1,194 research outputs found
Simple identification tools in FishBase
Simple identification tools for fish species were included in the FishBase information system from its inception. Early tools made use of the relational model and characters like fin ray meristics. Soon pictures and drawings were added as a further help, similar to a field guide. Later came the computerization of existing dichotomous keys, again in combination with pictures and other information, and the ability to restrict possible species by country, area, or taxonomic group. Today, www.FishBase.org offers four different ways to identify species. This paper describes these tools with their advantages and disadvantages, and suggests various options for further
development. It explores the possibility of a holistic and integrated computeraided strategy
Plant pest surveillance: from satellites to molecules
Open Access Article; Published online: 15 Mar 2021Plant pests and diseases impact both food security and natural ecosystems, and the impact has been accelerated in recent years due to several confounding factors. The globalisation of trade has moved pests out of natural ranges, creating damaging epidemics in new regions. Climate change has extended the range of pests and the pathogens they vector. Resistance to agrochemicals has made pathogens, pests, and weeds more difficult to control. Early detection is critical to achieve effective control, both from a biosecurity as well as an endemic pest perspective. Molecular diagnostics has revolutionised our ability to identify pests and diseases over the past two decades, but more recent technological innovations are enabling us to achieve better pest surveillance. In this review, we will explore the different technologies that are enabling this advancing capability and discuss the drivers that will shape its future deployment
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Plant pest surveillance: from satellites to molecules
Plant pests and diseases impact both food security and natural ecosystems, and the impact has been accelerated in recent years due to several confounding factors. The globalisation of trade has moved pests out of natural ranges, creating damaging epidemics in new regions. Climate change has extended the range of pests and the pathogens they vector. Resistance to agrochemicals has made pathogens, pests, and weeds more difficult to control. Early detection is critical to achieve effective control, both from a biosecurity as well as an endemic pest perspective. Molecular diagnostics has revolutionised our ability to identify pests and diseases over the past two decades, but more recent technological innovations are enabling us to achieve better pest surveillance. In this review, we will explore the different technologies that are enabling this advancing capability and discuss the drivers that will shape its future deployment
An intelligent mobile-enabled expert system for tuberculosis disease diagnosis in real time
This paper presents an investigation into the development of an intelligent mobile-enabled expert system to perform an automatic detection of tuberculosis (TB) disease in real-time. One third of the global population are infected with the TB bacterium, and the prevailing diagnosis methods are either resource-intensive or time consuming. Thus, a reliable and easyโto-use diagnosis system has become essential to make the world TB free by 2030, as envisioned by the World Health Organisation. In this work, the challenges in implementing an efficient image processing platform is presented to extract the images from plasmonic ELISAs for TB antigen-specific antibodies and analyse their features. The supervised machine learning techniques are utilised to attain binary classification from eighteen lower-order colour moments. The proposed system is trained off-line, followed by testing and validation using a separate set of images in real-time. Using an ensemble classifier, Random Forest, we demonstrated 98.4% accuracy in TB antigen-specific antibody detection on the mobile platform. Unlike the existing systems, the proposed intelligent system with real time processing capabilities and data portability can provide the prediction without any opto-mechanical attachment, which will undergo a clinical test in the next phase.</p
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Development and evaluation of point-of-care diagnostic technologies for providers and consumers
Point-of-care (POC) diagnostic technologies aim to expand access to traditional laboratory-based testing to near-patient settings. These settings can range from emergency or intensive care-units (ICUs) in the United States, to remote health posts in sub Saharan Africa. Differences in budget and infrastructure play a role in characterizing the wide array of possible โnear patientโ settings and must be taken into consideration in the engineering design process. In this dissertation we use translational engineering to develop practical and accessible microfluidic POC immunoassays for diverse settings, that include both provider and consumer facing applications.
First, we examined Lyme Disease in the U.S., where existing diagnostic technologies face the challenge of rapid and accurate serodiagnosis in the face of largely non-specific clinical symptoms. We developed a multiplexed rapid test that could replicate enzyme-linked immunosorbent assay (ELISA) performance for Lyme Disease diagnosis. After screening candidate biomarkers, we evaluated performance of the multiplexed microfluidic test against ELISA using clinical serum samples and illustrated the potential to streamline current clinical algorithms requiring two immunoassays (ELISA and Western Blot) into one standalone test suitable for physicianโs offices or urgent care clinics in the U.S. We also showed exploratory work towards a similar multiplexed test design for another bacterial spirochete infection, Leptospirosis.
