Data science techniques, including artificial intelligence (AI) and machine learning (ML), share a symbiotic relationship with healthcare and can help solve many critical healthcare challenges. Whereas data science involves drawing meaningful insights through the analysis of data, healthcare collects and generates vast amounts of data, both critical and general, to provide best possible services to the patients. The adoption of Electronic Health Records (EHRs) — that use a data science toolset for the benefit of medical operations — has emerged from the confluence of these two domains.
Within data science, many different techniques are available for classification, analysis, and interpretation of data. Two of the most common techniques used are artificial intelligence (AI) and machine learning (ML). Whereas AI mimics human brain functioning for data analysis, ML is a subset of AI that employs complex pattern-matching algorithms to make conclusions based on historical data. In healthcare, the use of AI can make data collection more up to the mark, relevant, and accurate whereas ML algorithms can expedite workflows, update databases, and improve diagnostic accuracy.
As the name suggests, Electronic Health Records is the digital collection of health reports of patients. These records are maintained by providers from time to time and may comprise all relevant clinical data of patients who are either under treatment or given a treatment in the past.
The EHR system is getting adopted into the healthcare infrastructure at a rapid pace, owing to some of its key benefits, such as easy availability of patient records, workflow optimization, and security. Data captured through an EHR system can be organized and processed in real time for further data science operations, such as predictive analytics, diagnostics, descriptive analytics, and more. These data are secured through encryption, anonymization, and data loss protection routines, allowing only authorized users to access the data. In addition, the integration of EHRs streamlines the daily workflow of healthcare professionals.
Artificial intelligence (AI) and machine learning (ML) have become an integral part of the technological infrastructure, including health information technology (IT). Healthcare IT is especially benefitted by these technologies in systems like electronic health records (EHRs), where AI and ML can render their strengths in full capacity.
EHR devices integrate with devices and their specialized algorithms can manage vast amounts of data more precisely and much faster than healthcare professionals and human scientists. This helps in rapidly discovering patterns to increase the speed of disease diagnosis, improve public health and safety, and inform treatment plans.
In the healthcare domain, AI can be used to evaluate a DNA sample to diagnose diseases and, through AI-based smartphone apps, identify concussions and monitor other concerns such as lung infections, jaundice in newborns, blood pressure and hemoglobin levels, and cough, among many other diseases.
ML use in healthcare helps connect patient’s EHR data comprising diagnoses and medication to quantify disease risk. In addition, based on the available data, ML algorithms can predict the future concerns related to a particular disease. This helps clinicians and other medical professionals suggest the appropriate care and treatment plans for their patients.
According to American Medical Association (AMA) estimates, clinicians spend about half of their time on repetitive chores, including updating papers, recording orders and patient information, and billing. This reduces the amount of time professionals can devote to improving patient care and diagnosis.
However, using AI, the amount of time clinicians spend on mundane, repetitive tasks can be cut by half or entirely removed. Natural language processing (NLP) models, which transform handwriting and speech records to text and assist therapists update pertinent information in real time, are largely responsible for these time savings.
Healthcare services need to be delivered as quickly as feasible, especially in emergency situations, such as when patients are admitted because of an accident. Thus, clinicians and other healthcare professionals should be able to swiftly get the precise information they require about their patients to begin treatment. They cannot afford to scroll through pages of material to find what they are looking for at that time. The use of AI addresses this issue by direct extraction of the important and exact data. Abstractors are featured in several cloud based EHR portals that allow professionals to retrieve certain details, notes, or data about a patient.
One of the main advantages of AI in EHRs is improved healthcare administration automation. The mere presence of vast amounts of data is sufficient to enable advanced automation and seamless healthcare management.
Appointment management, roster development, staffing, bed management, staff morale, and other issues might be readily resolved with AI. Predictive analytics-powered AI modules can assist administrators in forecasting readmissions, appointment calendars for the day or the week, patient fatality rates, recovery rates, and even in managing the hospital inventory supply chains.
AI has proven its worth in upscaling modern-day healthcare infrastructure and improving the range and quality of healthcare services. However, healthcare data is also helping build better AI models, thus outlining the interdependent nature of healthcare IT. The use of vital patient and treatment efficacy data from EHRs, such as those related to the progress of chronic diseases like diabetes, can make AI models more efficient at recognizing future disease risks and suggesting the best treatment plans.
Large volumes of image data can also be analyzed using ML. Convolutional neural networks (CNNs) are algorithms that use learnable features or weights to discern details from input images. CNNs have been used to diagnose heart illness using echocardiography pictures and films and to identify skin malignancies from images of skin lesions.
Increased integration of AI and ML technologies can be of significant help in improving the standards of EHR features. AI can help in improving the diagnostic and treatment capabilities of clinicians in healthcare services, whereas ML can help in the effective data management of patient records.
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