ORCID Profile
0000-0002-8328-5317
Current Organisations
University of Technology Sydney
,
Macquarie University
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Publisher: Springer Science and Business Media LLC
Date: 22-11-2019
DOI: 10.1038/S41746-019-0190-1
Abstract: Clinicians spend a large amount of time on clinical documentation of patient encounters, often impacting quality of care and clinician satisfaction, and causing physician burnout. Advances in artificial intelligence (AI) and machine learning (ML) open the possibility of automating clinical documentation with digital scribes, using speech recognition to eliminate manual documentation by clinicians or medical scribes. However, developing a digital scribe is fraught with problems due to the complex nature of clinical environments and clinical conversations. This paper identifies and discusses major challenges associated with developing automated speech-based documentation in clinical settings: recording high-quality audio, converting audio to transcripts using speech recognition, inducing topic structure from conversation data, extracting medical concepts, generating clinically meaningful summaries of conversations, and obtaining clinical data for AI and ML algorithms.
Publisher: Wiley
Date: 24-03-2023
DOI: 10.1111/TRF.17315
Abstract: Managing critical bleeding with massive transfusion (MT) requires a multidisciplinary team, often physically separated, to perform several simultaneous tasks at short notice. This places a significant cognitive load on team members, who must maintain situational awareness in rapidly changing scenarios. Similar resuscitation scenarios have benefited from the use of clinical decision support (CDS) tools. A multicenter, multidisciplinary, user‐centered design (UCD) study was conducted to design a computerized CDS for MT. This study included analysis of the problem context with a cognitive walkthrough, development of a user requirement statement, and co‐design with users of prototypes for testing. The final prototype was evaluated using qualitative assessment and the System Usability Scale (SUS). Eighteen participants were recruited across four institutions. The first UCD cycle resulted in the development of four prototype interfaces that addressed the user requirements and context of implementation. Of these, the preferred interface was further developed in the second UCD cycle to create a high‐fidelity web‐based CDS for MT. This prototype was evaluated by 15 participants using a simulated bleeding scenario and demonstrated an average SUS of 69.3 (above average, SD 16) and a clear interface with easy‐to‐follow blood product tracking. We used a UCD process to explore a highly complex clinical scenario and develop a prototype CDS for MT that incorporates distributive situational awareness, supports multiple user roles, and allows simulated MT training. Evaluation of the impact of this prototype on the efficacy and efficiency of managing MT is currently underway.
Publisher: SAGE Publications
Date: 29-08-2020
Abstract: To inform the development of automated summarization of clinical conversations, this study sought to estimate the proportion of doctor-patient communication in general practice (GP) consultations used for generating a consultation summary. Two researchers with a medical degree read the transcripts of 44 GP consultations and highlighted the phrases to be used for generating a summary of the consultation. For all consultations, less than 20% of all words in the transcripts were needed for inclusion in the summary. On average, 9.1% of all words in the transcripts, 26.6% of all medical terms, and 27.3% of all speaker turns were highlighted. The results indicate that communication content used for generating a consultation summary makes up a small portion of GP consultations, and automated summarization solutions—such as digital scribes—must focus on identifying the 20% relevant information for automatically generating consultation summaries.
Publisher: Elsevier BV
Date: 2021
Publisher: Elsevier BV
Date: 03-2022
Publisher: ACM
Date: 25-08-2010
Publisher: Elsevier BV
Date: 07-2021
Publisher: Springer Science and Business Media LLC
Date: 16-10-2018
DOI: 10.1038/S41746-018-0066-9
Abstract: Current generation electronic health records suffer a number of problems that make them inefficient and associated with poor clinical satisfaction. Digital scribes or intelligent documentation support systems, take advantage of advances in speech recognition, natural language processing and artificial intelligence, to automate the clinical documentation task currently conducted by humans. Whilst in their infancy, digital scribes are likely to evolve through three broad stages. Human led systems task clinicians with creating documentation, but provide tools to make the task simpler and more effective, for ex le with dictation support, semantic checking and templates. Mixed-initiative systems are delegated part of the documentation task, converting the conversations in a clinical encounter into summaries suitable for the electronic record. Computer-led systems are delegated full control of documentation and only request human interaction when exceptions are encountered. Intelligent clinical environments permit such augmented clinical encounters to occur in a fully digitised space where the environment becomes the computer. Data from clinical instruments can be automatically transmitted, interpreted using AI and entered directly into the record. Digital scribes raise many issues for clinical practice, including new patient safety risks. Automation bias may see clinicians automatically accept scribe documents without checking. The electronic record also shifts from a human created summary of events to potentially a full audio, video and sensor record of the clinical encounter. Digital scribes promisingly offer a gateway into the clinical workflow for more advanced support for diagnostic, prognostic and therapeutic tasks.
Publisher: JMIR Publications Inc.
