Summary
Objectives:
To describe the use and promise of conversational agents in digital health—including health promotion andprevention—and how they can be combined with other new technologies to ...provide healthcare at home.
Method:
A narrative review of recent advances in technologies underpinning conversational agents and their use and potential for healthcare and improving health outcomes.
Results:
By responding to written and spoken language, conversational agents present a versatile, natural user interface and have the potential to make their services and applications more widely accessible. Historically, conversational interfaces for health applications have focused mainly on mental health, but with an increase in affordable devices and the modernization of health services, conversational agents are becoming more widely deployed across the health system. We present our work on context-aware voice assistants capable of proactively engaging users and delivering health information and services. The proactive voice agents we deploy, allow us to conduct experience sampling in people's homes and to collect information about the contexts in which users are interacting with them.
Conclusion:
In this article, we describe the state-of-the-art of these and other enabling technologies for speech and conversation and discuss ongoing research efforts to develop conversational agents that “live” with patients and customize their service offerings around their needs. These agents can function as ‘digital companions’ who will send reminders about medications and appointments, proactively check in to gather self-assessments, and follow up with patients on their treatment plans. Together with an unobtrusive and continuous collection of other health data, conversational agents can provide novel and deeply personalized access to digital health care, and they will continue to become an increasingly important part of the ecosystem for future healthcare delivery.
Summary
Objectives
: To summarise the state of the art during the year 2018 in consumer health informatics and education, with a special emphasis on the special topic of the International Medical ...Informatics Association (IMIA) Yearbook for 2019: “Artificial intelligence in health: new opportunities, challenges, and practical implications”.
Methods
: We conducted a systematic search of articles published in PubMed using a predefined set of queries that identified 99 potential articles for review. These articles were screened according to topic relevance and 14 were selected for consideration as best paper candidates. The 14 papers were then presented to a panel of international experts for full paper review and scoring. Three papers that received the highest score were discussed in a consensus meeting and were agreed upon as best papers on artificial intelligence in health for patients and consumers in the year 2018.
Results
: Only a small number of 2018 papers reported Artificial Intelligence (AI) research for patients and consumers. No studies were found on AI applications designed specifically for patients or consumers, nor were there studies that elicited patient and consumer input on AI. Currently, the most common use of AI for patients and consumers lies in secondary analysis of social media data (e.g., online discussion forums). In particular, the three best papers shared a common methodology of using data-driven algorithms (such as text mining, topic modelling, Latent Dirichlet allocation modelling), combined with insight-led approaches (e.g., visualisation, qualitative analysis and manual review), to uncover patient and consumer experiences of health and illness in online communities.
Conclusions
: While discussion remains active on how AI could 'revolutionise' healthcare delivery, there is a lack of direction and evidence on how AI could actually benefit patients and consumers. Perhaps instead of primarily focusing on data and algorithms, researchers should engage with patients and consumers early in the AI research agenda to ensure we are indeed asking the right questions, and that important use cases and critical contexts are identified together with patients and consumers. Without a clear understanding on why patients and consumers need AI in the first place, or how AI could support individuals with their healthcare needs, it is difficult to imagine the kinds of AI applications that would have meaningful and sustainable impact on individual daily lives.
Objectives: To understand the role of health information technology (IT) vendors and health IT functionality in supporting advanced primary care. Study Design: We synthesized multiple rounds of ...surveys and interviews (2017-2022) from a mixed-methods evaluation of Comprehensive Primary Care Plus (CPC+), a multipayer model developed by CMS. CPC+ was the first federal advanced primary care reform effort that formalized health IT vendors' roles in supporting health IT implementation and specified detailed health IT requirements for practices. Methods: We conducted content analysis to identify cross-cutting themes related to health IT for both practices and vendors, comparing similarities and differences across participants and (when possible) over time. Results: Vendors and practices reported advances in registries and dashboards for improved information management within the practice as well as strengthened relationships between vendors and practices that supported health IT implementation. However, CPC+ practices noted several gaps or challenges using existing functionalities, and both vendors and practices reported broader challenges for more transformative health IT change, particularly the lack of interoperable health information exchange needed to support care management and care coordination. Key factors constraining vendors' investment in further advances included long product development schedules, making it difficult to respond to rapidly evolving model requirements. Vendors also shared that CPC+ practices represented a small fraction of their client base, so investing in developing new functionality was not strategic unless it was more broadly relevant outside CPC+. Conclusions: Continued collaboration among health IT vendors, practices, policy makers, and payers could support continued technological improvements, particularly related to information exchange and communication. Aligning requirements more closely with other federal and private models could also help mitigate the risk for vendors.
