FIGURE 1: Estimated Percentage of Tasks to be Automated from Select Roles

As a result, new capabilities will be required to meet patient needs and appropriately leverage tools. When hiring talent, organizations should consider how skill needs will change as digital solutions evolve, and how they can leverage new sourcing channels to fill gaps as needed.

This redesign of talent roles and functions also aligns with a shift within healthcare payers towards project work: 79% of payer executives note that work in the future will be structured by projects rather than job functions.[2] This enables payers to develop recruiting strategies and programs aimed at reskilling workers, including training on AI-enabled tools, and to invest in automation, digital tools and Artificial Intelligence solutions that will improve employee performance and engagement.

Given the maturity timeline of Specialized AI of 10+ years, the near-term integration of machine learning tools will augment the intelligence of providers rather than outright replace a majority of current tasks. As a result, human responsibilities (e.g., clinical, administrative and technology) will extend to training models, explaining results, and sustaining model efficacy. In return, machines will enhance human capabilities by amplifying speed and accuracy, interacting with patients and providers, and learning to embody provider characteristics.

PREDICTION 2: PHYSICIANS SHOULD BECOME MORE INTEGRATED IN THE DESIGN OF AI TOOLS FOR OPTIMAL SUCCESS

The value potential created by providers leveraging AI tools is only possible with those who are willing to adopt such technologies. While generating substantial evidence is a key facilitator in gaining provider adoption, integrating provider knowledge early into the design of AI-powered tools not only increases willingness of user adoption and confidence in the technology, but it also improves accuracy of the model. Strategic physician insights and their ability to handle edge cases that break rules and patterns will become increasingly important in model design to improve efficacy.

Physician input can help identify key checkpoints and ultimately improve algorithm performance.  Given that for many tools, physicians only gain insight into the results and have limited transparency into key assumptions of the process, involving providers early can highlight areas where increased transparency or checkpoints are required. Physicians can also provide insight on how humans handle anomalies or conflicting pieces of clinical information that software might overlook in favor of the dominant pattern. Additionally, physicians might be better equipped to identify biases than programmers working off of decontextualized data.

As AI evolves from Specialized to Generalized and begins to operate on deep learning protocols – where machines learn to reprogram themselves without human intervention—provider experience and institutional knowledge will be critical for designing the most doctor-like decision making process.

PREDICTION 3: AI WILL ENABLE GREATER PATIENT SELF-CARE

Our thesis at 7wireVentures is that successful healthcare leaders will drive towards empowering an Informed, Connected Health Consumer. Aligning with this belief, as the trend of consumerism perpetuates, patients will become more familiar with and increasingly want to receive care on their own terms similar to other non-healthcare products and services.

Accenture estimates that self-care will meet up to 10% of demand for healthcare services by 2030. Healthcare consumers are already using technology-enabled tools, such as biometric devices and wearables for self-care. Physician willingness to integrate tools also matches their interest- with nearly 66% of physicians noting they “would prescribe an app to help patients manage their chronic diseases.”

Tools such as virtual nurses, technologically-enabled home-monitoring devices, and digital therapeutics are applications that enable personalized patient self-care powered by Artificial Intelligence.

The future healthcare consumer will be familiar with patient self-care and expect to engage in self-care activities both prior to engaging with a provider and as part of ongoing treatment post engagement.

[1] Accenture Strategy 2030 Healthcare Workforce Research, 2017

[2] Accenture Technology Vision research, 2016.