This Week Health
December 6, 2021

High-Performing Healthcare Systems and Human-Centered Design

Paula Edwards, Consultant, Analytics, and Data Governance Practice Leader, has partnered with organizations who struggle with data quality problems or focusing analytics resources as high-performing healthcare systems. She has worked with IT teams to refresh processes with human-centered design in mind.

Paula Edwards This Week in Health IT

Paula Edwards, Consultant, Analytics, and Data Governance Practice Leader at Himformatics

At Himformatics, Edwards has worked with large health systems and other healthcare providers that want the most value in technology and data investments. She has come alongside these organizations to curate analytic strategies, roadmaps, and data governance strategies.

Though most have some level of data governance in place, Himformatics fills the gaps blocking high-performing healthcare systems. Edwards outlined the characteristics systems need to succeed with their data analytics, governance, data literacy, and how Himformatics can help.

Characteristics of High-Performing Healthcare Systems

A data-driven culture, especially in an organization's leadership, has proven essential for Edwards. Leaders must be data-driven, investing in analytics and holding the organization accountable for utilizing information to inform decisions.

It is not enough to put out a balanced scorecard and check the box, Edwards emphasized. Leadership must keep people accountable for performance.

“And if things are going in the wrong direction, they have to be able to answer why this is happening and have data to back it up," she said.

According to Edwards, without this culture, it is impossible to be a high-performing healthcare system.

Getting Started: Both Big and Small

According to Edwards, health systems can take steps to implement a stronger emphasis on data no matter their size.

"There are a lot of things people can do with data these days. If you give them the right tools and have the data available," she said.

Small health systems can start by doubling down on the tools already available, she advised. Data platforms and analytical tools focus on information, visualization, dash-boarding, and reporting. Core EHR vendors can also provide valuable reporting content.

Additionally, an organization’s level varies based on the realities of budget, system sophistication, and talent availability. As having fewer employees impacts the ability to customize, Edwards suggested smaller systems align to industry-standard metrics and definitions. More advanced organizations can also grow with advanced analytic tools powered by user analysts and data scientists.

The Influence of Academic Medical Centers

Academic medical centers tend to have the talent to navigate data their way. According to Edwards, they have been able to improve data platforms and drive healthcare forward beyond the industry standard.

“They're thinking down the road…they should be doing those things to kind of push the industry forward,” she said.

AI Creates Process Efficiency

AI and machine learning have influenced how the industry delivers care. As they continue to learn from data patterns, these tools have presented greater efficiency for high-performing healthcare systems. The pandemic forced innovation through AI, exemplified through recent case tracking, and vaccine development.

In the beginning, the Johns Hopkins’ map was the first to utilize analytics and metrics to discern how the pandemic spread. Edwards explained that this was a way to make information actionable for individuals on both a broad and local spectrum.

Developing a Data-Literate Health System

Data literacy is necessary for all health systems, Edwards explained.

"You can have a great culture, and you can have great platforms and great data governance, but if you don't have a data literate population that can put all of those things into practice, they're never going to have your analytics really impact care delivery or organizational outcomes," she said.

A literacy program becomes part of professional development, according to Edwards. If there is training for clinical and other competencies, data should also be included.

"A lot of organizations, where they fail in terms of adoption and utilization of analytics, is they train people on the tools, but they don't actually train them to understand the data and how to act when they see the data," she said.

The skills and knowledge taught in the training vary depending on the constituent. Edwards recommended programs stay modular and flexible to meet everyone's needs.

The Usability and Efficiency in Human-Centered Design

Human-centered design refers to the tools and methods within human-integrated systems that ensure the end-user is always at the front when designing a system. This allows work to be efficient and optimize user experience.

"A lot of what you hear now with user experience has a lot of its foundation in human-centered design methods. And a lot of what you see in agile methods is actually very much found in some of the user-centered design methods," she said.

Human-centered design methods have aimed to involve users and key stakeholders earlier in the design life cycle process. The users can then provide valuable insight into the context for system use.

Within healthcare, this is integral for electronic health records. According to Edwards, by not having user-centered designs in earlier versions, these iterations of current EHRs lacked usability.

"Once you have poor usability baked in, it's really hard to recover from that. So that's why some of the usability is so bad there," she explained.

The Strength of Healthcare Data Translators

Edwards recommended analytic teams involve healthcare data translators. These are people that use their technology, business and clinical knowledge to translate between business and IT personnel.

These translators help both sides articulate goals and capabilities, working with the technology team to deliver the promise.

Overall, healthcare translators have facilitated conversations so all parties can focus on the commonalities and building standards. There has been a need to standardize for efficiency and quality user experiences, Edwards explained.

"You have to be able to facilitate those conversations to get everybody to a workflow that works well for everyone, even if it's not necessarily their ideal workflow. You have got to get a little bit of give and take," she said.

Layered Data Governance for Health Systems

Strong data governance has two layers within organizations: executive and tactical stewardship. These levels are necessary for every organization, Edwards explained.

The Executive Layer

As cross-disciplinary executives are in charge of making strategic decisions with analytics, Edwards emphasized the need for executives with varying disciplines like compliance risk and finance.

Third-party access to data has been an additional aspect facing organizations, requiring further decisions for sharing access and patient data safety. This level additionally serves as an escalation path for policies and access when steward groups cannot achieve a consensus.

Understanding Tactical Stewardship in Regards to Data

For the second layer, people attending must know, use, and create data regularly.

When organizations first implement this layer, there are often problems with conflicting data. To increase data literacy, tactical groups must come together to decide how the metrics should count moving forward, Edwards explained.

Another struggle in data management is naming conventions for different EHR inputs, Edwards noted. For example, organizations most likely cannot settle on one definition of the length of stay. This could be an acceptable conclusion if there is a valid business or clinical reason to have two or more definitions.

However, if multiple definitions are decided upon, health systems must learn to differentiate through names. At one organization, Edwards found an example of deviance as clinical or billed patient length of stay.

"We solved the problem by saying, there are two needs. We're going to have two metrics, but we're going to call them something different, and this is how we're going to do it going forward," she explained.

Charters vs. Data Governance

Edwards recommended charters for governance groups to help organizations effectively define roles. Some health systems have deviated from this method and opted fora defined data governance policy. However, both operate out of the same playbook, whether a charter or policy-based approach, she explained. Either way, it is important people are given outlined roles, responsibilities, and accountabilities.

"It's really, how does the organization want to drive their data governance program?" she said.

Finding the Middle Ground for Analytic Models

There are many models for organizing analytics. According to Edwards, what works in an organization is both cultural and context-dependent. The chosen model influences the functionality of an organization.

However, settling too far on either the decentralized or centralized spectrum does not work. Edwards emphasized how fully decentralized models have silos of information and conflicting definitions while centralized models cannot meet demand.

Finding a hybrid model is the emerging best practice, according to Edwards. This allows for specific core resources to stay centralized, focusing on the enterprise platform build.

For Edwards, it is a constant battle to navigate the rogue areas in between the model.

Having the executive level governance keep people accountable, especially regarding purchases, has proven a prime way to prevent this. High-performing healthcare systems have leadership that emphasizes this accountability, Edwards explained.


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