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Data-Mining in Doctor's Office Helps Solve Medical Mysteries
When hospitals turn to Microsoft Corp., it's no longer just for the latest office software. Some are asking the technology giant for help in diagnosing their patients.
In one instance, a hospital in Washington, D.C., asked Microsoft to examine its medical records to determine why certain patients were getting sick soon after being discharged. The company crunched the data from MedStar Washington Hospital Center and found something surprising: Patients who stayed in the same room had come down with the same infection.
Read the full story: http://hcp.lv/JdeGpw
Source: Bloomberg Businessweek
Emily Goldberg Shares Insights as a Genetic Counselor for Breast Cancer Risk Screening
October 30th 2023On this episode of Managed Care Cast, Emily Goldberg, MS, CGC, a genetic counselor at JScreen, breaks down how genetic screening for breast cancer works and why it is so important to increase awareness and education around these screening tools available to patients who may be at risk for cancer.
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Provisional CDC data show that US babies gained roughly a year in life expectancy in 2022; study findings indicate that the COVID-19 pandemic significantly raised the risk of preterm birth for expectant California mothers; New York Attorney General Letitia James said about 4 million New Yorkers were affected by a data breach of the medical transcription company Perry Johnson & Associates.
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Examining Telehealth Uptake to Increase Equitable Care Access
January 26th 2023To mark the publication of The American Journal of Managed Care®’s 12th annual health IT issue, on this episode of Managed Care Cast, we speak with Christopher M. Whaley, PhD, health care economist at the RAND Corporation, who focuses on health economics issues, including the influence of the COVID-19 pandemic on health care delivery.
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Researchers Identify Potential Biomarkers for SMA Motor Improvement Following Nusinersen Treatment
November 17th 2023A retrospective, exploratory analysis identifies biomarkers predictive of spinal muscular atrophy (SMA) response to nusinersen and suggests the efficacy of using machine learning algorithms to anticipate patient outcomes.
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