Use of eHealth technologies to enable the implementation of musculoskeletal Models of Care: Evidence and practice

Musculoskeletal (MSK) conditions are the second leading cause of morbidity-related burden of disease globally. EHealth is a potentially critical factor that enables the implementation of accessible, sustainable and more integrated MSK models of care (MoCs). MoCs serve as a vehicle to drive evidence into policy and practice through changes at a health system, clinician and patient level. The use of eHealth to implement MoCs is intuitive, given the capacity to scale technologies to deliver system and economic efficiencies, to contribute to sustainability, to adapt to low-resource settings and to mitigate access and care disparities. We follow a practice-oriented approach to describing the ‘what’ and ‘how’ to harness eHealth in the implementation of MSK MoCs. We focus on the practical application of eHealth technologies across care settings to those MSK conditions contributing most substantially to the burden of disease, including osteoarthritis and inflammatory arthritis, skeletal fragility-associated conditions and persistent MSK pain

Models of Care for musculoskeletal health: Moving towards meaningful implementation and evaluation across conditions and care settings

Models of Care (MoCs) are increasingly recognised as a system-level enabler to translate evidence for ‘what works’ into policy and, ultimately, clinical practice. MoCs provide a platform for a reform agenda in health systems by describing not only what care to deliver but also how to deliver it. Given the enormous burden of disease associated with musculoskeletal (MSK) conditions, system-level (macro) reform is needed to drive downstream improvements in MSK healthcare – at the health service (meso) level and at the clinical interface (micro) level. A key challenge in achieving improvements in MSK healthcare is sustainable implementation of reform initiatives, whether they be macro, meso or micro level in scope. In this chapter, we introduce the special issue of the Journal dedicated to implementation of MSK MoCs. We provide a contextual background on MoCs, a synthesis of implementation approaches across care settings covered across the chapters in this themed issued, and perspectives on the evaluation of MoCs

Information Retrieval of Opioid Dependence Medications Reviews from Health-Related Social Media

Social media provides a convenient platform for patients to share their drug usage experience with others; consequently, health researchers can leverage this potential data to gain valuable information about users’ drug satisfaction. Since the 1990s, opioid drug abuse has become a national crisis. In order to reduce the dependency of opioids, several drugs have been presented to the market, but little is known about patient satisfaction with these treatments. Sentiment analysis is a method to measure and interpret patients’ satisfaction. In the first phase of this study, we aimed to utilize social media posts to predict patients’ sentiment towards opioid dependency treatment. We focused on Suboxone, a well-known opioid dependence medication, as our targeted treatment and Drugs.com, an online healthcare forum as our data source. For the purpose of our analysis, we first collected 1,532 posts to create a training dataset, split the posts to sentences, and annotated 1100 sentences for sentiment analysis. To predict patients’ sentiment, we extracted features from patients’ posts, including bigrams, trigrams, and features extracted from topic modeling. To develop the prediction model, we used two machine learning methods, Naïve Bayes and SVM, for predicting sentiment. We achieved the best performance using SVM, getting an accuracy of 61% for SVM. In the second phase of this study, we also aimed to understand the behavior of the patients toward the targeted medication. To accomplish this goal, we used the Health Belief Model (HBM), a social psychological model that describes and predicts patients’ health-related attitudes in action, benefit, barrier, and threat categories, for predicting such behavior from patients’ reviews. We also utilized the same combinations of features and machine learning methods that we used in the first phase of the study, and the best accuracy performance was 47% for the SVM classifier as compared to 43% as our baseline

Data Science in Healthcare

Data science is an interdisciplinary field that applies numerous techniques, such as machine learning, neural networks, and deep learning, to create value based on extracting knowledge and insights from available data. Advances in data science have a significant impact on healthcare. While advances in the sharing of medical information result in better and earlier diagnoses as well as more patient-tailored treatments, information management is also affected by trends such as increased patient centricity (with shared decision making), self-care (e.g., using wearables), and integrated care delivery. The delivery of health services is being revolutionized through the sharing and integration of health data across organizational boundaries. Via data science, researchers can deliver new approaches to merge, analyze, and process complex data and gain more actionable insights, understanding, and knowledge at the individual and population levels. This Special Issue focuses on how data science is used in healthcare (e.g., through predictive modeling) and on related topics, such as data sharing and data management

