R.H.M. Goossens
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95 records found
1
Understanding patients’ everyday experience is essential to improve patient centered care in sarcoidosis. So far, patient perspectives are based on survey- and qualitative research.
Aim
We aimed to assess patient-driven perspectives on their care trajectories using a novel machine learning-driven approach (MLD).
Methods
We used the largest Dutch sarcoidosis patient platform as the data source of patient stories. The patients’ stories were extracted with permission. We applied topic modelling (to generate topics among the posts), and sentiment analysis (to find tone of voice in the topics). To validate the findings, we read the top 50 most relevant posts of each topic. An in-depth patients’ disease trajectory map was made.
Results
Based on 4969 forum posts, 30 final topics and 10 upper themes were generated, which formed the basis for the “patient journey-map” which shows patients’ perspective across the care pathway. Important decision moments could be identified, as well as care “tracks” at home and hospital and topics associated with positive or negative emotions. Most patients’ perspectives were about symptoms (mainly negative sentiment), disease-modifying medication (mainly neutral sentiment), and quality of life (negative, neutral and positive).
Discussion
A major part of living with sarcoidosis takes place outside the view of the hospital, but this part often remains invisible. MLD is an innovative approach, providing a comprehensive overview of patients’ perspectives on health and care. Integrating, these findings in the design of health care delivery has the potential to improve patient-centered care. ...
Understanding patients’ everyday experience is essential to improve patient centered care in sarcoidosis. So far, patient perspectives are based on survey- and qualitative research.
Aim
We aimed to assess patient-driven perspectives on their care trajectories using a novel machine learning-driven approach (MLD).
Methods
We used the largest Dutch sarcoidosis patient platform as the data source of patient stories. The patients’ stories were extracted with permission. We applied topic modelling (to generate topics among the posts), and sentiment analysis (to find tone of voice in the topics). To validate the findings, we read the top 50 most relevant posts of each topic. An in-depth patients’ disease trajectory map was made.
Results
Based on 4969 forum posts, 30 final topics and 10 upper themes were generated, which formed the basis for the “patient journey-map” which shows patients’ perspective across the care pathway. Important decision moments could be identified, as well as care “tracks” at home and hospital and topics associated with positive or negative emotions. Most patients’ perspectives were about symptoms (mainly negative sentiment), disease-modifying medication (mainly neutral sentiment), and quality of life (negative, neutral and positive).
Discussion
A major part of living with sarcoidosis takes place outside the view of the hospital, but this part often remains invisible. MLD is an innovative approach, providing a comprehensive overview of patients’ perspectives on health and care. Integrating, these findings in the design of health care delivery has the potential to improve patient-centered care.
The rising number of cancer survivors and the shortage of health care professionals challenge the accessibility of cancer care. Health technologies are necessary for sustaining optimal patient journeys. To understand individuals’ daily lives during their patient journey, qualitative studies are crucial. However, not all patients wish to share their stories with researchers.
Objective:
This study aims to identify and assess patient experiences on a large scale using a novel machine learning–supported approach, leveraging data from patient forums.
Methods:
Forum posts of patients with colorectal cancer (CRC) from the Cancer Survivors Network USA were used as the data source. Topic modeling, as a part of machine learning, was used to recognize the topic patterns in the posts. Researchers read the most relevant 50 posts on each topic, dividing them into “home” or “hospital” contexts. A patient community journey map, derived from patients stories, was developed to visually illustrate our findings. CRC medical doctors and a quality-of-life expert evaluated the identified topics of patient experience and the map.
Results:
Based on 212,107 posts, 37 topics and 10 upper clusters were produced. Dominant clusters included “Daily activities while living with CRC” (38,782, 18.3%) and “Understanding treatment including alternatives and adjuvant therapy” (31,577, 14.9%). Topics related to the home context had more emotional content compared with the hospital context. The patient community journey map was constructed based on these findings.
Conclusions:
Our study highlighted the diverse concerns and experiences of patients with CRC. The more emotional content in home context discussions underscores the personal impact of CRC beyond clinical settings. Based on our study, we found that a machine learning-supported approach is a promising solution to analyze patients’ experiences. The innovative application of patient community journey mapping provides a unique perspective into the challenges in patients’ daily lives, which is essential for delivering appropriate support at the right moment. ...
