The Current Problem with HL7 and Electronic Health Data Exchange

Currently, there are several types of software and systems for storing patient data in place in the healthcare industry. Even though HL7v2 is commonly used in these software and systems, many different institutions use different methods for implementing HL7v2. When two systems that support HL7v2 communicate, an analysis procedure is typically involved.

Although patient information can be electronically exchanged via different systems and software using the same HL7v2 standard, it’s often incompatible, hard to read, time-consuming, or generally just confusing. The issue is not due to the usage of various versions of HL7v2 but rather how the standard has been implemented in different systems. With the nature of patient medical charts, this information should be quick and easy to exchange and read when it’s transferred between institutions.

FHIR As a Solution

Following the issues that come with HL7v2, FHIR was developed to help create a standard format of sharing data easily without confusion. FHIR is more standardized in its implementation, meaning that it will redefine how information is stored and exchanged between medical institutions. Overall, FHIR will make the exchange of information very easy since the layout and the organization of information within the file is standardized.

Using a deidentified ICU dataset available for public use, RediMinds created models using the FHIR file format. With the FHIR file format, RediMinds was able to develop models efficiently. Because FHIR data is standardized, data processing for model creation is easier and shorter. It also helps create more sophisticated analytical models.

Creating models with other file formats such as HL7v2 is not as simple since there are different variables and information in the file stored in different ways. On the other hand, with the FHIR file format, developing models becomes backend technology-agnostic since the file is standardized, and data will always be stored in the same fashion. Therefore, no matter the technology and software used by the hospital, the data will always be labeled the same.

Other Benefits of FHIR

There are a few other benefits of storing data in FHIR format. First of all, it is based on modern web standards, making it simple to use in mobile apps, online applications, and data sharing across different platforms. Since FHIR Resources are standardized, it has the same meaning across hospitals. On the contrary, despite HL7v2 being the primary standard of many medical institutions, its implementation varies across software, which causes confusion with the data.

With standardized data, insurance firms may use clinical data to enhance claims data to evaluate risks better, reduce costs, and optimize outcomes. In addition, patients may have more control over their health by obtaining medical information through applications that operate on smart devices. Because of its standard format, it cuts down on the amount of time spent on understanding different hospitals’ data and allows more time for decision-making. Efficiency and speed are both important features due to efficiency being necessary in the case of medical emergencies.

Furthermore, with minimal adjustments, Data Engineering and other activities that are done with FHIR data may be utilized across hospitals. Lastly, unlike HL7v2 and other standards, it is easy for humans to read and interpret without the computer running an analysis on the data.

Implications Of FHIR

Despite FHIR being a better choice for storing and sharing data, adopting FHIR may present challenges for intuition. FHIR was designed to simplify implementation but implementing an in-house FHIR implementation may mean acquiring new skills. Developing an interface or app is only the first step in the process. In the future, a team will need to maintain and grow a backend FHIR service, which may require extra skill in managing additional infrastructure.

Developing a FHIR-based cloud service may aid in overcoming adoption barriers. With the appropriate cloud implementation, you can speed up provisioning, personalize the implementation by integrating cloud-based services, and achieve adequate scalability for the future. Furthermore, the standardized file format of FHIR supports the development of AI solutions or advanced analytical solutions.

To learn more, check out this case study.

How 3D Reconstruction of Organs is Beneficial

As stated earlier, 2D images, such as CT scans and x-rays, have primarily been used for pre-surgical planning in procedures dealing with soft tissue-based procedures. However, with 3D technology, surgeons can get a better idea of the anatomical structure of the organ and the location of the organ relative to other soft tissue. With this invaluable insight, surgeons can create a route for the surgery and plan the procedure with more efficiency.

When only relying on x-rays or CT scans, there is a potential for valuable information to be missed during the pre-operative planning process. This could potentially slow down or create issues during the procedure. When using a 3D model of an object, the surgeon can easily rotate and view the object from many different angles. If printed, the object can be held and observed from a realistic perspective, which can give insight into the size of the object. Pre-operative planning using 3D models can lead to better diagnostic accuracy.

Furthermore, this technology enables more collaboration between surgeons. With 3D models, it is possible for teamwork to happen regardless of geography. For example, a surgeon who lives in another country can easily access a 3D model to diagnose a problem and plan an operation. This creates the possibility of experts in certain topics becoming more accessible when their advice would be beneficial for diagnosing or treating a patient.

AR/VR Surgical Solutions

Aside from 3D printing, 3D models could be made for the liver, lungs, or heart. These 3D models could provide the basis for AR/VR reconstruction of the anatomical object. Anatomical knowledge of the operative site is essential for soft tissue-based surgeries. VR and AR offer new ways to view three-dimensional anatomical structures for pre-operative planning.

VR allows the surgeon to evaluate and get familiar with complexity of the surgical area and object in a virtual environment. On the other hand, AR allows 3D objects to be projected onto the patient both before and during surgery. This technology could provide insight that could help the surgeon operate faster with more accuracy than when planning operations using only 3D images.