Next, we built on previous work towards a POC HIV-syphilis antenatal screening tool, to develop a smartphone-integrated, microfluidic assay for healthcare workers to use in low resource settings. The low-cost (34 to produce and provides results in 15 minutes. In this work, we focus on assay development efforts undertaken towards development of a fully integrated POC product suitable for deployment in the field, with practical considerations for the use of fingerstick blood, stability, scale-up and transport. We also streamlined the number of manual steps for end-user operation, through the use of lyophilized secondary antibodies, preloaded reagents on cassette, and an automatic result readout. While laboratory demonstration with clinical samples is important for initial characterization of POC devices, field evaluation reveals diagnostic performance under real-world conditions. We tested the device in the hands of minimally trained healthcare workers in Rwanda and saw comparable performance to other immunoassays run under field conditions. We also performed a follow-up pilot field study in Rwanda to evaluate the feasibility of the smartphone dongle platform for self-testing by patients/consumers in a low-resource setting, one of the most challenging use-cases for POC devices.
Finally, we sought to integrate intellectual frameworks from behavioral research and user-experience (UX) design in creating a new framework for evaluation of consumer-facing microfluidic devices, specifically towards HIV home-testing in the U.S. While overall rates of HIV are decreasing in the U.S., the population of gay, bisexual and other men who have sex with men (MSM) are disproportionately affected. Self-testing products for sexually transmitted infection (STI) testing could address unmet needs for these target populations in both increasing access and frequency of testing, as well as integrating use with sexual partners for early diagnosis or even prevention. We worked with a cohort of MSMs at high risk for HIV/STI transmission in New York City, and performed for the first time, a structured assessment of completely naรฏve users interacting with a smartphone interfaced microfluidic diagnostic device (โSMARTtestโ). We integrated UX design value model of device usability, credibility, accessibility and acceptability into our evaluation framework, which influence userโs information, knowledge, motivation and behavioral skills towards engaging with a prevention method (โIMBโ model). Thus far, such frameworks have rarely been applied to other consumer health monitoring devices, including microfluidic POC devices. As the microfluidic field moves towards more field demonstrations of devices, more untrained and minimally trained users will have access to such tools. It is important to understand how they use devices, what the device failure points are and what the most relevant design features are to spur user adoption and meaningful usage.