The rising number of cancer survivors and the shortage of health care professionals challenge the accessibility of cancer care. Health technologies are necessary for sustaining optimal patient journeys. To understand individuals’ daily lives during their patient journey, qualitative studies are crucial. However, not all patients wish to share their stories with researchers.
Objective:
This study aims to identify and assess patient experiences on a large scale using a novel machine learning–supported approach, leveraging data from patient forums.
Methods:
Forum posts of patients with colorectal cancer (CRC) from the Cancer Survivors Network USA were used as the data source. Topic modeling, as a part of machine learning, was used to recognize the topic patterns in the posts. Researchers read the most relevant 50 posts on each topic, dividing them into “home” or “hospital” contexts. A patient community journey map, derived from patients stories, was developed to visually illustrate our findings. CRC medical doctors and a quality-of-life expert evaluated the identified topics of patient experience and the map.
Results:
Based on 212,107 posts, 37 topics and 10 upper clusters were produced. Dominant clusters included “Daily activities while living with CRC” (38,782, 18.3%) and “Understanding treatment including alternatives and adjuvant therapy” (31,577, 14.9%). Topics related to the home context had more emotional content compared with the hospital context. The patient community journey map was constructed based on these findings.
Conclusions:
Our study highlighted the diverse concerns and experiences of patients with CRC. The more emotional content in home context discussions underscores the personal impact of CRC beyond clinical settings. Based on our study, we found that a machine learning-supported approach is a promising solution to analyze patients’ experiences. The innovative application of patient community journey mapping provides a unique perspective into the challenges in patients’ daily lives, which is essential for delivering appropriate support at the right moment.
Utilizing the Leap Motion Controller for skill tracking in surgical training
Solving line-of-sight issues
Minimally invasive surgery (MIS) requires mastery of complex skills, for which diverse training methods have been developed. While some methods focus on precise instrument tracking and others on realistic practice scenarios, combining these aspects leads to increased costs and impractical setups.
The Leap Motion Controller (LMC) is a cost-effective device offering precise motion tracking, but previous studies found its utility in surgical training is limited by line-of-sight issues. This study aims to address this challenge.
Methods
A novel interface was developed for use of LMC for tracking MIS instruments during practice. To resolve the line-of-sight problem, the traditional enclosed working area was replaced with a single vertical barrier concealing the task while allowing the LMC to maintain a clear horizontal view of the instrument. Performance metrics included time to task completion and total path length of the instrument. Twenty-eight medical students participated, performing 40 consecutive trials each.
Results
The LMC provided precise tracking, effectively resolving line-of-sight issues. Participants improved significantly, with task completion time decreasing from 61 s (SD = 40) to 19 s (SD = 8) and path length from 2390 mm (SD = 2569) to 574 mm (SD = 348). Performance plateaued after 20 trials, with reduced variance for all outcomes.
Conclusions
The study successfully leveraged the LMC for tracking surgical instruments, overcoming previous limitations. The setup enables real-time monitoring, continuous movement tracking, and tactile interaction with physical objects. Its affordability and simplicity make it a promising tool for traditional and home-based MIS training, especially in resource-limited settings. ...
Minimally invasive surgery (MIS) requires mastery of complex skills, for which diverse training methods have been developed. While some methods focus on precise instrument tracking and others on realistic practice scenarios, combining these aspects leads to increased costs and impractical setups.
The Leap Motion Controller (LMC) is a cost-effective device offering precise motion tracking, but previous studies found its utility in surgical training is limited by line-of-sight issues. This study aims to address this challenge.
Methods
A novel interface was developed for use of LMC for tracking MIS instruments during practice. To resolve the line-of-sight problem, the traditional enclosed working area was replaced with a single vertical barrier concealing the task while allowing the LMC to maintain a clear horizontal view of the instrument. Performance metrics included time to task completion and total path length of the instrument. Twenty-eight medical students participated, performing 40 consecutive trials each.
Results
The LMC provided precise tracking, effectively resolving line-of-sight issues. Participants improved significantly, with task completion time decreasing from 61 s (SD = 40) to 19 s (SD = 8) and path length from 2390 mm (SD = 2569) to 574 mm (SD = 348). Performance plateaued after 20 trials, with reduced variance for all outcomes.
Conclusions
The study successfully leveraged the LMC for tracking surgical instruments, overcoming previous limitations. The setup enables real-time monitoring, continuous movement tracking, and tactile interaction with physical objects. Its affordability and simplicity make it a promising tool for traditional and home-based MIS training, especially in resource-limited settings.
