Medical cannabis is used to treat health problems like pain, anxiety, and long-term illnesses. It is important to write down cannabis use correctly for patient safety, good care, and health studies.
A study in Washington State, where medical and recreational cannabis is legal, looked at almost 300,000 outpatient visits with over 200,000 patients. It showed that cannabis use was recorded in about 5.6% of visits. This means that cannabis use is part of medical records, but the system to record this information is still being improved.
Documenting cannabis use is more than just noting if a patient uses it. It also includes why they use it, how much, and how they take it. This information helps doctors make better decisions and avoid bad drug interactions. But doctors do not always record this clearly or in the same way.
One problem in recording cannabis use is that doctors use many different words to describe it. The Washington State study found 125 terms for medical use, 28 for non-medical use, and 41 unclear terms that did not show why the patient used cannabis.
Sometimes cannabis use is described in indirect ways, like “edible THC nightly for lumbar pain.” These notes are harder for computer systems to find than direct phrases like “continues medical cannabis use.” This makes it difficult for simple software to find and understand the notes.
This creates a challenge for automatic systems that rely on keyword searches. Those in charge of medical records and IT have a hard time improving the process without more advanced tools that can understand natural language.
NLP is a type of artificial intelligence that understands human language. In healthcare, NLP can read large amounts of written notes in patient files and pull out useful information. This helps make the records easier to use.
In the Washington State study, the NLP system found 54% of the medical cannabis notes automatically. The other 46% needed help from people checking the results to be sure they were correct. This shows that NLP helps, but it still needs human support to work well.
To make NLP better at finding cannabis information, developers need many examples of labeled data. These examples teach the AI how to find different terms and meanings. Gathering this data takes a lot of work but is necessary to improve results.
For doctors, good NLP tools mean better support when making treatment decisions. Knowing if a patient uses cannabis helps doctors check for drug interactions and plan care properly. It also helps research public health by showing how cannabis affects people.
Using AI to automate tasks can help doctors and medical staff keep better records of cannabis use. Tools like Simbo AI work with phone calls and front-office tasks, showing how AI can improve health care work.
Although Simbo AI mainly handles front-office services, the same ideas can be used to help with clinical notes using NLP. Medical offices can use these tools to reduce manual work and make fewer mistakes in recording cannabis use.
Here are some ways AI and automation improve cannabis documentation:
Investing in AI tools can make workflows faster and give doctors better patient information. This is helpful in states like Washington and California, where cannabis laws require clear records.
Research with NLP shows that medical records need to change to keep up with new challenges. Healthcare leaders must be ready to invest in NLP tools and work with doctors, IT staff, and AI experts to build good systems.
Organizations must also follow privacy and security rules since cannabis information is sensitive and protected by laws like HIPAA.
Training doctors to write clearly and use standard terms will help NLP systems work better and make patient records more consistent. With AI tools, this will improve data quality and help clinical decisions.
In states where cannabis is legal, good documentation helps with public health efforts. Reliable data helps plan policies and study the effects of cannabis use over time.
Medical practice managers, owners, and IT staff should think about using NLP and AI tools to solve problems in documenting medical cannabis use. Doing this helps care for patients, meets legal rules, and prepares healthcare systems for changes in cannabis laws and medical practices. By knowing how cannabis use is currently recorded and using new AI tools, healthcare providers can offer better care and help improve the health system overall.
The study investigates the documentation of medical cannabis use in electronic health records (EHRs) among primary care patients, using natural language processing (NLP) to analyze this documentation.
The researchers applied an NLP system, combined with NLP-assisted manual review, to identify clinician-documented medical cannabis use from patient encounter notes.
Medical cannabis use was documented in 5.6% of outpatient encounters, totaling 16,684 out of 299,597 screened encounters.
The documentation included 125 terms indicating medical use, 28 terms for non-medical use, and 41 ambiguous terms.
Implicit documentation of medical cannabis use was more prevalent, represented by phrases like ‘edible THC nightly for lumbar pain’, compared to explicit documentation.
The study highlighted that clinicians often use diverse and ambiguous language for documenting cannabis use, complicating automated extraction efforts.
Automating extraction could facilitate clinical decision support and further epidemiological research on medical cannabis usage.
A large amount of gold standard training data is required to effectively develop and validate NLP systems for extracting documentation on cannabis use.
The study takes place in Washington State, where both medical and recreational cannabis use is legal.
The research underscores the importance of improving the accuracy and utility of documented patient information in EHRs, which impacts clinical care and research.
Simbo is using AI agents to automate all phone related workflows. Connect now to know more.
Schedule AI Agents Demo Now© Since 2019, Simbo AI.