Underlying our work in creating accessible and practical POC immunoassay tools for infectious disease detection, is the illustration of the translational development roadmap from proof-of-concept assay development to field studies and user-based evaluations for intended end-use settings that range from U.S. based primary care clinics, rural health centers in low-resource settings as well as self-testing environments in both. Incorporating an understanding of the target use-case setting is critical in translating technologies for clinical use, whether in the infrastructure and services that are available, or end-user needs and constraints such as clinical workflow patterns, level of technical expertise and perceptions of usefulness and value. We show how user/use-case focused application of downstream translational engineering and testing informs upstream design choices and accelerates development of POC devices for real-world use. The sum of this work aims to illustrate tenets of translational engineering design and testing to advance insight into building POC products that are poised for greater adoption by target end users, whether they are health providers or consumers
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A roadmap for the clinical implementation of optical-imaging biomarkers
Clinical workflows for the non-invasive detection and characterization of disease states could benefit from optical-imaging biomarkers. In this Perspective, we discuss opportunities and challenges towards the clinical implementation of optical-imaging biomarkers for the early detection of cancer by analysing two case studies: the assessment of skin lesions in primary care, and the surveillance of patients with Barrettโs oesophagus in specialist care. We stress the importance of technical and biological validations and clinical-utility assessments, and the need to address implementation bottlenecks. In addition, we define a translational roadmap for the widespread clinical implementation of optical imaging-technologies
Developments in Transduction, Connectivity and AI/Machine Learning for Point-of-Care Testing
We review some emerging trends in transduction, connectivity and data analytics for Point-of-Care Testing (POCT) of infectious and non-communicable diseases. The patient need for POCT is described along with developments in portable diagnostics, specifically in respect of Lab-on-chip and microfluidic systems. We describe some novel electrochemical and photonic systems and the use of mobile phones in terms of hardware components and device connectivity for POCT. Developments in data analytics that are applicable for POCT are described with an overview of data structures and recent AI/Machine learning trends. The most important methodologies of machine learning, including deep learning methods, are summarised. The potential value of trends within POCT systems for clinical diagnostics within Lower Middle Income Countries (LMICs) and the Least Developed Countries (LDCs) are highlighted
Use of smartphone-based interventions to support smoking cessation and pharmacotherapy use
