A shift from local servers to the data cloud may be one of the best investments in quality improvement that a healthcare facility can make. The cloud makes it possible to increase the flexibility of data collection, improve the level of cybersecurity, and solve the problem of servicing and server infrastructure development.
However, the biggest advantage in the long term is the flexibility to combine data from different sources and share it within a local health ecosystem. The IT infrastructure in a healthcare facility is constantly developing and covering new systems and devices – only the cloud can ensure convenient scalability of new digital services. The cloud also makes it possible to analyse data for clinical, scientific and administrative purposes. In addition, it helps implement new solutions, such as artificial intelligence systems, applications, web portals for patients, etc.
Clinical decision support systems (CDSS)
The gap between available medical knowledge and its application in everyday clinical practice is growing. The reason is known: staff shortages and quickly increasing medical knowledge. It is estimated that about 1 million new scientific publications on life sciences are published each year in hundreds of sources. No doctor, regardless of the time they have, is capable of following all guidelines, recommendations and new indications, which means that it is impossible to adjust the decision-making process to scientific advances on an ongoing basis.
Clinical decision support systems (CDSS) enable doctors to quickly check whether there are new research results or recommendations applicable to a given disease entity and whether they could be used to treat patients. CDSS combines insights and updates from leading life-sciences journals in one place, while the content is usually supervised by doctors working as editors. In this way, a doctor can be sure that patients receive top-quality treatment and care in line with the principles of evidence-based medicine (EBM).
Artificial intelligence (AI) in healthcare is widely discussed, but its application is still marginal. Even tech-savvy managers do not know where to start implementing AI. Experts advise to first identifying the areas in which automation could improve workflows. The area in which AI is still the most commonly used is medical imaging, where AI supports the evaluation of X-rays and CT scans. However, high-end systems which analyse the ECG signal are already available in cardiology. When it comes to primary healthcare, algorithms already help identify rare diseases on the basis of non-specific symptoms (symptom checkers).
If a medical facility has already digitized patients’ health records, it is possible to use a more advanced tool for identifying patients from risk groups, for example, systems that assess the risk of conditions such as sepsis in hospitalized patients (population health management). A cost-free and the least risky option is a pilot implementation of one of these solutions in cooperation with startups or mature IT companies. Such companies often look for clinical partners willing to test AI algorithms.
Even though possibilities of using virtual reality (VR) in medicine continue to be discovered, there are already dozens of ready-made solutions that healthcare facilities can apply. Such solutions include pain-relief systems, solutions for young patients to reduce their fear of medical procedures, and training courses for healthcare workers (for example, how to handle difficult patients) and doctors (surgery simulations).
VR is perfect for education. For example, a 3D image of a sick organ speaks to the patient’s imagination better when discussing a planned surgery. The progress in clinical applications of VR in recent years is impressive, backed by research on its effectiveness. Moreover, VR is relatively easy to use – you only need to buy a reasonably cheap VR headset and an appropriate system.
Digital therapeutics (DTx)
There are high hopes for DTx because they are bridging the gap in treating diseases in which factors such as behaviour modification, patient involvement and following the doctor’s orders have therapeutic significance. DTx can be used in combination with drugs (for example, in cardiovascular diseases where medication is supplemented with behaviour change) or as individual therapies (for example, to treat back pain or mild depression).
They apply gamification, behavioural economics (which studies the effects of psychological, social, cognitive and emotional factors on the process of decision making), and artificial intelligence to modify the behaviour, attitude and actions necessary for the patient’s health or health prevention.
An advantage of such therapies is that they enable the users to track progress and can be adjusted to the achieved results on the fly. In addition, the effectiveness of digital therapeutics has been demonstrated in a growing number of clinical trials, so doctors can be sure that the application of such therapies is safe and produces tangible results. Some countries even start to reimburse DTx, which can sometimes even be prescribed by doctors.