Introduction
The global pandemic is a reminder of how fragile and volatile human interactions can be. While strong, warm and friendly customer service is best delivered through one on one interactions, social distancing and lockdown/curfew protocols have minimized the ability to cater to the same.
The healthcare industry is built on trust and comfort. At a time where human beings find themselves at their most vulnerable, being able to walk into a certified institution to receive the appropriate treatment and results can be difficult. Especially with growing cases and lack of staff availability to address the same.
Enter AI; Artificial Intelligence describes the concept of machines being able to perform tasks that typically require human intelligence to complete. AI can be customized to any industry requiring automation at higher scales and has been especially useful in shaping the growth of the healthcare industry.
What is AI?
Market Research is successful when it takes on a top-down approach; a comprehensive approach that begins from a birds-eye view (global positioning) and moves down to local competitors and landscape. Additionally bringing in unstructured data such as potential earnings and position availability within the field offer supplementary insight that could help the business come out on top.
AI is categorized by its ability to justify and take the relevant actions based on the best possible way to achieve specific goals. On a more common scale, machine learning and deep learning are two commonly employed practices that assist with automation and the streamlining of unstructured data.
As technology evolves, previous methods of employment become obsolete and a new, optimized method to complete the same action takes its place. AI has slowly become a more prevalent technology in day to day consumer living with self-driving cars and automated financial processing; the application potential for the same stretches as far as one’s imagination.
AI and Healthcare
The healthcare industry offers services that segregate between life and death. This means little room for error or miscommunication. All activities need to be precise and managed with the utmost care. In order to avoid potential errors or oversight, a growing number of healthcare providers employ AI powered tools and solutions for the best possible results. Applications include;
1. AI Assisted Surgery
The healthcare industry is worth over USD $40 billion. With a growing number of patients, and a non-proportionate growth in the healthcare profession, introducing robots could change the definition of roles and responsibilities. This includes robotic surgery.
Robots are able to offer analytics around preoperative medical records that guide surgeons at the time of surgery. They can offer assistance around new surgical techniques and supplementary information that could help make the procedure more efficient and effective.
A study conducted showed 379 orthopedic patients on the receiving end of AI assisted procedures experience five times fewer complications in comparison to undergoing surgeries where the doctor was operating on his own. Additionally more complex procedures could use additional support. Heart surgeons for example can be assisted by Heartlander, a tiny robot that is able to enter a small incision on the chest and perform mapping/therapy over the heart for a precise reading of what the prevailing situation is and what needs to be done.
2. Virtual Assistance
Introducing virtual nursing and assistance could help the healthcare industry save over USD $20 billion annually in terms of staffing and supplementary costs. Employing human staff to take on long shifts and a revolving door of patients is not only exhausting but could cause small scale errors with large repercussions.
Shifting this responsibility to robots that are able to respond at optimum capacity 24/7 is quickly becoming a widely adopted method. A preprogrammed bank of questions and answers can be programmed into the software along with other protocols such as patient monitoring and alerting medical professionals as needed. This also makes it easier for offsite patients to receive care and attention without the tedious process of getting readmitted and going through strenuous paperwork.
3. Optimized Judgements and Diagnosis
While still in its nascent stages, AI can be used to diagnose health concerns or problems in patients. Stanford University for example was able to employ AI to detect skin cancer in comparison to human dermatologists and performed at par.
Other use cases include voice and tone detection within speech patterns to understand cardiac arrests and severity of situations around emergency calls and using deep learning algorithms to identify breast cancer metastasis.
The application capabilities of AI for early detection, assessing symptoms and understanding health patterns/habits is vast and only limited to programming capabilities.
4. The Back End
Employing AI models at a back end capacity could save the healthcare industry USD $18 billion annually. Using machines to assist doctors and nurses with administrative tasks not only saves on time but minimizes possible data entry errors and allows the human staff to focus on higher value/priority tasks.
Technology such as voice to text transcription services could be extremely useful to schedule tests, create prescriptions and take notes. Additionally, the capacity at which the information is processed is significantly faster and at higher volumes. AI is also able to harness this big data and streamline it down to relevant information for better business decision making.
5. Image Analysis
Image analysis is a close to real-time assessment of the human body or a part. In a research study conducted by MIT, it was found that machine learning algorithms were able to understand 3D scans up to a thousand times faster than manually conducting the same. The application potential for this in radiology and other fields that require testing and deciphering would create more accurate and faster results leading to better healthcare provision.
Additionally, for harder to see places, image analysis could provide the input needed to ensure efficient remedial measures and guide doctors to problem areas that might not be as transparent to the naked eye. On a smaller scale, for issues like cuts, bruises and rashes, doctor visits can be avoided and instead a picture can be sent for a diagnosis.
6. Personalizing Medicine
Medication is a very personal thing. People have different needs, concerns, allergies and resistances that must be taken into consideration before anything is prescribed. Within oncology departments for example, over 75% of patients over a number of cancer variants do not positively respond to at least one type of available treatment drugs. The best possible way to create medication that works is to develop new biomarkers and genomic tests but this process is not only time consuming but offers no guarantees and is expensive.
Introducing artificial intelligence into this process could act as a cost effective and more accurate tool to develop precision medicine. AI algorithms offer prognostic insights and assist oncologists with segregating patients into smaller, homogenous groups. This assists with personalizing treatment options based on data provided. The AI driven solutions are likely to see better success rates and augment generic molecular testing.
Conclusion
In an industry that thrives off precision and efficiency, artificial intelligence offers a cost-effective method to avoid human error and produce the results needed at higher volumes. With the rising cases of declining health worldwide, cutting down on human staff but avoiding a compromise with results is critical. Artificial intelligence offers the ability to bridge this gap.
For healthcare providers looking to stay relevant, staying user friendly and streamlining unnecessary costs and introducing artificial intelligence systems works wonderfully. While the initial investment may be heavy, the long term benefits speak for themselves. Introducing an inhouse IT department may be something to consider alongside implementation for easier upkeep and modifications as needed.