This thesis reports findings from seven studies to develop and provide a preliminary evaluation of three smartphone apps tackling a different aspect of quitting. Study 1 was a pragmatic randomised controlled trial (RCT) of the NRT2Quit app that focused on improving adherence to nicotine replacement therapy (NRT) during quitting. Due to slow recruitment, the study was terminated early, but there was some evidence that the app could aid cessation. Study 2 was a theory-informed qualitative study of smokersโ and ex-smokersโ use of NRT, which identified barriers in capability, opportunity and motivation to NRT use and engagement with support on NRT use, which could also explain the poor recruitment into the NRT2Quit trial. Study 3 was a think-aloud study about NRT2Quit that showed that smokers were interested in the advice offered within the app, but preferred more comprehensive support, including craving management tools (CMTs). Study 4 was a pragmatic RCT of the BupaQuit app that offered CMTs versus an app version without them and found no detectable impact on cessation and several challenges to conducting pragmatic RCTs of apps. Study 5 identified barriers to verification of abstinence in such trials using personal carbon monoxide (CO) monitors. Study 6 involved follow-up interviews with the BupaQuit trial participants and found that while they were interested in CMTs, the app failed to meet their perceived needs, and many used unassigned cessation support. Study 7 used a mixed-methods approach to explore smokersโ views on personal, smartphone-enabled CO monitors and associated apps, which found that smokers were interested in such support but also highlighted challenges for the development and evaluation of such programmes. This PhD suggests that smokers can articulate a number of desired features in cessation apps, but making these appealing, engaging and effective remains a major challenge, and many barriers exist to appropriate evaluation
ํฌ์ค์ผ์ด ์๋น์ค์์ ๋ฐ์ดํฐ ๊ธฐ๋ฐ ์ปค๋ฎค๋์ผ์ด์ ์ ์ํ ๋์์ธ ์ฐ๊ตฌ
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :์ตํฉ๊ณผํ๊ธฐ์ ๋ํ์ ์ตํฉ๊ณผํ๋ถ(๋์งํธ์ ๋ณด์ตํฉ์ ๊ณต),2020. 2. ์ด์ค์.์ค๋งํธํฐ๊ณผ ์จ์ด๋ฌ๋ธ ๊ธฐ๊ธฐ์ ๋ณด๊ธ์ผ๋ก ์ธํด ํ์ ์์ฑ ๊ฑด๊ฐ ๋ฐ์ดํฐ(Patient-Generated Health Data; PGHD)๊ฐ ํฌ๊ฒ ์ฆ๊ฐํ์๊ณ , ์ด๋ ์์ฌ-ํ์ ์์ฌ ์ํต์ ๊ฐ์ ํ์ฌ ๋ฐ์ดํฐ ์ค์ฌ์ผ๋ก ๋ฐ์ ํ ์์๋ ์๋ก์ด ๊ธฐํ๋ฅผ ์ ๊ณตํ๋ค. PGHD๋ฅผ ์ฌ์ฉํ ๋ฐ์ดํฐ ์ค์ฌ ์ปค๋ฎค๋์ผ์ด์
์ ํตํด ํ์์ ์์ฌ๋ ๊ธฐ์กด ์์ ๋ฐ์ดํฐ๋ฅผ ๋ณด์ํ์ฌ ์ดํด์ ์ฐจ์ด๋ฅผ ๋ฉ์ธ ์ ์์ผ๋ฉฐ, ํ์ ๊ฑด๊ฐ์ ๋ํ ํฌ๊ด์ ์ธ ๊ด์ ๋ ํ๋ํ ์ ์๋ค. ๊ทธ๋ฌ๋, ์ด๋ฌํ ์๋ก์ด ์ ํ์ ๋ฐ์ดํฐ์ ๊ธฐ์ ์ ๊ธฐ์กด ์๋ฃ ์ปค๋ฎค๋์ผ์ด์
์ ํตํฉํ๋ ๋ฐ์๋ ์ฌ์ ํ ์ด๋ ค์์ด ๋จ์ ์๋ค. ํ์๋ ์ข
์ข
๋ฐ์ดํฐ ์์ง์ ๋ํ ์ฐธ์ฌ์ ๋๊ธฐ๋ฅผ ์์ด๋ฒ๋ฆฌ๋ฉฐ, ์ด์ ๋ฐ๋ผ ์์งํ ๋ฐ์ดํฐ๋ ๋ถ์์ ํด์ง๋ ๋ฌธ์ ๊ฐ ๋ฐ์ํ๋ค. ๋ํ PGHD๊ฐ ์จ์ ํ๊ฒ ์์ง ๋๋๋ผ๋ ์์ฌ์ ํ์๋ ์๋ฃ ๊ดํ์์ ์ด๋ฌํ ๋ฐ์ดํฐ๋ฅผ ํ์ฉํ๋ ๋ฐ ์ด๋ ค์์ ๊ฒช๊ฒ ๋๋ค. ๋ํ, ์๊ฐ๊ณผ ์ ๋ณด์ ๋ถ์กฑ์ผ๋ก ์ธํด ํ์ฌ ์ํฌ ํ๋ก์ฐ์์ ํ์์ ์์ฌ ๋ชจ๋๊ฐ PGHD๋ฅผ ํตํด ํ์
ํ๋ ๊ฒ์ ๋งค์ฐ ์ด๋ ค์ด ์ผ๋ก ์๋ ค์ ธ ์๋ค. HCI ์ฐ๊ตฌ ๊ด์ ์์, PGHD๋ฅผ ํ์ฉ ํ ๋ฐ์ดํฐ ์ค์ฌ ํต์ ์ ์ง์ํ๋ ์์คํ
์ ์ค๊ณํ๋ฉด ์ด๋ฌํ ๊ณผ์ ๋ฅผ ํด๊ฒฐํ ์ ์๋ ์ ์ฌ๋ ฅ์ด ์์ผ๋ฉฐ, ์ด๋ ๋ฐ์ดํฐ ์์ง(collection), ํํ(representation), ํด์(interpretation) ๋ฐ ํ์
(collaboration)์ ๋ค ๊ฐ์ง ์ค๊ณ ๊ณต๊ฐ(design space)์์ ์ถ๊ฐ์ ์ธ ํ์์ ์๊ตฌํ๋ค. ๋ฐ๋ผ์, ์ด ๋
ผ๋ฌธ์์๋ ์์คํ
์ค๊ณ ๋ฐ ํ์ฅ ๋ฐฐํฌ ์ฐ๊ตฌ๋ฅผ ์ํํ์ฌ, ๊ฐ ์ค๊ณ ๊ณต๊ฐ์์ ํด๊ฒฐ๋์ง ์์ ์ง๋ฌธ์ ํ์ํ๊ณ ๊ฒฝํ์ ์ฐ๊ตฌ ๊ฒฐ๊ณผ ๋ฐ ์ค๊ณ ์ง์นจ์ ์ ๊ณตํ๋ ๊ฒ์ ๋ชฉํ๋ก ํ๋ค.
๋จผ์ , ๋ฐ์ดํฐ ์์ง์ ๋ํ ์ค๊ณ ๊ณต๊ฐ์ ์ฐ๊ตฌ๋ก์, ์ ๊ทผ์ฑ ๋์ ๋ฐ์ดํฐ ์ถ์ ๋๊ตฌ๊ฐ ํ์๊ฐ ๋ค์ํ ์ ํ์ PGHD, ํนํ ์์ฌ ๋ฐ์ดํฐ๋ฅผ ์์งํ๋ ๋ฐ ์ด๋ค ๋์์ ์ค ์ ์๋์ง์ ๋ํด ์ฐ๊ตฌํ๊ณ ์ ํ์๋ค. ์ด๋ฅผ ์ํด, ์ ๊ทผ์ฑ ๋์ ๋ฐ์ดํฐ ์ถ์ ๋๊ตฌ์ธ mFood Logger์ ๋์์ธํ ํ, 20 ๋ช
์ ํ์์ 6 ๋ช
์ ์์์๋ฅผ ๋์์ผ๋ก ์ค์ฆ์ ์ฐ๊ตฌ๋ฅผ ์ํํ๋ค. ๊ทธ ๊ฒฐ๊ณผ, ํ์์ ์์์๊ฐ ๋ฐ์ดํฐ ๊ธฐ๋ฐ ์ปค๋ฎค๋์ผ์ด์
์ ์ํด ์ํ๋ ๋ฐ์ดํฐ ์ ํ์ด ๋ฌด์์ธ์ง ํ์
ํ ์ ์์๊ณ , ์์์ ๋งฅ๋ฝ์์ ๋ฐ์ดํฐ๋ฅผ ์์ง ํ ๋์ ๋์ ๊ณผ ๊ธฐํ๋ฅผ ๋ฐ๊ฒฌํ๋ค.
