Stock Markets June 9, 2026 01:13 PM

Philips-Sponsored Survey Finds AI Boosts Clinician Productivity and Cuts Costs

North American Philips executive says artificial intelligence is streamlining administrative work and allowing clinicians to see more patients weekly

By Maya Rios
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A survey sponsored by Philips indicates artificial intelligence is improving patient care accuracy while delivering time and cost savings. Jeff DiLullo, CEO of Philips' North American division, highlighted notable gains in labor productivity, saying the median clinician covered five additional patients per week. The technology is primarily being applied to administrative functions, with clinicians retaining responsibility for complex medical decisions. Separately, earlier research found AI does not outperform other methods for guiding healthcare decisions.

Philips-Sponsored Survey Finds AI Boosts Clinician Productivity and Cuts Costs
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Key Points

  • Survey sponsored by Philips finds AI improves accuracy in patient care while reducing time and cost burdens for health systems.
  • Philips' North American CEO Jeff DiLullo said the median clinician can see five additional patients per week thanks to productivity gains associated with AI.
  • Healthcare professionals reported using AI mainly for administrative tasks such as compiling data and scheduling, while clinicians retain responsibility for complex clinical decisions; patients are also increasingly turning to AI for health advice.

Artificial intelligence is producing measurable gains in clinical productivity and cost efficiency, according to a survey carried out with support from Philips, the company said. Jeff DiLullo, CEO of Philips' North American division, said the survey's findings point to both improved accuracy in patient care and meaningful time and cost savings for health systems.

DiLullo noted a concrete productivity benefit. "The median number of additional patients they can meet per week is five," he said, adding that the increase carries economic implications for health systems that could translate into improved throughput and resource use.

Philips, a Netherlands-based provider of diagnostic, imaging, and cloud technology to the healthcare sector, reported that respondents primarily deploy AI to support administrative tasks. Typical uses cited in the survey include compiling and organizing clinical data and handling scheduling duties. The company emphasized that clinicians continue to make the complex clinical judgments and higher-order decisions that AI tools are not being used to replace.

The survey also found patients are increasingly seeking health guidance from AI-driven tools. However, the company acknowledged limits in how AI is being used for decision support: earlier research referenced in the survey indicated that AI does not necessarily provide more assistance than other available methods when it comes to making healthcare decisions.

Taken together, the survey results suggest a growing role for AI in the administrative backbone of healthcare operations, coupled with a cautious approach to clinical decision-making. The productivity gain cited by Philips' North American CEO - a median of five additional patients seen per clinician each week - is the most explicit quantified outcome the company released from the survey.


Context and implications

By concentrating AI use on tasks such as data compilation and scheduling, health systems may capture efficiency improvements without shifting responsibility for complex care away from trained clinicians. Philips' position as a supplier of diagnostic, imaging, and cloud technologies places the company at the intersection of clinical operations and IT deployment, where such administrative AI applications are most readily integrated.

Limitations

The survey materials also reference prior research that found no clear superiority of AI over other methods for helping patients make healthcare decisions. That finding highlights an area of uncertainty regarding the effectiveness of AI as a decision-support tool for patients and clinicians.

Risks

  • Uncertainty about AI's effectiveness for healthcare decision-making - earlier research cited in the survey found AI does not provide more help than other methods for making healthcare decisions. This affects clinical decision support and patient-facing applications.
  • Dependence on AI for administrative gains may not address limits in clinical judgment - clinicians continue to handle complex decisions, indicating AI's current role is supportive rather than substitutive. This preserves clinician workload for complex cases and limits potential cost reductions.
  • Economic implications for health systems are noted but not quantified beyond the median of five additional patients per week, leaving questions about how productivity gains translate into system-wide financial outcomes.

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