Bringing Artificial Intelligence into a Practice

AI/ML is not about replacing human decision-making but rather providing intelligent recommendations to help practitioners make better and faster decisions.

We had the pleasure of watching another incredible webinar from the HLTH team titled “Using AI to Fix Broken Math and Optimize Use of Expensive Health Assets”. HLTH has an innate ability to bring together captivating panelists to discuss incredibly relevant topics, and this webinar topic was of particular interest to the team at DOCPACE since artificial intelligence and machine learning are such a huge part of what we do. 

Fortunately, the use of AI and ML is becoming more and more prevalent, although still often misunderstood. Since the scope of what AI and ML can do is so large across different industries, here are some quick examples of how major health systems are currently using artificial intelligence and machine learning: 

  • Resource allocation: The simple task of making an appointment actually has many moving parts; from the number of available intake staff to patient beds and testing equipment, a patient’s availability is measured against countless variables; and AI is beginning to be able to assist with creating recommendations. 

  • Prioritization of backlogged appointments: there is currently an extreme number of backlogged elective surgeries due to the pandemic. AI and ML can help to allocate resources as well as schedule the surgeries in order of highest need.

  • Deploying vaccinations. AI can help with matching the availability of vaccines with patients in the most effective and equitable way - while also accounting for the logistics of a vaccine that needs to be stored at a specific temperature.

  • Predicting no-shows: Additionally, many vaccination centers need to have a plan for no-show vaccine appointments in order to make sure that all possible doses are given out. AI can help predict the likelihood of a patient being a no-show 

So, the question remains, how exactly does AI and ML help solve these issues? It’s important to distinguish that AI and ML are not about replacing human decision-making. AI is so useful because it is able to comb through massive amounts of data, mining and classifying it in ways that would be incredibly difficult and time-intensive for a practice to do on their own. We understand that staffers are making dozens of high-stakes decisions a day. When faced with busy schedules and stress, AI and ML can help them to make faster and better decisions. However, it’s important to remember that ultimately the decision is still up to them. 

AI is not about making a decision for a practitioner but is providing an intelligent recommendation so they can make decisions based on data.

When introducing AI and ML to a practice, it’s important to start with one data set to solve a particular issue, instead of trying to solve every problem within an organization at once. 

The amount of data accessible to practitioners can be extremely overwhelming - so partnering with an AI expert like DOCPACE to help analyze your data can be extremely beneficial. One of the major keys when it comes to using AI and ML to look at data is that the data must be actionable. Analytics without action cannot help a practitioner improve their performance or give patients better care. 

During the webinar, Rebecca Kaul, Chief Innovation Officer, MD Anderson, noted that when bringing in AI and ML, the following steps are integral for effective adaptation and use of AL and ML:

1) Contextualize the data for what you’re using it for 

2) Focus on what problem you’re trying to solve 

3) Define what data you need in order to solve that problem 

4) Contextualize the data to extract what you need out of it 

DOCPACE’s simple AI solution can help providers reduce idle time which ultimately helps to increase revenue. If you’re interested in learning more about how we can help your practice increase revenue, schedule a demo today! 


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