๋์งธ, ์์์๋ฅผ ์ํ ๋ฐ์ดํฐ ํํ์ ํ์
ํ๊ธฐ ์ํด, 18๋ช
์ ๋ค์ํ ์ดํด ๊ด๊ณ์(e.g., ์์์, EMR ๊ฐ๋ฐ์)์ ์ฐธ์ฌ์ ๋์์ธ(participatory design) ํ๋ก์ธ์ค๋ฅผ ํตํด PGHD๋ฅผ ํ์ํ๋ DataMD๋ฅผ ์ค๊ณํ๊ณ ๊ตฌํํ๋ค. ์ฐธ์ฌ์ ๋์์ธ ์ํฌ์ต์ ํตํด ์์๋ธ ๊ฒ์, ์๋ฃ์ ์ํฉ์ ์ ์ฝ ๋๋ฌธ์ ์์์๊ฐ ์ํ๋ ๋ฐ์ดํฐ ํํ ๋ฐฉ์์ด ํจ์จ์ฑ๊ณผ ์น์ํจ์ผ๋ก ์๋ ด๋๋ค๋ ์ ์ด์๋ค. ์์์๋ ํ์ต์ ๊ฑธ๋ฆฌ๋ ์๊ฐ ๋ฌธ์ ๋ก ์ธํด ์๋ก์ด ์๊ฐํ ๋ฐฉ๋ฒ์ ์ฌ์ฉํ์ง ์์๊ณ , ํ ๋ฒ์ ๋ง์ ์์ ๋ฐ์ดํฐ๋ฅผ ๋ณด๊ณ ์ถ์ดํ๋ค. ์ด๋ฌํ ์๊ตฌ ์ฌํญ์ ๊ณ ๋ คํ์ฌ, ๋ค์ํ ์ ํ์ PGHD๊ฐ ํ ๋์ ๋ณด์ฌ์ง๋ฉฐ, ์ฌ๋ฌ ๊ฐ์ง ์์ ์ํฉ์ ๊ณ ๋ คํ, DataMD๋ฅผ ์ค๊ณํ๊ณ ๊ตฌํํ๋ค.
์
์งธ, ๋ฐ์ดํฐ ๊ธฐ๋ฐ ์ปค๋ฎค๋์ผ์ด์
์ ์ค์ํ ์ธก๋ฉด์ผ๋ก์, ํ์๋ฅผ ์ํ ๋ฐ์ดํฐ ํด์ ์ ๋ต์ ์ ์ํ์ฌ ํจ๊ณผ์ ์ธ ๋ฐ์ดํฐ ํด์์ ๋๋ ์ค๊ณ ์ง์นจ์ ์ ๊ณตํฉ๋๋ค. 20๋ช
์ ๋ง์ฑ ์งํ ํ์์์ ์ธํฐ๋ทฐ๋ฅผ ํตํด, ํ์๋ค์ด PGHD๋ฅผ ํด์ํ ๋, ๋
ผ๋ฆฌ์ ์ฆ๊ฑฐ๊ฐ ์๋ ์์ ์ ๊ณผ๊ฑฐ ๊ฒฝํ์ ๊ฐํ๊ฒ ์์กดํ๋ค๋ ์ ์ ๋ฐํ๋๋ค. ํ์๋ค์ ์์ ์ ์ ๋
๊ณผ ๊ฒฝํ์ ๋ฐ๋ผ ์ฌ๋ฌ ๋ฐ์ดํฐ ์ฌ์ด์ ๊ด๊ณ๋ฅผ ๊ฐ์ ํ๋ฉฐ, ์ด๋ฅผ ํ์ธํ๊ธฐ ์ํด ๋ค ๊ฐ์ง์ ๋ฐ์ดํฐ ํด์ ์ ๋ต์ ๊ตฌ์ฌํ๋ค. ์ด๋ฌํ ์ดํด๋ ์ค๊ณ์์ ์ฐ๊ตฌ์์ด ๋ฐ์ดํฐ ํด์์ ์ง์ํ๋ ์์คํ
์ค๊ณ๋ฅผ ๋ฐ์ ์ํค๋ ๋ฐ ๋์์ด ๋ ์ ์๋ค.
