OpenScrum: Scrum methodology to improve shared understanding in an open-source community

While we continue to see rise in the adoption of agile methods for software development, there has been a call to study the appropriateness of agile methods in open-source and other emerging contexts. This paper examines Scrum methodology adopted by a large, globally distributed team which builds an open-source electronic medical records platform called OpenMRS. The research uses a mixed method approach, by doing quantitative analysis of source-code, issue tracker as well as community activity (IRC logs, Mailing lists, wiki) in pre and post Scrum adoption, covering a period of 4 years. Later we conducted semi-structured interviews with core developers and followed it up with group discussions to discuss the analysis of the quantitative data and get their views on our findings. Since the project is "domain heavy", contributors (developers and implementers) need to have certain health informatics understanding before making significant contributions. This puts knowledge-sharing and "bus factor" as critical points of management for the community. The paper presents ideas about a tailored Scrum methodology that might better suited for open-source communities to improve knowledge-sharing and community participation, instead of just agilit

Implementing Guided Inquiry Learning and Measuring Engagement Using an Electronic Health Record System in an Online Setting

In many courses, practical hands-on experience is critical for knowledge construction. In the traditional lab setting, this construction is easy to observe through student engagement. But in an online virtual lab, there are some challenges to track student engagement. Given the continuing trend of increased enrollment in online courses, learning sciences need to address these challenges soon. To measure student engagement and actualize a social constructivist approach to team-based learning in the virtual lab setting, we developed a novel monitoring tool in an open-source electronic health records system (EHR). The Process Oriented Guided Inquiry Learning (POGIL) approach is used to engage students in learning. In this paper, we present the practice of POGIL and how the monitoring tool measures student engagement in two online courses in the interdisciplinary field of Health Information Management. To the best of our knowledge, this is the first attempt at integrating POGIL to improve learning sciences in the EHR clinical practice. While clinicians spend over 52% of a patient visit time on computers (called desktop medicine), there is very little focus on learning sciences and pedagogy to train clinicians. Our findings provide an approach to implement learning sciences theory to eHealth use training

Artificial Intelligence for Global Health: Learning From a Decade of Digital Transformation in Health Care

The health needs of those living in resource-limited settings are a vastly overlooked and understudied area in the intersection of machine learning (ML) and health care. While the use of ML in health care is more recently popularized over the last few years from the advancement of deep learning, low-and-middle income countries (LMICs) have already been undergoing a digital transformation of their own in health care over the last decade, leapfrogging milestones due to the adoption of mobile health (mHealth). With the introduction of new technologies, it is common to start afresh with a top-down approach, and implement these technologies in isolation, leading to lack of use and a waste of resources. In this paper, we outline the necessary considerations both from the perspective of current gaps in research, as well as from the lived experiences of health care professionals in resource-limited settings. We also outline briefly several key components of successful implementation and deployment of technologies within health systems in LMICs, including technical and cultural considerations in the development process relevant to the building of machine learning solutions. We then draw on these experiences to address where key opportunities for impact exist in resource-limited settings, and where AI/ML can provide the most benefit.Comment: Accepted Paper at ICLR 2020 Workshop on Practical ML for Developing Countrie

Exploring the potential and challenges of using mobile based technology in strengthening health information systems: Experiences from a pilot study

This paper empirically examines the challenges of introducing a mobile based reporting system (called SCDRT) within the public health system in India to strengthen the health information systems, and also discusses the approaches to address these challenges. Taking an “infrastructure” perspective, various socio-technical challenges relating to technology, operator and usage are discussed. Scaling, in geographical and functional terms, is discussed with a focus on aspects of “attractors” and “motivation.

Overview, not Overwhelm: Framing Operational BI Tools using Organizational Capabilities

In contexts where fragmentation of information systems is a problem, data warehouse (DW) has brought disparate sources of information together. While bringing data together from multiple health programs and patient record systems, how does one make sense of huge amounts of integrated information? Recent research and industry uses the term, “Operational BI” for decision making tools used in operational activities. In this paper, we highlight the use of DHIS 2, a large-scale, open-source, Health Management Information System (HMIS) that acts as a DW. Firstly, we present the results of a survey done in 13 countries to assess how Operational BI Tools are used. We then show 3 generations of BI Tools in DHIS 2 that have evolved from action-research done over 18 years in more than 30 countries. Secondly, we develop the Overview-Overwhelm (O-O) analytical framework for large-scale systems that need to work with Big Data. The O-O framework combines lessons from DHIS 2 BI Tools design and implementation survey results

Electronic Patient Records as a Substrate for Collaboration for Distributed Care in Low-Resource Contexts

Collaboration to provide patient care in low-resource contexts has been a challenge due to heavy patient load, limited connectivity, and knowledge-gap between primary and tertiary care. Through the design, development, and implementation of a private social network-connected, large-scale hospital information system, which has scaled to several zonal and district hospitals in a small hilly country in South East Asia, we present the case study of a system that has enabled collaboration. Using coordination mechanisms as a theoretical framework, we discuss some methods of collaboration. In the paper, we present electronic patient records (EPR) as the substrate that enables collaboration between providers, departments, developers throughout the health systems. In our analysis, we present useful learnings of collaboration between provider-provider, developer-developer, provider-patient, implementer-provider, and how the balance of these is a necessary condition to create a useful substrate for collaboration

