Integrating the Health Enterprise (IHE) International has announced that the Mobile Access to Health Documents (MHD) profile has been released for public comment by any interested parties.
The MHD implementation guide is designed to simplify access to health documents for patients and healthcare providers using mobile devices. Documents can be delivered to a mobile device using a health information exchange (HIE), an electronic health record (EHR), a personal health record, and/or a patient portal.
It describes a simplified application programming interface (API) supporting access to health documents. The API is based upon Web-accessible resources using representational state transfer (RESTful) approaches to query for metadata on health documents.
The guide is designed for patient kiosks used in hospital registration departments, patient or provider apps enabling access to or submission of medical history data, personal health record apps publishing information to an EHR or HIE, and electronic measurement devices accessing patient medical histories from an EHR or HIE.
The MHD implementation guide may be accessed here. Comments may be submitted here between now and 4 July 2012.
![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
