Journal of Medical Internet Research

Symptom checkers (SCs) for laypersons’ self-assessment and preliminary self-diagnosis are widely used by the public. Little is known about the impact of these tools on health care professionals (HCPs) in primary care and their work. This is relevant to understanding how technological changes might affect the working world and how this is linked to work-related psychosocial demands and resources for HCPs.

Previously, most studies used 5-star and 1-star ratings to represent reviewers’ positive and negative attitudes, respectively. However, this premise is not always true because individuals’ attitudes have more than one dimension. In particular, given the credence traits of medical service, to build durable physician-patient relationships, patients may rate their physicians with high scores to avoid lowering their physicians’ web-based ratings and help build their physicians’ web-based reputations. Some patients may express complaints only in review texts, resulting in ambivalence, such as conflicting feelings, beliefs, and reactions toward physicians. Thus, web-based rating platforms for medical services may face more ambivalence than platforms for search or experience goods.

The World Health Organization recommends regular hand hygiene monitoring and feedback to improve hand hygiene behaviors and health care–associated infection rates. Intelligent technologies for hand hygiene are increasingly being developed as alternative or supplemental monitoring approaches. However, there is insufficient evidence regarding the effect of this type of intervention, with conflicting results in the literature.

Silent paroxysmal atrial fibrillation (AF) may be difficult to diagnose, and AF burden is hard to establish. In contrast to conventional diagnostic devices, photoplethysmography (PPG)–driven smartwatches or wristbands allow for long-term continuous heart rhythm assessment. However, most smartwatches lack an integrated PPG-AF algorithm. Adding a standalone PPG-AF algorithm to these wrist devices might open new possibilities for AF screening and burden assessment.

Obesity is a public health issue worldwide. Conversational agents (CAs), also frequently called chatbots, are computer programs that simulate dialogue between people. Owing to better accessibility, cost-effectiveness, personalization, and compassionate patient-centered treatments, CAs are expected to have the potential to provide sustainable lifestyle counseling for weight management.

Mentorship is vital for professional development in academic research and clinical practice, yet it faces challenges due to a limited number of experienced mentors and a lack of protected time for mentorship that may disproportionately affect women mentors in midcareer who are doing much of this “invisible work.” The Push-Pull Mentoring Model offers a potential solution by emphasizing shared responsibility and active engagement between mentors and mentees; it fosters a flexible and collaborative approach that is mutually (though not necessarily equally) supportive of both individuals’ career goals, with mentees pushing mentors up and facilitating opportunities in their realm of influence, including but not limited to sponsorship, while mentors are simultaneously pulling them up. The Push-Pull Mentoring Model provides a promising alternative to traditional mentoring models and may help institutions address the challenges associated with limited mentorship resources.

This article focuses on the importance of mentorship and sponsorship for women in academic medicine, including trainees and faculty, and emphasizes the need for flexible and expanded definitions. Both the benefits and potential harms associated with sponsorship are described. There are 6 actionable strategies illustrated that may be added to a multidimensional mentoring model in order to better support women in medicine.

Although randomized controlled trials (RCTs) are the gold standard for establishing the efficacy and safety of a medical treatment, real-world evidence (RWE) generated from real-world data has been vital in postapproval monitoring and is being promoted for the regulatory process of experimental therapies. An emerging source of real-world data is electronic health records (EHRs), which contain detailed information on patient care in both structured (eg, diagnosis codes) and unstructured (eg, clinical notes and images) forms. Despite the granularity of the data available in EHRs, the critical variables required to reliably assess the relationship between a treatment and clinical outcome are challenging to extract. To address this fundamental challenge and accelerate the reliable use of EHRs for RWE, we introduce an integrated data curation and modeling pipeline consisting of 4 modules that leverage recent advances in natural language processing, computational phenotyping, and causal modeling techniques with noisy data. Module 1 consists of techniques for data harmonization. We use natural language processing to recognize clinical variables from RCT design documents and map the extracted variables to EHR features with description matching and knowledge networks. Module 2 then develops techniques for cohort construction using advanced phenotyping algorithms to both identify patients with diseases of interest and define the treatment arms. Module 3 introduces methods for variable curation, including a list of existing tools to extract baseline variables from different sources (eg, codified, free text, and medical imaging) and end points of various types (eg, death, binary, temporal, and numerical). Finally, module 4 presents validation and robust modeling methods, and we propose a strategy to create gold-standard labels for EHR variables of interest to validate data curation quality and perform subsequent causal modeling for RWE. In addition to the workflow proposed in our pipeline, we also develop a reporting guideline for RWE that covers the necessary information to facilitate transparent reporting and reproducibility of results. Moreover, our pipeline is highly data driven, enhancing study data with a rich variety of publicly available information and knowledge sources. We also showcase our pipeline and provide guidance on the deployment of relevant tools by revisiting the emulation of the Clinical Outcomes of Surgical Therapy Study Group Trial on laparoscopy-assisted colectomy versus open colectomy in patients with early-stage colon cancer. We also draw on existing literature on EHR emulation of RCTs together with our own studies with the Mass General Brigham EHR.

Telehealth has gained substantial attention during the COVID-19 pandemic, and reimbursement policies in health care settings have increased access to remote modes of care delivery. Telehealth has the potential to mitigate care concerns for people living with dementia and their family caregivers. There is a paucity of knowledge on the performance of telehealth services and user experiences, especially among caregiving dyads during the pandemic.

Behavioral activation is an effective treatment for reducing depression. As depressive disorders affect many people worldwide, internet-based behavioral activation (iBA) could provide enhanced treatment access.

Childhood vaccines are a safe, effective, and essential component of any comprehensive public health system. Successful and complete child immunization requires sensitivity and responsiveness to community needs and concerns while reducing barriers to access and providing respectful quality services. Community demand for immunization is influenced by multiple complex factors, involving attitudes, trust, and the dynamic relationship between caregivers and health workers. Digital health interventions have the potential to help reduce barriers and enhance opportunities for immunization access, uptake, and demand in low- and middle-income countries. But with limited evidence and many interventions to choose from, how do decision makers identify promising and appropriate tools? Early evidence and experiences with digital health interventions for immunization demand are presented in this viewpoint to help stakeholders make decisions, guide investment, coordinate efforts, as well as design and implement digital health interventions to support vaccine confidence and demand.

Many US hospitals are classified as nonprofits and receive tax-exempt status partially in exchange for providing benefits to the community. Proof of compliance is collected with the Schedule H form submitted as part of the annual Internal Revenue Service Form 990 (F990H), including a free-response text section that is known for being ambiguous and difficult to audit. This research is among the first to use natural language processing approaches to evaluate this text section with a focus on health equity and disparities.