Date: 09-02-2020
DOI: 10.2196/15823
Abstract: Conversational agents (CAs) are systems that mimic human conversations using text or spoken language. Their widely used ex les include voice-activated systems such as Apple Siri, Google Assistant, Amazon Alexa, and Microsoft Cortana. The use of CAs in health care has been on the rise, but concerns about their potential safety risks often remain understudied. This study aimed to analyze how commonly available, general-purpose CAs on smartphones and smart speakers respond to health and lifestyle prompts (questions and open-ended statements) by examining their responses in terms of content and structure alike. We followed a piloted script to present health- and lifestyle-related prompts to 8 CAs. The CAs’ responses were assessed for their appropriateness on the basis of the prompt type: responses to safety-critical prompts were deemed appropriate if they included a referral to a health professional or service, whereas responses to lifestyle prompts were deemed appropriate if they provided relevant information to address the problem prompted. The response structure was also examined according to information sources (Web search–based or precoded), response content style (informative and/or directive), confirmation of prompt recognition, and empathy. The 8 studied CAs provided in total 240 responses to 30 prompts. They collectively responded appropriately to 41% (46/112) of the safety-critical and 39% (37/96) of the lifestyle prompts. The ratio of appropriate responses deteriorated when safety-critical prompts were rephrased or when the agent used a voice-only interface. The appropriate responses included mostly directive content and empathy statements for the safety-critical prompts and a mix of informative and directive content for the lifestyle prompts. Our results suggest that the commonly available, general-purpose CAs on smartphones and smart speakers with unconstrained natural language interfaces are limited in their ability to advise on both the safety-critical health prompts and lifestyle prompts. Our study also identified some response structures the CAs employed to present their appropriate responses. Further investigation is needed to establish guidelines for designing suitable response structures for different prompt types.
Publisher: MDPI AG
Date: 20-07-2022
DOI: 10.3390/MTI6070060
Abstract: Immersive virtual reality (iVR) has gained considerable attention recently with increasing affordability and accessibility of the hardware. iVR applications for older adults present tremendous potential for erse interventions and innovations. The iVR literature, however, provides a limited understanding of guiding design considerations and evaluations pertaining to user experience (UX). To address this gap, we present a state-of-the-art scoping review of literature on iVR applications developed for older adults over 65 years. We performed a search in ACM Digital Library, IEEE Xplore, Scopus, and PubMed (1 January 2010–15 December 2019) and found 36 out of 3874 papers met the inclusion criteria. We identified 10 distinct sets of design considerations that guided target users and physical configuration, hardware use, and software design. Most studies carried episodic UX where only 2 captured anticipated UX and 7 measured longitudinal experiences. We discuss the interplay between our findings and future directions to design effective, safe, and engaging iVR applications for older adults.
Publisher: JMIR Publications Inc.
Date: 17-06-2019
DOI: 10.2196/10896
Publisher: ACM
Date: 08-05-2021
Publisher: ACM
Date: 25-04-2020
Publisher: JMIR Publications Inc.
Date: 15-11-2022
DOI: 10.2196/38525
Abstract: Health care and well-being are 2 main interconnected application areas of conversational agents (CAs). There is a significant increase in research, development, and commercial implementations in this area. In parallel to the increasing interest, new challenges in designing and evaluating CAs have emerged. This study aims to identify key design, development, and evaluation challenges of CAs in health care and well-being research. The focus is on the very recent projects with their emerging challenges. A review study was conducted with 17 invited studies, most of which were presented at the ACM (Association for Computing Machinery) CHI 2020 conference workshop on CAs for health and well-being. Eligibility criteria required the studies to involve a CA applied to a health or well-being project (ongoing or recently finished). The participating studies were asked to report on their projects’ design and evaluation challenges. We used thematic analysis to review the studies. The findings include a range of topics from primary care to caring for older adults to health coaching. We identified 4 major themes: (1) Domain Information and Integration, (2) User-System Interaction and Partnership, (3) Evaluation, and (4) Conversational Competence. CAs proved their worth during the pandemic as health screening tools, and are expected to stay to further support various health care domains, especially personal health care. Growth in investment in CAs also shows the value as a personal assistant. Our study shows that while some challenges are shared with other CA application areas, safety and privacy remain the major challenges in the health care and well-being domains. An increased level of collaboration across different institutions and entities may be a promising direction to address some of the major challenges that otherwise would be too complex to be addressed by the projects with their limited scope and budget.
Publisher: BMJ
Date: 21-12-2021
DOI: 10.1136/BJSPORTS-2020-102892
Abstract: To determine the effectiveness of physical activity interventions involving mobile applications (apps) or trackers with automated and continuous self-monitoring and feedback. Systematic review and meta-analysis. PubMed and seven additional databases, from 2007 to 2020. Randomised controlled trials in adults (18–65 years old) without chronic illness, testing a mobile app or an activity tracker, with any comparison, where the main outcome was a physical activity measure. Independent screening was conducted. We conducted random effects meta-analysis and all effect sizes were transformed into standardised difference in means (SDM). We conducted exploratory metaregression with continuous and discrete moderators identified as statistically significant in subgroup analyses. Physical activity: daily step counts, min/week of moderate-to-vigorous physical activity, weekly days exercised, min/week of total physical activity, metabolic equivalents. Thirty-five studies met inclusion criteria and 28 were included in the meta-analysis (n=7454 participants, 28% women). The meta-analysis showed a small-to-moderate positive effect on physical activity measures (SDM 0.350, 95% CI 0.236 to 0.465, I 2 =69%, T 2 =0.051) corresponding to 1850 steps per day (95% CI 1247 to 2457). Interventions including text-messaging and personalisation features were significantly more effective in subgroup analyses and metaregression. Interventions using apps or trackers seem to be effective in promoting physical activity. Longer studies are needed to assess the impact of different intervention components on long-term engagement and effectiveness.
