How Artificial Intelligence (AI) is Poised to Address Healthcare Business Problems

The adoption of artificial intelligence in healthcare is set to solve several problems for patients, clinics, hospitals and the industry as a whole. The healthcare industry is gradually evolving toward an ecosystem of supportive and collaborated machine learning. Every healthcare industry professional is pondering on whether artificial intelligence is suited for their own personal professional environment. If nothing else, it is a clear indication that the healthcare industry is seeking intelligent solutions to its present problems. However, there is still pervasive confusion on the significance and derivatives of AI in healthcare. Several computer-related solutions that are present in the healthcare business industry are not directly related to computer intelligence, but are erroneously considered to be part of Artificial Intelligence (AI). The above-mentioned solutions are merely manually created algorithms that assist with data analysis and treatment recommendation.

Machine learning, on the other hand, is a system wherein the computer is able to intelligently perform tasks that are similar to those undertaken by the human brain. These types of solutions have complex interwoven analysis techniques, which are designed to able to undertake intelligent tasks and predict outcomes based on historic feedback and the recognition of patterns. It addresses prevalent problems in the healthcare industry through predictive analytics and AI medical applications.

Artificial Intelligence Poised to Address Business Challenges

Question: How to identify if the business is complex enough to require artificial intelligence?

Answer: The first and most important question is whether your business intelligence stands to improve through the use of machine learning. This requires an initial analysis and understanding of the challenges facing the business and if these can be addressed through the incorporation of Artificial Intelligence (AI), more specifically through the deployment of machine learning.

If the business or organization in question is struggling with a massive amount of unstructured data that needs to be organized and efficiently transmitted, then you might be wise in opting for data solutions that are offered within the artificial intelligence purview. These solutions work towards structuring and indexing data that can then be used meaningfully, without the wastage of human hours. Structured data increases the efficiency of the entire lifecycle of healthcare.

Next in line is the need for predictive analysis to develop patterns within the organization. If the business or organization stands to benefit from prediction through the analysis of tends and past patterns, be it for claims and reimbursement or treatment analysis, then AI medical applications may be the right direction for business investments.

An unnecessarily large amount of expenses can be easily managed through the deployment of AI-assisted medical care that will go a long way to reduce added costs and expenses for the patient as well as the organization. Finance-based solutions are particularly helpful for hospitals and large healthcare organizations.

In a nutshell, the following factors are pointing toward the need for AI-assisted medical care:

Question: How can AI wearables stop denial claims before they occur?

Answer: The management of claims denials is among the top priorities of most healthcare organizations. It has a lasting and direct effect on the patient and healthcare provider alike. This is because every claim denied translates into lost revenue. Several AI wearables can contribute to a drastic reduction of denied claims, causing a significant increase in revenues for the organization and better healthcare accessibility for the patient.

Claims denials usually occur due to inaccurate documentation, uninsured claims, missing information, etc. It is of utmost importance to enter accurate and complete information while filing a claim. More often than not, basic patient information is incorrectly entered due to common errors, such as typos, that end up resulting in a denied claim. Incorrect coding is another significant reason why claims are denied. Timely filing of claims is another prerequisite to avoid claims being denied.

Artificial intelligence solutions for denials management have the ability to develop complex patterns, through the following means:

Question: How can AI help payers achieve their goals in medical care?

Answer: With the advances in technology related to AI-based medical service, applications that are tailor-made to specific organizational needs are emerging in the market. There are specific solutions that are designed toward the benefit of payers and consumers as well. Healthcare payers stand to gain an enormous edge above its competitors through the application of these specific technologies. Below are some examples:

Improved Health Results – On a general basis, healthcare providers procure patient information prior to their physical interaction with them. Deploying an AI-driven solution instantly uplifts these interactions and the efficiencies involved by devising effective strategies of treatment plans. This is executed through an analysis of the patient’s history and care notes that allow the healthcare providers to offer enhanced healthcare. It can further suggest appropriate areas of information procurement from the patient.

Improved Patient Experience – Going through the entire range of applicable benefits is a tedious task for most administrative employees and healthcare providers. AI technology can be compared to virtual administrative officers that are capable of holistically improving the patient experience through prompt resolutions, efficient query rectification, and suggested solutions.

