Properly managed healthcare analytics have the potential to change and alter the standard of care within many hospital systems, but many hospitals have been slow to utilize business intelligence to gather data.
However, COVID-19 and healthcare analytics have changed the way data is viewed as hospitals rushed to share data during the early stages of the disease as medical researchers and doctors across the world attempted to get a deeper understanding of the coronavirus.
In fact, predictive analytics have been employed to help change the overall outcomes of COVID-19 cases and to identify which patient populations are at the highest risk from the deadly virus.
Increased Transparency
However, the COVID-19 has pushed the healthcare industry closer to a place where it can truly become a more transparent and collaborative industry with the proper user and exchange of healthcare analytics.
Throughout the pandemic markets that previously would have competed against each other and protected information learned to mobilize and work together to share and exchange data. In addition, instead of isolating data to regions, data is being shared in a collaborative fashion to create access to care both in terms of bed capacity data between neighboring hospitals to ventilate availability across states.
The same transparency has helped regional and nationwide trends become more evident in terms of which groups are more high-risk for COVID-19 and which treatments are working better than others. The ability to share detailed outcomes in an organized fashion without worrying about proprietary is foreign to the healthcare industry, but its value is undeniable. Given the ongoing nation of pandemic, it is reasonable to expect this trend to continue which could benefit the study and treatments of many other health conditions in the process.
Democratization of Data
At the beginning of the pandemic, one of the largest problems was that data could not be properly accessed and shared. As patients exhibiting signs of coronavirus started to arrive in different states, regions, and countries teams were not able to access data sets that allowed them to share experiences.
This hindered research institutions that were unable to gather a quantitative amount of health analytics to actually create models or proper data infrastructure. This is partially due to the fact that most hospitals choose limited pre-purchased and pre-packaged analytic data tools. What is more valuable to the health industry, are data solutions built within the health organization from scratch.
Data Pooling
This was painfully obvious at the beginning of the coronavirus crisis when it was quickly revealed that pooling data from hospitals across the nation was nothing short of a nightmare for data technicians. The software did not allow for information to be shared across platforms, and hospitals all chose to use their own platforms making it difficult to retrieve and share pertinent information with others.
Even if data could be pulled from a hospital database, it was hard to consistently pull useful data. Updating to electronic health records was supposed to bring the healthcare community into a new era, but the pandemic proved that despite the massive adoption of EHRs, the healthcare industry is far from there yet.
The Future
What will likely become more prominent within the study of Covid-19 and healthcare analytics as the pandemic evolves is further concentration on barrier analytics or the close observation of barriers that may be preventing access to treatment or positive outcomes from occurring. By studying and observing data scientists can determine the barriers that are preventing patients from receiving adequate care and then address them. Commonly accepted barriers to date include compliance, access to equity care based on race and income, and the emergence of telemedicine to combat the fear of infection.
