The use of artificial intelligence in healthcare is becoming popular. However, as with the adoption of AIOps in other industries, some healthcare providers find it difficult. Since AIOps is constantly evolving and its scope is expanding, it is understandable that not everyone will be able to understand it. From this lack of knowledge, most challenges in AIOps adoption arise. However, AIOps can be a massive boon for the healthcare industry once these challenges are overcome.

Challenges in AIOps Adoption for Healthcare Providers

Implementing AIOps is not an entirely novel idea, and this technology has existed for quite some time now. However, AIOps adoption is still difficult for several healthcare providers. This is because many find it tough to trust such widespread use of technology in an industry as critical as healthcare. It is why healthcare providers face the following challenges when it comes to adopting and integrating AIOps in various operations and services.

• Lack of Clinical Use Cases – Since the use of AIOps is relatively new in most industries, many are still unsure about its benefits. This can prove to be a challenge for adopting the technology. This is because there are not many use cases of clinical AIOps. However, the use of AIOps needs to start somewhere for there to be actual studies and cases. There is also an issue with the adoption of AIOps solutions. It is that the end-users are not always supportive of it. This, too, stems from the fact that there is a lack of knowledge regarding clinical AIOps, and it is often difficult for the end-users to understand the existing use cases if any. This makes it challenging to integrate AIOps into the existing workflows.

• Cost of AIOps Adoption – While it is true that AIOps, in the long run, can lead to cost-effective operations, the initial adoption and maintenance can lead to higher costs for a certain period. It would take some time for the healthcare providers to reach a point where AIOps implementation ensured lower costs than traditional, manually operated systems. There is a cost in hiring AIOps experts during the adoption process. These experts would need to work with the existing healthcare workers to help them navigate the solutions for a while. There is also a cost in developing AI-led product engineering services in healthcare, without which it is not possible to transform the traditional systems completely. The cost of AIOps adoption is a significant challenge, particularly for those healthcare providers who cannot spend a considerable amount on new technology that might take a while to be streamlined.

• Shift from Manual Clinical Processes – The healthcare industry is focused on traditional systems. This is because most do not trust automated systems to take over, especially when it is a matter of life and death. Healthcare providers, especially those who rely on traditional methods of diagnosis and treatment, find it challenging to adopt AIOps. In fact, they are pretty suspicious of the solutions. This suspicion arises from finding AIOps to be unethical, to an extent. Automating certain parts of the medical practice can be difficult, mainly if they are related to the treatment of a severe medical condition. However, the shift from manual systems is impossible without automation and digital transformation. Unless the question of ethics is clarified, the change to AIOps systems can be pretty tough. This delay in adopting automation limits the potential of AIOps and what it could do for the healthcare industry, especially in terms of increased efficiency of services and enhanced patient care.

• Inadequate Transparency – There is a question regarding the transparency in AIOps systems. While all of the challenges in adopting AIOps come from a lack of understanding, the transparency issue is almost only focused on the healthcare industry. In healthcare, there is a need for absolute transparency because it can determine the treatment and the medical outcome of a patient. Since there is a lack of adequate data on clinical trials with AIOps, the problem with transparency increases. However, there has been much progress with AI-led operations management services in healthcare and solutions like imaging informatics. However, this continues to be a niche area, and not all healthcare providers are well-versed in it. As long as there are inadequate information and use cases, the question about transparency will continue. Adopting AIOps will be a challenge, especially for those who continue to use and trust traditional manual processes.

Why is AIOps Adoption Necessary in the Healthcare Industry?

Despite there being many challenges, many healthcare providers are approaching AIOps solutions. They are beginning to implement these solutions in certain aspects of healthcare services, and that is a good start. This is because AIOps has multiple benefits for the healthcare industry.

AIOps can ensure better patient care through machine learning and predictive analytics tools. Implementing AIOps solutions through healthcare predictive analytics software services can help to process and analyze patient data and help physicians arrive at accurate insights. These insights can determine efficient diagnostics and treatments without forcing the patient to undergo numerous tests, scans, or unnecessary medications.

AIOps can also enhance cybersecurity and risk management services in healthcare. AIOps-led systems are secure and constantly monitored to ensure no error, thus reducing the risk of malicious attacks. This ensures that no sensitive medical information or patient data is compromised.

Other benefits of AIOps adoption include:

• Cost-effective solutions
• Optimization of healthcare systems
• The possibility of accurate self-diagnosis
• Safer and quicker record-keeping
• Healthcare data interoperability

Conclusion

There are various AI tools for digital transformation in the healthcare industry that help to shift from on-premises databases to cloud-based systems quickly. Such tools will allow healthcare providers to provide better patient care and services. The challenges in AIOps adoption can be overcome through the availability of information. It is essential for experts who deal with clinical AIOps to provide this information to others. There is a need for integrating and streamlining AIOps solutions into clinical workflows. Once that is possible, the process of adoption will no longer be as difficult.