Maggie Helms HealthPartners: 7 Powerful Lessons in Healthcare AI and Data Innovation.
Introduction: Bringing Data, Digital Technology, and AI Together
Healthcare organizations are under increasing pressure to improve patient experiences, support clinical teams, manage operating costs, and deliver reliable care. At the same time, artificial intelligence, advanced analytics, and digital platforms are creating new opportunities to address these challenges.
The real challenge is determining which technologies can make a meaningful difference and how to implement them responsibly.
Maggie Helms, Senior Vice President and Chief Data, AI and Digital Officer at HealthPartners, is helping lead this transformation. She has spent more than a decade at HealthPartners, advancing through leadership positions while contributing to the organization’s health informatics, data operations, and technology capabilities. Her expanded executive role brings data, analytics, AI, and digital technology together under a unified strategy.
According to HealthPartners’ official announcement, this strategy is designed to improve outcomes for patients, members, care teams, employer partners, and colleagues while supporting affordability.
Helms’ approach reflects an important principle in modern healthcare technology: innovation should solve practical problems rather than simply introduce new tools.
From establishing reliable data foundations to supporting clinicians with AI and improving digital experiences, her leadership provides valuable lessons for healthcare organizations navigating digital transformation.
1. Building a Strong Foundation Through Healthcare Data
Artificial intelligence depends on the quality of the information used to develop and operate it. If healthcare data is incomplete, inconsistent, outdated, or difficult to access, even advanced technology can produce unreliable results.
Healthcare organizations often manage information across multiple systems, departments, and business functions. Medical records, insurance claims, laboratory results, appointment systems, and administrative platforms may use different definitions for similar information.
These inconsistencies can make reporting difficult and reduce confidence in analytical insights.
Helms has emphasized the importance of establishing a reliable data foundation at HealthPartners. In a Becker’s Hospital Review interview, she discussed the organization’s data lakehouse strategy as a common source of reference for information across the business.
A shared data foundation can help teams work from consistent definitions, improve reporting, and prepare information for analytics and AI applications.
Why data quality matters
- It helps clinicians and operational teams access more consistent information.
- It supports reliable reporting across different departments.
- It reduces confusion caused by conflicting definitions.
- It creates a stronger foundation for AI development.
- It helps organizations make decisions using more dependable evidence.
The role of data management in healthcare
| Data challenge | Possible solution | Expected benefit |
| Inconsistent definitions | Standardized data terminology | More reliable reporting |
| Disconnected systems | Data integration | Better information availability |
| Incomplete information | Data quality monitoring | Improved analytical reliability |
| Limited access to insights | Governed analytics platforms | More informed decisions |
| Unclear data ownership | Defined accountability | Stronger data governance |
A strong data strategy is not simply a technical project. It requires agreement between departments about how information should be defined, maintained, protected, and used.
2. Using Healthcare AI Responsibly
Artificial intelligence (AI) has the potential to grow exponentially, but healthcare companies cannot rely on adopting new technologies fast to measure success.
The field of medicine presents a challenging environment where decisions have far-reaching consequences, require situational and human considerations, and have the potential to cause great harm if done incorrectly. Therefore, AI systems should always be reviewed with caution before implementing their use in clinical or enterprise settings.
Helms has focused on the importance of trustworthiness when it comes to HealthPartners’ strategy for AI. As Helms stated in the interview with Becker’s Hospital Review in March 2026, the company would not pursue “technological capabilities for the sake of competitors.”
HealthPartners has an excellent track record of testing various solutions in front of specialists before bringing them to the company’s members and patients, putting emphasis on the power of human expertise.
This lesson can be valuable to many healthcare leaders, as a “successful proof of concept” of a solution is not enough to prove its readiness for actual customers.
Essential principles for responsible healthcare AI
Clinical validation: Evaluate whether a system performs reliably for its intended purpose.
Human oversight: Ensure qualified professionals can review outputs and intervene when necessary.
Privacy and security: Protect sensitive health information throughout the technology lifecycle.
Transparency: Help users understand the appropriate role and limitations of AI-generated information.
Continuous monitoring: Review performance after implementation and investigate unexpected results.
Responsible AI implementation framework
| Implementation stage | Key activity | Success indicator |
| Identify | Define a specific healthcare problem | Clear project objectives |
| Evaluate | Test the tool with relevant experts | Acceptable performance |
| Validate | Assess safety, accuracy, and limitations | Documented validation |
| Deploy | Introduce the tool with appropriate oversight | Safe workflow integration |
| Monitor | Review outcomes and user feedback | Sustained performance |
Responsible AI does not mean avoiding innovation. It means creating the conditions under which innovation can earn the confidence of patients, clinicians, and healthcare leaders.
