Revolutionising Healthcare: How Artificial Intelligence is Making a Difference and Assisting the Sector
Artificial Intelligence to the Rescue: Tackling the Data Overload in Healthcare and Making Sense of Health Data | Article
“AI in Healthcare: A Holistic Overview of Medical Data for Improved Patient Health”
Artificial Intelligence (AI) is revolutionising the Healthcare industry by tackling the overwhelming amount of data and making sense of it. With the rapid advancements in technology, healthcare providers can now collect and store vast amounts of patient data, but the challenge lies in making sense of it all. From electronic health records to medical imaging, the sheer volume of data can make it difficult for doctors and medical personnel to identify trends, make accurate diagnoses, and provide optimal care. However, AI solves this problem by providing a holistic overview of medical data and identifying patterns that would have otherwise gone unnoticed. As a result, healthcare providers are better equipped to track patient health and make informed decisions that lead to improved patient outcomes. This introduction will explore how AI is used in healthcare to tackle data overload and make sense of health data.
Interpret a high volume of data with AI; you will be surprised what you find out.
AI in healthcare provides a holistic overview of medical data, enabling healthcare providers to track patient health in minuscule increments. With the ability to analyse large amounts of data in real-time, AI can identify patterns and trends that would have otherwise gone unnoticed. This allows healthcare providers to make informed decisions that lead to improved patient outcomes.
One of the key advantages of using AI in healthcare is that it can track patient health over time, even in minuscule increments. By analysing data such as electronic health records, lab results, and medical imaging, AI can detect changes in a patient’s health that may not be apparent to the human eye. This is particularly important for conditions that progress slowly, such as chronic illnesses. By tracking patient health over time, AI can help healthcare providers detect early warning signs of a decline in health and take action to prevent it from getting worse.
Furthermore, AI can also be used to predict patient outcomes. By analysing historical data, AI can identify patterns that are indicative of a particular result, such as a successful recovery or a relapse. This allows healthcare providers to anticipate potential problems and take steps to prevent them. AI can also help healthcare providers identify patients at a higher risk of certain health conditions, such as heart disease or diabetes, and take preventative measures to reduce their risk.
Overall, AI in healthcare provides a holistic overview of medical data and allows healthcare providers to track patient health in minuscule increments. By identifying patterns and trends that would have otherwise gone unnoticed, AI helps healthcare providers make informed decisions that lead to improved patient outcomes. By using AI, healthcare providers can provide more effective care and improve patient outcomes in the long run.
‘AI tools for extracting data from healthcare compliance documents’
‘AI and ML can provide medical personnel with data-driven clinical decision support (CDS).’
AI in Healthcare is a predominant term used to describe the use of Machine Learning algorithms and software, or Artificial Intelligence, to simulate human perception in the analysis, presentation, and comprehension of complex medical and healthcare data.
Here are some interesting facts and statistics about artificial intelligence and healthcare!
- According to an Accenture report, AI has the potential to create $150 billion in annual savings for the healthcare economy by 2026.
- A survey by Deloitte found that 72% of healthcare organizations are investing in AI and machine learning.
- A study published in the Journal of the American Medical Association (JAMA) found that AI-assisted diagnostic systems could identify skin cancer with similar accuracy as human dermatologists.
- Research by Frost & Sullivan found that the global market for AI in healthcare is expected to grow from $2.1 billion in 2018 to $36.1 billion by 2025.
- A PwC survey found that 64% of consumers are willing to use AI-powered virtual health assistants to schedule appointments and manage their health records.
- According to ResearchAndMarkets.com, the global AI market in healthcare is projected to reach $22.8 billion by 2025, growing at a CAGR of 42.2% from 2020 to 2025.
- A survey by the American Medical Association (AMA) found that 75% of physicians believe that AI will play a significant role in the future of healthcare.
- According to a Journal of Medical Internet Research study, AI-powered chatbots can help improve patient engagement and adherence to treatment plans.
- A survey by the National Institutes of Health (NIH) found that AI has the potential to help improve the accuracy of diagnostic imaging and reduce the workload of radiologists.
- According to a report by the World Economic Forum, AI has the potential to improve patient outcomes, increase access to care, and reduce costs in the healthcare industry.
Extract medical information swiftly and precisely
Powered by advanced machine learning models, AI and ML comprehend and identify complex medical information quickly and more accurately. For example, the system can extract “methicillin-resistant Staphylococcus aureus” (often input as “MRSA”), link it to the “J15.212” ICD-10-CM code, and provide context such as whether a patient has tested positive or negative, to make the extracted term meaningful.
Protect confidential patient information
An array of tools for AI and ML provides several capabilities to help the Healthcare sector stay firmly compliant and protect patient data. The service is HIPAA-qualified and can identify protected health information (PHI) stored in medical record systems while adhering to the General Data Protection Regulation (GDPR). In addition, our developers can deploy data privacy and robust security solutions by extracting and then identifying relevant patient identifiers as described in HIPAA’s Safe Harbor method of de-identification.
