How AI in Medical Documentation Helps to Reduce Physician Burnout

Key Takeaways

• Generative AI is a useful tool for medical documentation and can reduce admin work for healthcare providers. It is trained to summarize patient information in less time, thus decreasing clinician burnout.
• NLP is used in medical documentation to convert unstructured data into a structured format for easy access. This technology has certain benefits such as language translation, which is quite useful for non-native patients.
• AI in medical documentation ensures that all records are properly tagged, so physicians don’t encounter any difficulties while searching for records. AI tool is also trained to keep track of all documents and alert the user if any document is to be updated.

Physician burnout is accelerating with time. One of the major reasons for physician burnout is excessive medical documentation. According to a study published in the Annals of Internal Medicine, physicians spend more of their time entering data than with patients. The study further stated that physicians give 27 percent of their office time to patients, and spend 49.2 percent of their office day on desk work and EHRs.

Medical documentation is not only limiting physicians’ time spent with patients but also enhancing their burnout rate due to documentation workload. With the help of technology such as artificial intelligence (AI), physicians can wrap up documentation work in less time. They can contribute more of their time to patients and less to documentation with the help of AI.

Contribution of AI in Medical Documentation

1. Speech Recognition

Speech recognition involves the conversion of spoken language into text in real-time. So, rather than dictating information and transcribing it later, speech recognition makes use of AI and NLP technology to create medical documents in real time. Physicians save time by simply documenting patient data right away, thereby saving themselves from burnout. Further, speech recognition technology is cost-effective, user-friendly, and less tiring to use.

2. Summarizing Records

Healthcare providers have a hectic schedule and they try to give maximum time to their patients. A comprehensive patient document with all the essential points helps to diagnose and treat patients faster. So, physicians leverage AI in medical documentation to summarize records and review them quickly. A recent example includes the launch of an artificial intelligence software platform by Wisedocs. The software was created for insurance companies to quickly summarize tons of medical records and garner insights from documents.

3. Document Organization

Document organization is a necessity for smooth operations at healthcare organizations. AI can automate document organization to make it more effective and efficient. One of the key challenges faced during medical documentation is naming files and categorizing them in folders. Artificial intelligence technology can be used to automatically differentiate between various documents and catalog them.

Additionally, AI in medical documentation ensures that all records are properly tagged, so physicians don’t encounter any difficulties while searching for records. AI tool is also trained to keep track of all documents and alert the user if any document is to be updated. It also points out if any records are duplicated and eliminates additional copies.

Types of AI in Medical Documentation

1. Generative AI

Generative AI is a type of artificial intelligence technology that has recently created a buzz due to the launch of ChatGPT by OpenAI. It can produce various types of content such as graphics, poetry, and videos, and assist in research. This newfound technology has opened lucrative opportunities for creators, researchers, and healthcare providers in the form of simplifying admin tasks and educational content generation. However, it has unlocked certain issues such as deepfakes, forged images and videos, and cybersecurity concerns.

In the healthcare sector, the use of generative AI like ChatGPT for clinical reasoning, decision-making, or diagnosis is debatable because of the misinformation provided by AI. As per an article published in The New York Times, researchers stated that generative AI will be a powerful tool for spreading misinformation and may do more damage than good. Patients who lack medical expertise and are seeking generative AI to ask medical questions need to be aware of the risks offered by this technology.

Apart from the cons of generative AI, it is quite a useful tool for medical documentation and can reduce admin work for healthcare providers. ChatGPT is trained to summarize patient information in less time, thereby reducing clinician burnout. Furthermore, a recent development by Microsoft’s Nuance Communication includes the announcement of a new clinical documentation tool powered by GPT-4. This tool is called the Dragon Ambient eXperience and will enable healthcare providers to automate clinical documentation only by listening to physician-patient consultations.

2. NLP

Use cases of NLP (Natural Language Processing) in healthcare include computer-assisted coding, data mining research, clinical trial matching, decision support, and much more. However, out of the use cases, clinical documentation has always been at the top. NLP can understand spoken words, sentiments, and text in the same way as humans. So, it can be a useful tool in identifying patient feelings at the time of diagnosis and documenting patient data during patient-physician conversations.

Ways in which NLP contributes to medical documentation are:

• Text Extraction: Unstructured data is difficult to read and access, so NLP is used to extract useful data, and also convert unstructured data into structured format. This data can be further used to gain insights and analyze it to improve patient care.
• Sentiment Analysis: As humans, we express different emotions while speaking. So, NLP reviews the data while documentation analyzes it, and finds a pattern. It makes assumptions about the feelings of a patient during the documentation process. Understanding correct emotional situations allows doctors to reassure their patients and help them calm down during tense conditions.
• Language Translation: Natural language processing can automatically translate documents from one language to another. This feature can be used for patients with a different native language.

3. Machine Learning (ML)

One of the many reasons why machine learning is used in healthcare is to improve productivity and efficiency via automation. This technology is trained to automate the normalization and de-identification of patient data. It can also be used for data mining and profiling. From the context of medical documentation, ML helps in indexing, searching, and retrieving medical records. It can also eliminate duplicate patient records. Duplicate data are often created due to data entry and discrepancies during patient registration. Duplicate records not only result in misdiagnosis of patients but also compel patients to repeat their tests, thus costing them money.

Another use of machine learning in documentation is to deteriorate risk index and poor outcomes. A study published in Pediatric Critical Care Medicine stated that EHR contains too much clinical data, so the ML model helps to relieve the cognitive burden by automatically analyzing risks. It has so far timely identified hospitalized children at risk of mortality and morbidity.

Wrapping Up

From the above studies, it is safe to conclude that AI has a crucial role in organizing, translating, searching, and capturing data to simplify the lives of physicians and reduce their burnout. With AI technology, healthcare providers can free up space to accommodate more patients and give time to their loved ones and themselves. The role of AI in healthcare doesn’t just stop at documentation, it has more benefits such as answering patient queries via chatbots and aiding in medical imaging.

Arkenea offers AI services to healthcare organizations and businesses who are looking to leverage AI for their clients. We provide a range of services such as predictive modeling, NLP, generative AI, machine learning development, and much more. Connect with us to get best-in-class AI development tools that meet your requirements and industry standards.