Health systems must meet evolving consumer expectations while tackling aging infrastructure, strained budgets, and workforce shortages. In addition, they must modernize the way they deliver care.
Natural language processing alleviates some administrative provider tasks by promptly answering online queries, scanning customer feedback, and analyzing survey results. It also helps gather valuable real-time intelligence that improves the customer experience.
1. Machine Learning
Machine learning is a cornerstone of many healthcare advancements, from diagnostics to personalized patient care. It can improve outcomes, streamline operations, and lower costs.
ML can detect patterns in large data sets to help medical professionals make informed decisions quickly and efficiently.
It enables them to create customized treatment plans for each patient, taking into account their genetics and medical history. This personalized approach can result in more effective treatments with fewer side effects.
Machine-learning tools can be used to detect diseases, including cancer and heart disease, at an early stage, allowing physicians to take preventive measures before the symptoms appear.
In addition, ML algorithms can analyze medical images like X-rays and CT scans to identify and differentiate between healthy and diseased tissue.
ML can also help reduce the time to market for new pharmaceuticals by optimizing drug discovery and development.
In the future, patients can monitor their health remotely using wearable devices that collect and analyze data.
This information will be automatically sent to a PCP’s office via telemedicine, which can then provide the physician with insights and alerts.
These advances will allow doctors to focus more on patient care and treatment than administrative tasks.
Efficiency gains from ML also contribute to cost reductions. For example, predictive analytics can help hospitals anticipate surges in patient admissions and allocate resources appropriately.
Additionally, ML-enabled catboats can automate everyday healthcare tasks like scheduling appointments and answering basic questions.
2. Predictive Analytics
Natural language processing is widely used in healthcare to process patient medical records and facilitate clinical research.
It is also utilized to develop catboats or virtual assistants that assist patients in scheduling appointments, re-ordering prescriptions, and answering questions about their health.
The benefits of natural language processing in healthcare are that it analyzes the data, produces insightful findings, and can help make medical diagnosis easier.
For example, NLP can analyze doctor notes and EHRs to identify patterns and trends. This information can then be used to predict future needs and outcomes, such as identifying patients at risk for a specific disease or needing a particular type of care.
It can save money by preventing hospital overcrowding and reducing the number of unnecessary procedures.
NLP can also be applied to a variety of other healthcare processes. One example is automated medical coding, which involves analyzing clinical notes and patient EHRs to derive billable codes that can be submitted to insurance companies.
NLP can eliminate this manual task and increase efficiency by automating a large portion of the coding process.
NLP can also assist with clinical trial matching by analyzing patient medical histories and comparing them to available trials. It can improve patient access to life-saving treatments.
3. Natural Language Processing
Despite the vast data generated by EHRs, telemedicine, and other medical technologies, most healthcare data still needs to be more structured.
As such, machine learning tools can be helpful in the medical industry. These technologies can structure the unstructured information into meaningful insights for healthcare and health research.
In the case of healthcare, this means that patients can access their data, and hospitals can use natural language to improve services for their customers.
It could include providing better customer service, ensuring employees use inclusive language, and scanning call logs to identify the most common problems.
Recognizing specific words and semantics can infer important details that human doctors might miss.
For example, a physician might not mention that a patient has social support in their notes, but it might be implied by the presence of a relative at the bedside.
A basic keyword-driven NLP will see these two phrases as the same, but a more sophisticated NLP model will understand that they are different and have distinct meanings.
One area where NLP in healthcare is already used is determining the best patient medication. Using NLP in a clinical decision support system (CDS) can make this process faster and more accurate by identifying critical factors in each patient’s medical history.
Another application of NLP in healthcare is to match patients with appropriate clinical trials. It is a task that would take years to complete manually, but NLP can do it in minutes or hours.
4. Big Data
In the same way that companies use data to optimize their operations and personalize customer experiences, healthcare providers are employing big data analytics to improve patient care, drive efficiency, reduce costs, and gain insights for future business decisions.
One of healthcare’s most significant big data sources is electronic health records (EHRs).
The data in these records, including a patient’s demographic information, medications, test results, and more, are used to create clinical documentation for each visit.
It enables physicians to spend more time caring for their patients rather than on administrative tasks like reviewing complex EHRs.
The data from these EHRs is also used for diagnostic purposes and monitoring a patient’s health. Big data is helping medical professionals make more accurate diagnoses, which can lead to better treatment outcomes.
It can also help prevent complications, such as hospital readmissions, by detecting patterns that could indicate a worsening health state.
The volume of big data is growing exponentially as the world becomes increasingly connected. Sensors, social media, and embedded chips—now in everything from airplanes to refrigerators and even bodily implants—contribute to the growing mounds of data.
As a result, the use of big data in healthcare is accelerating. It’s already being used for managing large clinical data sets, accelerating clinical trial matching, and other applications disrupting the industry.