Lower medical document processing charges
The service makes it easy to automate and lower the cost of processing and coding unstructured medical text from patient records, billing, and clinical indexing. Our team of developers can integrate into existing workflow systems and applications.
How can we help you use AI in Healthcare?
Everyday use of Artificial Intelligence in Healthcare involves Natural Language Processing (NLP) applications that can understand and classify clinical documentation. For example, NLP systems can analyze unstructured clinical notes on patients, giving incredible insight into understanding quality, improving methods, and better patient results.
‘Today, much of health data is free from medical text like doctors’ notes, clinical trial reports, and patient health records. However, manually extracting the data is a time-consuming process, and automated, rule-based attempts to extract the data don’t capture the whole story as they fall short of taking context into account. Because of that, data remains unusable in large scale analytics needed to advance the Healthcare and life sciences industry, improve patient outcomes, and create efficiencies.’
AI-driven extraction from electronic health records (EHR)
Features
Innovative items that enhance the data sets to new levels. Extract information from unstructured medical text accurately and quickly.
Medical Reports
Today, much of health data is free from medical text, such as doctors’ notes, clinical trial reports, and patient health records. However, manually extracting the data is time-consuming, and automated, rule-based attempts to extract the data don’t capture the whole story as they fail to take context into account. Because of that, data remains unusable in large-scale analytics needed to advance the Healthcare and life sciences industry, improve patient outcomes, and create efficiencies.
Track and Measure
The proper selection criteria must be quickly discovered to recruit patients for clinical trials in many medical sectors. Artificial Intelligence and Machine Learning understand and identify complex medical information in unstructured text to help make indexing and searching easier. Subsequently, an insight into the patient’s clinical history.
How can AI be integrated and collaborated in healthcare?
Artificial Intelligence can help manage and analyze data, make decisions, and conduct conversations in health care, so it is destined. To remove the burden of tedious tasks and give time back to medical personnel to change clinicians’ roles and everyday practices.
Considerably Improve Diagnosis
Finding the correct diagnosis in the patient notes that should be mapped to the valid code in the International Classification of Diseases (ICD) for a hospital or clinic can be time-consuming and tedious. In addition, extracting diagnoses that can be represented in different ways is particularly challenging. For example, “atrial fibrillation” is sometimes called “AF.” AI and ML can accurately identify abbreviations, misspellings, and typos in the medical text within our system. This reduces the time a medical coder must spend analyzing unstructured notes, decreases the time burden on clinical staff, and improves efficiency.
Intelligent Search
With petabytes of unstructured data being produced in hospital systems every day, our goal is to convert this information into valuable insights that can be efficiently accessed and understood. We embrace AI and ML to comprehend and provide the functionality to help our clients by quickly extracting and constructing information from medical documents to build a comprehensive, longitudinal view of patients and enable decision support and population analytics.
Comprehend Medical
Medical Named Entity and Relationship Extraction (NERe), API returns medical information such as medication, medical condition, test, treatment and procedures (TTP), anatomy, and Protected Health Information (PHI). It also identifies relationships between extracted sub-types associated with Medications and TTP. There is also contextual information as entity “traits” (negation, or if a diagnosis is a sign or symptom). The table below shows the extracted data with relevant sub-types and entity traits.
Application Programming Interface (API) – Connector
With a simple API, we can quickly and accurately extract information such as medical conditions, medications, dosages, tests, treatments and procedures, and protected health information while retaining the context of the data. We can identify the relationships among the extracted information to help you build applications for use cases like population health analytics, clinical trial management, pharmacovigilance, and summarization.
Medical Ontology Concerning
The Medical Ontology Linking APIs identify medical information and link it to standard medical ontologies’ codes and concepts. For example, medical conditions are linked to ICD-10-CM codes (e.g. “headache” is related to the “R51” code) with the InferICD10CM API. In contrast, medications are linked to RxNorm codes (“Acetaminophine / Codeine” is linked to the “C2341132” cui). In addition, the Medical Ontology Linking APIs also detect contextual information as entity traits (e.g. negation).
‘We can help you deploy the latest innovative technology, Artificial Intelligence, and Machine Learning to comprehend, analyse and search Petabytes of unstructured medical data.’
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Artificial Intelligence | Holistic Care | Patient Recovery | Healthcare | Data Analysis | Real-Time Analysis | Chronic Illnesses | Prediction | Patient Outcomes | Early Warning Signs | Preventative Measures | Health Conditions | Heart Disease | Diabetes | Effective Care | Healthcare Providers | Medical Data | Patient Health | Trends | Patterns | Chronic Illness Management | Early diagnosis | Patient Monitoring | Cloud | Servics | Providers | Scalability | Flexibility | Cloud Based Comprehending with AI/ML/NLP
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Stefan Czarnecki
The Blog Post, originally penned in English, underwent a magical metamorphosis into Arabic, Chinese, Danish, Dutch, Finnish, French, German, Hindi, Hungarian, Italian, Japanese, Polish, Portuguese, Spanish, Swedish, and Turkish language. If any subtle content lost its sparkle, let’s summon back the original English spark.
