Epilepsy affects many people around the world. A large number have a type called drug-resistant focal epilepsy. These patients often have seizures that keep happening and make life difficult. For some, surgery can stop the seizures. About half to 60% of people who have surgery become free of seizures. But finding these surgery candidates early is hard in healthcare.
Because there is a lot of clinical data, technologies like Natural Language Processing (NLP) can help. NLP can take useful information from doctor’s notes and electronic health records. This article looks at how NLP helps hospitals and clinics in the United States find epilepsy surgery candidates earlier than usual.
Natural Language Processing is a type of artificial intelligence. It helps computers understand and work with human language. In healthcare, NLP is mainly used to read clinical notes. These notes are often in regular language, not in clear data fields. NLP can find important details like diagnoses, symptoms, or past treatments from many patient records.
A big benefit of NLP is that it can work quickly, even in real time. It can find useful information hidden in free-text notes. In epilepsy, NLP can spot signs of drug-resistant epilepsy early. This helps doctors think about surgery sooner and avoid long delays for patients.
A recent review looked at six studies about NLP helping to find epilepsy surgery candidates. These studies were chosen from 1,369 articles. The review showed that NLP could find candidates one to two years before doctors usually refer patients for surgery.
Most studies used machine learning methods like support vector machines (SVMs). One study used random forest models and gradient boosted machines. These systems scan through electronic health records and notes to find patterns linked to drug-resistant focal epilepsy. This form makes up about 30% of epilepsy cases.
Finding candidates early is very important. If seizures do not stop with medicine, surgery might help. If patients are not found in time, they miss surgery that can improve their life.
The studies showed that NLP methods have fair to good accuracy in finding surgery candidates. This shows that NLP could work well in big hospitals or clinics with many patients.
However, none of the studies looked at how adding NLP systems to hospital routines changes patient results or how well the system runs. More research is needed to see how these AI tools affect referrals, surgery results, and hospital work.
Still, these NLP tools can help hospital staff. They can find patients sooner, lead to surgery consultations earlier, avoid long, ineffective treatments, and help manage clinical resources better.
AI, including NLP, is often used to make healthcare work smoother for staff. In the United States, hospital administrators and IT teams can use AI tools to help patients move through epilepsy care faster.
For instance, automated phone systems using conversational AI can set up follow-up appointments for patients flagged as surgery candidates. This reduces work for staff and lets them do other tasks. AI can also connect with hospital records to send alerts or referrals automatically once NLP finds relevant information.
At epilepsy clinics, workflow automation can speed up referrals by telling doctors about patients who might need surgery. This helps make sure no patient is forgotten or delayed.
AI tools can keep checking patient records regularly, not just once. These checks can quickly flag patients with medicine-resistant epilepsy so referrals happen without wait.
Besides spotting patients, AI can answer simple patient questions, reducing phone load on staff. This helps with booking, reminders, and follow-ups. It makes things easier for both patients and administrators.
Hospitals in the U.S. work in a busy and changing environment. Using AI and NLP together can make patient identification and referral faster and more reliable.
Even though NLP shows promise, using it in real U.S. hospitals needs good planning. Most studies were done in a few centers, so the models may not work the same everywhere. Hospitals must get NLP tools that fit their records, patient groups, and how doctors write notes.
NLP depends on good and full clinical data. If records are missing or wrong, results will drop. Hospitals need strong data rules and good note-keeping.
Data privacy and following laws like HIPAA are very important. Any AI system must keep patient info safe and private.
Finally, people still matter most. NLP can suggest patients who might need surgery, but doctors and care teams make the final choices. Staff training and working together with IT is important to get the best from AI tools.
The studies did not check what happens after using NLP systems, but there are clear benefits. Finding patients earlier might:
In the U.S., where referrals often get delayed, these benefits can make patients happier and lower costs. It might also reduce emergency visits caused by bad seizures.
Health administrators in charge of epilepsy programs might work with AI companies that focus on NLP. By watching how the new tools work, they can improve results and make the AI work better for their patients.
Nonprofit groups and medical schools have helped this research. They spread information about AI tools for epilepsy surgery candidate identification. For example, CURE Epilepsy shared articles about NLP’s use in this area as of July 2023.
Academic medical centers collect data from many places and test AI systems on large patient groups. Cooperation among providers, hospital leaders, data experts, and AI makers is needed to bring these tools into patient care across the U.S.
As epilepsy care changes, AI tools like NLP offer new ways to find patients earlier and make office work easier. Clinic and hospital leaders in the U.S. should think about how NLP fits with their notes and referral steps.
Some steps to try include:
NLP could help patients with hard-to-treat epilepsy get surgery sooner. This might change many lives. More research is needed to see the long-term effects, but current studies say NLP and AI can be useful in U.S. epilepsy care.
The studies focus on the effectiveness of natural language processing (NLP) in identifying patients suitable for epilepsy surgery, which can help a significant portion of patients achieve seizure-free outcomes.
The review found that NLP showed moderate-to-high performance in identifying suitable candidates for epilepsy surgery before clinical referrals.
Approximately 50% to 60% of patients become seizure-free after undergoing epilepsy surgery.
NLP is utilized for information extraction, information retrieval, document categorization, text summarization, and generating meaningful information like diagnosis or prognosis from electronic health record data.
The data search identified 1369 publication results, leading to 58 full-text articles being reviewed before narrowing it down to 6 studies for analysis.
Five of the six studies utilized support vector machines, while one study employed random forest models and gradient boosted machines for NLP tasks.
No, none of the studies evaluated the influence of implementing these NLP algorithms on healthcare systems or patient outcomes.
Some studies indicated that NLP could identify suitable candidates 1 to 2 years prior to the initial clinician referral.
Drug-resistant focal epilepsy accounts for about 30% of individuals diagnosed with epilepsy.
NLP is viewed as a promising technology for identifying patients who may benefit from referrals for epilepsy surgery evaluation.
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