One of the biggest problems that most industries along with the healthcare industry are facing is the non-utilization of data. 270 healthcare professionals were made a part of a KPMG survey. According to the survey, a small number of people (only 10%) are investing in buying tools that collect data and have capabilities of making predictions. How much is all this data worth? You will never know unless you are making use of all this data. What needs to be understood here is that this data cannot only be utilized for improving healthcare technology but to help make healthcare institutes become financially strong and profitable.
This profitability will not result in shooting up the price of healthcare facilities for patients instead it endeavors to bring the costs down for both, the patients and the healthcare professionals. How data can be used to make healthcare industry more profitable, treatments more reliable, patient-doctor relation more beneficial etc. depends on how much interest healthcare professionals take in understanding the tools that collect data and their workings. Talks, seminars and webinars are being held to discuss the importance of healthcare analytics. Take the example of Hasummit.com registering people to attend a healthcare analytics summit to disclose to the industry related personnel the power of data and how it can be harnessed. Read more at Evolution of Healthcare Analytics.
The world has not reached this point all of a sudden. Changes have been made along the way and an evolution was taking place in healthcare analytics since past few decades. Take a look at how data was collected in healthcare industries in the past.
Healthcare Analytics in 1960s
Medicaid and Medicare played the most important role in healthcare analytics during this decade. IT did exist at that time but it was so expensive that utilizing them would have resulted in higher costs and lower benefits. Hospitals had their own accounting systems in this decade and they used to share it with each other as a way to use data to their advantage.
Healthcare Analytics in 1970s
The computers were becoming smaller with time. At this time they had become small enough to be used individually in individual departments of a healthcare facility. However, they were not so good at keeping records, and finding trends and predicting certain patterns was still beyond the computational power of these systems. These systems, however, proved to be the first step in the digitization of healthcare data.
Healthcare Analytics in 1980s
This was the time when computers started to become powerful enough to be integrated into hospital and healthcare environments. A bridge was created between the financial and clinical activities. When hospitals pulled data from their systems, the effects of certain data could be seen on both, clinical systems and financial systems. Software applications had started to land in the market for hospitals to make use of.
Healthcare Analytics in 1990s
Computers were powerful now and hospitals had realized their importance in not only collecting data but also utilizing that data to their advantage. However, the use of these computers and software systems at this time was more for competition than it was to take healthcare industry to a height. The networks were robust in this era and IDN integration was given birth too.
Healthcare Analytics in 2000s
The applications and computers had become powerful enough in this era to give hospitals and healthcare facilities information about clinical activities, financial movements and their effects on their future. This was the time for outcome-based reimbursements. Healthcare facilities had realized that they had to integrate more applications, software and system to collect data and analyze the data to provide better healthcare services and improve profitability along with it.
Today, doctors, hospitals and any healthcare facilities are making use of big data. Data is not burden anymore instead it is acting as a resource. A doctor can talk to a patient while looking at that patient’s past 15 years of data. Automated software applications can be used on this data to predict future interactions with the patient. Doctors can send automated reminders to their patients for when they need to take a shot for a particular condition. Even patients will have access to data that will assist them in helping their own situation and making better decisions about their conditions without consulting with the doctors.