Publisher: BCS Learning & Development
Date: 07-2018
Publisher: ACM
Date: 11-06-2012
Publisher: Oxford University Press (OUP)
Date: 26-08-2020
Abstract: The study sought to understand the potential roles of a future artificial intelligence (AI) documentation assistant in primary care consultations and to identify implications for doctors, patients, healthcare system, and technology design from the perspective of general practitioners. Co-design workshops with general practitioners were conducted. The workshops focused on (1) understanding the current consultation context and identifying existing problems, (2) ideating future solutions to these problems, and (3) discussing future roles for AI in primary care. The workshop activities included affinity diagramming, brainwriting, and video prototyping methods. The workshops were audio-recorded and transcribed verbatim. Inductive thematic analysis of the transcripts of conversations was performed. Two researchers facilitated 3 co-design workshops with 16 general practitioners. Three main themes emerged: professional autonomy, human-AI collaboration, and new models of care. Major implications identified within these themes included (1) concerns with medico-legal aspects arising from constant recording and accessibility of full consultation records, (2) future consultations taking place out of the exam rooms in a distributed system involving empowered patients, (3) human conversation and empathy remaining the core tasks of doctors in any future AI-enabled consultations, and (4) questioning the current focus of AI initiatives on improved efficiency as opposed to patient care. AI documentation assistants will likely to be integral to the future primary care consultations. However, these technologies will still need to be supervised by a human until strong evidence for reliable autonomous performance is available. Therefore, different human-AI collaboration models will need to be designed and evaluated to ensure patient safety, quality of care, doctor safety, and doctor autonomy.
Publisher: ACM
Date: 25-04-2020
Publisher: BCS Learning & Development
Date: 2018
Publisher: Elsevier BV
Date: 12-2007
Publisher: ACM
Date: 16-08-2010
Publisher: JMIR Publications Inc.
Date: 07-11-2019
DOI: 10.2196/15360
Abstract: The personalization of conversational agents with natural language user interfaces is seeing increasing use in health care applications, shaping the content, structure, or purpose of the dialogue between humans and conversational agents. The goal of this systematic review was to understand the ways in which personalization has been used with conversational agents in health care and characterize the methods of its implementation. We searched on PubMed, Embase, CINAHL, PsycInfo, and ACM Digital Library using a predefined search strategy. The studies were included if they: (1) were primary research studies that focused on consumers, caregivers, or health care professionals (2) involved a conversational agent with an unconstrained natural language interface (3) tested the system with human subjects and (4) implemented personalization features. The search found 1958 publications. After abstract and full-text screening, 13 studies were included in the review. Common ex les of personalized content included feedback, daily health reports, alerts, warnings, and recommendations. The personalization features were implemented without a theoretical framework of customization and with limited evaluation of its impact. While conversational agents with personalization features were reported to improve user satisfaction, user engagement and dialogue quality, the role of personalization in improving health outcomes was not assessed directly. Most of the studies in our review implemented the personalization features without theoretical or evidence-based support for them and did not leverage the recent developments in other domains of personalization. Future research could incorporate personalization as a distinct design factor with a more careful consideration of its impact on health outcomes and its implications on patient safety, privacy, and decision-making.
Publisher: Springer Science and Business Media LLC
Date: 04-05-2020
Publisher: JMIR Publications Inc.
Date: 06-04-2022
Abstract: ealthcare and wellbeing are two main interconnected application areas of conversational agents (CAs). There is a significant increase in research, development, and commercial implementations in this area. In parallel to the increasing interest, new challenges in designing and evaluating CAs have emerged. his study aims to identify key design, development, and evaluation challenges of CAs in healthcare and wellbeing research. The focus is on the very recent projects with their emerging challenges. review study was conducted with 17 invited studies, most of which were presented at the ACM CHI2020 conference workshop on CAs for health and wellbeing. Eligibility criteria required the studies to involve a CA applied to a health or wellbeing project in an ongoing or recently finished project. The participating studies were asked to report on their projects' design and evaluation challenges. We used thematic analysis to review the studies. he findings include a range of topics from primary care to caring for older adults to health coaching. We identified four major themes: i) domain information and integration, ii) user-system interaction and partnership, iii) evaluation, and iv) conversational competence. hile some challenges are shared with other CA application areas, safety and privacy remain the major challenges in the healthcare and wellbeing domains. An increased level of collaboration across different institutions and entities may be a promising direction to address some of the major challenges which otherwise would be too complex to be addressed by the projects with their limited scope and budget.
Publisher: JMIR Publications Inc.
