Some of the most exciting breakthroughs in the future of medical research are beginning to emerge where clinical research, technology, and big data meet. Healthcare is one industry where the vast amount of data created every day offers great promise. The amount of data generated by EHRs, medical equipment, and health-related apps (among other places) now exceeds what humans can reasonably process. This is where AI comes in. AI can assist a clinical research organization like Veristat and medical researchers in analyzing large data sets and identifying trends, which can lead to better medical diagnostics and healthcare services. AI is already being used to improve and speed up disease diagnosis and screening accuracy, create new medicines, and promote public health initiatives.
AI/IOT for remote patient monitoring
Some startups are creating their own monitoring devices and sensors, and then layering on machine learning to analyze the data to enable remote patient monitoring for virtual clinical trials. Others are focusing solely on AI software development and combining it with existing third-party home monitoring systems. Real-time medication adherence studies could be made easier with the help of connected devices. In contrast, some approaches center on gathering physiological information.
AI in Preventative Health Care
Using AI for preventive health care has far-reaching and significant implications. Researchers are currently looking into the application of AI to predict a wide variety of diseases and disorders, not just heart attacks. In memory care clinics, for instance, AI can foresee which patients may develop dementia within two years. More importantly, AI is already being utilized in emergency rooms and ICUs to aid physicians in caring for some of the most fragile and at-risk patients. Electronic medical records, laboratory reports, vital sign recordings, and prescription logs all include enormous amounts of information. Using AI algorithms, healthcare providers can better detect changes in patient health or the emergence of potentially life-threatening complications.
Quality of Electronic Health Records (EHR)
If you were to ask any healthcare worker what they hate most about their job, they would almost certainly mention complicated electronic health record (EHR) systems. Historically, clinicians recorded their observations and patient data manually, with varying degrees of consistency from one to the next. It was often done after the patient’s visit, which left room for error. On the other hand, with AI and deep learning-based speech recognition technology, interactions with patients, clinical diagnoses, and possible treatments can be improved and recorded more accurately and in near real-time.
What does this data mean for clinical trials?
The growing use of mobile devices has positioned Google at the core of the healthcare data ecosystem, providing real-time data that was previously inaccessible while simultaneously obtaining data from EHRs that was challenging to combine. AI and machine learning can be used for early diagnosis, drug creation, study enrollment, and remote patient monitoring. In many studies, participants are randomly assigned to either the study drug group or the placebo group. Then, it will be possible to contrast the symptoms that the experimental group and the control group experienced. The use of patient-generated data for digital twin creation could do away with the necessity for a control group, which would further ease recruitment bottlenecks.
The healthcare industry has always been swamped with an inflow of massive amounts of sophisticated data arriving at a dizzying rate. Although big data and artificial intelligence are frequently hailed as breakthroughs that can improve practically every aspect of the pharmaceutical value chain, integration, and data quality remain critical priorities.