The measurement of blood pressure (BP) is critical to the treatment and management of many medical conditions. High blood pressure is associated with many chronic disease conditions, and is a major ...source of mortality and morbidity around the world. For outpatient care as well as general health monitoring, there is great interest in being able to accurately and frequently measure BP outside of a clinical setting, using mobile or wearable devices. One possible solution is photoplethysmography (PPG), which is most commonly used in pulse oximetry in clinical settings for measuring oxygen saturation. PPG technology is becoming more readily available, inexpensive, convenient, and easily integrated into portable devices. Recent advances include the development of smartphones and wearable devices that collect pulse oximeter signals. In this article, we review (i) the state-of-the-art and the literature related to PPG signals collected by pulse oximeters, (ii) various theoretical approaches that have been adopted in PPG BP measurement studies, and (iii) the potential of PPG measurement devices as a wearable application. Past studies on changes in PPG signals and BP are highlighted, and the correlation between PPG signals and BP are discussed. We also review the combined use of features extracted from PPG and other physiological signals in estimating BP. Although the technology is not yet mature, it is anticipated that in the near future, accurate, continuous BP measurements may be available from mobile and wearable devices given their vast potential.
Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically ...requires extraction of curated predictor variables from normalized EHR data, a labor-intensive process that discards the vast majority of information in each patient's record. We propose a representation of patients' entire raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format. We demonstrate that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization. We validated our approach using de-identified EHR data from two US academic medical centers with 216,221 adult patients hospitalized for at least 24 h. In the sequential format we propose, this volume of EHR data unrolled into a total of 46,864,534,945 data points, including clinical notes. Deep learning models achieved high accuracy for tasks such as predicting: in-hospital mortality (area under the receiver operator curve AUROC across sites 0.93-0.94), 30-day unplanned readmission (AUROC 0.75-0.76), prolonged length of stay (AUROC 0.85-0.86), and all of a patient's final discharge diagnoses (frequency-weighted AUROC 0.90). These models outperformed traditional, clinically-used predictive models in all cases. We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios. In a case study of a particular prediction, we demonstrate that neural networks can be used to identify relevant information from the patient's chart.
Supervised machine learning algorithms have been a dominant method in the data mining field. Disease prediction using health data has recently shown a potential application area for these methods. ...This study ai7ms to identify the key trends among different types of supervised machine learning algorithms, and their performance and usage for disease risk prediction.
In this study, extensive research efforts were made to identify those studies that applied more than one supervised machine learning algorithm on single disease prediction. Two databases (i.e., Scopus and PubMed) were searched for different types of search items. Thus, we selected 48 articles in total for the comparison among variants supervised machine learning algorithms for disease prediction.
We found that the Support Vector Machine (SVM) algorithm is applied most frequently (in 29 studies) followed by the Naïve Bayes algorithm (in 23 studies). However, the Random Forest (RF) algorithm showed superior accuracy comparatively. Of the 17 studies where it was applied, RF showed the highest accuracy in 9 of them, i.e., 53%. This was followed by SVM which topped in 41% of the studies it was considered.
This study provides a wide overview of the relative performance of different variants of supervised machine learning algorithms for disease prediction. This important information of relative performance can be used to aid researchers in the selection of an appropriate supervised machine learning algorithm for their studies.
The use of machine learning (ML) with electronic health records (EHR) is growing in popularity as a means to extract knowledge that can improve the decision-making process in healthcare. Such methods ...require training of high-quality learning models based on diverse and comprehensive datasets, which are hard to obtain due to the sensitive nature of medical data from patients. In this context, federated learning (FL) is a methodology that enables the distributed training of machine learning models with remotely hosted datasets without the need to accumulate data and, therefore, compromise it. FL is a promising solution to improve ML-based systems, better aligning them to regulatory requirements, improving trustworthiness and data sovereignty. However, many open questions must be addressed before the use of FL becomes widespread. This article aims at presenting a systematic literature review on current research about FL in the context of EHR data for healthcare applications. Our analysis highlights the main research topics, proposed solutions, case studies, and respective ML methods. Furthermore, the article discusses a general architecture for FL applied to healthcare data based on the main insights obtained from the literature review. The collected literature corpus indicates that there is extensive research on the privacy and confidentiality aspects of training data and model sharing, which is expected given the sensitive nature of medical data. Studies also explore improvements to the aggregation mechanisms required to generate the learning model from distributed contributions and case studies with different types of medical data.
Despite growing interest from both patients and healthcare providers, there is little clinical guidance on how mobile apps should be utilized to add value to patient care. We categorize apps ...according to their functionality (e.g. preventative behavior change, digital self-management of a specific condition, diagnostic) and discuss evidence for effectiveness from published systematic reviews and meta-analyses and the relevance to patient care. We discuss the limitations of the current literature describing clinical outcomes from mHealth apps, what FDA clearance means now (510(k)/de novo FDA clearance) and in the future. We discuss data security and privacy as a major concern for patients when using mHealth apps. Patients are often not involved in the development of mobile health guidelines, and professionals' views regarding high-quality health apps may not reflect patients' views. We discuss efforts to develop guidelines for the development of safe and effective mHealth apps in the US and elsewhere and the role of independent app reviews sites in identifying mHealth apps for patient care. There are only a small number of clinical scenarios where published evidence suggests that mHealth apps may improve patient outcomes.
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