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Sep 21, 2022
Data Strategy And Roadmap

How AI Can Unlock the Full Potential of Healthcare Data

Artificial intelligence (AI), machine learning (ML), and other related technologies are gaining popularity in business and society and are slowly being leveraged in the healthcare industry. These technologies have the potential to transform and revolutionize several healthcare aspects, including patient care and administrative processes within the provider, payer, and pharmaceutical companies.

There are already a couple of studies and research papers indicating that AI can perform as well as or better than human physicians at primary healthcare tasks, including disease diagnostics. At present, algorithms are already surpassing human radiologists in terms of detecting cancerous tumors and assisting researchers in constructing cohorts for expensive medical trials.

According to a 2021 Healthcare AI Survey by Gradient Flow, the industry is likely to invest in technologies such as data integration (45%), natural language processing (36%), and business intelligence (33%) in order to unlock the full potential of their structured and unstructured data. These are not just magnificent goals – they are backed by huge investments – and make up around 8round 80% of all healthcare data.

Why is the Majority of Medical Data Highly Unstructured?

The majority of medical data is highly unstructured because medical imaging, EHR, EMR, and medical diagnostics were never intended to be structured in the first place. In fact, structured data is generated mostly to fit into prebuilt table architectures, while medical data does not fit into any such bracket as it mostly contains free-form fields.

Other industries such as e-commerce, retail, hospitality, finance, logistics, and so on, are composed of homogenous data and it does not require much interpretation. On the other hand, MRIs, patient data, medical images, prescriptions, and X-rays, when digitized, need to follow certain codes and healthcare standards. These factors make healthcare data difficult to interpret by machines and hence most of it cannot be translated to a relational database or table architecture.

While EHR systems have grown in popularity in the last few years, a huge volume of data still exists in physical formats such as fax. In order to translate scanned records into a machine-interpretable format, additional tech support through Optical Character Recognition (OCR) is required, which can help utilize these records in analysis tools.

What is the Role of AI in Structuring Data?

The 4th annual Optum Survey carried out on AI in healthcare suggested that experts believe in the potential of AI to help enhance patient outcomes, reduce costs, and promote health equity. All of this will be achieved through the application of advanced forms of AI to accelerate the data flow between payers, providers, and patients.

For medical AI to fulfill the promises of technologists moving forward, the responsibility is on medical companies to better manage unstructured data and offer smart systems with high-quality data to measure and improve accuracy and reliability of their training. This not only builds a continuous cycle of learning but also lays the foundation for future insight.

A lot of industry experts are eager to see where the technology provides the most profit. Healthcare managers are ready to integrate AI to improve the quality and reach of data. A study by IT services and consulting firm Accenture indicated that almost 80% clinicians see the role of AI moving beyond administrative use cases to more robust use cases.

However, the secondary and tertiary use cases will become more complex unless a strong data infrastructure is first put into place. Clinicians and industry experts must demonstrate the reliability and ability of AI with more sophisticated tasks to earn and maintain vendor trust in the technology.

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Why Timely, Accurate Information is Required in Healthcare Delivery, Management

The medical industry has a growing data problem that is getting in the way of personalized patient care. Unstructured data is not being utilized to build an overall image of a person’s health and well-being. With incomplete knowledge, decision-making can easily prove insignificant, thereby resulting in poor outcomes and high expenses.

AI is the answer to timely access to accurate data, which, in turn, will provide clinicians and healthcare managers with the information required for achieved affirmative patient outcomes. Physicians understand the potential of high-quality data. A research study involving respondents suggested that 96% of them believed easier access to primary data will help save lives. Moreover, the study also indicated that 95% considered data interoperability as the pillar of improving patient outcomes. 86% of respondents believed efficient exchange of healthcare information will lead to reduced costs, faster diagnosis, and accurate outcomes.

In any case, physicians are experiencing several issues lately. Studies suggest that almost 62% are responsible for entering the same patient into different EHR systems while a whopping 68% are having difficulty finding information in multiple EHR system records.  The use of unreliable EHR technology is the fundamental reason physicians are compromising on their ability to deliver quality. One of the major factors that could result in improved personalized care is reducing the overall time spent in finding and updating patient data.

In the medical industry, increased efficiency and yield represent the most sought-after improvements across businesses. With more and more innovations in AI having reached a production environment, medical institutions must leverage the technology to make sure that their providers are making informed clinical decisions based on well-validated and reliable information.

If organizations fail to secure investments in AI solutions that make high-quality data accessible to clinicians, it will limit the applications and use cases of AI in healthcare over the long haul. Machine learning will allow smart systems to leverage NLP to recognize and extract key data elements that will accelerate clinical decision-making and reduce the overall time to patient care. Consider a scenario where unstructured data becomes structured efficiently and securely. That’s the goal that healthcare professionals and care managers have, and technology can turn that vision into a reality.

With companies and businesses increasing their AI investments over the next 5 years, AI scientists and researchers must base their analytical insights on a secure infrastructure of precise, complete, and coherent data. The initial step to achieving this is by turning unstructured data into actionable information. Medical and healthcare institutions will then be prepared to utilize future advancements in AI that is poised to transform patient care.