches to identify maternal and infant risk and asset factors using Peridata.Net: a WI-MIOS study.","authors":"Jeana M Holt,&nbsp;AkkeNeel Talsma,&nbsp;Teresa S Johnson,&nbsp;Timothy Ehlinger","doi":"10.1093/jamiaopen/ooad080","DOIUrl":"10.1093/jamiaopen/ooad080","url":null,"abstract":"<p><strong>Objective: </strong>To analyze PeriData.Net, a clinical registry with linked maternal-infant hospital data of Milwaukee County residents, to demonstrate a predictive analytic approach to perinatal infant risk assessment.</p><p><strong>Materials and methods: </strong>Using unsupervised learning, we identified infant birth clusters with similar multivariate health indicator patterns, measured using perinatal variables from 2008 to 2019 from <i>n</i> = 43 969 clinical registry records in Milwaukee County, WI, followed by supervised learning risk-propagation modeling to identify key maternal factors. To understand the relationship between socioeconomic status (SES) and birth outcome cluster assignment, we recoded zip codes in Peridata.Net according to SES level.</p><p><strong>Results: </strong>Three self-organizing map clusters describe infant birth outcome patterns that are similar in the multivariate space. Birth outcome clusters showed higher hazard birth outcome patterns in cluster 3 than clusters 1 and 2. Cluster 3 was associated with lower Apgar scores at 1 and 5 min after birth, shorter infant length, and premature birth. Prediction profiles of birth clusters indicate the most sensitivity to pregnancy weight loss and prenatal visits. Majority of infants assigned to cluster 3 were in the 2 lowest SES levels.</p><p><strong>Discussion: </strong>Using an extensive perinatal clinical registry, we found that the strongest predictive performance, when considering cluster membership using supervised learning, was achieved by incorporating social and behavioral risk factors. There were inequalities in infant birth outcomes based on SES.</p><p><strong>Conclusion: </strong>Identifying infant risk hazard profiles can contribute to knowledge discovery and guide future research directions. Additionally, presenting the results to community members can build consensus for community-identified health and risk indicator prioritization for intervention development.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"6 3","pages":"ooad080"},"PeriodicalIF":2.1,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/97/3c/ooad080.PMC10500218.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10286996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced phenotypes for identifying opioid overdose in emergency department visit electronic health record data","authors":"Ralph Ward, Jihad S Obeid, Lindsey Jennings, Elizabeth Szwast, William Garrett Hayes, Royal Pipaliya, Cameron Bailey, Skylar Faul, Brianna Polyak, George Hamilton Baker, Jenna L McCauley, Leslie A Lenert","doi":"10.1093/jamiaopen/ooad081","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooad081","url":null,"abstract":"Abstract Background Accurate identification of opioid overdose (OOD) cases in electronic healthcare record (EHR) data is an important element in surveillance, empirical research, and clinical intervention. We sought to improve existing OOD electronic phenotypes by incorporating new data types beyond diagnostic codes and by applying several statistical and machine learning methods. Materials and Methods We developed an EHR dataset of emergency department visits involving OOD cases or patients considered at risk for an OOD and ascertained true OOD status through manual chart reviews. We developed and validated prediction models using Random Forest, Extreme Gradient Boost, and Elastic Net models that incorporated 717 features involving primary and second diagnoses, chief complaints, medications prescribed, vital signs, laboratory results, and procedural codes. We also developed models limited to single data types. Results A total of 1718 records involving 1485 patients were manually reviewed; 541 (36.4%) patients had one or more OOD. Prediction performance was similar for all models; sensitivity varied from 94% to 97%; and area under the receiver operating characteristic curve (AUC) was 98% for all methods. The primary diagnosis and chief complaint were the most important contributors to AUC performance; primary diagnoses and medication class contributed most to sensitivity; chief complaint, primary diagnosis, and vital signs were most important for specificity. Models limited to decision support data types available in real time demonstrated robust prediction performance. Conclusions Substantial prediction performance improvements were demonstrated for identifying OODs in EHR data. Our e-phenotypes could be applied in surveillance, retrospective empirical applications, or clinical decision support systems.","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136070908","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":"A chatbot for hypertension self-management support: user-centered design, development, and usability testing.","authors":"Ashley C Griffin, Saif Khairat, Stacy C Bailey, Arlene E Chung","doi":"10.1093/jamiaopen/ooad073","DOIUrl":"10.1093/jamiaopen/ooad073","url":null,"abstract":"<p><strong>Objectives: </strong>Health-related chatbots have demonstrated early promise for improving self-management behaviors but have seldomly been utilized for hypertension. This research focused on the design, development, and usability evaluation of a chatbot for hypertension self-management, called \"Medicagent.\"</p><p><strong>Materials and methods: </strong>A user-centered design process was used to iteratively design and develop a text-based chatbot using Google Cloud's Dialogflow natural language understanding platform. Then, usability testing sessions were conducted among patients with hypertension. Each session was comprised of: (1) background questionnaires, (2) 10 representative tasks within Medicagent, (3) System Usability Scale (SUS) questionnaire, and (4) a brief semi-structured interview. Sessions were video and audio recorded using Zoom. Qualitative and quantitative analyses were used to assess effectiveness, efficiency, and satisfaction of the chatbot.</p><p><strong>Results: </strong>Participants (<i>n</i> = 10) completed nearly all tasks (98%, 98/100) and spent an average of 18 min (SD = 10 min) interacting with Medicagent. Only 11 (8.6%) utterances were not successfully mapped to an intent. Medicagent achieved a mean SUS score of 78.8/100, which demonstrated acceptable usability. Several participants had difficulties navigating the conversational interface without menu and back buttons, felt additional information would be useful for redirection when utterances were not recognized, and desired a health professional persona within the chatbot.</p><p><strong>Discussion: </strong>The text-based chatbot was viewed favorably for assisting with blood pressure and medication-related tasks and had good usability.</p><p><strong>Conclusion: </strong>Flexibility of interaction styles, handling unrecognized utterances gracefully, and having a credible persona were highlighted as design components that may further enrich the user experience of chatbots for hypertension self-management.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"6 3","pages":"ooad073"},"PeriodicalIF":2.1,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10491950/pdf/ooad073.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10570803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}