Date: 09-08-2019
Abstract: onversational agents (CAs) are systems that mimic human conversations using text or spoken language. Their widely used ex les include voice-activated systems such as Apple Siri, Google Assistant, Amazon Alexa, and Microsoft Cortana. The use of CAs in health care has been on the rise, but concerns about their potential safety risks often remain understudied. his study aimed to analyze how commonly available, general-purpose CAs on smartphones and smart speakers respond to health and lifestyle prompts (questions and open-ended statements) by examining their responses in terms of content and structure alike. e followed a piloted script to present health- and lifestyle-related prompts to 8 CAs. The CAs’ responses were assessed for their appropriateness on the basis of the prompt type: responses to safety-critical prompts were deemed appropriate if they included a referral to a health professional or service, whereas responses to lifestyle prompts were deemed appropriate if they provided relevant information to address the problem prompted. The response structure was also examined according to information sources (Web search–based or precoded), response content style (informative and/or directive), confirmation of prompt recognition, and empathy. he 8 studied CAs provided in total 240 responses to 30 prompts. They collectively responded appropriately to 41% (46/112) of the safety-critical and 39% (37/96) of the lifestyle prompts. The ratio of appropriate responses deteriorated when safety-critical prompts were rephrased or when the agent used a voice-only interface. The appropriate responses included mostly directive content and empathy statements for the safety-critical prompts and a mix of informative and directive content for the lifestyle prompts. ur results suggest that the commonly available, general-purpose CAs on smartphones and smart speakers with unconstrained natural language interfaces are limited in their ability to advise on both the safety-critical health prompts and lifestyle prompts. Our study also identified some response structures the CAs employed to present their appropriate responses. Further investigation is needed to establish guidelines for designing suitable response structures for different prompt types.
Publisher: Oxford University Press (OUP)
Date: 22-04-2019
DOI: 10.1093/JAMIA/OCZ046
Abstract: The objective of this study is to characterize the dynamic structure of primary care consultations by identifying typical activities and their inter-relationships to inform the design of automated approaches to clinical documentation using natural language processing and summarization methods. This is an observational study in Australian general practice involving 31 consultations with 4 primary care physicians. Consultations were audio-recorded, and computer interactions were recorded using screen capture. Physical interactions in consultation rooms were noted by observers. Brief interviews were conducted after consultations. Conversational transcripts were analyzed to identify different activities and their speech content as well as verbal cues signaling activity transitions. An activity transition analysis was then undertaken to generate a network of activities and transitions. Observed activity classes followed those described in well-known primary care consultation models. Activities were often fragmented across consultations, did not flow necessarily in a defined order, and the flow between activities was nonlinear. Modeling activities as a network revealed that discussing a patient’s present complaint was the most central activity and was highly connected to medical history taking, physical examination, and assessment, forming a highly interrelated bundle. Family history, allergy, and investigation discussions were less connected suggesting less dependency on other activities. Clear verbal signs were often identifiable at transitions between activities. Primary care consultations do not appear to follow a classic linear model of defined information seeking activities rather, they are fragmented, highly interdependent, and can be reactively triggered. The nonlinearity of activities has significant implications for the design of automated information capture. Whereas dictation systems generate literal translation of speech into text, speech-based clinical summary systems will need to link disparate information fragments, merge their content, and abstract coherent information summaries.
Publisher: Oxford University Press (OUP)
Date: 03-2019
DOI: 10.1093/IWC/IWZ015
Abstract: Varying understandings of UX in conversational interfaces literature. A UX assessment framework with UX dimensions and their relevant attributes. Descriptions of the six main questionnaires for evaluating conversational interfaces. A comparison of the six questionnaires based on their coverage of UX dimensions.
Publisher: SAGE Publications
Date: 2022
DOI: 10.1177/20552076221115017
Abstract: To investigate the feasibility of the be.well app and its personalization approach which regularly considers users’ preferences, amongst university students. We conducted a mixed-methods, pre-post experiment, where participants used the app for 2 months. Eligibility criteria included: age 18–34 years owning an iPhone with Internet access and fluency in English. Usability was assessed by a validated questionnaire engagement metrics were reported. Changes in physical activity were assessed by comparing the difference in daily step count between baseline and 2 months. Interviews were conducted to assess acceptability thematic analysis was conducted. Twenty-three participants were enrolled in the study (mean age = 21.9 years, 71.4% women). The mean usability score was 5.6 ± 0.8 out of 7. The median daily engagement time was 2 minutes. Eighteen out of 23 participants used the app in the last month of the study. Qualitative data revealed that people liked the personalized activity suggestion feature as it was actionable and promoted user autonomy. Some users also expressed privacy concerns if they had to provide a lot of personal data to receive highly personalized features. Daily step count increased after 2 months of the intervention (median difference = 1953 steps/day, p-value .001, 95% CI 782 to 3112). Incorporating users’ preferences in personalized advice provided by a physical activity app was considered feasible and acceptable, with preliminary support for its positive effects on daily step count. Future randomized studies with longer follow up are warranted to determine the effectiveness of personalized mobile apps in promoting physical activity.
Publisher: JMIR Publications Inc.
