A health information system (HIS) refers to a system designed to manage healthcare data. This includes systems that collect, store, manage and transmit a patient’s electronic medical record (EMR), a hospital’s operational management or a system supporting healthcare policy decisions.
Health information systems also include those systems that handle data related to the activities of providers and health organizations. As an integrated effort, these may be leveraged to improve patient outcomes, inform research, and influence policy-making and decision-making. Because health information systems commonly access, process, or maintain large volumes of sensitive data, security is a primary concern.
Point of Care is a powerful combination of software and hardware designed to help care providers deal with administrative tasks, rapid diagnostics, medical records storage and transmission. Point of Care platforms are often confused with Point of Care Testing (POCT) that allows running medical tests and get rapid diagnostics. POCT can include glucose monitoring, lateral flow assays, immunoassays and more. However, POCT devices can be integrated into the Point of Care system providing real-time updates on important health indicators.
Point of Care can optimize the internal and external hospital administration processes like registration, scheduling, billing and even suggesting the right specialist basing on patients’ health complaints. As PoC can provide a feature of creating and managing profiles by using a unique ID number, patients can easily get access to their health-related data, history of visits, insurance information and more or make an appointment independently.
An electronic health record (EHR) is a digital version of a patient’s paper chart. EHRs are real-time, patient-centered records that make information available instantly and securely to authorized users. While an EHR does contain the medical and treatment histories of patients, an EHR system is built to go beyond standard clinical data collected in a provider’s office and
can be inclusive of a broader view of a patient’s care. EHRs are a vital part of health IT and can:
One of the key features of an EHR is that health information can be created and managed by authorized providers in a digital format capable of being shared with other providers across more than one health care organization. EHRs are built to share information with other health care providers and organizations – such as laboratories, specialists, medical imaging facilities, pharmacies, emergency facilities, and school and workplace clinics – so they contain information from all clinicians involved in a patient’s care.
Artificial intelligence in healthcare is an overarching term used to describe the utilization of machine-learning algorithms and software, or artificial intelligence (AI), to emulate human cognition in the analysis, interpretation, and comprehension of complicated medical and healthcare data. Specifically, AI is the ability of computer algorithms to approximate conclusions based solely on input data.
What distinguishes AI technology from traditional technologies in health care is the ability to gather data, process it and give a well-defined output to the end-user. AI does this through machine learning algorithms and deep learning. These algorithms can recognize patterns in behavior and create their own logic. To gain useful insights and predictions, machine learning models must be trained using extensive amounts of input data. AI algorithms behave differently from humans in two ways: (1) algorithms are literal: once a goal is set, the algorithm learns exclusively from the input data and can only understand what it has been programmed to do, (2) and some deep learning algorithms are black boxes; algorithms can predict with extreme precision, but offer little to no comprehensible explanation to the logic behind its decisions aside from the data and type of algorithm used
The primary aim of health-related AI applications is to analyze relationships between prevention or treatment techniques and patient outcomes. AI programs are applied to practices such as diagnosis processes, treatment protocol development, drug development, personalized medicine, and patient monitoring and care. AI algorithms can also be used to analyze large amounts of data through electronic health records for disease prevention and diagnosis.