How to Add AI to Your Existing Healthcare Software?

There are two ways in which AI in healthcare can be used: by developing AI-powered healthcare software from scratch or by adding AI to the existing healthcare software. The former takes significant time and money, so the latter is a preferred option for emerging health tech companies and practitioners who use healthcare software. 

Adding AI to the existing software offers a range of benefits such as NLP for text-to-speech translation, AI-powered chatbots for virtual assistance, and unstructured data analysis of EHR.

This article discusses how easily one can add AI to their existing healthcare software. Let’s start by looking at the current market conditions.

AI in Healthcare: Market Overview 

The launch of ChatGPT by OpenAI in 2023 has taken the world by storm. This generative AI tool is all set to leave its mark in the healthcare sector. It is already showing positive results in clinical research writing and ensuring error-free medical documents. A lot more can be expected from AI in healthcare. The market estimates that AI will be worth almost $188 by 2030, rising at a CAGR of 37 percent from 2022.

The use of AI in healthcare is rising because it can quickly scan surplus unstructured data and provide accurate analysis. For instance, Fred Hutchinson Cancer Center in Seattle, made use of NLP in Amazon Comprehend Medical to review unstructured clinical data and match patients with clinical cancer studies. 

Additionally, 85 percent of healthcare executives have an AI strategy in mind and they share a deep need for AI to address issues such as hospital readmissions, workforce shortages, and chronic illnesses.

Advantages of Augmenting an Existing Healthcare Software as Opposed to Developing a New One

An existing healthcare software needs additional functions and features to keep up with the changing technological trends. The medical and non-medical staff is well acquainted with the existing software features, and may not need rigorous training to get accustomed to the additional software functions. However, the staff may require hard-core training if a new software solution is being developed. Training requires money, however, the amount spent on educating staff about a new software product will be higher as opposed to the amount spent on training a couple of additional features. Healthcare facilities may end up spending a lot of money if they opt for new software, so augmenting existing software can be a plausible option.  

Furthermore, the introduction of AI, machine learning, the Internet of Things (IoT), big data, and more have compelled developers to modify their healthcare software along the lines of the changing technology, only for the betterment of the users. Integration of AI, for example in the existing healthcare software will prove to be much cheaper compared to full-fledged AI software development. Currently, the cost of AI healthcare software development ranges from $100,000 to $500,000, which can be cut down if AI is added to the existing software. Moreover, a delayed software upgrade is likely to cost more compared to a timely upgrade.

Additionally, healthcare software that falls short on technology augmentation is less likely to be preferred by providers, healthcare organizations, and clinics. Patients are looking for ease, comfort, and convenience while using healthcare software, hence if it fails to fulfill these criteria then no one is going to use it. 

Purchasing new software is a cost and resource-intensive activity. Building AI healthcare software from scratch requires resources who are experts in healthcare software development and AI integration, which get added to the overall costs. However, for adding AI to software, you can ramp up additional AI-powered features to the existing software. This saves you tons of time and resources you would otherwise have to spend bringing the staff up to speed on the new software development.

Steps to Add AI to Existing Software

1. Familiarize Yourself With AI And Identify Problems It Can Solve

C-suite executives need to have a good understanding of what AI can and cannot do. In-depth research can help you to start strategizing on how AI can be implemented in healthcare software. One of the ways is to get your hands dirty and try to learn the functioning of the most popular AI models such as DALL-E-2, ChatGPT, robots, text analytics for health, chatbots, etc. 

Practical knowledge will give you an idea about what AI is capable of and how you can use it in healthcare software development. Also, hands-on experience can help to brainstorm ideas for integrating AI into existing software. It will also allow you to rethink what doctors and patients expect from AI-based software. 

Once there’s a clear understanding of artificial intelligence, then you can start focusing on what problems AI can solve and what are upcoming opportunities in the field. For instance, one of the most common problems faced by doctors and non-medical staff is burnout due to heavy documentation work. With the help of artificial intelligence, documentation work can be automated. It also leaves no room for errors.

2. Augment Existing Software Features With AI

Augmenting existing features with AI increases the efficiency of healthcare software. With enhanced features, providers and patients can expect a reduction in medical errors, 24/7 service via chatbots, and automation of tasks. Adding chatbots to healthcare software via AI frameworks or chatbot platforms helps to engage patients. It stimulates them to ask questions and get their doubts solved. 

Further, AI streamlines patient appointment scheduling features too. According to research presented at the ASA Advance 2022, AI-based scheduling can help to reduce burnout and improve physician engagement. 

Additionally, as per Nature’s article one of the issues faced by doctors is the time taken to document a patient visit. The article stated that AI algorithms could document the conversation between a patient and a doctor, phrase the text and use it to fill in the information in the patient’s EHR. All of these aspects suggest that AI can effectively augment existing software features.

3. Partner With AI Development Experts And Choose Technology Stack

AI development experts are like a guiding light toward successful AI-based healthcare software development. They have the necessary skill set and an understanding of what works best for existing software. However, it is necessary to know what type of AI skillset you’re looking for. There may be some who are experts in NLP, whereas some may know only about generative AI. 

Consider partnering with an AI software development company like Arkenea, that can guide and provide you with the necessary resources required to augment existing healthcare software. Once experts are on board, it is time to choose the necessary technology stack. 