Date: 20-03-2023
DOI: 10.2196/44542
Abstract: Mental health interventions delivered through mobile health (mHealth) technologies can increase the access to mental health services, especially among university students. The development of mHealth intervention is complex and needs to be context sensitive. There is currently limited evidence on the perceptions, needs, and barriers related to these interventions in the Southeast Asian context. This qualitative study aimed to explore the perception of university students and mental health supporters in Singapore about mental health services, c aigns, and mHealth interventions with a focus on conversational agent interventions for the prevention of common mental disorders such as anxiety and depression. We conducted 6 web-based focus group discussions with 30 university students and one-to-one web-based interviews with 11 mental health supporters consisting of faculty members tasked with student pastoral care, a mental health first aider, counselors, psychologists, a clinical psychologist, and a psychiatrist. The qualitative analysis followed a reflexive thematic analysis framework. The following 6 main themes were identified: a healthy lifestyle as students, access to mental health services, the role of mental health promotion c aigns, preferred mHealth engagement features, factors that influence the adoption of mHealth interventions, and cultural relevance of mHealth interventions. The interpretation of our findings shows that students were reluctant to use mental health services because of the fear of stigma and a possible lack of confidentiality. Study participants viewed mHealth interventions for mental health as part of a blended intervention. They also felt that future mental health mHealth interventions should be more personalized and capable of managing adverse events such as suicidal ideation.
Publisher: JMIR Publications Inc.
Date: 15-12-2022
Abstract: ypoglycemia is a frequent and acute complication in type-1 diabetes mellitus (T1DM) and is associated with a higher risk of car mishaps. Currently, hypoglycemia can be detected and signaled through flash glucose monitoring (FGM) or continuous glucose monitoring (CGM) devices. These devices, however, require manual and visual interaction, removing the focus of attention from the driving task. Hypoglycemia is known to cause a decrease in attention, challenging the safety of using such devices behind the wheel. Here, we present an investigation of hands- and distraction-free technology: an in–vehicle voice–assistant–based warning. esigning and assessing a voice–assistant–based health warning for hypoglycemia while driving and addressing the limitations of the current warning solutions. e developed and assessed the warning in three studies, where participants received the warning while driving. In all studies, we measured participants’ self-reported technology readiness, perception of the warning, and compliance behavior (whether they stopped the car and their reaction time), and assessed any room for improvement through participants’ feedback. In Study 0, 10 healthy participants drove in a simulator and assessed the feasibility of using a voice assistant to deliver a warning. In Study 1, 18 participants with T1DM drove in a simulator and assessed the revised version of the warning. In Study 2, 20 participants with T1DM undergoing hypoglycemia assessed a further revised version of the warning while driving in a real car on a test track. In all studies, we also measured self-reported technology readiness, and acceptance of the warning, and assessed compliance behavior and reaction time in response to the warning. n all studies, 100% of participants complied with the warning. In Study 0, healthy participants perceived the warning as usable and useful, and their feedback suggested reducing speech rate and increasing driver-assistant interaction. In Study 1, participants with T1DM reported good perception and their feedback suggested the warning to be less instructive. In Study 2, we observed moderate perception (lower than in Study 1), and participants’ feedback revealed the warning was too overloading. o the best of the authors’ knowledge, this is the first study investigating the feasibility of an in–vehicle voice–assistant–based warning for hypoglycemia while driving. Drivers find such an implementation useful and effective, although in iduals with T1DM preferred a simple and direct voice warning, rather than a conversational one. This may reflect the utility and unfamiliarity of proactive behavior in voice assistants. We anticipate this research to be a starting point for the combination of driver-state warnings and for voice–assistant–based health support, and to be a guide for the design of such a combination. linicalTrials.gov NCT04035993, NCT04569630
Publisher: JMIR Publications Inc.
Date: 19-11-2021
DOI: 10.2196/22890
Abstract: Healthy behaviors are crucial for maintaining a person’s health and well-being. The effects of health behavior interventions are mediated by in idual and contextual factors that vary over time. Recently emerging smartphone-based ecological momentary interventions (EMIs) can use real-time user reports (ecological momentary assessments [EMAs]) to trigger appropriate support when needed in daily life. This systematic review aims to assess the characteristics of smartphone-delivered EMIs using self-reported EMAs in relation to their effects on health behaviors, user engagement, and user perspectives. We searched MEDLINE, Embase, PsycINFO, and CINAHL in June 2019 and updated the search in March 2020. We included experimental studies that incorporated EMIs based on EMAs delivered through smartphone apps to promote health behaviors in any health domain. Studies were independently screened. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed. We performed a narrative synthesis of intervention effects, user perspectives and engagement, and intervention design and characteristics. Quality appraisal was conducted for all included studies. We included 19 papers describing 17 unique studies and comprising 652 participants. Most studies were quasi-experimental (13/17, 76%), had small s le sizes, and great heterogeneity in intervention designs and measurements. EMIs were most popular in the mental health domain (8/17, 47%), followed by substance abuse (3/17, 18%), diet, weight loss, physical activity (4/17, 24%), and smoking (2/17, 12%). Of the 17 studies, the 4 (24%) included randomized controlled trials reported nonstatistically significant effects on health behaviors, and 4 (24%) quasi-experimental studies reported statistically significant pre-post improvements in self-reported primary outcomes, namely depressive (P .001) and psychotic symptoms (P=.03), drinking frequency (P .001), and eating patterns (P=.01). EMA was commonly used to capture subjective experiences as well as behaviors, whereas sensors were rarely used. Generally, users perceived EMIs to be helpful. Common suggestions for improvement included enhancing personalization, multimedia and interactive capabilities (eg, voice recording), and lowering the EMA reporting burden. EMI and EMA components were rarely reported and were not described in a standardized manner across studies, h ering progress in this field. A reporting checklist was developed to facilitate the interpretation and comparison of findings and enhance the transparency and replicability of future studies using EMAs and EMIs. The use of smartphone-delivered EMIs using self-reported EMAs to promote behavior change is an emerging area of research, with few studies evaluating efficacy. Such interventions could present an opportunity to enhance health but need further assessment in larger participant cohorts and well-designed evaluations following reporting checklists. Future research should explore combining self-reported EMAs of subjective experiences with objective data passively collected via sensors to promote personalization while minimizing user burden, as well as explore different EMA data collection methods (eg, chatbots). PROSPERO CRD42019138739 www.crd.york.ac.uk rospero/display_record.php?RecordID=138739
Publisher: JMIR Publications Inc.