Contemporary Topics in Graduate Medical Education

Graduate medical education (GME) is a continually evolving, highly dynamic area within the complex fabric of the modern health-care environment. Given the rapidly changing regulatory, financial, scientific and technical aspects of GME, many institutions and programs face daily challenges of "keeping up" with the most recent developments within this ever-more-sophisticated operational environment. Organizational excellence is a requirement for the seamless functioning of GME programs, especially when one consider the multiple disciplines and stakeholders involved. The goal of the current book cycle, titled Contemporary Topics in Graduate Medical Education, beginning with this inaugural tome, is to provide GME professionals with a practical and readily applicable set of reference materials. More than 20 distinguished authors from some of the top teaching institutions in the US, touch upon some of the most relevant, contemporary, and at times controversial topics, including provider burnout, gender equality issues, trainee wellness, scholarly activities and requirements, and many other theoretical and practical considerations. We hope that the reader will find this book to be a valuable and high quality resource of a broad range of GME-related topics. It is the Editors' goal to create a multi-tome platform that will become the definitive go-to reference for professionals navigating the complex landscape of modern graduate medical education

Answering a calling: medical professionals' digital careers in crowdsourcing

One of the most striking trends in individuals’ careers over the last decade has been the dramatic increase in the proportion of the labor force working beyond their employers’ physical boundaries because of the digital revolution in the gig economy. This trend has drawn much attention in the changing nature of work, workplace and careers. However, little empirical research has explored how and why individuals behave in the interface between online platforms and traditional organizations. In my dissertation, I explore these questions by studying medical professionals’ digital careers in the Chinese healthcare crowdsourcing industry, also known as “mobile doctors.” First, by analyzing approximately 240-hour observations and 43 interviews with Chinese physicians, I identify a key issue in this new career – time conflict between crowdsourcing and traditional work. The findings show that physicians respond to time conflict in a variety of ways, including time theft, an essential yet under-researched construct in the crowdsourcing literature which reflects the tension between traditional work and crowdsourcing. Second, by analyzing archival data of 4,034 doctors’ 3.1 million time records on a Chinese healthcare platform across half a year, I show that time theft for crowdsourcing is related to the traditional work context, including hospitals’ boundary control and offline crowd worker social groups. Finally, I further explore, via interview data, why such seemingly costly and deviant time theft is adopted by mobile doctors. The findings reveal that medical professionals assume the extra burden of working for crowdsourcing with the hope of answering unfulfilled occupational callings in traditional work and adding meaning to their work. Overall, these findings contribute to a better understanding of the shifting nature of work and careers in the digital economy by documenting and explaining mobile doctors’ participation in this new world of work

Explaining the Absence of Surgical Procedure Regulation

Systematic evaluation of both existing and innovative surgical procedures is needed to make important safety and efficacy data available to surgeons, facilitating optimal treatment decisions. High quality risk-benefit data is also essential if the healthcare system is to honor its obligation to inform patients of relevant benefits and risks prior to obtaining their consent to treatment. Yet for a variety of reasons, surgical procedures are not subject to direct regulation. As a result, surgeons consulting the available literature may find it inadequate to answer fundamental questions about optimal treatment choices. This failure of information increases the chance that, for years or even generations, patients will undergo painful or risky surgeries until further study eventually exposes them as either inferior to alternatives, or worse, as causing more harm than good. Unfortunately, history has repeatedly borne witness to such medical embarrassments, from the centuries-old practice of bloodletting to the disconcertingly recent examples of radical mastectomies and autologous bone marrow transplants, among many others. The federal government is well-positioned to support needed surgical research activities at the national level, both through the identification of evidence gaps and the funding of studies to fill those gaps. The government is also well-positioned to coordinate the private surgical research marketplace through activities such as the aggregation of country-level adverse event data and the setting of standards for medical terminology and guidelines for study protocols. In partnership with state governments and private organizations, the federal government can contribute to appropriate regulation that improves evidence development without imposing disproportionate burdens on surgeons or others

Explaining the Absence of Surgical Procedure Regulation

Systematic evaluation of both existing and innovative surgical procedures is needed to make important safety and efficacy data available to surgeons, facilitating optimal treatment decisions. High quality risk-benefit data is also essential if the healthcare system is to honor its obligation to inform patients of relevant benefits and risks prior to obtaining their consent to treatment. Yet for a variety of reasons, surgical procedures are not subject to direct regulation. As a result, surgeons consulting the available literature may find it inadequate to answer fundamental questions about optimal treatment choices. This failure of information increases the chance that, for years or even generations, patients will undergo painful or risky surgeries until further study eventually exposes them as either inferior to alternatives, or worse, as causing more harm than good. Unfortunately, history has repeatedly borne witness to such medical embarrassments, from the centuries-old practice of bloodletting to the disconcertingly recent examples of radical mastectomies and autologous bone marrow transplants, among many others. The federal government is well-positioned to support needed surgical research activities at the national level, both through the identification of evidence gaps and the funding of studies to fill those gaps. The government is also well-positioned to coordinate the private surgical research marketplace through activities such as the aggregation of country-level adverse event data and the setting of standards for medical terminology and guidelines for study protocols. In partnership with state governments and private organizations, the federal government can contribute to appropriate regulation that improves evidence development without imposing disproportionate burdens on surgeons or others