Every one of us would like to have a good medical care system and physicians are expected to be medical experts and take good decisions all the time.
Physicians would like to stay up to date with the latest technology and research. But it’s highly unlikely to memorize all the knowledge, patient history, records needed for every situation.
Even they have all the massive amount of data and information, it’s difficult to compare and analyze the symptoms of all the diseases and predict the outcome.
So, integrating information into patient’s personalized profile and performing an in-depth research is beyond the scope a physician.
“Ever heard of a personalized healthcare plan – exclusively crafted for an individual? “
Predictive analytics is the process to make predictions about the future by analyzing historical data. For health care, it would be convenient to make best decisions in case of every individual.
Prediction modeling uses artificial intelligence to create a prediction from past records, trends, individuals, diseases and the model is deployed so that a new individual can get a prediction instantly. Health and Medicare units can use these predictive models to accurately assess when a patient can safely be released.
Predictive analysis can help the physician in many ways :
1) Predicting the accuracy of diagnosis
2) Preventing a disease
3) Providing individual analysis of a patient and better-targeted treatment
Nowadays, all the healthcare and medical records are digitized, healthcare providers and professionals are digitizing huge amounts of data (patient name, medical history, diseases, diagnostic tests, and prescriptions, insurance). But maintaining and storing the data is expensive and challenging and moreover, it’s a time-consuming process.
The expenses related to health care are rising at an alarming rate and there is an urgent need to implement a smart structure to store the data. Predictive analytics is basically a structure of historical data and trends that can predict new patterns.
Implementing predictive analysis in healthcare is challenging and it’s pretty expensive for healthcare providers.
Some of the key challenges in adopting predictive analysis in healthcare:
• Data Security: When data is stored at high volume, there is a huge risk of data theft. Since patient data is confidential and it’s stored in the centralized system; it becomes highly susceptible to attacks.
• Data maintenance and transfer: Data is stored in cloud nowadays and there is a different layer of security related to retrieving and transferring data. Companies need to spend a considerable amount of money to maintain the data.
• Data structuring: Data in healthcare is more diverse, unstructured, fragmented, dispersed than other industries. It’s pretty difficult to analyze and aggregate such data.
• Lack of Skilled resources: It takes considerable highly skilled professionals to work on the complex data structure of healthcare industry. Although technology is evolving day by day, it’s difficult to find skilled resources to manage high volume healthcare database.
Healthcare organizations rely heavily on data analytics to improve research & development, healthcare trends, disease study, genome study and much more. The better we understand the medical history, records, individual patients, the better we can treat it with a personalized healthcare approach.
#Predictive analysis #Digital Health #Employee Health benefits #Corporate #Wellness #Digitizing medical records