Effective healthcare provision relies heavily on the databases maintained and while great efforts are made to digitize the necessary data, there is often too much to look through and it could possibly be old and irrelevant. Real-time data is obtained to ensure that the input available to the healthcare professionals is collected at the point of care and is easily accessible. Real time data acquisition involves the recording and observation of clinical data ranging from the assessment of patients to action taken with the patient. To achieve the efficient gathering process of real-time healthcare data, the medical sector turns to the implementation of Artificial Intelligence and machine learning.
AI adoption in Healthcare
AI has seen its fast-paced advancement over the last decade and has been embraced in all walks of life, including the management of healthcare IT. AI has been used for monitoring of trends of healthcare data. Beside the observation of disease and recovery of patients, AI has also found use in the management of the hospital databases, keeping track of information like the available hospital bed, date of discharge of patients, demand of bays, etc.
AI as a part of EHRs
A hospital’s Electronic Health Records are intended to reduce the workload of the healthcare professionals, however with redundant, obsolete and unorganized data it may often lead to greater difficulties and an inefficient system. With the integration of AI and machine learning, the EHRs may be streamlined to make the data more comprehensible and accessible. The adoption of rule-based systems in the EHRs allow the machine to make clinical decisions. However, ethical decisions are to be taken only by the physician and not by binary systems.
Real time data entry permits the reduction of time required for the computing of the lab results assisting the healthcare professionals in decision making. Additionally, researchers are able to view the data collected from thousands of patients to recognize trends and aid in determining trends in deterioration or recovery of a patient.
Internet of Medical Things
The Internet of Things (IoT) is a network or system connecting devices and physical objects with a computing and processing ability, having sensors and other technology that connects and exchanges data across the devices and systems in the network. In the healthcare industry, the IoT aims to expand the scope of medical care by using large amounts of healthcare data to optimize healthcare services through machine learning. In addition to this it also replaces large, inconvenient machinery with smaller robotic instruments and devices posing the ability to transfer data into the respective EHRs.
Conclusion
Real-time data entry permits the functioning of healthcare professionals to be more efficient. The use of AI in the acquisition of real time data allows simple management of healthcare data and IT. It also ensures accessible EHRs with no redundancy of information. Machine learning allows easy observation of trends and making of clinical decisions. The IoT aims to ensure easy transfer of data between devices and expands the scope of medicine.
