Healthcare eco-systems includes health insurance organizations, care providers and patients. With the growing expectation on quality and transparency of treatment, providers are forced to do more at lesser cost.
Healthcare providers are expected to deliver accuracy in diagnosis and treatment which is cumbersome with the traditional ways of clinical decision making.
Health Insurance organizations are continuously looking for lowering cost incurred due to redundant tests or procedures and longer stay in hospitals.
Leveraging today’s technology has opened doors of endless opportunities to make precise and efficient interventions at right time thereby improving the quality of care to the patients.
ArtificialIntelligenceinHealthcare
Artificial Intelligence is a technology that uses software programs with complex algorithms to emulate human cognition.
The explosion of healthcare data is continually growing with data received from multiple sources. To derive meaningful insights, there is a need to analyze data using Artificial Intelligence and continuous learning models.
Adaption of artificial intelligence has lot of benefits in the healthcare industry. It can improve the efficiency in diagnosis of a disease at early stages and assist physicians in making better decisions while treating patients.
Artificial intelligence can be used in the health insurance industry to lower healthcare cost and reduce the response turn-around-time using past data patterns through unsupervised learning.
By augmenting supervised learning and leveraging experiences from different phases of clinical trials, AI brings huge potential to solve numerous challenges in clinical trials.
How will Artificial Intelligence (AI) benefit the Pharma industry?
Pharma industry is very competitive industry where years of research and billions of dollars are spent in clinical trials.
Number of investigative sites from different geographies are reduced with nearly 30%-40% of enrolled trial patients dropping due to longer investigation period and increased risk of adverse effects.
“AI has huge potential to maximize recruitment and retention at different sites during the four phases of clinical trials and can help in enhancing the research techniques and risk monitoring from collected data”
AI driven pattern designs and algorithms with deep learning techniques can drive intelligent clinical trials. AI can enable site-less virtual trials by analyzing historical operational centralized data.
AI can provide predictive data to research team that can help determine if a drug under research will result into a positive or negative outcome or if the drug may have an adverse effect on certain segment of patients before it is being administered.
AI can help analyze radiology images, pathological results and large volume of past research data to derive insights, beyond a human ability, which can help make decisions to continue or drop trial during a research phase.
What change Artificial Intelligence brings in delivering efficient patient care?
The medical industry is building lot of algorithms for analyzing images that can diagnose genetic diseases, tumors using pattern analysis, facial recognition techniques.
An experienced doctor has handled complex procedures while treating a patient. Today, AI technology leverages complex algorithms and continuous learning methods to deliver what an experienced doctor could do with quicker turn-around and accuracy in problem identification.
The healthcare industry is also focusing on Robotic surgery to perform complex surgeries reducing the risk of surgical complications.
It is always said that AI by itself makes use of the algorithms and deep learning but there will be situations where certain decisions need to be made by a Human and hence “Human-in-loop” is necessary in every AI implementation.
Using Artificial Intelligence in Healthcare industry will bring accuracy in diagnosis and procedures, lower the re-admission rate and reduce longer duration of stay in hospitals for recovery.
“AI led chat bot using NLP can act as a Digital Practitioner to capture patient problems and symptoms while it can also assist in patient recovery process”
Artificial Intelligence technology is considered to bring lot of changes in the Health Insurance Industry. In what processes will AI be more relevant?
Healthcare payers are under pressure of regulations and compliance while they are continuously looking at ways of lowering cost and growing revenue.
Adaption to newer technology to meet the market expectations is a challenge while maintaining the current legacy applications.
Machine Learning, Natural Language Processing and Automation applied on large data is best for faster processing but not totally for abstract thinking unlike humans. With a human in step most process like Prior Authorizations, Appeals & Grievances and Revenue Cycle Management can gain accuracy in processing with tremendous cost savings.
While Prior Authorization process is being looked as a potential to automate the workflow from Provider submission, the approval of the submitted request from the insurer has greater opportunity for AI led automation.
Appeals & Grievance is another complex and labor-intensive process dealing with various sources of input. This SLA driven process can be more efficient with analysis of data, natural language processing, automation and deep learning.
Premium payment details received from the bank’s lockbox has always been a challenge when it comes to applying un-matched payments. AI led BOT implementation, driven through a workflow process can minimize the manual efforts and challenges with discrepancies and delinquencies in premium payments.
One of the most beneficial area of AI can be in Claims processing. With the use of AI on historical claims, fraud pattern analysis can be performed to determine common fraud in healthcare claims. Further using AI, insurers can now predict the cost of claim by analyzing pre-conditions of a patient that has led to increased claim cost in certain complex procedures.
Health Insurance companies offer a wellness and health risk assessment platforms to the insured members. Using the right algorithms and deep learning techniques matching with the patients claims having similar health conditions can bring accuracy in health-related risks.
There are benefits of implementing AI in Healthcare, however not implementing for the required outcome can be a nightmare with huge losses in technology adaption.
Overall, AI can benefit in faster and accurate processing, deliver greater insights for decision making and reduce the potential human errors.
“Today, machines need to store and analyze large volume of data, build patterns through continuous learning models, automate to optimize the workflow and with human expertise manage and make informed decisions to improve productivity in Healthcare