e-ESAS: Evolution of a Participatory Design-based Solution for Breast Cancer (BC) Patients in Rural Bangladesh

Healthcare facility is scarce for rural women in the developing world. The situation is worse for patients who are suffering from diseases that require long-term feedback-oriented monitoring such as breast cancer. Lack of motivation to go to the health centers on patients’ side due to sociocultural barriers, financial restrictions and transportation hazards results in inadequate data for proper assessment. Fortunately, mobile phones have penetrated the masses even in rural communities of the developing countries. In this scenario, a mobile phone-based remote symptom monitoring system (RSMS) with inspirational videos can serve the purpose of both patients and doctors. Here, we present the findings of our field study conducted on 39 breast cancer patients in rural Bangladesh. Based on the results of extensive field studies, we have categorized the challenges faced by patients in different phases of the treatment process. As a solution, we have designed, developed and deployed e-ESAS—the first mobile-based RSMS in rural context. Along with the detail need assessment of such a system, we describe the evolution of e-ESAS and the deployment results. We have included the unique and useful design lessons that we learned as e-ESAS evolved through participatory design process. The findings show how e-ESAS addresses several challenges faced by patients and doctors and positively impact their lives

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Project HealthDesign: Rethinking the Power and Potential of Personal Health Records: Round One Final Report

Describes an initiative to develop prototypes for next-generation personal health record applications on a common platform focused on self-management for better health. Outlines grantees' prototypes for user-centered daily monitoring and lessons learned

The Serums Tool-Chain:Ensuring Security and Privacy of Medical Data in Smart Patient-Centric Healthcare Systems

Digital technology is permeating all aspects of human society and life. This leads to humans becoming highly dependent on digital devices, including upon digital: assistance, intelligence, and decisions. A major concern of this digital dependence is the lack of human oversight or intervention in many of the ways humans use this technology. This dependence and reliance on digital technology raises concerns in how humans trust such systems, and how to ensure digital technology behaves appropriately. This works considers recent developments and projects that combine digital technology and artificial intelligence with human society. The focus is on critical scenarios where failure of digital technology can lead to significant harm or even death. We explore how to build trust for users of digital technology in such scenarios and considering many different challenges for digital technology. The approaches applied and proposed here address user trust along many dimensions and aim to build collaborative and empowering use of digital technologies in critical aspects of human society

Can mHealth Improve Risk Assessment in Underserved Populations? Acceptability of a Breast Health Questionnaire App in Ethnically Diverse, Older, Low-Income Women.

Background: Use of mobile health (mHealth) tools has expanded rapidly but little research has been done on its acceptability by low-income, diverse, older patient populations. Objective: To assess the attitudes of a diverse group of underserved women on the acceptability and usability of mHealth tools in a clinical setting using a breast health questionnaire application (app) at a public hospital mammography clinic. Methods: Semi-structured interviews were conducted in a breast-imaging center of an urban safety net institution from July-August 2012. Interviews included pre- and post-questions. Women completed the Athena breast health questionnaire app on an iPad and were asked about their experience and ways to improve the tool. Results: Fifteen women age 45-75 years from diverse ethnic and educational backgrounds were interviewed. The majority of women, 11 of 15, preferred the Athena app over a paper version and all the women thought the app was easy to use. Two Spanish-speaking Latinas preferred paper; and two women, with limited mobile phone use, did not have a preference. Many women indicated that it would be necessary to have staff available for instruction and assistance if the app were to be implemented. Conclusions: mHealth tools are an acceptable, if not preferred, method of collecting health information for diverse, older, low-income women. Further studies are required to evaluate the reliability and accuracy of data collection using mHealth tools in underserved populations. mHealth tools should be explored as a novel way to engage diverse populations to improve clinical care and bridge gaps in health disparities

Use of digital healthcare solutions for care delivery during a pandemic-chances and (cyber) risks referring to the example of the COVID-19 pandemic

During pandemics, regular service provisioning processes in medical care may be disrupted. Digital health promises many opportunities for service provisioning during a pandemic. However, a broad penetration of medical processes with information technology also has drawbacks. Within this work, the authors use the COVID-19 pandemic to analyze the chances and the risks that may come with using digital health solutions for medical care during a pandemic. Therefore, a multi-methods approach is used. First we use a systematic literature review for reviewing the state of the art of digital health applications in healthcare. Furthermore, the usage of digital health applications is mapped to the different processes in care delivery. Here we provide an exemplary process model of oncological care delivery. The analysis shows that including digital health solutions may be helpful for care delivery in most processes of medical care provisioning. However, research on digital health solutions focuses strongly on some few processes and specific disciplines while other processes and medical disciplines are underrepresented in literature. Last, we highlight the necessity of a comprehensive risk-related debate around the effects that come with the use of digital healthcare solutions

The future of laboratory medicine - A 2014 perspective.

Predicting the future is a difficult task. Not surprisingly, there are many examples and assumptions that have proved to be wrong. This review surveys the many predictions, beginning in 1887, about the future of laboratory medicine and its sub-specialties such as clinical chemistry and molecular pathology. It provides a commentary on the accuracy of the predictions and offers opinions on emerging technologies, economic factors and social developments that may play a role in shaping the future of laboratory medicine

Pain Level Detection From Facial Image Captured by Smartphone

Accurate symptom of cancer patient in regular basis is highly concern to the medical service provider for clinical decision making such as adjustment of medication. Since patients have limitations to provide self-reported symptoms, we have investigated how mobile phone application can play the vital role to help the patients in this case. We have used facial images captured by smart phone to detect pain level accurately. In this pain detection process, existing algorithms and infrastructure are used for cancer patients to make cost low and user-friendly. The pain management solution is the first mobile-based study as far as we found today. The proposed algorithm has been used to classify faces, which is represented as a weighted combination of Eigenfaces. Here, angular distance, and support vector machines (SVMs) are used for the classification system. In this study, longitudinal data was collected for six months in Bangladesh. Again, cross-sectional pain images were collected from three different countries: Bangladesh, Nepal and the United States. In this study, we found that personalized model for pain assessment performs better for automatic pain assessment. We also got that the training set should contain varying levels of pain in each group: low, medium and high