Medical science is never stagnant and is constantly evolving. However, within the healthcare industry, there remains a lack of personalization. The same healthcare services and medications are available for all patients. While such treatment plans may work for a majority, they may fail to be effective for others. This is because every patient has a different medical history and can also be sensitive to certain forms of medications. This is why there is a need for personalized healthcare. While this may sound impossible and expensive, it is very much possible and quite cost-effective as well. To ensure personalized healthcare, there is a need for the implementation of advanced technology like AIOps. The use of AI in healthcare IT solutions is already popular, but solutions like machine learning, artificial intelligence, and predictive analytics are not limited to data processing. It can also impact treatment plans and various other healthcare services.
Can AIOps Provide Personalized Healthcare Services?
AIOps is instrumental in ensuring the best outcomes within the healthcare industry. It is possible to utilize AIOps solutions for developing healthcare services that are customized to specific patient needs. Healthcare workers can also implement AIOps tools for drug development and in medical research.
Following are the ways in which personalized healthcare can be provided to patients with the help of AIOps.
- Accurate Analysis of Medical Imaging – AIOps solutions can support and automate the reviewing of images and scans. The implementation of AIOps tools like predictive analytics will help to gain important insights regarding medical imaging. This helps doctors identify problems that they might have missed otherwise. In clinical studies, there might be multiple reports of medical imaging, and processing each manually will inevitably lead to errors. The use of AIOps along with a doctor’s knowledge will help to accurately analyze and determine the diagnosis. AIOps solutions like patient access analytics can also assist in processing scans and ensuring the correct reading of them so that there are no discrepancies in the electronic health records (EHRs).
- Reduction in Drug Development Costs – There is a cost to develop drugs and if these drugs are the same as those already available, then that research can become unnecessary. AIOps solutions can obtain data on molecular structures of both existing and potential medicines. Analysis and comparative study of these will help to develop drugs that are potent and effective. This analysis can occur within a very short period of time, thus cutting down on time and cost. The overall cost of drug development is usually high because of the time spent on various different processes and research. If this can be minimized, the cost will reduce automatically.
- Disease Forecast – AIOps can assist healthcare workers to analyze a patient’s conditions and determine the risk of certain diseases. This is possible through the implementation of healthcare predictive analytics AI/ML services software. An example of this is the detection of acute kidney injury (AKI). AKI is usually difficult to detect through the usual methods of diagnosis. But it can lead to a rapid deterioration of health and even become life-threatening. This leads to life-long treatment plans and kidney dialysis which is very expensive. This is why AIOps solutions are necessary. Through predictive analysis, it is possible to monitor patient health and analyze specific conditions. Predictive analytics can help to determine if there is a risk of AKI. Early detection, in this case, can help the patient to live a happy and healthy life, and that is what AIOps offers.
There are several more ways in which AIOps can ensure the availability of personalized care and medication. It can aid cancer research and help develop treatment plans that are convenient to the patient. It can also assist in the development of genetic medicine. Apart from medical research, AIOps is instrumental in ensuring automated medical emergency assistance. This can be personalized according to the needs of the patient at that point in time.
Scope of Precision Medicine with AIOps Implementation
Precision medicine is a medical model or form of healthcare that proposes to customize healthcare services available to patients. It is based on the belief that treatment plans, products or drugs, and medical assistance can be personalized according to the needs of specific patient subgroups.
Even today, the healthcare industry functions on a model that advocates for a one-drug-suits-all viewpoint. This is not only ineffective for many patients but can also be harmful to those with specific sensitivities. Therefore, it is crucial to shift to a more flexible form of healthcare, one that caters to different medical needs and can be customized.
AIOps plays an important role in enabling the use of precision medicine. Precision medicine relies on data from various medical technologies, most of which are disruptive. These include at-home health sensors, genome sequencing, or even advanced biotechnology. Precision medicine is also based on specific deep learning algorithms that are available through advanced supercomputing. Such algorithms can identify the risk of specific diseases in a certain patient subgroup, and then analyze that data to come up with the best course of treatment.
Efficient detection through analysis of multiple sources is not possible through manual processes or traditional statistics. This is why the use of artificial intelligence in healthcare is critical. The application of precision medicine can affect numerous patients and enable them to access effective and relevant medication and healthcare services.
Healthcare needs to be both universal and personalized. It is important to understand that each patient has different needs and the same healthcare services cannot be convenient for everyone. The healthcare industry needs to take a collective step towards personalizing the services available. There is already a move towards AI for health cloud enablement services and while digital transformation is essential, it is equally important to extend that towards diagnosis, treatment, and research for specific patient requirements.