Date: 27-04-2018
Abstract: ontext-aware systems, also known as context-sensitive systems, are computing applications designed to capture, interpret, and use contextual information and provide adaptive services according to the current context of use. Context-aware systems have the potential to support patients with chronic conditions however, little is known about how such systems have been utilized to facilitate patient work. his study aimed to characterize the different tasks and contexts in which context-aware systems for patient work were used as well as to assess any existing evidence about the impact of such systems on health-related process or outcome measures. total of 6 databases (MEDLINE, EMBASE, CINAHL, ACM Digital, Web of Science, and Scopus) were scanned using a predefined search strategy. Studies were included in the review if they focused on patients with chronic conditions, involved the use of a context-aware system to support patients’ health-related activities, and reported the evaluation of the systems by the users. Studies were screened by independent reviewers, and a narrative synthesis of included studies was conducted. he database search retrieved 1478 citations 6 papers were included, all published from 2009 onwards. The majority of the papers were quasi-experimental and involved pilot and usability testing with a small number of users there were no randomized controlled trials (RCTs) to evaluate the efficacy of a context-aware system. In the included studies, context was captured using sensors or self-reports, sometimes involving both. Most studies used a combination of sensor technology and mobile apps to deliver personalized feedback. A total of 3 studies examined the impact of interventions on health-related measures, showing positive results. he use of context-aware systems to support patient work is an emerging area of research. RCTs are needed to evaluate the effectiveness of context-aware systems in improving patient work, self-management practices, and health outcomes in chronic disease patients.
Publisher: JMIR Publications Inc.
Date: 11-05-2020
DOI: 10.2196/17203
Abstract: People with low back pain (LBP) in the community often do not receive evidence-based advice and management. Community pharmacists can play an important role in supporting people with LBP as pharmacists are easily accessible to provide first-line care. However, previous research suggests that pharmacists may not consistently deliver advice that is concordant with guideline recommendations and may demonstrate difficulty determining which patients require prompt medical review. A clinical decision support system (CDSS) may enhance first-line care of LBP, but none exists to support the community pharmacist–client consultation. This study aimed to develop a CDSS to guide first-line care of LBP in the community pharmacy setting and to evaluate the pharmacist-reported usability and acceptance of the prototype system. A cross-platform Web app for the Apple iPad was developed in conjunction with academic and clinical experts using an iterative user-centered design process during interface design, clinical reasoning, program development, and evaluation. The CDSS was evaluated via one-to-one user-testing with 5 community pharmacists (5 case vignettes each). Data were collected via video recording, screen capture, survey instrument (system usability scale), and direct observation. Pharmacists’ agreement with CDSS-generated self-care recommendations was 90% (18/20), with medicines recommendations was 100% (25/25), and with referral advice was 88% (22/25 total 70 recommendations). Pharmacists expressed uncertainty when screening for serious pathology in 40% (10/25) of cases. Pharmacists requested more direction from the CDSS in relation to automated prompts for user input and page navigation. Overall system usability was rated as excellent (mean score 92/100, SD 6.5 90th percentile compared with similar systems), with acceptance rated as good to excellent. A novel CDSS (high-fidelity prototype) to enhance pharmacist care of LBP was developed, underpinned by clinical practice guidelines and informed by a multidisciplinary team of experts. User-testing revealed a high level of usability and acceptance of the prototype system, with suggestions to improve interface prompts and information delivery. The small study s le limits the generalizability of the findings but offers important insights to inform the next stage of system development.
Publisher: JMIR Publications Inc.
