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Off late, artificial intelligence (AI) has become the buzzword for almost all industry sectors; this is no different for the healthcare arena. However, AI is a massive infrastructure undertaking, and most organizations get lost in the process of injecting this technology into their IT environment.
As many organizations continue to look at big data analytics to improve healthcare, the scope of machine learning and deep learning solutions in healthcare also increases simultaneously. Machine learning and deep learning are no newbies for the healthcare industry; however, their progress is. This makes AI hard to be deployed and efficiently managed. The lack of ability to efficiently manage AI stops organizations from using real-time analytics at the point of care. The Internet of Things (IoT) in its part is also in need of more powerful analytics infrastructure. Wearable medical devices can evidently reduce the number of patients visiting doctors, as they act as precaution tools, helping the patient analyze his physique. These devices endow physicians with the following benefits; remotely monitor the patients, get a better understanding of the patient’s lifestyle and habits, and collect accurate data. However, organizations that do not have functioning AI solutions cannot obtain, handle, or process this data in real time.
The harder the analytic solutions get to deploy in the healthcare industry, the more promising sign it leaves behind for the future of population health, predictive analytics, and quality patient care
Imagine a future where each individual will have a healthcare treatment and care plan exclusively crafted for him/her? Exciting, right? Thankfully, we’re not very far away from that dream. Predictive analytics and Big Data are rapidly transforming the face of healthcare as we speak.
Today, as each step in the healthcare chain is digitized, healthcare providers and professionals are accumulating huge amounts of data (patient names, medical history, diseases, prescriptions, diagnostic tests, medical insurance, etc.). However, storing and managing this data in a way that is helpful to the healthcare provider or the patient is immensely challenging because of its sheer size and cost entailed.
In fact, according to a McKinsey report (2013),
“After more than 20 years of steady increases, healthcare expenses now represent 17.6 percent of GDP —nearly $600 billion more than the expected benchmark for a nation of the United States’s size and wealth.”
This emphasizes that expenses in healthcare are rising at an alarming rate and there is an urgent need to implement a smart structure where health records and patient history can be stored in an organized manner, helping to provide both better care and treatment as well as cut down costs.
Predictive Analytics is basically a structure of artificial neural networks and decision trees that can predict new patterns or trends in data rooting on the knowledge present in the historical data. By incorporating analytics in healthcare, we will be equipped to predict the future. Vinnie Ramesh, Chief Technology Officer, Co-founder of Wellframe, states:
“Predictive analytics is…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. 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.”
Though predictive analytics presents before us a huge opportunity to scale up the quality of healthcare and treatment, we cannot ignore some of the major challenges facing it.
One of the primary challenges is implementing analytics within the healthcare systems, which means incurring IT costs, which a lot of providers can’t afford.
Let us look at some other hurdles that prevent the adoption of analytics in the field of healthcare:
Data structure issues: A bulk of this data in healthcare is unstructured, fragmented, and dispersed. This makes it very difficult to analyze and aggregate data in such format. Furthermore, it is a known fact that data in healthcare is much more diverse as compared to data from other fields.
Security and Compliance issues: When data is stored in such large bulk, security becomes a major concern since we’re talking about confidential patient data. Being stored in a centralized system, this data is freely available and hence, becomes highly susceptible to attacks.
Data Storage and Transfer issues: Organizations integrating and analyzing data have to bear a considerable amount of data storage and transfer costs. When data is stored in the cloud, there is a different layer of security related to retrieving, transferring, and loading of patient data.
Lack of Skilled Professionals: While it is true that technology is evolving and advancing at a rapid pace, the amount of experienced and skilled individuals who are constantly updating themselves in sync with these new trends is considerably low. Managing such large amount of confidential healthcare data demands a certain level of skill and efficiency.
Irrespective of these challenges, predictive and big data analytics hold a tremendous potential to transform the face of the healthcare industry. Healthcare organizations that rely on data analytics can improve on areas as R&D, surgery, genome study, and so much more. The better we can understand the medical history and needs of individual patients, the better we can treat them using a personalized treatment approach.
The good news is that Big Data and predictive analytics have already been put to good use in the field of healthcare in many countries.
Doctors and patients can now easily keep track of their health through Electronic Health Record (EHR), a digital record where every individual patient’s medical history, demographics, diagnostics tests, etc., are stored and maintained. While doctors can easily make the changes in this as they come, it involves no paperwork and one doesn’t have to worry about data replication. Furthermore, EHRs are programmed in a way that they can remind patients about their upcoming tests, or prescription drugs refill.
Tools such as Clinical Decision Support (CDS) allow healthcare organizations to analyze medical data in real-time and offer doctors and medical professionals with the necessary advice as they write out prescriptions. Researchers are coming up with personal analytics devices that will continuously gather the patient data and store it in the cloud. Also, symptom calculators are gradually becoming the “recommendation engines” of the healthcare industry enabling patients to self-diagnose their health problems.
Suppose you have the flu, you just need to go online and enter your symptoms in the symptom calculator and the software’s algorithm will match you with others who have had similar symptoms and also show you the diagnosis that was most common. Approaches like ask a doctor online, online consultation, digital prescriptions etc have also emerged lately.
Charlie Farah, the Asia-Pacific market development healthcare and public sector director of Qlik, stated:
“There is a real buzz around India on the absolute benefits analytics and data discovery can deliver to an organization. Wockhardt Hospitals Ltd., one of India’s leading super-specialty care, deployed the Qlik Platform as a key tool in its journey on establishing a company wide adoption of data analytics.”
Thus, it is needless to say that with predictive analytics in the scene, the future of healthcare industry sure does look promising.