๋ง์ง๋ง์ผ๋ก, ๋ฐ์ดํฐ๋ฅผ ํตํ ํ์
์ ์ง์ํ๊ธฐ ์ํด ์์ ์ฐ๊ตฌ์์ ๋์์ธํ ์์คํ
์ ๊ธฐ๋ฐ์ผ๋ก PGHD๋ฅผ ๊ณต์ ํ๊ณ ํ์ฉํจ์ผ๋ก์จ, ์์์์ ํ์๊ฐ ์ด๋ป๊ฒ ํ์
ํ๋์ง๋ฅผ ์กฐ์ฌํ๊ณ ์ ํ๋ค. ํ์์ ๋ฐ์ดํฐ ์์ง ๋ฐ ํด์์ ๋๋ ์ฑ์ธ MyHealthKeeper์ ์์์๋ฅผ ์ํ ์ธํฐํ์ด์ค์ธ DataMD๋ก ๊ตฌ์ฑ๋ ํตํฉ ์์คํ
์ ์์ ํ์ฅ์ ๋ฐฐํฌํ๋ค. 80๋ช
์ ์ธ๋ํ์์์ ์์์ํ ๊ฒฐ๊ณผ์ ๋ฐ๋ฅด๋ฉด PGHD๋ฅผ ํตํ ํ๋ ฅ์ผ๋ก ํ์๊ฐ ํ๋ ๋ณํ์ ์ฑ๊ณตํ ์์์๋ค. ๋ํ, ์ฑ ์ฌ์ฉ ๋ก๊ทธ์ ๋ฐ๋ฅด๋ฉด ํ์๋ ์ง์ ์ ์ธ ์ํธ ์์ฉ ์์ด๋ ์์์์ ์๊ฒฉ์ผ๋ก ํ์
ํ ์๋ ์๋ ๊ฒ์ผ๋ก ๋ํ๋ฌ๋ค. ์ด๋ฌํ ๊ฒฐ๊ณผ๋ฅผ ๋ฐํ์ผ๋ก, ์ด ์ฐ๊ตฌ์์๋ ์์์์ ํ์ ์ฌ์ด์ ํ๋ ฅ์ ์ง์ํ ์์๋ ์ฃผ์ ๊ธฐํ๊ฐ ๊ธฐ์กด ์์ ์ํฌํ๋ก์ฐ์ PGHD ์ฌ์ฉ์ ํตํฉํ๋ ๊ฒ์ ์์์ ์ ์ํ๋ค.
์์ ์ฐ๊ตฌ๋ค์ ํตํด, ๋ฐ์ดํฐ ๊ธฐ๋ฐ ์ปค๋ฎค๋์ผ์ด์
์ ์ํ ๋์์ธ์ด ํ์์ ์์ฌ๊ฐ PGHD๋ฅผ ํตํด ํ์
ํ๋ ๋ฐ ๋์์ด ๋ ์ ์์์ ๋ฐ๊ฒฌํ๋ค. PGHD๊ฐ ๋ค ๊ฐ์ ์ค๊ณ ๊ณต๊ฐ ๋ด์์ ๊ธฐ์กด ์์ฌ-ํ์ ํต์ ์ ๋ฐ์ดํฐ ์ค์ฌ ํต์ ์ผ๋ก ๊ฐ์ ํ ์์๋ ๋ฐฉ๋ฒ์ ๊ฐ๋
ํํจ์ผ๋ก์จ, ์ด ์ฐ๊ตฌ๋ ํ์์ ์์ฌ ๊ฐ์ ๋ฐ์ดํฐ ๊ธฐ๋ฐ ์ปค๋ฎค๋์ผ์ด์
์ ์ํ ๋์์ธ์ด ์ด๋ป๊ฒ ๋์ถ๋์ด์ผ ํ๋์ง์ ๋ํ ์๋ก์ด ์๊ฐ์ ์ ๊ณตํ ๊ฒ์ผ๋ก ๊ธฐ๋ํ๋ค. ์ด ์์
์ HCI, CSCW๊ณผ ๊ฑด๊ฐ ์ ๋ณดํ ์ปค๋ฎค๋ํฐ์ ๊ฒฝํ์ ์ดํด๋ฅผ ๋์ด๊ณ , ์ค์ฉ์ ์ธ ์ค๊ณ ์ง์นจ์ ์ ๊ณตํ๋ฉฐ, ์ด๋ก ์ ํ์ฅ์ ๊ธฐ์ฌํ๋ค. ๋ํ, ์ด ์ฐ๊ตฌ๋ ํฅํ ๋ค๋ฅธ ๋ถ์ผ์์ ๋ฐ์ดํฐ ๊ธฐ๋ฐ ์ปค๋ฎค๋์ผ์ด์
์ ์ง์ํ๋ ์์คํ
์ ์ค๊ณ๊ฐ ์ด๋ป๊ฒ ์ด๋ค์ ธ์ผ ํ๋์ง์ ๋ํ ๊ธฐ์ด๋ฅผ ์ ๊ณตํ๋ค.The prevalence of smartphones and wearable devices has led to a dramatic increase in patient-generated health data (PGHD). The growing interest in PGHD has offered new opportunities to improve doctor-patient communication to become more data-driven. Data-driven communication using PGHD enables patients and physicians to fill in gaps between understandings by supplementing existing clinical data, as well as providing a more comprehensive picture of ongoing patient health. However, challenges in integrating such new types of data and technologies into existing healthcare communications remain. Patients often lose their engagement and motivation in data collection, resulting in incomplete data. Even if PGHD is wholly collected, physicians and patients encounter challenges in utilizing such data--representation and interpretation--in healthcare practices. Furthermore, it is challenging for both patients and physicians to collaborate through PGHD in the current workflow due to the lack of time and information overload. From the HCI research perspective, designing a system supporting data-driven communication utilizing PGHD has the potential to address such challenges, which calls for further exploration in four design spaces: data collection, representation, interpretation, and collaboration. Therefore, in this dissertation work, I aim to explore unsolved questions in each design space by conducting a series of design and deployment studies and provide empirical findings and design guidelines.
