Simon-Konstantin Thiem, Lucas Küppers, Benjamin Aretz, Arezoo Bozorgmehr, Arian Karimzadeh, Frauke Leupold, Birgitta Weltermann
{"title":"Language complexity of patient-physician chat communication on hypertension control: results of the cluster-randomised PIA study.","authors":"Simon-Konstantin Thiem, Lucas Küppers, Benjamin Aretz, Arezoo Bozorgmehr, Arian Karimzadeh, Frauke Leupold, Birgitta Weltermann","doi":"10.1186/s12916-025-04006-0","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>High language complexity impairs patients' understanding and medical outcomes. While messengers accelerate communication, the language complexity of chats between patients and providers is poorly studied. This study analyses language complexity and communication characteristics of chat data from the PIA study, which significantly improved blood pressure control after 6 to 12 months.</p><p><strong>Methods: </strong>The cluster-randomised controlled PIA study enrolled 848 hypertension patients (412 intervention, 436 control) from 64 German general practices. The PIA technology enabled a secured communication of blood pressure readings, medication plans and messages. The chats were analysed regarding frequency, length, response time and content. Language complexity was measured using the Flesch index with seven levels from 'hard' to 'very simple'. The study is registered in the German Clinical Trials Register (DRKS00012680).</p><p><strong>Results: </strong>In total, 4231 messages were sent between 24 general practitioners and 363 patients of the intervention arm between 09/20 and 09/21: 22% messages (n = 941) were automated (new medication plan or prescription available), while 78% were non-automated (n = 3290), with 41.1% of these messages originating from patients and 58.9% from practices. The average chat dialogue lasted 176.8 days (SD 9.8). Patients' messages had a mean of 22.6 words (SD 22.6) compared to 16.8 (SD 19.4) by practices. Most messages (88.92%) from practices and 51.9% from patients addressed medication or treatments. Simple or very simple language was used in 90.5% of the messages both by patients and by physicians regardless of sociodemographic characteristics. BP improved with increased frequency of messages (p < 0.001).</p><p><strong>Conclusions: </strong>This communication showed a remarkably low language complexity by physicians and patients and better control with more messages. The results support the use of digital communication for topics such as chronic hypertension care.</p><p><strong>Trial registration: </strong>German Clinical Trials Register, DRKS00012680. Registered May 10th, 2019, https://www.drks.de/drks_web/setLocale_EN.do .</p>","PeriodicalId":9188,"journal":{"name":"BMC Medicine","volume":"23 1","pages":"174"},"PeriodicalIF":7.0000,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11934458/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12916-025-04006-0","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
引用次数: 0
Abstract
Background: High language complexity impairs patients' understanding and medical outcomes. While messengers accelerate communication, the language complexity of chats between patients and providers is poorly studied. This study analyses language complexity and communication characteristics of chat data from the PIA study, which significantly improved blood pressure control after 6 to 12 months.
Methods: The cluster-randomised controlled PIA study enrolled 848 hypertension patients (412 intervention, 436 control) from 64 German general practices. The PIA technology enabled a secured communication of blood pressure readings, medication plans and messages. The chats were analysed regarding frequency, length, response time and content. Language complexity was measured using the Flesch index with seven levels from 'hard' to 'very simple'. The study is registered in the German Clinical Trials Register (DRKS00012680).
Results: In total, 4231 messages were sent between 24 general practitioners and 363 patients of the intervention arm between 09/20 and 09/21: 22% messages (n = 941) were automated (new medication plan or prescription available), while 78% were non-automated (n = 3290), with 41.1% of these messages originating from patients and 58.9% from practices. The average chat dialogue lasted 176.8 days (SD 9.8). Patients' messages had a mean of 22.6 words (SD 22.6) compared to 16.8 (SD 19.4) by practices. Most messages (88.92%) from practices and 51.9% from patients addressed medication or treatments. Simple or very simple language was used in 90.5% of the messages both by patients and by physicians regardless of sociodemographic characteristics. BP improved with increased frequency of messages (p < 0.001).
Conclusions: This communication showed a remarkably low language complexity by physicians and patients and better control with more messages. The results support the use of digital communication for topics such as chronic hypertension care.
Trial registration: German Clinical Trials Register, DRKS00012680. Registered May 10th, 2019, https://www.drks.de/drks_web/setLocale_EN.do .
期刊介绍:
BMC Medicine is an open access, transparent peer-reviewed general medical journal. It is the flagship journal of the BMC series and publishes outstanding and influential research in various areas including clinical practice, translational medicine, medical and health advances, public health, global health, policy, and general topics of interest to the biomedical and sociomedical professional communities. In addition to research articles, the journal also publishes stimulating debates, reviews, unique forum articles, and concise tutorials. All articles published in BMC Medicine are included in various databases such as Biological Abstracts, BIOSIS, CAS, Citebase, Current contents, DOAJ, Embase, MEDLINE, PubMed, Science Citation Index Expanded, OAIster, SCImago, Scopus, SOCOLAR, and Zetoc.