Shawn M. Carter
Online Content Editor at Nephrology News & Issues
Deputy editor, Forbes Health (@healthonforbes) Previously: @CNBC/@FoxBusiness/@GoHealio. Views, my own. Reach out at [email protected].
Articles
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2 weeks ago |
healio.com | Shawn M. Carter
Read more Key takeaways:Before introduction of stratified benchmarks, dialysis centers with more low-income patients had higher penalty rates vs. those with fewer disadvantaged patients. After implementation, penalty rates were similar. U.S. dialysis facilities serving patients with social risk factors faced fewer financial penalties after stratified benchmarks were added to the End-Stage Renal Disease Treatment Choices model, according to study data.
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3 weeks ago |
healio.com | Shawn M. Carter |Kamyar Kalantar-Zadeh
Read more Eating during hemodialysis is not often talked about in nephrology. Some clinics deter the practice due to safety and hygiene issues, but others may promote it. Emerging research may signal a shift in how eating during dialysis is viewed.
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3 weeks ago |
healio.com | Shawn M. Carter
Key takeaways:Of young adults using peritoneal dialysis, 71% and 37% continued with that type at 1 and 5 years, respectively. Common reasons for switching to hemodialysis were infection, missed sessions and mechanical issues. Young adults with kidney disease have a high risk of transitioning from peritoneal dialysis to hemodialysis due to infections, nonadherence to treatment schedule and mechanical issues, data show.
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3 weeks ago |
healio.com | Shawn M. Carter
Key takeaways: From an enrollment of more than 5 million, crude prevalence of autosomal dominant polycystic kidney disease was 67.3 per 100,000 persons. Prevalence was lowest for Native American and Alaska Native populations. BOSTON — Autosomal dominant polycystic kidney disease rates in the Military Health System are comparable to the general population, but with variability among certain groups, according to a speaker.
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3 weeks ago |
healio.com | Shawn M. Carter
Key takeaways: During two prospective evaluations, forecasts had a mean absolute error of 3.1 patients per day. Using AI-forecast schedules would have reduced overstaffing by 67% and understaffing by 79%. LAS VEGAS — The use of AI deep learning can help forecast acute dialysis needs and enhance staffing efficiency in hospitals, according to a speaker.
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