BERG Analytics provides real time predictive and prescriptive solutions that optimize patient treatments and improve population health. BERG’s Artificial Intelligence platform, bAIcis® uses powerful technology to deliver new insight, validate clinical trials and drive better performance for healthcare organizations. BERG Analytics provides healthcare stakeholders with actionable Intelligence promoting best practices while supporting value based care initiatives. Slava Akmaev, Ph.D., Head of BERG Analytics
Value-based care is on its way toward becoming the dominant care delivery model in health care. What technologies do health-care organizations need to implement to succeed under this emerging model?
The pay-for-performance model is a critical next stage in health-care dynamics – not only in the U.S., but around the world. Health-care services, procedures and medical interventions are becoming more sophisticated and, at the same time, more expensive. Because of the high cost of new gene-editing technologies – some have price tags of up to $1 million – patients and payers want the reassurance of treatment efficacy at the individual level. As more sophisticated, expensive and complicated treatments come to market, the concept of assessing intervention success on “average” will fade away. The industry can not justify 20% efficacy statistics, as on the other side, 80% of the population have no benefit from the intervention. Currently, payers reimburse failed treatments en masse, and this has only been possible in health care. But this approach is rapidly changing as healthcare costs rise. Precision medicine tools – specifically advanced analytics and artificial intelligence – will gain significant market share in health information technology within the next five years. Deep analysis and interpretation of the available molecular data (such as genomics and metabolomics) is necessary to drive the specificity of care. Also, new waves of mobile data, thanks to wearables, are flooding the health-care market.
Improved patient engagement is an imperative for healthcare organizations. How do you envision health-care organizations using smart devices and other consumer-facing tools to more fully engage patients in their care?
Many organizations are already collecting and/or monitoring patients’ vital data via mobile tech, so I see substantial growth in the telemedicine market over the next decade. Smart mobile devices and wearables are making tremendous strides, starting with heart-rate monitoring and the recent advancement of EKG recording on smart watches. Most likely, we’ll see widespread adoption of telemedicine in Asia and the developing world before it impacts the U.S. and Western Europe.
Staffing has emerged as a major challenge for health care. What staffing strategies can healthcare organizations adopt that will enable them to fully leverage technology innovations?
Training in IT, data science and machine learning are requirements for the successful implementation of data-driven strategies in any health-care organization. Where telemedicine, advanced analytics and artificial intelligence are typically not well developed within health-care environments, health-care organizations must decide whether to buy or hire. While some of the largest pharmaceutical companies are widely adopting data-driven approaches within their organizations, and making the transition to the data-and-medicine philosophy, growing such capabilities internally is a formidable task for most health-care organizations.
Artificial intelligence is having a significant impact on how businesses and consumers use technology. How can health-care organizations best leverage AI technologies?
Artificial intelligence is a big term that is often overused. Data research has played a vital role in health care for decades, with statisticians and medical informaticians successfully using data-analysis tools to predict outcomes. It’s important to understand that AI allows researchers and clinicians to identify key insight in myriads of data points in a hypothesis-free manner. Advanced predictive methods can help health-care organizations to better manage complex treatment pathways, identify patients at risk of complications and adverse drug reactions, and ultimately provide personalized care leading to better outcomes.