Some of these include:

• Front-end Framework: Angular or React
• API Gateway: Kong or Amazon API Gateway
• Programming Language: Python or Java
• Cloud Computing: AWS, Google Cloud, or Microsoft Azure
• ML Frameworks: PyTorch or Keras
• NLP Libraries: NLTK or Stanford CoreNLP
• Data Visualization: Seaborn, Matplotlib, or Bokeh libraries  

4. Develop APIs to Integrate AI

APIs are an integral part of the software as it ensures smooth interaction between features. To develop an API for AI integration it’s necessary to bring in three types of professionals on board. Firstly, the data scientists who are experts in developing algorithms. Next, healthcare software developers will prevent disruption of existing software and ensure smooth AI integration. Lastly, you’ll need DevOps experts who can take the model created by the data scientists and put it into production. 

Once the team is assembled, start with the API development. During development, make sure that the API is semantic and any person should be able to figure out what it does and how to use it. Avoid using confusing terms such as ‘HP’, which could either mean ‘hit points’ or ‘hyper-parameters’. 

Further, ascertain that the language used for API is compatible with integrating AI. Be cautious about the technical terms used in the software functions as it directly relates to the API. Additionally, make APIs accessible for AI because, at the end of the day, AI will be running the API. 

Apart from this, if you’re looking out to cut down costs then approach an AI development company as they would be able to embed APIs within existing software to bring down development time and cost. Arkenea, a healthcare software development company provides API integration services, along with AI development for Healthtech organizations.   

5. Risk Mitigation

Several risks associated with AI and its solutions include:

Fragmented Data

Training AI algorithms for existing healthcare software requires surplus data. Sources of which include EHRs, insurance claims, pharmacy records, wearables, patient portals, etc. The issue is that healthcare data is fragmented and spread across multiple sources. Aside from this, patients tend to switch their providers, causing data to be split across systems. This data fragmentation increases the risk of errors and increases the expense of data gathering. 

The solution is to have strong technical support for quality data gathering. Another way is to leverage the services of FHIR, which is capable of connecting two EHR systems for data sharing and also ensures interoperability. 

Inequality and Biases

AI learns from the data provided to it. For example, if artificial intelligence is trained for treating only chronic diseases, it will have zero knowledge about acute diseases. So, if a patient is suffering from acute disease, the AI healthcare software may not those patients effectively.

Similarly, AI is estimated to perform worse when a race or gender is underrepresented in AI data training. So, it is essential to provide all the relevant data to AI algorithms and then add it to the existing software. 

Privacy Concerns

Large datasets are needed to train AI, so developers are compelled to seek data from patients. This is a major concern for patients as it directly violates their privacy. If done without authorization, then it can lead to filing legal lawsuits against software developers. One way is to get written consent for data sharing from the patients. Another is to obtain data from clinical research surveys and results. 

How Can Healthcare Practitioners Benefit from Including AI in Existing Software?

When fed with the right data, AI healthcare software with predictive analytics is bound to offer accurate reports. Predictive analytics helps physicians in detecting diseases, and plan out treatment, and medications.

Let’s look at how AI-driven existing software can help healthcare practitioners.

EHR

According to a survey by Athenahealth, 58 percent of physicians often feel overloaded with EHR information, which increases stress levels. To address this issue, the Georgetown University Medical Center adopted a text-mining tool. When integrated with EHR, this tool leveraged NLP to address information overload and allowed physicians to skim through surplus medical data easily. This approach not only reduced stress levels but also helped to streamline workflow. 

Additionally, as per a report by the JAMA Network Open published in 2021, physicians found that an AI tool that extracted patient data cut EHR time by 18 percent. AI-driven healthcare software is likely to review data at a faster rate and provide insights to healthcare practitioners to make clinical decisions.

AI for Mental Health Software

AI assistants in the form of chatbots can be integrated into mental health software and it can be used as an initiation to vent out problems by people who may not be comfortable in face-to-face conversations with a therapist. Note that AI is not built to give treatment plans, but rather is used to document patient data and review it. It is the physician whose say matters in the therapy. HHS advisory committees and the FDA are trying to regulate policies on how AI can be used in the best way possible for mental health software.

Practice Management Software

AI-powered practice management software is used to automate appointment scheduling and patient reminders. With artificial intelligence, it can streamline the reimbursement and medical billing process. Moreover, AI assistance helps physicians to generate detailed reports on patient medical records.    

AI in Healthcare: The Way Ahead

AI in healthcare has tremendous scope for growth and opportunities. ChatGPT is one of the recent examples that has already left its mark in the healthcare space. The use of generative AI for administrative work, documenting error-free data, and creating images is just a stepping stone for artificial intelligence. 

With the speed at which it is growing, AI is set to change the way healthcare functions in the coming years. With robotic surgeries, chatbots, drug development, clinical research, and much more, artificial intelligence has left no stone unturned. 

Development companies are investing heavily in the research and development of AI technology. So, to keep up with the growing competition, leverage services from the experts in AI development. Arkenea offers a range of AI development services such as AI-assisted chatbots, machine learning, NLP, robotic process automation, and much more. Our team of experts can help you add AI to your existing software, thus saving you loads of development costs. Connect with us to get a quote.