3. Supporting Clinicians With Practical AI Applications
Healthcare professionals spend significant time on documentation, reviewing patients’ histories, coordinating care, and other administrative tasks. While these activities are essential, they may create a burden on the professional and take attention and time away from the caring process.
Artificial intelligence can help to resolve this issue by automating some routine tasks and assisting in organizing and retrieving the required information.
HealthPartners has tested different use cases related to clinicians. For example, the company has utilized an ambient digital scribe, implemented previsit planning and discharge summary assisted by artificial intelligence. All three mentioned cases are examples of how AI can be applied in healthcare settings to resolve specific problems.
An ambient documentation tool can help to electronically capture the conversation between a clinician and a patient and convert it into a clinical note. However, it does not mean that the professional can completely delegate this responsibility to a computer. In the case of previsit planning, the tool can help to organize and present the information needed for a particular case. A similar situation can occur when a patient is discharged; in this case, AI-enabled healthcare assistance can create a summary for the clinician based on the information related to the discharge.
To conclude, it is possible to mention several examples of how artificial intelligence can be applied in healthcare settings in order to resolve specific issues and problems.
Where clinical AI can help
- Documentation: Assist with preparing clinical notes.
- Previsit planning: Organize relevant patient information.
- Discharge summaries: Help clinicians review follow-up requirements.
- Administrative support: Reduce repetitive tasks in operational workflows.
- Information retrieval: Help staff locate relevant information more efficiently.
The effectiveness of these tools depends on accuracy, workflow integration, staff training, and the ability to correct errors.
Technology should reduce unnecessary effort rather than create additional work through complicated interfaces or repeated verification.
4. Connecting Digital Innovation With Better Patient Experiences
Digital transformation is often discussed in terms of platforms, applications, and infrastructure. For patients, however, its value is much more personal.
Patients want to understand their healthcare options, access services conveniently, communicate with their providers, and receive relevant information at the right time.
HealthPartners’ integrated model combines healthcare delivery and health plan services. This creates opportunities to connect different parts of the patient and member experience, while also introducing the challenge of coordinating information across different systems.
Digital tools can support appointment access, care navigation, benefit information, reminders, and communication.
In March 2026, Helms discussed the organization’s use of digital engagement and AI-supported recommendations, while explaining that its approach to more advanced patient-facing AI remained cautious. The organization was testing solutions with experts rather than rushing to launch fully autonomous patient-facing agents.
This distinction is important. A digital tool should be introduced because it addresses a real need and can be operated safely, not simply because the technology is available.
What effective digital healthcare should deliver
Convenience: Make common healthcare tasks easier to complete.
Clarity: Present information in language that patients can understand.
Consistency: Provide reliable information across appropriate digital channels.
Accessibility: Consider different abilities, languages, and levels of digital confidence.
Human assistance: Provide clear routes to qualified staff when a situation requires personal support.
Digital healthcare is most useful when it simplifies the patient’s journey while preserving the human relationships that remain central to medical care.
5. Applying Analytics to Population Health and Preventive Care
Population health management refers to understanding the health status of populations and the ways by which their health can be improved.
Analytics can be used in healthcare to identify patterns of healthcare utilization, assess care quality, and spot gaps in care. For instance, an organization might use analytics to examine emergency care use and find appropriate interventions for patients. Analytics helps to uncover if patients are getting the recommended treatment and services and what barriers to care they encounter.
HealthPartners has reported improvements in specific areas of care quality, consumer engagement, and unnecessary use of emergency departments through their combination of analytics, artificial intelligence, and human expertise. According to the March 2026 interview by Helms, the reductions in their emergency care use ranged from about 4% in some settings to over 16% in others.
The figures are related to the specific programs that were mentioned in the interview. They cannot be generalized to all settings and do not mean that all the programs that utilize AI solutions have a positive ROI.
However, the main takeaway is that analytics helps to drive processes that lead to improvements in care and should not be viewed as a solution that will deliver instant results.
Potential applications of healthcare analytics
| Application | How analytics can help | Potential outcome |
| Emergency department utilization | Identify patterns and opportunities for alternative care | More appropriate use of services |
| Preventive care | Highlight care gaps | Better follow-up planning |
| Patient engagement | Analyze interaction patterns | More relevant communication |
| Workforce planning | Estimate service demand | Improved staffing preparation |
| Quality improvement | Monitor performance indicators | More targeted improvement efforts |
Analytics should complement clinical knowledge and individual circumstances. Statistical patterns can help guide attention, but they should not automatically determine an individual’s care.