In the design space of data collection, I investigated how the semi-automated tracking tool can support patients to track various types of PGHD, especially food journaling. With the design of mFood Logger, a semi-automated data tracking tool, I conducted an empirical study with 20 patients and 6 clinicians. I identified desired data types for data-driven communication from the patients' and clinicians' sides and uncovered the challenges and opportunities in collecting data within clinical contexts. I was able to understand the feasibility and acceptability of PGHD in clinical practices, as well as clinicians' presence--either remotely or in-person--as an enabler that encourages patients to keep tracking PGHD in the longer-term. Incorporating critical topics regarding data collection from the literature and findings from my work, I discuss the applicability of PGHD and data tracking modes.
To support data representation for clinicians, I designed and implemented DataMD that displays PGHD, considering situational constraints through a participatory design process with 18 various stakeholders (e.g., clinicians, EMR developers). Through the participatory design workshop, I found that the ways of data representation that clinicians desired converged to efficiency and familiarity due to the situational constraints. Clinicians wanted to see a large amount of data at once, avoiding using novel visualization methods due to the issue of learnability. Considering those requirements, I designed and implemented DataMD, in which various types of PGHD are represented with considerations of clinical contexts. I discussed the role of data representation in data-driven communication.
As the critical aspect of data-driven communication, I present different data-interpretation strategies from patients, providing design guidelines to help effective data-interpretation. By conducting interviews with 20 chronic disease patients, I found that they shaped their interests and assumptions by incorporating prior experiences rather than logical evidence. I also identified four data-interpretation strategies: finding evidence to confirm assumptions, discrediting data to preserve initial assumptions, discovering new insights, and deferring drawing hasty conclusions from data. These understandings help designers and researchers advance the design of systems to support data-interpretation.
Lastly, to support collaboration via data, I demonstrate how clinicians and patients collaborate by sharing and utilizing PGHD based on the system I designed. I deployed the integrated system consisting of a patient app, MyHealthKeeper, and a clinician interface, DataMD. I investigated how the system could support collaboration via data. Clinical outcomes revealed that collaboration via PGHD led patients to succeed in behavior change. App usage log also showed that patients could even remotely collaborate with clinicians without direct interactions. Findings from these studies indicate that the key opportunities to facilitate collaboration between clinicians and patients are the integration of data prescriptions into the clinician's workflow and intervention based on natural language feedback generated within clinical contexts.
