Artificial intelligence (AI) and machine learning (ML) can diagnose and predict patient outcomes with up to 90% accuracy, according to research. This technology is reshaping healthcare by making EMR systems smarter, more accessible and efficient.
What this means for healthcare is that these models can analyze huge amount of data, identify patterns, and support the provider’s decisions. Electronic Medical Record software (EMR), although a precursor to EHR systems, lets a healthcare practitioner manage their practice by digitizing some or most parts of it.
The software offers a suite of services to the doctor to improve their patient care process. AI and ML can improve most aspects of the EMR software resulting in enhanced patient engagement, data security, better administrative processes, and more. Custom healthcare software development plays a crucial role in integrating these advanced technologies into existing systems, ensuring that the solutions are tailored to meet the specific needs of healthcare providers.
But these technologies are currently facing a few challenges that need to be addressed. These include data privacy and security, data quality, integration with existing systems, regulatory compliance, and more. Stay tuned as we discuss how healthcare providers can get the most out of these technologies and improve patient care.
AI and ML to Improve EMR Systems
EMR systems sometimes struggle with data overload that leads to usability issues. Also, this is an older technology so it’s a bit slower than other options available nowadays. It requires manual data entry, which is susceptible to human errors, leading to further administrative overload down the line.
AI and ML integration into existing EMR systems can address most of these challenges. For example, you won’t need to manually enter the patient details, check insurance eligibility, and file the claim. All of these processes can be automated with the help of AI and ML.
These systems are capable of predictive analytics because they’re trained on similar data and are smart enough to analyze similar patterns and make assumptions. This can have potentially far-reaching benefits. Imagine identifying at-risk patients and recommending preventive measures and treatment.
This integration can lower administrative burden while also improving the overall patient experience.
Image Recognition and NLP to Support Clinical Decisions
Here is how AI and ML can support healthcare providers in making better decisions for patient care.
Clinical Decision Support
Since these models are trained on gigantic data sets containing real-world scenarios and results, they can analyze historical trends and make future projections as well. This lets healthcare providers identify at-risk patients e.g., people with diabetes or heart conditions, and take preventive measures before their condition worsens.
Because it has more data than a doctor, it can recall thousands of similar cases instantly and determine the best treatment plan. This leads to improved patient care and satisfaction.
Natural Language Processing (NLP)
NLP allows computers and software to understand human language and respond in a way that we can understand. Think of it like a computer learning the language you speak so it can communicate with you. This also allows the model to read and interpret text like clinical notes and turn those into structured data.
AI models use NLP to automate transcriptions and coding, and help reduce errors in clinical documentation. This equates to fewer denied claims and quick turnaround times.
Older search systems use keywords or phrases to satisfy search intent but it’s not very accurate. By using NLP, searching for things becomes easier and simpler.
Image Recognition and Analysis
The healthcare process of most patients involves taking test recommended by the doctor. And some of those tests like the X-rays, MRIs, and CT scans are images, that the doctor analyzes for abnormalities.
AI can do it better and quicker than humans because it doesn’t have any physical limitations, making it ideally suited for such applications. Moreover, the doctor can use the AI analysis to make more accurate diagnosis.
These models can also track changes over time by comparing different tests. This can help healthcare providers monitor disease progression over time and offer better care.
The AI Advantage: Supercharged EMRs
We’ve already mentioned that AI and ML models can predict patient outcomes with up to 90% median accuracy, according to a study. This was for all included classifiers in the study, one of those classifiers reached a maximum of 98.5% accuracy. This is remarkably close to an always right output. Imagine what it can do for the healthcare sector.
This will lead to better patient care and consequently outcomes with higher accuracy. These models can also reduce the number of misdiagnoses and medical errors. Another benefit of reduced medical errors would be reduced costs resulting in higher profit margins for the healthcare providers. Providers can also save cost by automating billing and streamlining other workflows.
Since the data is structured and organized, healthcare providers can use it for better population health management. And this can have a tangible community-wide impact outside of the practice.
Challenges and Considerations in AI/ML Implementation
Here are some important challenges AI and ML are facing in healthcare.
Data privacy concerns
AI has to be trained on actual patient data so it can analyze and identify patterns and learn from key findings in historical cases. And the patient data is also protected under strict regulations by HIPAA and HITRUST, and doesn’t allow unauthorized access. Training AI models on sensitive data while maintaining patient confidentiality is a challenge.
Integration with existing systems
It may sound a little hard to believe but some healthcare providers are still using legacy EMR systems or even paper records in smaller practices. In contrast, most organizations are suing EHR software from different vendors with unique features and workflows. This is why AI integration into an existing system can be complex and expensive.
Ethical considerations
In AI and ML domains, AI bias is a real thing. It refers to the AI model giving biased results because of the training data used. In other words, if the training data had some bias or the person designing the algorithm was biased, it would likely reflect in the model’s responses.
Conclusion
And these are just some examples of how AI and ML can turbocharge your EMR systems to reduce the burden and improve patient outcomes. Other benefits include efficiency and convenience for the practice, along with greater profitability.
There are nevertheless great challenges to be faced related with issues like data privacy, how old systems will integrate with the newer technology, and ethical considerations. This is how these technologies can contribute to driving more efficiency into your practice and becoming a better healthcare provider.
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