Studies on finger kinematics, especially the range of motion (RoM) measurements, are essential to understand the use of finger joints and the pathology of related disease. Limited literatures compared the active RoM (A-RoM) of finger joints with either their functional RoM (F-RoM) or passive RoM (p-RoM) using different measuring protocols and tools. This study aims to provide an overall comparison including all three types of RoMs. We measured A-RoM, F-RoM, and P-RoM, using a dynamic measurement system. Our goal is to investigate the relationships among the three RoMs by comparing their extreme rotation angles. The results suggested that P-RoM was the largest motion range, and F-RoM can exceed their A-RoM. The F-RoM of distal-interphalangeal joints may rotated 8–20° more than their A-RoM, mainly during precise and power manipulations. Besides to A-RoM, knowledge of F-RoM and P-RoM are also important for a comprehensive understanding for clinical practice, and thus, to support the optimization and evaluation of treatment devices for finger joint, such as implant replacement.
Timing, Indicators, and Approaches to Digital Patient Experience Evaluation
Umbrella Systematic Review
BACKGROUND: The increasing prevalence of DH applications has outpaced research and practice in digital health (DH) evaluations. Patient experience (PEx) was reported as one of the challenges facing the health system by the World Health Organization. To generate evidence on DH and promote the appropriate integration and use of technologies, a standard evaluation of PEx in DH is required. OBJECTIVE: This study aims to systematically identify evaluation timing considerations (ie, when to measure), evaluation indicators (ie, what to measure), and evaluation approaches (ie, how to measure) with regard to digital PEx. The overall aim of this study is to generate an evaluation guide for further improving digital PEx evaluation. METHODS: This is a 2-phase study parallel to our previous study. In phase 1, literature reviews related to PEx in DH were systematically searched from Scopus, PubMed, and Web of Science databases. Two independent raters conducted 2 rounds of paper screening, including title and abstract screening and full-text screening, and assessed the interrater reliability for 20% (round 1: 23/115 and round 2: 12/58) random samples using the Fleiss-Cohen coefficient (round 1: k1=0.88 and round 2: k2=0.80). When reaching interrater reliability (k>0.60), TW conducted the rest of the screening process, leaving any uncertainties for group discussions. Overall, 38% (45/119) of the articles were considered eligible for further thematic analysis. In phase 2, to check if there were any meaningful novel insights that would change our conclusions, we performed an updated literature search in which we collected 294 newly published reviews, of which 102 (34.7%) were identified as eligible articles. We considered them to have no important changes to our original results on the research objectives. Therefore, they were not integrated into the synthesis of this review and were used as supplementary materials. RESULTS: Our review highlights 5 typical evaluation objectives that serve 5 stakeholder groups separately. We identified a set of key evaluation timing considerations and classified them into 3 categories: intervention maturity stages, timing of the evaluation, and timing of data collection. Information on evaluation indicators of digital PEx was identified and summarized into 3 categories (intervention outputs, patient outcomes, and health care system impact), 9 themes, and 22 subthemes. A set of evaluation theories, common study designs, data collection methods and instruments, and data analysis approaches was captured, which can be used or adapted to evaluate digital PEx. CONCLUSIONS: Our findings enabled us to generate an evaluation guide to help DH intervention researchers, designers, developers, and program evaluators evaluate digital PEx. Finally, we propose 6 directions for encouraging further digital PEx evaluation research and practice to address the challenge of poor PEx.
Designing digital patient experiences
The digital health design framework
Autonomic responses to pressure sensitivity of head, face and neck
Heart rate and skin conductance
Building Understanding of Experience Design in Digital Health
Preliminary Results Based on Semi-Structured Interviews
Design is expanding its influence on shaping future healthcare. Ideally, designers apply human-centered design and human factors that introduce theory, principles, and methods to design to optimize people’s healthcare experiences in both digital and non-digital environments. To discuss and implement experience design in healthcare, consensus about experience design in healthcare is needed. Objectives: Therefore, the purpose of this study is to investigate designers’ views on experience design in health, and to uncover their understanding about three experience design concepts, i.e., user experience (UX), patient experience (PEx), and digital patient experience (dPEx). We conducted online semi-structured interviews study with convenience samples who met the eligibility. We used ATLAS.ti for an in-depth data coding following thematic analysis. 24 international designers of digital health solutions, either in industry or in academia took part in the interviews. We found the similarities and differences mentioned between healthcare design and non-healthcare design relate to (1) design principles, (2) user attributes, and (3) design contexts. Furthermore, the differences between UX, PEx, and dPEx can be mapped on five dimensions: people, contexts, purposes, means, and usage scenarios. These insights can help designers and human factors specialists build a common design language for experience design in healthcare. Our study can also assist designers and human factors specialists with experience design in digital health by pointing out the areas where design thinking generally is appropriate and the places where particular expertise in healthcare design is needed.
