Burdened by an overabundance of KPIs, the health care sector can look to machine learning to force a focus on the metrics that matter most.

Health care is an industry where innovation predominates and demands for greater accountability have intensified. In this enormous, complex, and highly regulated sector, there is much to measure to improve patient outcomes, lower costs, and maintain trust. Importantly, concerns over patient privacy increasingly dominate discussions about using first-party data. Organizations increasingly confront the challenge of keeping their key performance indicators (KPIs) from becoming unwieldy and unmanageable. Dr. John Halamka, CIO of Beth Israel Deaconess Medical Center, hints at the challenge when he observes that in a complex organization like Beth Israel — comprising tens of thousands of doctors, nurses, IT staff, and other employees, not to mention patients — KPIs vary from situation to situation. “For me, there may be four or five what I’ll call high-level key performance indicators,” he says. “But for the IT operation, there are over a hundred.” That kind of misaligned and conflicted KPI overflow is not sustainable.

This KPI overload — along with a tendency to rely on intuitive decision-making — may explain why health care companies have lower levels of engagement with machine learning (ML) than other industries we surveyed. (See Figure 1.)

A Reliance on Intuition

In the winter of 2018, we surveyed 1,600 senior North American marketing executives and managers about their use of KPIs and the role of ML in their marketing activities; 425 were from the health care sector. Sixty-two percent of health care respondents say that their organizations are investing in new skills or training to make marketing more effective in using automation and ML; 63% of respondents in the overall sample responded this way. Seventy-two percent of health care respondents believe that their current functional (marketing-specific) KPIs could be better achieved with greater investment in automation and ML. In the overall sample, that figure was 74%.