Date: 26-11-2019
Abstract: eople with low back pain (LBP) in the community often do not receive evidence-based advice and management. Community pharmacists can play an important role in supporting people with LBP as pharmacists are easily accessible to provide first-line care. However, previous research suggests that pharmacists may not consistently deliver advice that is concordant with guideline recommendations and may demonstrate difficulty determining which patients require prompt medical review. A clinical decision support system (CDSS) may enhance first-line care of LBP, but none exists to support the community pharmacist–client consultation. his study aimed to develop a CDSS to guide first-line care of LBP in the community pharmacy setting and to evaluate the pharmacist-reported usability and acceptance of the prototype system. cross-platform Web app for the Apple iPad was developed in conjunction with academic and clinical experts using an iterative user-centered design process during interface design, clinical reasoning, program development, and evaluation. The CDSS was evaluated via one-to-one user-testing with 5 community pharmacists (5 case vignettes each). Data were collected via video recording, screen capture, survey instrument (system usability scale), and direct observation. harmacists’ agreement with CDSS-generated self-care recommendations was 90% (18/20), with medicines recommendations was 100% (25/25), and with referral advice was 88% (22/25 total 70 recommendations). Pharmacists expressed uncertainty when screening for serious pathology in 40% (10/25) of cases. Pharmacists requested more direction from the CDSS in relation to automated prompts for user input and page navigation. Overall system usability was rated as excellent (mean score 92/100, SD 6.5 90th percentile compared with similar systems), with acceptance rated as good to excellent. novel CDSS (high-fidelity prototype) to enhance pharmacist care of LBP was developed, underpinned by clinical practice guidelines and informed by a multidisciplinary team of experts. User-testing revealed a high level of usability and acceptance of the prototype system, with suggestions to improve interface prompts and information delivery. The small study s le limits the generalizability of the findings but offers important insights to inform the next stage of system development.
Publisher: IEEE
Date: 2006
Publisher: Informa UK Limited
Date: 03-07-2022
Publisher: ACM
Date: 04-06-2016
Publisher: JMIR Publications Inc.
Date: 04-07-2019
Abstract: he personalization of conversational agents with natural language user interfaces is seeing increasing use in health care applications, shaping the content, structure, or purpose of the dialogue between humans and conversational agents. he goal of this systematic review was to understand the ways in which personalization has been used with conversational agents in health care and characterize the methods of its implementation. e searched on PubMed, Embase, CINAHL, PsycInfo, and ACM Digital Library using a predefined search strategy. The studies were included if they: (1) were primary research studies that focused on consumers, caregivers, or health care professionals (2) involved a conversational agent with an unconstrained natural language interface (3) tested the system with human subjects and (4) implemented personalization features. he search found 1958 publications. After abstract and full-text screening, 13 studies were included in the review. Common ex les of personalized content included feedback, daily health reports, alerts, warnings, and recommendations. The personalization features were implemented without a theoretical framework of customization and with limited evaluation of its impact. While conversational agents with personalization features were reported to improve user satisfaction, user engagement and dialogue quality, the role of personalization in improving health outcomes was not assessed directly. ost of the studies in our review implemented the personalization features without theoretical or evidence-based support for them and did not leverage the recent developments in other domains of personalization. Future research could incorporate personalization as a distinct design factor with a more careful consideration of its impact on health outcomes and its implications on patient safety, privacy, and decision-making.
Publisher: ACM
Date: 25-04-2020
Publisher: Association for Computing Machinery (ACM)
Date: 13-04-2023
DOI: 10.1145/3569893
Abstract: General Practitioners are among the primary users and curators of textual electronic health records, highlighting the need for technologies supporting record access and administration. Recent advancements in natural language processing facilitate the development of clinical systems, automating some time-consuming record-keeping tasks. However, it remains unclear what automation tasks would benefit clinicians most, what features such automation should exhibit, and how clinicians will interact with the automation. We conducted semi-structured interviews with General Practitioners uncovering their views and attitudes toward text automation. The main emerging theme was doctor-AI collaboration, addressing a reciprocal clinician-technology relationship that does not threaten to substitute clinicians, but rather establishes a constructive synergistic relationship. Other themes included: (i) desired features for clinical text automation (ii) concerns around clinical text automation and (iii) the consultation of the future. Our findings will inform the design of future natural language processing systems, to be implemented in general practice.
Publisher: ACM
Date: 29-11-2022
Publisher: JMIR Publications Inc.
Date: 08-03-2023
Abstract: ypoglycemia is a serious complication in diabetes, it impairs cognitive and psychomotor function, and is linked to driving mishaps. In-vehicle voice assistants (VAs) have been designed to proactively deliver a warning of hypoglycemia while driving. However, proactive VAs can cause driving impairments through startling. Hence, we complement the voice warning from the VA with ambient light-emitting diodes (LED) and investigate the effect of this addition on emotional reaction. esigning an in-vehicle voice warning for hypoglycemia and assessing its effect on the emotional reaction and technology acceptance. e present two studies investigating the emotional reactions of drivers with diabetes to different hypoglycemia warning modalities. The same procedure was replicated in two settings: simulated and real driving. A quasi-experimental design, with two independent variables (blood glucose phase and warning modality) and one main dependent variable (emotional reaction), was implemented. The material and apparatus included intravenous catheters to manipulate blood glucose and a tablet with an app to simulate hypoglycemia warnings. The warnings had three possible modalities: Standard, Voice, and Voice + LED. Objective emotional reaction (arousal) was measured physiologically via skin conductance response (SCR). Subjective emotional reaction was measured with the Affective Slider (valence and arousal). Both emotional reaction measures were tested with a mixed-effect linear model. Secondary outcomes included self-reported measures of technology acceptance. ur results showed that in the simulated-driving setting, the Voice + LED warning modality was preferred over other modalities. However, this advantage decreased in real-world driving because the LED was less visible outdoors. The Voice modality was more effective than the Standard warning modality in both simulated and real-world driving studies. The mixed model on self-reported emotional reaction yielded significant results. Self-reported arousal was higher during Decreasing blood glucose and Hypoglycemia for Voice and Voice + LED in the real-world driving study. In contrast, self-reported valence was influenced by blood glucose manipulation rather than the warning modality. The mixed model on SCR did not yield significant results. Participants consistently ranked the Voice and Voice + LED warning modalities superior to the Standard modality. his paper proposes using the in-vehicle VA and ambient lighting system installed in a car to deliver a warning of hypoglycemia. It investigated how the warning modality affects emotional response and acceptance in virtual and real-world environments. The study provides insight into the potential of implementing VA-based health warnings in cars and contributes to a better understanding of the challenges and opportunities of in-vehicle VA-based health interventions.
