Healthcare SystemsThe use of data interoperability, artificial intelligence (AI), and machine learning (ML) have been particularly impressive in the Healthcare sector.

Recently, the healthcare systems, overwhelmed by COVID-19 patients, relied heavily on technology to become more efficient. This meant streamlining in-patient care, relying on remote technologies, using big data analytics to make decisions, and even accepting the risks of weak cybersecurity measures. One big indicator of this is how IT teams have become part and parcel of hospital operations. From cybersecurity to AI applications, technology companies are developing more and more solutions for healthcare clients. The use of data interoperability, artificial intelligence (AI), and machine learning (ML) have been particularly impressive. The healthcare technology market trends reflect this increase in investment and the urgent need for advanced digital solutions. According to CB Insights, global healthcare funding hit a new record in 2020. There were over 5,500 deals that amounted to a total of $80.6 billion raised as equity funding. There were a record 187 healthcare mega-rounds ($100M+) as well.

During the first months of the pandemic, the percentage of healthcare consultations that were carried out remotely shot up from 0.1% to 43.5%. The reasons for this increase are obvious – but even when we take communicable diseases out of the equation, there are plenty of good reasons to develop capabilities to examine, diagnose and treat patients remotely. In remote regions and places where there are shortages of doctors (such as China and India) this trend has the potential to save lives by dramatically expanding access to medical treatment.

Extended reality (XR), virtual reality (VR), augmented reality (AR), and mixed reality (MR) have potentially transformative applications in the healthcare sector. VR headsets are used to train doctors and surgeons, allowing them to get intimately acquainted with the workings of the human body without putting patients at risk, or requiring a supply of medical cadavers. VR is also used in treatment. This can be a part of therapy, where it has been used to train children with autism in social and coping skills. It's also been used to facilitate cognitive-behavioral therapy (CBT) to assist with chronic pain, anxiety, and even schizophrenia, where treatments have been developed that aim to allow sufferers to work through their fears and psychosis in safe and non-threatening environments.

The high-level use case for AI in healthcare, as in other sectors, is in helping to make sense of the huge amount of messy, unstructured data that’s available to capture and analyze. In healthcare, this can take the form of medical image data – X-rays, CT, and MRI scans, as well as many other sources, including information on the spread of communicable diseases like covid, the distribution of vaccines, genomic data from living cells, and even handwritten doctors' notes.

In the medical field, current trends around the use of AI often involve the augmentation and upskilling of human workers. For example, the surgeons working with the assistance of AR, mentioned in the previous section, are augmented by computer vision – cameras that can recognize what they are seeing and relay the information. Another key use case is automating initial patient contact and triage in order to free up clinicians' time for more valuable work. Telehealth providers like Babylon Health use AI chatbots, powered by natural language processing, to gather information on symptoms and direct inquiries to the right healthcare professionals.

Another field of healthcare that will be deeply impacted by AI in the coming years is preventative medicine. Rather than reacting to illness by providing treatments after the diagnosis, preventative medicine aims to predict when and how the illness will occur and put solutions in place before it even happens. This can include predicting where outbreaks of contagious diseases will occur, hospital readmission rates, as well as where lifestyle factors like diet, exercise, and environment are likely to lead to health issues in different populations or geographical areas (for example, predicting opioid addiction in communities, or which patients who self-harm are most likely to attempt suicide.) AI makes it possible to create tools that can spot patterns across huge datasets far more effectively than traditional analytics processes, leading to more accurate predictions and ultimately better patient outcomes