6. Modernizing Technology to Enable Scalable Innovation
Healthcare facilities tend to design their operations around a combination of legacy systems, modern applications, data management tools, and various third-party technologies. The challenges stemming from the inability to properly leverage these technologies in tandem with one another pose serious obstacles to operational efficacy. Modernization efforts are intended to create a conducive environment for the effective use of digital tools, analytics, and AI.
At Helms’ new role, he will be leading an initiative that creates a more unified system for digital tools, analytics, and AI across HealthPartners. The organization has been focusing on the creation of a modern data ecosystem and governance infrastructure to scale its AI operations.
Unified data strategies and advanced software development are two interdependent forces that should be combined to achieve better results. Effective application development practices cannot yield desirable outcomes if the data used to train them is of poor quality. On the other hand, an equally compelling data analytics tool may not be useful if it is not easy to use.
Important priorities for technology modernization
- Improve integration between relevant information systems.
- Establish common standards for data access and management.
- Design digital tools around actual user workflows.
- Build security and privacy requirements into implementation.
- Create processes for maintaining and updating digital products.
- Evaluate whether technology investments deliver measurable benefits.
Modernization is a continuing process, not a one-time upgrade. Successful organizations need to balance new capabilities with reliability, maintainability, and the practical realities of daily healthcare operations.
7. Measuring Results Instead of Chasing Technology Trends
One of the key insights from HealthPartners’ experience is the importance of differentiating between the promise and the proof of value of a given technology.
An application may be able to attract a following among the users during a pilot, but it does not necessarily mean that it has been able to add value to processes at work. Similarly, a tool may be able to save some hours of work, but if it brings in additional costs or simply the workflow is not optimized enough, there is no overall value.
Helms speaks of the importance of differentiating between realization of value and demonstration of ROI, which is applicable to healthcare settings as well since the areas of impact may well go beyond the mere financial gains and losses. There are intangible benefits such as improvements in the quality of care, reduction of administrative burden, or optimization of the patient journey that should all be considered. Therefore, it is recommended that healthcare leaders set the goal that will be used as the reference point after the technology is implemented and measure the relevant metrics to see if the objective has been met.
Measuring the value of healthcare AI
| Measurement area | Example metric | Key question |
| Clinical workflow | Documentation time | Is the tool reducing unnecessary work? |
| Patient experience | Satisfaction and engagement | Are patients better supported? |
| Care quality | Relevant quality indicators | Is performance improving? |
| Operational efficiency | Processing time and error rates | Are workflows becoming more effective? |
| Financial performance | Total costs and attributable savings | Is the investment generating a measurable return? |
| Responsible use | Safety incidents and validation results | Is the technology operating appropriately? |
A balanced evaluation should consider the cost of implementation, training, maintenance, oversight, and ongoing monitoring.
It should also examine unintended consequences. If an application saves time in one department but increases work elsewhere, its overall value may be lower than expected.
By measuring results honestly, healthcare organizations can determine which initiatives deserve further investment and which need to be redesigned.
The Future of Healthcare AI at HealthPartners
Healthcare AI will continue to evolve and present opportunities to transform clinical documentation, care coordination and administration, analytical capabilities, and digital engagement.
The challenge is to fully integrate these capabilities into a broader ecosystem while avoiding fragmentation and reinforcing trust.
HealthPartners’ emphasis on reliable data, responsible AI, clinical expertise and meaningful outcomes underscores considerations for any healthcare organization seeking to improve care while balancing the needs of health plans and care providers.
The next frontier of innovation will require executive-level governance as well as effective cross-disciplinary collaboration, with technology and clinicians working together to demonstrate value.
AI is likely to become an important tool for streamlining and standardizing some processes, but success will depend on how thoughtfully these solutions are implemented and adopted.
The focus for healthcare leaders should be on solutions that make life easier for people – and not the other way around.
Conclusion
Maggie Helms’ experience at HealthPartners embodies the modern-day fusion of data analytics, digital empowerment, and artificial intelligence.
In her practice, there are several key principles that can be considered as successful examples for healthcare leaders. These include finding ways to ensure the reliability of data, encouraging clinicians to embrace new opportunities enabled by AI, optimizing digital experiences, and focusing on meaningful technology assessment.
Overall, the emphasis is on making healthcare more intelligent while balancing competing needs. An efficient data strategy is the foundation upon which the entire healthcare AI ecosystem is built. Experience shows that there is a need for both effective governance and engagement to maximize the benefits of digital disruption. Finally, the way forward is to maximize the value of the healthcare ecosystem by focusing on the benefits to patients and providers. In the end, this is where the promise of AI-enabled healthcare systems will be fulfilled or not. The core value propositions of such systems will include enhanced experiences, empowerment of clinicians, improved care delivery, and intelligent utilization of resources.
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