Running a digital publishing and technology company keeps me busy. Writing about it is therapy.
Artificial intelligence (AI), machine learning and data science are really starting to shape the delivery of healthcare services. We see it in almost every significant activity, from the management of patient scheduling through to physically assisting surgery.
Technology has long been the driving force in the evolution of healthcare. New medicines, procedures and equipment are constantly being invented to improve the range and quality of health services.
I find the new dimensions of technological progress, including AI and robotics, to be intriguing. The infiltration of autonomous procedures, quietly removed from the human touch, can be both reassuring and worrying. They mean fewer human errors but also less direct control by a thinking, feeling person.
Artificial intelligence, machine learning, data science, and other automation based procedures were invented to augment human effort. They are easy to operate and bring effectiveness and efficiency to work processes. AI has already been successfully used in banking, investment, mining, security, transportation, and a host of other sectors.
To understand the changes in healthcare, each new encroachment by computer algorithms needs to be looked individually. If we go through several of the major developments, we can see that AI, machine learning and data science are being used with good reason.
The motivation is understandable: the new technologies allow services to be delivered more cheaply and to a higher standard.
AI and machine learning can serve to decongest the waiting line via online booking of appointments. Patients are, for example, more likely to arrive at a hospital only when it is actually the time to see the doctor. These patients can also interact with AI-powered chatbots that can see to their immediate health needs before they gain access to a health professional.
The digital management of patient information can help in keeping the hospital paperless and information retrieval very effective. With this initiative, you can have the unobstructed flow of information between the concerned parties in a hospital.
Machine learning and natural language processing can assist doctors in keeping close tabs on each patient visit. Recent research shows that medical doctors can now use wearable technologies like the Apple watch to record patient visits. This allows physicians to concentrate on listening to the patients’ concerns and they can lodge data into electronic hospital records easily.
There is also a growing body of possibilities in telemedicine where home care robots can connect patients with their doctors via video calls for tips on how to stay healthy. This takes away the burden of having to check on the patient from time to time.
There has been a general increase in the adoption of robots in emergency situations. They can make healthcare available in cases of emergency or when the doctor is not readily in sight. People can interact with healthcare personnel on how to keep an accident victim alive before the arrival of the ambulance.
Machines can use previous data to diagnose the presence of a disease condition. Although, there is an increased concern on the accuracy of the results, it has been shown that machine-based diagnosis and prescription can be very accurate. It can predict the outbreak of a disease condition or identify individuals who are vulnerable to some disease conditions.
Digital monitoring can serve to keep the doctors informed of the latest and past happenings in the life of a patient and why the doctor may need to keep a close eye on them. AI-based systems can study the clinical signs presented by a client to prescribe the right medications to be taken.
Da Vinci robots are leading the race in the utilization of mechanical arms to assist doctors in carrying out surgical procedures. The various machines can handle delicate organs with speed and accuracy. They also assist the surgeon in gaining access to some organs and tissues that may be difficult to work with.
Although machines are far from attaining full autonomy -- i.e. the ability to work unsupervised -- researchers have identified how they can bring greater efficiency to surgical procedures.
The days may be numbered when nurses are busy and hurried by tasks such as drawing blood, keeping a close eye on patients, monitoring vital signs, and moving patients around. Nursing activities are being streamlined and simplified by the right application of AI-powered robots.
AI-powered systems can draw blood, help patients to move, and also monitor vital signs without the intervention of the nurses. Technological assistance gives nursing staff more time to focus on health-based services that require human hands, empathy and oversight.
The potential to use data science in medicine to harness the power of big data remains enormous. The healthcare industry generates huge volumes of biomedical data. Data from billions of patient encounters is recorded in electronic health records, by scientific instruments and in clinical decision support systems.
Tapping into the potential of big data requires data scientists to turn their hand to medical industry issues. And it's been suggested that many doctors could do well by learning data science.
Doctors could, for example, do online postgraduate courses in data science that are available to people who are not information technology professionals (examples here). This may enable them to better carry out tasks such as diagnosing patients using time-series or multi-parameter data, better interpreting visual representations of observational data, and better interpreting the results of large clinical studies.
Effectively applying data science is a challenge that the healthcare industry faces, along with other industries where valuable big data exists. Properly meeting this challenge will enable health service providers to continue improving patient outcomes.
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