Healthcare organizations are constantly seeking ways to improve patient care outcomes as well as achieving better care coordination. The pressure to improve sees providers in the sector turn to technology, and most recently predictive analytics.
If used properly, cutting-edge data analytics, can impact patient care positively. Analyzing the data at hand can lead to discovering which practices are most effective, cut costs, and improve the health of populations.
Data analytics refers to the practice of taking aggregated raw data and drawing crucial insights and intelligence contained in the evidence. With digital advancements, innovative software ad technology plays a vital role in examining large volumes of data for hidden information that could inevitably improve consumer satisfaction and the future of healthcare.
The healthcare system, which is extremely reliant on data can funnel this new information into equipping practitioners with new resources, track practitioner performance, and even identify people at risk for chronic illnesses.
In a study conducted by the Society of Actuaries, they reported that 93 percent of health organizations regard predictive analytics as important to the future of their business. While 89 percent of providers stated they use predictive analytics or plan to do so in the next five years.
Most of what affects health outcomes is due to external factors. These mitigating factors include health habits and behaviours of patients, socioeconomic status, including education and employment, as well as physical environment. To improve outcomes, the public health system can use data analytics to extend its boundaries to account for these mitigating circumstances. In data analytics, these metrics can be modeled to predict risk of chronic disease.
Predictive analytics is not reinventing the wheel. It’s applying what doctors have been doing on a larger scale. What’s changed is our ability to better measure, aggregate, and make sense of previously hard-to-obtain or non-existent behavioral, psychosocial, and biometric data,” says Vinnie Ramesh, Chief Technology Officer, Co-founder of Wellframe. “Combining these new datasets with the existing sciences of epidemiology and clinical medicine allows us to accelerate progress in understanding the relationships between external factors and human biology—ultimately resulting in enhanced reengineering of clinical pathways and truly personalized care”
In collecting and examining these forms of data, the healthcare industry is set to more effectively allocate resources while giving providers the tools to treat at-risk populations and anticipate long term systemic costs. While AI and data analytics won’t replace practitioners, it will give radiologists and oncologists new tools in treating illnesses like cancer.