Publisher: JMIR Publications Inc.
Date: 23-11-2022
Abstract: ental health interventions delivered through mobile health (mHealth) technologies can increase the access to mental health services, especially among university students. The development of mHealth intervention is complex and needs to be context sensitive. There is currently limited evidence on the perceptions, needs, and barriers related to these interventions in the Southeast Asian context. his qualitative study aimed to explore the perception of university students and mental health supporters in Singapore about mental health services, c aigns, and mHealth interventions with a focus on conversational agent interventions for the prevention of common mental disorders such as anxiety and depression. e conducted 6 web-based focus group discussions with 30 university students and one-to-one web-based interviews with 11 mental health supporters consisting of faculty members tasked with student pastoral care, a mental health first aider, counselors, psychologists, a clinical psychologist, and a psychiatrist. The qualitative analysis followed a reflexive thematic analysis framework. he following 6 main themes were identified: a healthy lifestyle as students, access to mental health services, the role of mental health promotion c aigns, preferred mHealth engagement features, factors that influence the adoption of mHealth interventions, and cultural relevance of mHealth interventions. The interpretation of our findings shows that students were reluctant to use mental health services because of the fear of stigma and a possible lack of confidentiality. tudy participants viewed mHealth interventions for mental health as part of a blended intervention. They also felt that future mental health mHealth interventions should be more personalized and capable of managing adverse events such as suicidal ideation.
Publisher: Centre of Sociological Research, NGO
Date: 31-05-2016
Publisher: ACM
Date: 06-07-2020
Publisher: JMIR Publications Inc.
Date: 26-07-2020
Abstract: ealthy behaviors are crucial for maintaining a person’s health and well-being. The effects of health behavior interventions are mediated by in idual and contextual factors that vary over time. Recently emerging smartphone-based ecological momentary interventions (EMIs) can use real-time user reports (ecological momentary assessments [EMAs]) to trigger appropriate support when needed in daily life. his systematic review aims to assess the characteristics of smartphone-delivered EMIs using self-reported EMAs in relation to their effects on health behaviors, user engagement, and user perspectives. e searched MEDLINE, Embase, PsycINFO, and CINAHL in June 2019 and updated the search in March 2020. We included experimental studies that incorporated EMIs based on EMAs delivered through smartphone apps to promote health behaviors in any health domain. Studies were independently screened. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed. We performed a narrative synthesis of intervention effects, user perspectives and engagement, and intervention design and characteristics. Quality appraisal was conducted for all included studies. e included 19 papers describing 17 unique studies and comprising 652 participants. Most studies were quasi-experimental (13/17, 76%), had small s le sizes, and great heterogeneity in intervention designs and measurements. EMIs were most popular in the mental health domain (8/17, 47%), followed by substance abuse (3/17, 18%), diet, weight loss, physical activity (4/17, 24%), and smoking (2/17, 12%). Of the 17 studies, the 4 (24%) included randomized controlled trials reported nonstatistically significant effects on health behaviors, and 4 (24%) quasi-experimental studies reported statistically significant pre-post improvements in self-reported primary outcomes, namely depressive ( i P /i & .001) and psychotic symptoms ( i P /i =.03), drinking frequency ( i P /i & .001), and eating patterns ( i P /i =.01). EMA was commonly used to capture subjective experiences as well as behaviors, whereas sensors were rarely used. Generally, users perceived EMIs to be helpful. Common suggestions for improvement included enhancing personalization, multimedia and interactive capabilities (eg, voice recording), and lowering the EMA reporting burden. EMI and EMA components were rarely reported and were not described in a standardized manner across studies, h ering progress in this field. A reporting checklist was developed to facilitate the interpretation and comparison of findings and enhance the transparency and replicability of future studies using EMAs and EMIs. he use of smartphone-delivered EMIs using self-reported EMAs to promote behavior change is an emerging area of research, with few studies evaluating efficacy. Such interventions could present an opportunity to enhance health but need further assessment in larger participant cohorts and well-designed evaluations following reporting checklists. Future research should explore combining self-reported EMAs of subjective experiences with objective data passively collected via sensors to promote personalization while minimizing user burden, as well as explore different EMA data collection methods (eg, chatbots). ROSPERO CRD42019138739 www.crd.york.ac.uk rospero/display_record.php?RecordID=138739
No related grants have been discovered for Ahmet Baki Kocaballi.