AI Redefines Healthcare Organizations: Now and In the Future

Responses by Rikin Patel, chief technologist, DXC Technology Americas.

Q: What is the state of artificial intelligence (AI) within healthcare organizations and services now?

Patel: In a 2019 DXC survey of more than 600 global companies, we found nearly half (48%) of healthcare executives and board members expect artificial intelligence (AI) and machine learning (ML) to play a significant role in their digital strategies three years from now — the most of any technology or industry practice. Nearly one in three (32%) expect that assessing and adopting new digital tools, such as AI and blockchain, will be one of the three most critical aspects of digital decision-making in the coming year.

Of course, while discussion about AI has become ubiquitous, and while the promise has been great, adoption in healthcare has been slow. Yet AI has the potential to address multiple shortcomings in healthcare, such as misdiagnosis, treatment errors, wasted resources, process inefficiencies and physician burnout. As the need for high-quality, affordable healthcare mounts, using data to make better-informed healthcare decisions is essential. Healthcare organizations will accelerate the use of AI to better anticipate patient needs, improve treatment decisions and reduce health risks in select cohorts moving forward.

Q: What’s an example of a way in which AI is already changing healthcare?

Patel: Some healthcare organizations’ prioritization of AI is focused on facilitating the rise of precision medicine, which aims to tailor medical treatment to each individual’s specific profile by employing digital analytic tools.

The goal of precision medicine is to provide more accurate information about the patient’s condition, the population subset they belong to, and the treatments that are most effective for that group. This enables both doctors and patients to decide on a course of action that’s more precisely suited to the individual’s profile. AI, and in particular ML, takes precision medicine to the next level with the ability to read large datasets and improve accuracy and prediction of outcome for patients.

Q: How can AI and providers work together to improve health outcomes?

Patel: There is a concern among some providers that AI will replace the need for human workers; however, AI should be viewed as a tool to enhance and scale providers through the automation of simple tasks, freeing up time for providers to spend with their patients (“high tech, high touch”). AI empowers doctors and other healthcare professionals to take better care of patients by identifying and streamlining the who, what, how and where of healthcare, giving providers more time to connect with patients and deliver personalized, compassionate care.

Some examples of more specialized AI to complete complex tasks include:

  • AI can transform personalized care by eliminating the need for doctors to rediscover lessons learned. Using AI to learn from millions of patients, diagnoses and treatments, doctors can spend less time searching for the most effective treatment.
  • Using AI to forecast admissions and plan staffing means administrators can spend less time managing hospital operations, and more time improving care. Executives can dedicate more time to creating chronic disease management programs and community outreach initiatives, such as charitable medical programs for underserved people.

Q: What can we expect in the future?

 Patel: There are three main areas in which AI technologies are being applied in healthcare that will truly come to the forefront in 2020. These three areas are:

  • Digitizing operations: Optimizing workflow to improve workforce productivity and deliver efficiency and effectiveness. This is the most mature area with the most near-term benefits. Machine learning in radiology has been used to detect both incidental and critical findings and could be used to automate basic screening for breast cancer, leaving the more complex interpretation, biopsy and treatment tasks for radiologists. Focus for this area going forward will be to improve back-office performance, talent management, employee turnover/burnout, service pricing, and procurement contract analysis.
  • Improving engagement: Improving how patients, members and consumers engage with healthcare. This is a maturing area as healthcare continues to shift to patient centricity. Focus for this is to better automate and develop data-driven engagement tools to improve experience; balance supply (patient volume) and demand (staff) to optimize staffing models; and improve call center execution through cognitive agents.
  • Supporting clinical diagnostics: Developing solutions that allow healthcare providers to diagnose or advise patients. This is an emerging area and offers the most potential for the long term. Focus for this is to improve accuracy of diagnosis, triage patient cases using unstructured data, clinical decision-making and more.

Finally, as providers, payers and other organizations become more aware of how much data’s value increases when it is shared across wider ecosystems, secure data sharing can lead to improved individual and population health outcomes.

However, for the healthcare ecosystem to flourish, we need trust mechanisms that validate both the individual’s right to share and the enterprise’s right to consume data. Self-sovereign identity standards such as Decentralized Identifiers (DIDs), verifiable credentials based on zero-knowledge proofs and blockchain-based consent are emerging to address these challenges. Through the combination of AI, IoT and distributed ledger technology, we will see providers and patients willing to share data in data exchanges to help improve healthcare outcomes for their families at home and communities throughout the world.

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