Temporal binding refers to a systemic bias in the perceived time interval between two related events, most frequently voluntary motor actions and a subsequent sensory effect. An inevitable component of most instrumental motor actions is tactile feedback. Yet, the role of tactile feedback within this phenomenon remains largely unexplored. Here, we used local anesthesia of the index finger to temporarily inhibit incoming sensory input from the finger itself, while participants performed an interval-estimation task in which they estimated the delay between a voluntary motor action (button press) and a second sensory event (click sound). Results were compared to a control condition with intact sensation. While clear binding was present in both conditions, the effect was significantly enhanced when tactile feedback was temporarily removed via local anesthesia. The results are discussed in light of current debates surrounding the underlying mechanisms and function of this temporal bias.
Pressure sensitivity research on the head, face, and neck is critical to develop ways to reduce discomfort caused by pressure in head-related products. The aim of this paper is to provide information for designers to be able to reduce the pressure discomfort by studying the relation between pressure sensitivity and soft tissue in the head, face and neck. We collected pressure discomfort threshold (PDT) and pressure pain threshold (PPT) from 119 landmarks (unilateral) for 36 Chinese subjects. Moreover, soft tissue thickness data on the head, face and neck regions of 50 Chinese people was obtained through CT scanning while tissue deformation data under the PDT and PPT states was obtained from literature. The results of the three-elements correlation analysis revealed that soft tissue thickness is positively correlated with deformation but not an important factor in pressure sensitivity. Our high-precision pressure sensitivity maps confirm earlier findings of more rough pressure sensitivity studies, while also revealing additional fine scale sensitivity differences. Finally, based on the findings, a high-precision "recommended map” of the optimal stress-bearing area of the head, face and neck was generated.
Design-Relevant Factors Affecting the Patient Experience in Digital Health
Preliminary Results of an Umbrella Systematic Review
Since Covid-19, digital health interventions (DHIs) have been embraced as never before. The pandemic led to many new challenges, including the patient experience in digital health care delivery. In this literature study, we identified and synthesized factors that impact patient experience in digital health (dPEx), and reviewed the methods and strategies relevant to its design and implementation. We conducted an umbrella review including 15 reviews representing 543 studies. Four themes were identified that describe design-relevant factors that impact dPEx: individual context, content, technical issues, and design features. We propose a preliminary framework to explain the relationship between each factor and support user-centered design efforts. Further research is needed to identify which factors have the most impact.
neck are brought into correspondence using a non-rigid iterative closest point technique. From the correspondence an average head, face, and neck geometry and soft tissue thickness map was calculated. Statistics of the overall soft tissue thickness of the head, face, and neck is extracted, and an accurate soft tissue thickness map of the Chinese head, face, and neck is generated. This study not only lays the groundwork for future simulation experiments on head-related product design, but it also has significant implications for the fields of facial reconstruction in China. ...
neck are brought into correspondence using a non-rigid iterative closest point technique. From the correspondence an average head, face, and neck geometry and soft tissue thickness map was calculated. Statistics of the overall soft tissue thickness of the head, face, and neck is extracted, and an accurate soft tissue thickness map of the Chinese head, face, and neck is generated. This study not only lays the groundwork for future simulation experiments on head-related product design, but it also has significant implications for the fields of facial reconstruction in China.
In hospitals, sinks act as reservoirs for bacterial pathogens. To assess the extent of splashing, fluorescein dye was added to four hospital sinks previously involved in pathogen dispersal to the environment and/or transmission to patients, and one sink that was not. Applying dye to the p-trap or tailpiece did not result in any fluorescent droplets outside of the drain. When applied to the drain, droplets were found in all but one wash basin, and this was more common in the absence of a drain plug. Sink design considerations to install drain plugs, reduce dripping and offset the tap may help to prevent transmission from drains.
