Along with the rest of the globe, 2021 healthcare trends across Asia-Pacific (APAC) countries will center on COVID-19 recovery and resuming the healthcare improvement journey. In the APAC region, however, a mix of developed and developing countries poses unique challenges, as healthcare access and basic infrastructure vary widely between urban and rural populations and economic levels. To shepherd healthcare out of the pandemic and enhance delivery overall in 2021, APAC nations will focus on increasing investment in digital health (including virtual care, machine learning, and EMR adoption), achieving universal health coverage, shifting more towards value, and improving payer-provider relationships.
Learn more about Farhana Nakhooda
Farhana Nakhooda, SVP Health Catalyst, Asia Pacific (APAC), leads the overall vision and growth strategy of the Health Catalyst business across Asia Pacific. She is passionate about improving healthcare outcomes, access to care, as well as decreasing healthcare costs across the APAC region. Nakhooda has extensive experience with engagement at the intersection of medicine, technology, AI, and analytics. Before joining Health Catalyst, she was the IBM Healthcare & Life Sciences solutions leader for APAC for 18 years, where she played a key role in digitally enabling healthcare transformation projects across APAC, Eastern Europe, and Middle East/Africa. Nakhooda has more than 25 years of international experience in the healthcare and life sciences industry as a medical researcher and subject matter expert. She has a bachelor of science degree (with honors), in which she majored in biology and biochemistry, at the University of British Columbia. She completed her MBA at Monash Mt Eliza Business School in Melbourne, Australia.
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In addition to driving COVID-19 understanding within the United States, a national disease registry is informing research beyond U.S. borders. Clinicians with the Singapore Ministry of Healthcare Office for Healthcare Transformation (MOHT) have used Health Catalyst Touchstone® COVID-19 data to develop a machine learning tool that helps predict the likelihood of COVID-19 mortality. With this national data set that leverages deep aggregated EHR data, the MOHT accessed the research-grade data it needed to build a machine-learning algorithm that predicts risk of death from COVID-19. The registry-informed prediction model was accurate enough to stand up to comparisons in the published literature and promises to help inform vaccine research and, ultimately, allocation of vaccines within populations.