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It can be challenging for the general public to understand data on medical risk. Weseley-Jones and Mordechai tackle this issue by conducting a survey to assess people's skill and comfort with understanding medical risk information in percentage and frequency formats.

Over the last few decades, childhood stunting has persisted as a major global challenge. This study hypothesized that TPTO (Tree-based Pipeline Optimization Tool), an AutoML (automated machine learning) tool, would outperform all pre-existing machine learning models and reveal the positive impact of economic prosperity, strong familial traits, and resource attainability on reducing stunting risk. Feature correlation plots revealed that maternal height, wealth indicators, and parental education were universally important features for determining stunting outcomes approximately two years after birth. These results help inform future research by highlighting how demographic, familial, and socio-economic conditions influence stunting and providing medical professionals with a deployable risk assessment tool for predicting childhood stunting.

Diabetes is a growing health concern in the developing world. This study aimed to develop a questionnaire that uses factors including age, blood pressure, BMI, and family history to predict whether Filipino participants are at risk for diabetes.

While the genetic basis of hip fracture risk has been studied extensively in adults, it is not known whether parental history of bone fractures affects their children's fracture risk. In this article, the authors investigated whether a parental history of bone fractures influences the rate of fractures in their children. They found that adolescent children whose parents had a more extensive history of fractures were more likely to have a history of fractures themselves, suggesting that parents' medical histories may be an important consideration in future pediatric health research.

In this article, the authors looked at developing a strategy that would allow for earlier diagnosis of Diabetes as that improves long-term outcomes. They were able to find that BMI, tricep skin fold thickness, and blood pressure are the risk factors with the highest accuracy in predicting diabetes risk.

Hemani et al. tackled the problem of rampant hospital waste by implementing staff training to help inform hospital workers about proper waste disposal. The authors observed a significant increase in proper waste disposal after the training, showing that simple strategies, such as in-person classroom training and posters, can have a profound effect on limiting improper waste handling.

Classifying medical text data using vector databases and text embedding

Black patients tend to have better health outcomes when cared for by Black physicians, yet Black doctors make up only 5% of U.S. physicians, despite Black people comprising 14% of the population. This analysis of data from Florida medical schools showed a higher enrollment of Black first-year students (13.5%) compared to the national average (9%), and a national increase from 6% in 2013 to 9% in 2021, aligning with the rise of social justice movements. Increasing Black medical student enrollment could reduce health disparities and improve outcomes for Black communities.

The electronic health record (EHR), along with its mobile application, has demonstrated the ability to improve the efficiency and accuracy of health care delivery. This study included data from 874 health care providers over a 12-month period regarding their usage of mobile phone (EPIC® Haiku) and tablet (EPIC® Canto) mEHR. Ambulatory and inpatient care providers had the greatest usage levels over the 12-month period. Awareness of workflow allows for optimization of mEHR design and implementation, which should increase mEHR adoption and usage, leading to better health outcomes for patients.

Microwave energy (ME) is used in the medical field to denature protein structures, resulting in inactivation or destruction of abnormal cells. Identifying the extent of destruction of abnormal tissue (cancer tissue or tissue with abnormal electrical activity) is essential for accomplishing successful therapy and reducing collateral damage. Our study was an ex vivo assessment of the changes on ultrasound scans (US) in chicken tissue exposed to ME. We hypothesized that any changes in tissue structures would be recognized on the reflected ultrasound waves. Ultrasound scans of tissues change with exposure to microwaves with increasing reflection of ultrasound waves. With exposure to microwaves, surface level brightness on the ultrasound scans increases statistically significantly. The findings could be used in heat related (ME and radiofrequency) procedures where clinicians would be able to actively assess lesions in real-time. Further studies are required to assess changes in tissue during active exposure to different types of energies.