Across these studies, I found that the design for data-driven communication can support patients and physicians to collaborate through PGHD. By conceptualizing how PGHD could improve the existing doctor-patient communication to data-driven communication within four design spaces, I expect that this work will shed new light on how the design should be derived for data-driven communication between patients and physicians in the real world. Taken together, I believe this work contributes to empirical understandings, design guidelines, theoretical extensions, and artifacts in human-computer interaction, computer-supported cooperative work, and health informatics communities. This work also provides a foundation for future researchers to study how the design of the system supporting data-driven communication can empower various users situated in different contexts to communicate through data in other domains, such as learning, beyond the context of healthcare services.1 Introduction 1
1.1 Background 1
1.2 Motivation 4
1.3 Topics of Interest 5
1.3.1 Design Spaces 5
1.3.2 Research Scope 11
1.4 Thesis Statements and Research Questions 13
1.5 Thesis Overview 15
1.6 Contribution 18
1.6.1 Empirical research contributions 18
1.6.2 Artifacts contributions 18
1.6.3 Theoretical contributions 19
2 Conceptual Background & Related Work 20
2.1 Data-driven Communication in Healthcare Services 20
2.1.1 Concept of Doctor-Patient Communication 21
2.1.2 Brief History of Patient-Centered Approach 25
2.1.3 Emergence of Patient-Generated Health Data 27
2.2 Four Design Spaces for Data-Driven Communication 30
2.2.1 Data collection 34
2.2.2 Data Representation 41
2.2.3 Data Interpretation 47
2.2.4 Collaboration via Data 50
3 Data Collection: Study of mFood Logger 54
3.1 Motivation 55
3.2 Preliminary Work & Tool Design 57
3.2.1 Clinical Requirements for Data Collection 57
3.2.2 Design of Data Collection Tool: mFood Logger 60
3.3 Study Design 63
3.3.1 Participants 63
3.3.2 StudyProcedure 64
3.4 Results 69
3.4.1 PatientSide 69
3.4.2 ClinicianSide 76
3.5 Limitations & Conclusion 80
3.6 Chapter 3 Summary 81
4 Data Representation: Design of DataMD 83
4.1 Motivation 84
4.2 Preliminary Work 86
4.2.1 Workflow Journey Maps 87
4.2.2 DesignGoals 89
4.3 Study Design 90
4.3.1 Participants 91
4.3.2 ParticipatoryDesignworkshop 91
4.4 Results 92
4.4.1 DesignRequirements 92
4.4.2 Implementation: DataMD 98
4.5 Limitations & Conclusion 102
4.6 Summary of Chapter4 102
5 Data Interpretation: Data-Interpretation Strategies 103
5.1 Motivation 103
5.2 Study Design 106
5.2.1 Participants 106
5.2.2 Study Procedure 108
5.2.3 Data Analysis 110
5.3 Results 111
5.3.1 Change of Interest in Data 111
5.3.2 Assumptions on Relationships between Data Types 113
5.3.3 Data-InterpretationStrategy 117
5.4 Limitations & Conclusion 124
5.5 Summary of Chapter5 125
6 Collaboration via Data: Deployment Study 126
6.1 Motivation 127
6.2 System Design 128
6.2.1 MyHealthKeeper: Patient App 128
6.2.2 DataMD: Clinician Interface 132
6.3 Study Design 133
6.3.1 Participants 134
6.3.2 Procedure 135
6.4 Data Analysis 138
6.4.1 Statistical Analysis of Clinical Outcomes 139
6.4.2 App Usage Log 139
6.4.3 Observation Data Analysis 139
6.5 Results 140
6.5.1 Behavior Change 140
6.5.2 Data-Collection & Journaling Rate 144
6.5.3 Workflow Integration & Communication Support 146
6.6 Limitations & Conclusion 150
6.7 Summary of Chapter6 151
7 Discussion 152
7.1 Towards a Design for Data-Driven Communication 152
7.1.1 Improve Data Quality for Clinical Applicability 153
7.1.2 Support Accessibility of Data Collection 154
7.1.3 Understand Clinicians Preference for Familiar Data Representation. 157
7.1.4 Embrace Lived Experience for Rich Data Interpretation 158
7.1.5 Prioritize Workflow Integration for Successful Data-Driven Communication 163
7.1.6 Consider Risks of Using Patient-Generated Health Data in Clinical Settings 165
7.2 Opportunities for Future Work 166
7.2.1 Leverage Ubiquitous Technology to Design Data CollectionTools 166
7.2.2 Provide Data-Interpretation Guidelines for People with Different Levels of Literacy and Goals 169
7.2.3 Consider Cultural Differences in Data-Driven Communication 170
8 Conclusion 173
8.1 Summary of Contributions 173
8.2 Future Directions 175
8.3 Final Remarks 176Docto
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