Reduced Costs – Through the use of artificial intelligence, healthcare professionals can undertake an analysis that introspects past claims and data to extradite eligibility scenarios and resolve queries. For members, the software is capable of identifying gaps in chronic healthcare and suggests preventive care measures. The vast amount of data is intelligently structured and applied meaningfully to improve member experience and reduce the costs of the healthcare organization.

Question: How high is the threat to cybersecurity for AI-driven solutions, with relation to Electronic Health Records (EHRs)?

Answer: With the evolution of the healthcare industry into an increasingly digital environment, cybersecurity is a concern on the mind of most healthcare professionals. The existence of crucial and private patient data and EHRs makes this an all-important priority. Although the threat is high, there are several solutions to safeguard against cyber threats.

Through the installation of advanced firewalls, inspection and antimalware programs, spam discovery filters, and network isolators, security control is enhanced in the virtual world. Access is limited through secure identities and authentication processes, without which data will not be visible.

Further, there are several solutions available that follow strict cybersecurity configurations for enhanced security. They also undertake threat modelling across devices for strict adherence to secure practices. Finally, through the use of encryption techniques, rights management options and inspection criteria, patient and professional medical data remains highly protected.

Question: How will AI augment image detection for clinical decisions in healthcare?

Answer: The advantage of AI-related medical services encompasses image detection in healthcare significantly. Electronic Medical Records (EMRs) are unstructured and cumbersome to sort through due to the accumulation in a hap hazard fashion. Physicians find these overwhelming volumes of data cumbersome to sort through and identify the ones that are clinically relevant. This is where AI steps in.

AI-driven image detection solutions are capable of augmenting the healthcare professional’s capability to identify relevant and key images and data for a holistic and accurate diagnosis. Through the initial reading of the image, AI solutions can identify and suggest potential findings. Recent solutions offer suggestions that are based on an initial detection of the image and compounded by patient history. They work towards clinical decision support by reducing the work of the healthcare professional. Artificial intelligence has made leaps and bounds in the following medical image detection areas:

Although the cost is high of deploying these image detection solutions, they are created with the ability to considerable enhance the professionals ability of image detection.

Question: How does AI offer predictive medical testing and applications?

Answer: Predictive analysis is a feature of artificial intelligence that has been highlighted as one of the main advantages of this technology. This predictive feature is not solely confined to claims management, but also extends itself toward medical diagnosis of medical tests that detect infections, conditions and diseases in laboratory settings. These tests are generally, in vitro diagnostics (IVD), wherein the same is taken from the human body (such as blood or tissue) to scan for results.

It is not surprising to note that many deaths have occurred due to diagnostic errors that are caused through inefficient correspondence, gaps in communication and overall inadequacies in the healthcare environment. The leveraging of AI solutions for medical diagnostics is a growing phenomenon. Applications, such as, chatbots, oncology, pathology and rare diseases are further enhanced through the use of AI. These applications are embedded with the following features:

However, medical diagnostics AI applications are still in their nascent stages and will be continually evolving over the next few decades. Its reliability and efficiency are still to be completely analyzed. Although, it is safe to say that this is going to be the next big thing in AI-driven solutions.

How to Start Applying Artificial Intelligence to your Business Problems?

The easiest way to explore the applications of AI is by segmenting it into three distinct sections based on the solution offerings:

Solutions Driven through Algorithms: These are applications that have built-in algorithms that are evidence-based and programmed manually. These algorithms allow the computer, through data inputs, to use the entered information and apply it to find a solution, such as, treatment solutions, claims denials predictions, suggested therapies, etc.

Visual Solutions: These are AI-driven tools that offer facial recognition, data recognition, etc. to create patterns that can easily escape a trained human eye. This is done through the use of intelligent machines that excel in the field of medical diagnostics.

Medical Practice Solutions: These solutions are provided to decrease the work load on healthcare professionals and administrative staff by allowing the software to perform the mundane, but crucial duties of patient data entries, claims management, etc.

The above three distinctions will help a business or organization analyse and decide their own areas of requirement and which AI tools are intended specifically to aid their personal work environment.

Conclusion – Key Takeaways