Using eMMA to Manage Medication

Tschanz, Mauro; Dorner, Tim Lucas; Holm, Jürgen; Denecke, Kerstin

doi:10.1109/mc.2018.3191254

Cited by 14 publications

(19 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tschanz led a group of researchers that developed an electronic medication management assistant (eMMA) at the Bern University of Applied Sciences [29]. The objective of their proposal is coaching patients, living in private homes, improving their medication intake precision, and reducing misuse.…”

Section: Tests and Evaluation Of The Results Obtainedmentioning

confidence: 99%

Commercial Devices-Based System Designed to Improve the Treatment Adherence of Hypertensive Patients

Silva

Souza

Cruz

et al. 2019

Sensors

View full text Add to dashboard Cite

This paper presents an intelligent system designed to increase the treatment adherence of hypertensive patients. The architecture was developed to allow communication among patients, physicians, and families to determine each patient’s medication intake and self-monitoring of blood pressure rates. Concerning the medication schedule, the system is designed to follow a predefined prescription, adapting itself to undesired events, such as mistakenly taking medication or forgetting to take medication on time. When covering the blood pressure measurement, it incorporates best medical practices, registering the actual values in recommended frequency and form, trying to avoid the known “white-coat effect.” We assume that taking medicine precisely and measuring blood pressure correctly may lead to good adherence to the treatment. The system uses commercial consumer electronic devices and can be replicated in any home equipped with a standard personal computer and Internet access. The resulting architecture has four layers. The first is responsible for adding electronic devices that typically exist in today’s homes to the system. The second is a preprocessing layer that filters the data generated from the patient’s behavior. The third is a reasoning layer that decides how to act based on the patient’s activities observed. Finally, the fourth layer creates messages that should drive the reactions of all involved actors. The reasoning layer takes into consideration the patient’s schedule and medication-taking activity data and uses implicit algorithms based on the J48, RepTree, and RandomTree decision tree models to infer the adherence. The algorithms were first adjusted using one academic machine learning and data mining tool. The system communicates with users through smartphones (anytime and anywhere) and smart TVs (in the patient’s home) by using the 3G/4G and WiFi infrastructure. It interacts automatically through social networks with doctors and relatives when changes or mistakes in medication intake and blood pressure mean values are detected. By associating the blood pressure data with the history of medication intake, our system can indicate the treatment adherence and help patients to achieve better treatment results. Comparisons with similar research were made, highlighting our findings.

show abstract

Section: Tests and Evaluation Of The Results Obtainedmentioning

confidence: 99%

Commercial Devices-Based System Designed to Improve the Treatment Adherence of Hypertensive Patients

Silva

Souza

Cruz

et al. 2019

Sensors

View full text Add to dashboard Cite

show abstract

“…Again, no hint about their underlying architecture is provided. Tschanz et al propose a chat-like smartphone app (eMMA) to manage patient medication through a chatbot [22] focused on patient interactions and medication management. Also, in [3] a chatbot has been developed to provide patients with medication reminders and a health tracker using patient data gathering, but without providing end-to-end encryption.…”

Section: Related Workmentioning

confidence: 99%

Microservice chatbot architecture for chronic patient support

Roca

Sancho

García

et al. 2020

Journal of Biomedical Informatics

View full text Add to dashboard Cite

Chatbots are able to provide support to patients suffering from very different conditions. Patients with chronic diseases or comorbidities could benefit the most from chatbots which can keep track of their condition, provide specific information, encourage adherence to medication, etc. To perform these functions, chatbots need a suitable underlying software architecture. In this paper, we introduce a chatbot architecture for chronic patient support grounded on three pillars: scalability by means of microservices, standard data sharing models through HL7 FHIR and standard conversation modelling using AIML. We also propose an innovative automation mechanism to convert FHIR resources into AIML files, thus facilitating the interaction and data gathering of medical and personal information that ends up in patient health records. To align the way people interact with each other using messaging platforms with the chatbot architecture, we propose these very same channels for the chatbot-patient interaction, paying special attention to security and privacy

show abstract

“…Dialog agents (chatbots) can be defined as systems that support conversational interaction with users by means of natural language, speech, or other modalities. 1 With the rise of artificial intelligence and its use for language understanding and interpretation, chatbots and their application in health care settings have gained in interest in recent years, coming to applications for diagnosing systems, 2 medication management, 3 treatment support of mental disorders, recommendation of ICD-10 codes 4 and many others. 5,6 A recent systematic review in this area examined characteristics, current applications, and evaluation measures of conversational agents in health care.…”

Section: Introduction Backgroundmentioning

confidence: 99%

Evaluation Metrics for Health Chatbots: A Delphi Study

et al. 2021

Self Cite

View full text Add to dashboard Cite

Background 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. Objectives 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. Methods 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). Results 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. Conclusion 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.

show abstract

Using eMMA to Manage Medication

Cited by 14 publications

References 9 publications

Commercial Devices-Based System Designed to Improve the Treatment Adherence of Hypertensive Patients

Commercial Devices-Based System Designed to Improve the Treatment Adherence of Hypertensive Patients

Microservice chatbot architecture for chronic patient support

Evaluation Metrics for Health Chatbots: A Delphi Study

Contact Info

Product

Resources

About