Digital Patient Experience
Umbrella Systematic Review
Background: The adoption and use of technology have significantly changed health care delivery. Patient experience has become a significant factor in the entire spectrum of patient-centered health care delivery. Digital health facilitates further improvement and empowerment of patient experiences. Therefore, the design of digital health is served by insights into the barriers to and facilitators of digital patient experience (PEx). Objective: This study aimed to systematically review the influencing factors and design considerations of PEx in digital health from the literature and generate design guidelines for further improvement of PEx in digital health. Methods: We performed an umbrella systematic review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. We searched Scopus, PubMed, and Web of Science databases. Two rounds of small random sampling (20%) were independently reviewed by 2 reviewers who evaluated the eligibility of the articles against the selection criteria. Two-round interrater reliability was assessed using the Fleiss-Cohen coefficient (k1=0.88 and k2=0.80). Thematic analysis was applied to analyze the extracted data based on a small set of a priori categories. Results: The search yielded 173 records, of which 45 (26%) were selected for data analysis. Findings and conclusions showed a great diversity; most studies presented a set of themes (19/45, 42%) or descriptive information only (16/45, 36%). The digital PEx-related influencing factors were classified into 9 categories: patient capability, patient opportunity, patient motivation, intervention technology, intervention functionality, intervention interaction design, organizational environment, physical environment, and social environment. These can have three types of impacts: positive, negative, or double edged. We captured 4 design constructs (personalization, information, navigation, and visualization) and 3 design methods (human-centered or user-centered design, co-design or participatory design, and inclusive design) as design considerations. Conclusions: We propose the following definition for digital PEx: "Digital patient experience is the sum of all interactions affected by a patient's behavioral determinants, framed by digital technologies, and shaped by organizational culture, that influence patient perceptions across the continuum of care channeling digital health." In this study, we constructed a design and evaluation framework that contains 4 phases-define design, define evaluation, design ideation, and design evaluation-and 9 design guidelines to help digital health designers and developers address digital PEx throughout the entire design process. Finally, our review suggests 6 directions for future digital PEx-related research.
Innovating health care
Key characteristics of human-centered design
Human-centered design is about understanding human needs and how design can respond to these needs. With its systemic humane approach and creativity, human-centered design can play an essential role in dealing with today's care challenges. 'Design' refers to both the process of designing and the outcome of that process, which includes physical products, services, procedures, strategies and policies. In this article, we address the three key characteristics of human-centered design, focusing on its implementation in health care: (1) developing an understanding of people and their needs; (2) engaging stakeholders from early on and throughout the design process; (3) adopting a systems approach by systematically addressing interactions between the micro-, meso- and macro-levels of sociotechnical care systems, and the transition from individual interests to collective interests.
Measuring the motion of human hand joints is a challenging task due to the high number of DOFs. In this study, we proposed a low-cost hand tracking system built on action cameras and ArUco markers to measure finger joint rotation angles. The lens distortion of each camera was corrected first via intra-calibration and the videos of different cameras were aligned to the reference camera using a dynamic time warping based method. Two methods were proposed and implemented for extracting the rotation angles of finger joints: one is based on the 3D positions of the markers via inter-calibration between cameras, named pos-based method; the other one is based on the relative marker orientation information from individual cameras, named rot-based method. An experiment was conducted to evaluate the effectiveness of the proposed system. The right hand of a volunteer was included in this practical study, where the movement of the fingers was recorded and the finger rotation angles were calculated with the two proposed methods, respectively. The results indicated that although using the rot-based method may collect less data than using the pos-based method, it was more stable and reliable. Therefore, the rot-based method is recommended for measuring finger joint rotation in practical setups.
Game Design in Mental Health Care
Case Study–Based Framework for Integrating Game Design into Therapeutic Content
While there has been increasing interest in the use of gamification in mental health care, there is a lack of design knowledge on how elements from games could be integrated into existing therapeutic treatment activities in a manner that is balanced and effective. To help address this issue, we propose a design process framework to support the development of mental health gamification. Based on the concept of experienced game versus therapy worlds, we highlight 4 different therapeutic components that could be gamified to increase user engagement. By means of a Dual-Loop model, designers can balance the therapeutic and game design components and design the core elements of a mental health care gamification. To support the proposed framework, 4 cases of game design in mental health care (eg, therapeutic protocols for addiction, anxiety, and low self-esteem) are presented.