Mobile-Application Based Cognitive Behavior Therapy (CBT) for Identifying and Managing Depression and Anxiety

Mobile technology is a cost effective and scalable platform for developing a therapeutic intervention. This paper discusses the development of a mobile application for people suffering with depression and anxiety. The application which we have developed is similar to a Cognitive Behavior Therapy (CBT) website, which is freely available on the internet. Past research has shown that CBT delivered over the internet is effective in alleviating the depressive symptoms in users. But, this delivery method is associated with some innate drawbacks, which caused user dropout and reduced adherence to the therapy. To overcome these shortfalls, from web based CBT delivery, a mobile application called MoodTrainer was developed. The application is equipped with mobile specific interventions and CBT modules which aim at delivering a dynamic supportive psychotherapy to the user. The mobile specific interventions using this application ensures that the user is constantly engaged with the application and focused to change the negative thought process. We present MoodTrainer as a self-efficacy tool and virtual CBT that is not meant to replace a clinical caregiver. Rather, it is a supportive tool that can be used to self-monitor, as well as a monitoring aid for clinicians

Comparison of Open-Source Electronic Health Record Systems Based on Functional and User Performance Criteria

Objectives: Open-source Electronic Health Record (EHR) systems have gained importance. The main aim of our research is to guide organizational choice by comparing the features, functionality, and user-facing system performance of the five most popular open-source EHR systems. Methods: We performed qualitative content analysis with a directed approach on recently published literature (2012-2017) to develop an integrated set of criteria to compare the EHR systems. The functional criteria are an integration of the literature, meaningful use criteria, and the Institute of Medicine's functional requirements of EHR, whereas the user-facing system performance is based on the time required to perform basic tasks within the EHR system. Results: Based on the Alexa web ranking and Google Trends, the five most popular EHR systems at the time of our study were OSHERA VistA, GNU Health, the Open Medical Record System (OpenMRS), Open Electronic Medical Record (OpenEMR), and OpenEHR. We also found the trends in popularity of the EHR systems and the locations where they were more popular than others. OpenEMR met all the 32 functional criteria, OSHERA VistA met 28, OpenMRS met 12 fully and 11 partially, OpenEHR-based EHR met 10 fully and 3 partially, and GNU Health met the least with only 10 criteria fully and 2 partially. Conclusions: Based on our functional criteria, OpenEMR is the most promising EHR system, closely followed by VistA. With regards to user-facing system performance, OpenMRS has superior performance in comparison to OpenEMR

Development and Evaluation of a Natural Language Conversational Bot for Identifying Appropriate Clinician Referral from Patient Narratives

Digitized for IUPUI ScholarWorks inclusion in 2021.Recent years have seen a significant increase in automated conversational agent chatbots. Conversational agents like chatbots for health may provide timely and cost-effective support in clinical care. Some studies show that chatbots could have an impact on patient engagement. Additionally, health systems are attempting to connect with patients over social networks, mainly where specialists are limited. By 2025, the Association of American Medical Colleges estimates that the United States will have a shortfall of 61,700-94,700 physicians and critical shortage in many specialties, delaying available appointments by months in many cases. Thus, we need innovative solutions that can manage the time of limited specialists appropriately. Recent research has demonstrated that deep learning methods are superior for natural language classification tasks compared to other machine learning methods. The primary objective of this study was to develop a telegram chatbot which reads patient narratives and acts as a conversational agent by redirecting the case to the appropriate specialist. Besides simply working on improving conversational capabilities of chatbots, we developed a novel method for referring the cases to specialists based on their responses to previous cases on a social network group. As far as we know, no other chatbot has the level of accuracy or referral system like our developed chatbot

Full Training versus Fine Tuning for Radiology Images Concept Detection Task for the ImageCLEF 2019 Challenge

Concept detection from medical images remains a challenging task that limits implementation of clinical ML/AI pipelines because of the scarcity of the highly trained experts to annotate images. There is a need for automated processes that can extract concrete textual information from image data. ImageCLEF 2019 provided us a set of images with labels as UMLS concepts. We participated for the rst time for the concept detection task using transfer learning. Our approach involved an experiment of layerwise ne tuning (full training) versus ne tuning based on previous reported recommendations for training classi cation, detection and segmentation tasks for medical imaging. We ranked number 9 in this year's challenge, with an F1 result of 0.05 after three entries. We had a poor result from performing layerwise tuning (F1 score of 0.014) which is consistent with previous authors who have described the bene t of full training for transfer learning. However when looking at the results by a radiologist, the terms do not make clinical sense and we hypothesize that we can achieve better performance when using medical pretrained image models for example PathNet and utilizing a hierarchical training approach which is the basis of our future work on this dataset