The Future of 3D Reconstruction of Anatomical Objects

Pre-surgical planning is critical to achieving the best possible surgical result and minimizing any problems during or after the surgery. While there are limits to planning with 2D images, creating 3D models of anatomical objects could result in more efficient pre-operative planning and, therefore, better outcomes for patients. This technology provides the building blocks towards a broader solution of developing AI-assisted surgical tools to provide better overall surgical outcomes.

To learn more, check out the case study.

What is Endoscopy?

An endoscopy, often known as “EGD,” is a minimally invasive technique that takes images of the upper digestive system without requiring complicated and possibly risky surgery.

With endoscopy, doctors can get comprehensive imaging of parts of the small intestine using a long, flexible tube with a small, high-quality camera and light connected to the end. The camera has two lenses that act as the left and right eye, which generates a perception of depth and provides information about how deep a surgeon can go during surgery.

An endoscopy can take biopsies in addition to getting comprehensive pictures of the upper digestive system. A biopsy involves the removal of a tiny sample of tissue. The tissue sample is subsequently submitted to the lab for additional testing to identify the existence of a disease.

Endoscopy may be performed to repair an issue if one is identified. Endoscopic procedures can be used to treat a variety of issues, including opening constricted regions, regulating bleeding, removing abnormal growths or tumors, removing stuck items, and banding any aberrant veins in the esophagus.

Why Does Depth Perception Matter to the Creation of AI Solutions?

Surgeons view pictures through an endoscope or assistive camera in minimally invasive surgery. Because the camera has both left and right eyes, the surgeon has good depth awareness after a few surgeries.

The difficulty is that the AI will need to comprehend depth to automate specific aspects of the procedure.

Therefore, depth perception is essential for AI models.

In minimally invasive operations, surgeons must mentally reconstruct the 3D patient anatomy from a restricted number of visual cues. Insufficient depth signals from the environment may cause surgeons to overestimate spatial depth, compromising patient safety.

Using AI surgical tools can lead to a better knowledge of the spatial connections between different surgical and anatomical objects, as well as ways for the surgical team to minimize risk while allowing human specialists to maintain control.

RediMinds built a model to test out how AI might be used in endoscopy for depth perception. The model showed promising results, with mean average inaccuracy of 3mm across two separate test scenarios – indicating depth perception could be predicted well.

A surgeon can view more perspectives if they have access to 3D structures of a situation. This could allow for the development of more accurate and helpful safety measures.

The Future of AI in Endoscopy

AI has the ability to enhance all aspects of endoscopy, from surgical planning to post-surgery outcomes. It’ll compensate for human mistakes and limitations by improving endoscopic treatments’ accuracy, consistency, and speed.

AI has performed well thus far in endoscopy use cases. However, more research, ethical approval, and new regulations will be required before such AI tools can be used in practice. Nevertheless, AI will be a significant advantage in future years, as it has the potential to revolutionize endoscopy.

To learn more check out the full case study.

How is this Beneficial?

With the application of ML models to surgery and establishment and implementation of situation-specific intervention procedures, the surgeon can take advantage of this window of opportunity to manage patient risks. Predictive models developed in this manner can assist surgeons in identifying high-risk groups, anticipating significant occurrences, and planning treatment measures to enhance patient outcomes. A great case of this in action was performed by RediMinds.

RediMinds worked with the Vattikuti Foundation to create models for predicting intraoperative complications, or in other words, complications before and after surgery, and 30-day morbidity. The models were built using a multi-institutional dataset that was focused on a specific cohort of kidney cancer patients who underwent Robotic Partial Nephrectomy.

Machine learning and AI models performed well in this study in predicting problems during surgery and 30-day complications following surgery. These tools could be applied to real life clinical settings, which would allow the centers to utilize the models to identify patients who are likely to experience a problem before or after surgery, as well as to innovate intervention methods and offer feedback on model performance. This would also allow researchers to track the models’ performance in a clinical context.

Model Development

Here’s a brief rundown of RediMinds’ model deployment process for this study. First, historical data from patients who underwent robot-assisted partial nephrectomy is obtained, including demographic, pre-operative, intra-operative, and follow-up information. The next step, the data engineering process, then includes pre-processing historical data and defining features that will allow machine-learning algorithms to be trained on the data.

Next, model training and model validation models are trained, and the model with the greatest performance on unseen test data is chosen. The model with the highest performance is put in a clinical setting for further evaluation. The final step is the model monitoring, which is the process of comparing model predictions against ground truth to see if the model is operating as expected.

Final Thoughts

Surgeons strive to make their actions and decisions to treat patients under their care as predictable as possible. With the successful application of ML, it is possible to predict complications before and after surgery, which could be consequential to the patient’s surgical outcome.

This approach and methodology may be used in any surgical circumstance across the healthcare spectrum. It is not restricted to Robotic Partial Nephrectomy patients. With adequate data and continued research, the next generation of assistive AI surgical tools can be built utilizing the technique that RediMinds used.

To learn more, check out the case study

This isn’t the only way that our research into AI in healthcare is improving patient outcomes.

Check out our guide to 6 exciting applications of AI in healthcare.