It is no longer news that with the technological advancement in the health sector, there has been a strong push for the practice of artificial intelligence (AI) in assisting patient care, improving efficiency, as well as minimizing costs within organizations.
There is a huge promise of the AI revolution when you look at its application in the health care sector. Understanding how to implement it can be a very difficult journey.
This article discusses how it may be useful to first undertake more limited AI initiatives while forming the opinion of how it can be useful in the digital transformation of clinical practices. Naturally it is heavily dependent on organizations goals, priorities and resources.
The Case for Starting Small
Among the reasons one may get for desperation to engage in large scale AI systems is the upside of doing so, however there are great benefits derived from the beginning of low hanging fruits:
- Reduced Complexity: Smaller projects typically have fewer stakeholders and are less likely to heavily disrupt existing procedures since they are almost self-contained, this makes them easy to execute as well as oversee.
- Lower Risk: Since the investment is minimal and the scope limited, the potential risk of failure in undertaking the project is less likely to occur.
- High Impact: Projects that can make a big impact in patient care and provider wellness and efficiency.
- Faster Implementation: Projects can be implemented and completed up in lesser time, enabling the organization to reap the benefits and acquire the needed expertise.
- Easier Adoption: Change is required but may not be such major ones and therefore most staff will be willing to make the change as there is reduced resistance and negatively impacting the success chances.
- Learning Opportunities: small trials allow experimenting with different aspects of AI including implementation, management of data and also change management with low risks and costs.
- Time to Value: simple AI projects are usually valuable at the beginning of use and so there is realization of value right from the start. Thus confidence in the use of AI technology is also built.
Characteristics of an Ideal Starter AI Project
For potential conduct during your first AI project, try to look for such solutions that have the following traits.
- Non-clinical Focus: Tools used within the medical field that do not contribute directly to clinical decision making.
- Software-Based: Limiting the use of hardware can ease the implementation and minimize the expenditure.
- Device Flexibility: Measures that are applicable on the already existing devices eliminate the need for additional infrastructure.
- Standalone Functionality: There are certain projects where no extensive modifications to the current dubbed systems such as EMRs are necessary. These projects tend to be implemented faster.
- Privacy-Preserving: In addition, tools that do not keep personal information shall assist in limiting privacy issues.
- User Friendly Interface: A keystroke friendly design lessens the time of training to increase the acceptance.
- Alignment With Current Processes: Features that add to current work systems rather than create new work systems are more likely to gain wider acceptance.
- Minimum Training Required: The lesser the time taken for training the more effective the execution is.
- Time to Value: Changes that will be beneficial on the first use will advocate for more heavy investment in the works for AI.
Example: No Barrier’s AI Medical Interpreter
No Barrier has designed an AI medical interpreter for healthcare providers, which is a perfect illustration of an AI project with a well-defined focus. This is an example of the advantages of building something in phases.
- Not A Clinical Decision Support System: It facilitates interaction, but has no direct effect on clinical decision making.
- Software Only: There is no need to purchase any amplifiers and hence reduces the effort.
- Device Independence: It is possible to use the application on any connected device, for example on the mobile phones of the providers.
- No Need For Integration: There is no need for integration with EMR systems.
- Data Protection: As a processor application, sensitive data does not reside on the system.
- Use Of Simple Interface: The ease of use permits any team member to utilize the application.
- The Process Fills Gaps: It operates as part of the already existing demand for interpreters.
- Minimal Training: It is possible to onboard providers within a couple of minutes.
- Time to Value: From the very first minute when the system is employed, the advantages become available as improvement of providers and patients interaction takes place straight away.
In summary, starting small with AI in healthcare offers numerous advantages, including reduced risk, faster implementation, and easier adoption. By focusing on non-clinical, software-based solutions that align with existing processes, organizations can gain valuable experience and immediate benefits. This approach, exemplified by projects like No Barrier's AI Medical Interpreter, allows healthcare providers to build confidence in AI technology while paving the way for broader digital transformation in the future.
