Interactive Journal of Medical Research

Physicians could improve the efficiency of the health care system if a reliable resource were available to aid them in better understanding, selecting, and interpreting the diagnostic laboratory tests. It has been well established and widely recognized that (1) laboratory testing provides 70%-85% of the objective data that physicians use in the diagnosis and treatment of their patients; (2) orders for laboratory tests in the United States have increased, with an estimated volume of 4-5 billion tests per year; (3) there is a lack of user-friendly tools to guide physicians in their test selection and ordering; and (4) laboratory test overutilization and underutilization continue to represent a pervasive source of inefficiency in the health care system. These inappropriate test orders not only lead to slower or incorrect diagnoses for patients but also add a significant financial burden. In addition, many ordered tests are not reimbursed by Medicare because they are inappropriate for the medical condition or were ordered with the incorrect International Statistical Classification of Diseases and Related Health Problems, Tenth Revision diagnostic code, not meeting the medical necessity. Therefore, current clinical laboratory test ordering procedures experience a quality gap. Often, providers do not have access to an appropriate tool that uses evidence-based guidelines or algorithms to ensure that tests are not duplicated, overused, or underused. This viewpoint lays out the potential use of an automated laboratory clinical decision support system that helps providers order the right test for the right disease and documents the right reason or medical necessity to pay for the testing.

Caregiver-employees (CEs) for older adults experience a high burden to fulfill their dual roles. Caregiver-friendly workplace policy (CFWP) has been used in many countries to balance employment and caregiving duties, but it is a relatively new concept in Hong Kong.

Medication adherence, or how patients take their medication as prescribed, is suboptimal worldwide. Improving medication-taking habit might be an effective way to improve medication adherence. However, habit is difficult to quantify, and conventional habit metrics are self-reported, with recognized limitations. Recently, several objective habit metrics have been proposed, based on objective medication-taking data.

Gestational diabetes mellitus (GDM) is characterized by hyperglycemia in pregnancy and typically resolves after birth. Women with GDM have an increased risk of developing type 2 diabetes mellitus (T2DM) later in life compared to those with normoglycemic pregnancy. While diabetes prevention interventions (DPIs) have been developed to delay or prevent the onset of T2DM, few studies have provided process evaluation (PE) data to assess the mechanisms of impact, quality of implementation, or contextual factors that may influence the effectiveness of the intervention.

Incorporating artificial intelligence (AI) into medical education has gained significant attention for its potential to enhance teaching and learning outcomes. However, it lacks a comprehensive study depicting the academic performance and status of AI in the medical education domain.

Large language models (LLMs) are artificial intelligence tools that have the prospect of profoundly changing how we practice all aspects of medicine. Considering the incredible potential of LLMs in medicine and the interest of many health care stakeholders for implementation into routine practice, it is therefore essential that clinicians be aware of the basic risks associated with the use of these models. Namely, a significant risk associated with the use of LLMs is their potential to create hallucinations. Hallucinations (false information) generated by LLMs arise from a multitude of causes, including both factors related to the training dataset as well as their auto-regressive nature. The implications for clinical practice range from the generation of inaccurate diagnostic and therapeutic information to the reinforcement of flawed diagnostic reasoning pathways, as well as a lack of reliability if not used properly. To reduce this risk, we developed a general technical framework for approaching LLMs in general clinical practice, as well as for implementation on a larger institutional scale.

Although uroflowmetry and bladder diaries are widely used for noninvasive evaluation of lower urinary tract symptoms, they still have limitations in diagnostic capability and users’ convenience. The aim of this paper is to discuss potential solutions by reviewing (1) the evolution and current clinical use of uroflowmetry and bladder diary, including clinical guidelines, daily practice applications, and their historical development; (2) a growing trend toward using home devices with various technologies; and (3) a comprehensive comparison of the strengths and weaknesses of these home devices. In our opinion, the following points can be highlighted: (1) the emerging trend of using home devices can enhance diagnostic capabilities through repeated measurements and the convenience of at-home testing and (2) home devices, which provide both frequency-volume and uroflowmetry information, have the potential to transform the management of lower urinary tract symptoms.

Psychoeducation positively influences the psychological components of chronic low back pain (CLBP) in conventional treatments. The digitalization of health care has led to the discussion of virtual reality (VR) interventions. However, CLBP treatments in VR have some limitations due to full immersion. In comparison, augmented reality (AR) supplements the real world with virtual elements involving one’s own body sensory perception and can combine conventional and VR approaches.

Chronic diseases such as diabetes and cardiovascular disease are global health challenges, affecting millions of people worldwide. Traditional health care often falls short in chronic disease management. This has led to the exploration of innovative solutions, such as mobile health (mHealth) technologies. mHealth, which leverages mobile and wireless technologies, has the potential to transform health care delivery by providing continuous, accessible, and personalized care. However, the effectiveness of mHealth, particularly when integrated with traditional health care interventions delivered by professionals, warrants comprehensive investigation. Understanding the combined impact of mHealth and professional-led interventions is critical to maximizing the potential of mHealth to improve patient outcomes and adherence.

Interstep variations in step riser height and tread depth within a stairway could negatively impact safe stair negotiation by decreasing step riser height predictability and, consequently, increasing stair users’ fall risk. Unfortunately, interstep variations in riser height and depth are common, particularly in older stairways, but its impact may be lessened by highlighting steps’ edges using a high-contrast stripe on the top front edge of each step.

Adherence to antiretroviral therapy is a critical component in achieving viral suppression in people living with HIV in addition to increasing overall quality of life. Several indirect methods have been used to measure adherence including the Simplified Medication Adherence Questionnaire (SMAQ).

Good communication between health authorities and citizens is crucial for adherence to preventive measures during a pandemic. Crisis communication often appeals to worries about negative consequences for oneself or others. While worry can motivate protective behavior, it can also be overwhelming and lead to irrational choices or become a mental health problem. Also, the levels and consequences of worry can differ between different groups of citizens. Little is known about the evolution of worries during the pandemic and adherence to measures in distinct groups.