Journal of Mind and Medical Sciences

Contemporary clinical medicine relies on the integration of clinical observation with physiological and pathological mechanisms to improve diagnosis, therapeutic decision-making, and patient outcomes. However, most current biomedical research interprets these mechanisms predominantly through the lens of upward emergence, according to which higher-order biological functions arise from the interaction of simpler lower-level components. Although indispensable for understanding visceral diseases, this perspective provides only partial access to biological complexity. Accumulating evidence from neuroscience, developmental biology, endocrinology, psychiatry, and regenerative medicine shows that higher-level systemic functions can also reorganize, modulate, or generate lower-level structures, a phenomenon known as downward emergence. Together, upward and downward emergence form a bidirectional framework that more accurately reflects the complex organizational pattern of biological systems. This editorial argues that clinical practice and biomedical research must explicitly acknowledge this bidirectional dynamic, as many diseases (including malignancy) cannot be fully understood through upward emergence alone. Downward emergent processes explain phenomena such as morphogenesis, regeneration, matrix remodeling, immunological reprogramming, endocrine-neurovegetative integration, and forms of pathological transformation that are difficult to interpret through classical reductionism. Viewing cancer as the pathological expression of a disturbed supracellular program provides a coherent explanation of its complex biology and highlights the possibility that malignant progression could be responsive to higher-order regulatory instructions. In this context, the Journal of Mind and Medical Sciences is undertaking a conceptual and editorial realignment, positioning itself as a journal of bidirectional emergence in health and disease. Rather than diminishing its clinical mission, this shift strengthens it by providing a more comprehensive framework for understanding physiological and pathological organization, one that integrates structure–function and function–structure relationships. As medicine moves toward increasingly integrative and mechanistic models of disease, adopting a bidirectional perspective becomes not only scientifically justified but also necessary for advancing diagnostic accuracy, therapeutic innovation, and the development of novel supracellular strategies for human health.

Background: Medication errors pose significant health risks and economic burdens globally. In Saudi Arabia, the reported error rates range from 1.6% to 84.8%; yet, the contributing factors remain inadequately understood. This systematic review aims to identify the associated factors and predictors of medication errors across Saudi healthcare settings. Methods: Electronic databases (EMBASE, CINAHL, and PubMed) were searched for peer-reviewed articles published from January 2010 to January 2025. Studies reporting statistically significant factors associated with medication errors or error reporting in Saudi Arabia were included. A quality assessment was conducted using the Appraisal tool for Cross-Sectional Studies (AXIS). Results: Thirteen studies met the inclusion criteria. Healthcare-worker-related factors included age (workers < 35 years are more prone to errors), experience level (4–5 years optimal for reporting), negative attitudes toward errors (AOR = 14.08), and a lack of training (AOR = 7.29). Patient-related factors included advanced age (1.0–2.7-times increased risk), males, polypharmacy (1.1–5.3-times increased risk), and high-risk medications (hypoglycemic drugs, warfarin, and antibiotics). System-related factors included day shift timing (AOR = 1.1), oral medication route (AOR = 0.4), ICU setting (3.3-times increased risk), medical unit setting (1.7-times increased risk), confusing packaging, and look-alike/sound-alike medications. Conclusions: Our findings emphasize that medical errors arise from a complex interplay between healthcare-worker-related factors (age, experience, and attitudes) and hospital-administration-related factors (reporting mechanisms, documentation practices, shift timing, and workload).

Unexpected in-hospital cardiac arrest (IHCA) in the emergency department is defined as an unexpected cardiac arrest during the stay in the emergency department with measured vital signs when entering the emergency department, requiring immediate emergency treatment to save a life. Since IHCA is an urgent medical event, especially in the emergency department, this study explored the risk prediction of IHCA events in the emergency department. IHCA not only has a high mortality rate, but is also likely to cause permanent neurological damage. In the emergency environment, due to the complexity and rapid changes in the patient’s condition, traditional assessment tools often fail to identify high-risk cases in a timely manner. In view of this, this study uses both the Power BI visual analysis platform and the binary decision tree model to construct a data-driven risk prediction tool. Power BI analysis successfully presented the dynamic ranking of influencing factors, and the decision tree prediction model showed excellent performance, with an accuracy of 91%, a recall rate of 89%, an F1-score of 89%, and an overall accuracy of 100%; this prediction system is expected to improve the efficiency of emergency medical care, identify high-risk patients in a timely manner, and assist medical staff in intervening in advance and implementing preventive measures. This study provided two different approaches: Power BI and decision tree. Power BI requires no coding and can be used by medical professionals without a programming background, while decision tree is designed for professionals with a programming background. While the structures of Power BI and decision tree differ slightly, they are generally similar and can both serve as intelligent clinical tools.