5 ways to let AI raise patient satisfaction, elevate provider performance
It’s been years since AI proponents started promising big returns on healthcare providers’ investments in the technology. The results have yet to catch up with the pitches. What’s the holdup?
Analysts at McKinsey & Company may have the answer.
“Organizations are caught between the excitement to quickly seize the opportunity and a lack of alignment on where to start, alongside general caution given the potential risks related to deploying AI,” they write in a report released Nov. 15.
To help accelerate the use of AI in upgrading the consumer experience—while also boosting providers’ bottom lines—the authors propose five steps. Here are key excerpts.
1. Tackle the 70% problem of data readiness.
Delivering tangible value for healthcare consumers through AI requires integrated data ready for consumption—a “challenging task that represents, on average, 70% percent of the work when developing AI-based solutions,” the McKinsey subject-matter experts write. More:
‘To surface meaningful insights, AI adopters can complement their clinical and patient data with information on social determinants of health, patient-reported outcomes, retail purchases and wellness trackers.’
2. Zero in on consumer experience priorities to ensure AI success.
This is a critical step to avoid trying to do too much at once, which can limit meaningful progress, the authors point out. “To prioritize areas of focus, it is imperative to engage cross-functional leaders in the organization,” they add.
‘Clinical leadership in particular has firsthand insight on patients’ pain points and what exactly isn’t working in care delivery and consumer experience.’
3. Optimize real-time insights for AI-powered interventions.
By analyzing details such as a patient’s appointment preferences and how or when they have responded to outreach, McKinsey notes, AI can tailor the timing, frequency and message themes to provide recommendations most likely to resonate. More:
‘Gen AI can further enhance the effectiveness of these timed interventions with hyperpersonalized message content.’
4. Map AI risks in healthcare and develop mitigation plans.
Besides data-use transparency, organizations “can provide consumers with clear logs and documentation on AI systems, including bias mitigation strategies and training protocols such as details on the population profiles used.” More:
‘Mature, integrated data repositories built to power AI can become valuable targets for cyberattacks: 2023 broke the record for healthcare data breaches, logging some 725 breaches of 500 or more records, more than twice what was reported in 2017.’
5. Level up your team’s AI capabilities.
“One way to increase the likelihood of success in AI implementation is to employ a copilot model, where employees work alongside AI tools to make incremental process improvements,” the McKinsey analysts write. “This capitalizes on AI’s speed and capacity with the checks and balances of human skill and intuition to mitigate errors and risks.” More:
‘Importantly, this process includes periods of capability testing and learnings collection within a small set of users prior to scaling across the enterprise. Such a test-and-learn tactic allows organizations to de-risk scaling and to measure impact and adoption within existing workflows.’
Dave P. has worked in journalism, marketing and public relations for more than 30 years, frequently concentrating on hospitals, healthcare technology and Catholic communications. He has also specialized in fundraising communications, ghostwriting for CEOs of local, national and global charities, nonprofits and foundations.