If AI isn’t going to replace the doctor, then what is it going to do? Replace the mundane. It will start to replace or improve things you already outsource or things that currently stretch your resources. We’re talking about repetitive tasks that don’t require advanced degrees or training.
Recent research from Accenture got to the core of what is coming. According to the study, AI will adapt and flex to automate complex tasks and self-learn for repeatability at scale. It will help humans focus on adding value and complement and enhance human ability and improve capital efficiency.Why is this? Let’s look at what AI is and how it works to uncover the answer to that question.
First, artificial intelligence is a broad term used to talk about several different things. It includes advanced analytics, machine learning (ML), natural language processing (NLP), and true artificial intelligence. Let’s define those:
Advanced analytics is a part of data science that uses high-level methods and tools to focus on projecting future trends, events, and behaviors. This gives organizations the ability to perform advanced statistical models such as ‘what-if’ calculations, as well as future-proof various aspects of their operations. The major areas that make up advanced analytics are predictive data analytics, big data, and data mining. The process of advanced analytics includes all three areas at various times.
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves. Machine learning algorithms build a mathematical model based on sample data, known as “training data,” in order to make predictions or decisions without being explicitly programmed to perform the task.
Natural Language Processing is the technology used to aid computers to understand the human’s natural language. Natural Language Processing, usually shortened as NLP, is a branch of artificial intelligence that deals with the interaction between computers and humans using the natural language.
Artificial Intelligence: True or strong artificial intelligence is an artificial consciousness, a hypothetical machine that possesses awareness of external objects, ideas, and/or self-awareness.There are also three stages of AI, and in a recent blog post, Dave deBrokart talked about this:
Narrow AI is what’s real today, more or less. Narrow AI can do a stupendous job of recognizing familiar patterns and doing the right thing at very high speed—for specific tasks. Narrow is what runs “autonomous cars,” it’s IBM’s Watson that won Jeopardy!, it’s what runs “personal assistants” like Siri, Alexa, and Microsoft’s Cortana, and it’s even the kind that plays chess. Narrow is very fast and dependable.
General AI is when the technology really seems to be a person. We ain’t there yet, but people are sure working on it. Many, many skills ... plus feelings. Yes, feelings.
Super Intelligence AI is, as the name suggests, above and beyond what any human could do.”
Narrow AI is just what we need right now. Sixty-one percent of respondents in a recent survey said they would use an artificially intelligent virtual assistant to handle financial transactions, schedule appointments, or explain their health insurance coverage options.These are easy ways to use AI because they are things that we already use today. We use chatbots. We use voice assistants.
We get information served up online. It’s familiar territory for patients and healthcare organizations at least to some degree.In addition, AI is only as good as the data it has to work with. The more complicated the task or the data system, the harder it is to build good AI solutions. According to John Lynn, founder of Healthcarescene, this is the reason most AI solutions will come from existing companies that use it to enhance their existing products with their own data.
There are many simple and repetitive tasks involved in patient communication and other patient interactions. When done manually, they can be very time consuming for staff and patients.
Patients are frustrated about these issues and others, including poor communication, long wait times, and lack of digital tools. However, many of these problems can be addressed with automation and further improved with AI.
AI can be used today to:
Identify best practices: Many AI solutions will come from existing companies because they have the data. That data can be analyzed to provide insights. One recent study looked at how the cadence and frequency of automated reminders affected patient confirmation rates. The insights showed that the use of automated reminders, combined with the right timing, could increase confirmation rates up to 156 percent.
Respond 24/7: Because narrow AI is good at interacting in specific ways, it is great for SMS or website chat. It can be used to provide an interactive experience for patients where they can ask questions and get an automated response or be directed appropriately to the right person.
Identify potential no-shows and fill appointments: AI can be used to analyze patient behavior to predict the possibility of no-shows so the schedule can be adjusted accordingly. Other similar use cases include things such as the likelihood patients will pay bills on time or follow through on treatments. It can also automatically reach out to patients waiting for specific appointment types when they are available.
Automate post-appointment care plans: AI can be used to establish the right cadence and content for post-care plans based on prescribed treatment and patient behavior.
Automate post-appointment care plans: AI can be used to establish the right cadence and content for post-care plans based on prescribed treatment and patient behavior.Analyze patient sentiment:AI is able to review patient reviews and surveys to identify trends and key words so organizations can see patterns in that feedback.
These use cases are practical and available now, but it is important to note that they still require human intervention. This is why AI isn’t replacing everyone in healthcare. And it never will. Patients are interested in using AI to save them time and get more convenience, but they still want to get to a person when they need it. And, while the AI-driven tools can deliver insights, someone still needs to apply those insights.
The best-case scenario today for AI is to use it to remove mundane tasks and free staff for the more complicated tasks and to focus on patients when they do need a person. This means that it is important to ensure that there is the right balance between the technology and the human touch.