MedCity News
Providers look to predictive modeling to help rebuild finances in 2021
After a challenging 2020, health systems are looking to deploy strategies for recovery, especially to bolster financial health. One strategy health executives are considering is investing in predictive modeling technology that can help them manage patient demand and outcomes in the coming year.
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Fluctuating patient demand in the midst of a once-in-a-century pandemic made it hard for health systems to manage their finances in 2020. To help turn the financial tide this year, health executives are looking to invest in predictive modeling technology.
In fact, 74% of health executives responding to a PwC survey last August and September said their organizations would invest more in predictive modeling in the coming year, signaling their keen interest. These tools can be used in several ways, from predicting clinical outcomes to identifying patient preferences to enhancing provider workflows.
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Clinical research requires that data be mined for insights. Machine learning, which develops algorithms to find patterns, has difficulty doing this with data related to health records because this type of information is neither static nor regularly collected. A new study developed a transparent and reproducible machine learning tool to facilitate analysis of health information. The tool can be used in clinical forecasting, which can predict trends as well as outcomes in individual patients.
The study, by a researcher at Carnegie Mellon University (CMU), appears in
Proceedings of Machine Learning Research. Temporal Learning Lite, or TL-Lite, is a visualization and forecasting tool to bridge the gap between clinical visualization and machine learning analysis, explains Jeremy Weiss, assistant professor of health informatics at CMU s Heinz College, who authored the study. While the individual elements of this tool are well known, their integration into an interactive cl
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Although guidelines do not recommend use of opioids to manage pain for individuals with knee osteoarthritis, a recent study published early online in
Arthritis Care & Research, an official journal of the American College of Rheumatology and the Association of Rheumatology Professionals, estimates that 858,000 Americans use opioids such as tramadol and oxycodone for their knee pain, equating to $14 billion in lifetime opioid-related societal costs, or nearly $0.5 billion annually.
A team led by Elena Losina, PhD, Robert W. Lovett Professor of Orthopedic Surgery, of Brigham and Women s Hospital, used a computer simulation to estimate the annual and lifetime contribution of opioids to knee osteoarthritis-related costs. The researchers show the direct medical cost of knee osteoarthritis treatment including opioids totals $7.45 billion or 53 percent of the total lifetime costs. The remaining 47 percent of lifetime costs to society is used to pay for lost productivity at wor
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UNIVERSITY PARK, Pa. When healthcare workers become ill during a disease outbreak, overall case counts and mortality rates may significantly increase, according to a new model created by researchers at Penn State. The findings may help to improve interventions that aim to mitigate the effects of outbreaks such as COVID-19. Each year dozens of potentially lethal outbreaks affect populations around the world. For example, Ebola ravaged western Africa in 2014; Zika damaged lives in the Americas in 2015; and now we are in the midst of a worldwide pandemic COVID-19, said Katriona Shea, professor of biology and Alumni Professor in the Biological Sciences, Penn State. Healthcare workers are essential to providing care during such outbreaks. Yet, their exposure to the diseases they treat means they too may become victims of the outbreak. Conventional epidemic models do not usually consider this important driver of quality of care, and may thus underestimate epidemic burdens