Chief Information Officers – CIOs play an important role in an organization as they are supposed to help develop strategic approaches to diversify revenue sources and accelerate data-driven innovation, while retaining or, preferably, enhancing the technology invested by their company.They are constantly analyzing current and future needs of technology. The COVID-19 pandemic has made the work even more difficult for CIOs of Healthcare organizations as they are constantly deploying large-scale remote work and telemedicine tasks, both of which are demanding from technology and logistical point of view.
When the COVID-19 hit, healthcare organisations learned of the tremendous lack of resources that prevents existing data from being used effectively to leverage high-level technological operations or even made available for collaboration. Machine learning can help CIOs as they have a timely opportunity to integrate machine learning to unlock the value of their data and help their organisations by enabling new revenue opportunities and leading to potentially life-changing, data driven innovation. A machine learning framework that allows artificial intelligence algorithms to learn from data distributed across multiple locations and owners — allows for collaboration with other healthcare organisations to develop more impactful data-driven insights without having to share sensitive data directly.
Machine learning can help CIOs in Healthcare and when implemented in healthcare organisations to process and operate high-level data can have numerous benefits such as:
Privacy and Compliance
It is the duty of the CIOs to safely manage the data of their organisations for purposes related to revenues and analysis. Machine learning fulfils privacy obligations based on consent standards and legislation, such as HIPAA and the General Data Protection Law of the European Union, by holding all data stored in the networks of healthcare organisations. With earlier attempts to secure healthcare data by de-identification falling short (in some cases, de-identification being able to bypass HIPAA), alternative approaches to data protection need to be explored. Indeed, machine learning can help overcome the infamous data de-identification problem that has not been adequately de-identified to re-identify patients and data that has been de-identified to be useless for AI algorithm training.
Security and Access
By holding all data stored only in networks of healthcare organisations, machine learning makes transfers of physical data extremely risky and sluggish a thing of the past. Also gone is the need to rely on data users to return or dispose of data properly after they have finished operating with it. With machine learning systems, anyone allowed to access data can do that safely and efficiently, regardless of where they are located.
Handling existing data
There is an infinite range of health-care data to deal with from X-ray and MRI images, blood pressure measurements and handwritten notes from doctors. Machine learning can benefit by easily integrating with existing healthcare systems or EHRs and structuring all data in order to make it easier to search and act.
Comprehensive security
Key to clinical research is the ability to correlate different data sets, since local data sets are often too small or skewed. This, however, brings with it the troublesome likelihood of correlating data sets being related and potentially de-anonymised. Although it is possible that there will still be a trade-off between data privacy and efficiency, machine learning can help facilitate more safe data correlation by providing differential privacy, which enables data sharing by identifying community trends within data sets although maintaining information about individuals.
