National Committee on Vital and Health Statistics (NCVHS)
Ad Hoc Work Group on Secondary Uses of Health Data

July 18, 2007

The Quality Improvement Perspective on Uses of Health Data

I’m Jennifer Lundblad, President and CEO of Stratis Health, an independent non-profit quality improvement organization based in Minnesota that collaborates with providers and consumers to improve health care. When describing the opportunities and uses of health data and health information exchange, quality is often mentioned but typically fairly low on the list. I’m pleased that the committee recognizes the important opportunities in quality that are available through the use of health data, and I’m glad to share my expertise and perspective with the Committee as part of the panel today. I hope to contribute helpful information from Stratis Health’s role generally as an organization that translates research into practice to improve health care, and specifically related to the Quality Performance Improvement Alliance, or QPIA.

The QPIA initiative is a partnership between VHA Upper Midwest and the organizations that hold the Medicare QIO contracts in Minnesota, Wisconsin, Illinois, North Dakota, and South Dakota, which includes Stratis Health. Through this partnership, we are providing support and technical assistance to more than 40 high performing hospitals across the five states who have committed to achieving 100% performance in the areas of heart failure, AMI, pneumonia, and surgical care at their facilities. The QPIA initiative is a data-driven quality improvement effort and therefore a great example to draw from in examining the questions posed to the panelists for this Committee.


In the context setting questions posed to testifiers, you first asked about policies, enablers, or restrictions that facilitate or impede the benefits of using electronically transmissible personal health data (and I’ll add…for purposes of quality improvement and patient safety, the lens through which Stratis Health views its work).

One of the key remaining barriers to fully gaining the benefits of electronic data is the lack of implementation of standards for data transmission and sharing across providers using different vendors – vendor systems simply don’t talk with one another. This is particularly problematic in an integrated health system state like Minnesota, where the best vendor product choice for a group of hospitals that are part of a large health system may not be the best for the physician practices that are part of that same health system. They then are either forced to compromise how well they meet their setting-specific requirements and goals, or how well they are able to achieve health information exchange across their system. And this is within the same integrated health system; once you are trying to exchange data outside of the same system, the problems are similar but exacerbated.

In addition, from Stratis Health’s perspective in supporting quality improvement across the continuum of care (i.e., hospitals, clinics, nursing homes, home health), we hope and expect that the next generation of quality performance measures will include patient-centered measures across the continuum of care that a patient experiences for any given episode or condition. There will be a corresponding need for data to reflect this multi-provider perspective in ways that promote accountability for the entire patient experience and allow identification of improvement opportunities across the continuum. Given the current uses of EHRs and the capabilities of HIE, achieving this will be no small task.

The Committee also asked whether current laws provide sufficient privacy and security protection for identifiable health data to be used in quality measurement, reporting, and improvement; and whether these were well understood by those that needed to implement the protections.

HIPAA is the national floor, so to speak, of data privacy and security protection laws. There is a patchwork of state-based regulations that come in to play above and beyond HIPAA, creating a sometimes fractured and confusing environment for health care providers.

In Minnesota, we have a tradition of very strong patient content laws. So in our state, the combination of HIPAA and our state laws give sufficient privacy and security protections. Yet even with, or perhaps because of, strong patient privacy and consent laws, there were barriers perceived in the ways that the Minnesota patient consent requirements impeded the electronic exchange of information. Specifically, there were undefined terms and ambiguous concepts, difficulties in determining application of the laws to the electronic environment, and the need to update Minnesota’s laws to facilitate electronic exchange while respecting patients’ control of their information.

As a result, in the state legislative session that ended in May, the 2007 Health Records Act passed which seeks to provide solutions to these barriers. I’ve included a 2-page summary of the Act in the materials, where you can see the summary of the new requirements, and it includes everything from precision in definitions of “health records”, “medical emergency”, and “related health care entity”, to exceptions for long-term care providers caring for residents who are physically or mentally unable to provide consent, to a framework for record locator services to support data exchange.

The Health Records Act resulted from the Minnesota Privacy and Security Workgroup, which, under funding from RTI, was a broad, multi-stakeholder group that was able to work through the issues and barriers and develop the solutions that made it into the new law.

The final component that will help assure that this information is widely understood and used in Minnesota is the educational efforts. We have a strong state eHealth Advisory Committee, of which I am a member, and we just held our third annual Minnesota eHealth Summit, drawing more than 400 participants for learning, sharing, and networking around these crucial topics of EHR use, health information exchange, and data privacy and security.

While these are important advances in health data privacy and security to support health information exchange in Minnesota, if you think about the QPIA initiative that I briefly described, you can begin to see how the “patchwork” across states can impede quality improvement – what we pass in Minnesota isn’t the same as what is in place in our neighboring states, so that our many Minnesota health systems that cross the borders — and the QPIA initiative which crosses multiple borders — have a complex crosswalk of regulations and laws to understand in order to be able to accomplish their collaborative quality improvement work.

The Committee also asked about the opportunity to optimize use of health information for the improvement of the nation’s health and healthcare delivery system through the nationwide health information network (NHIN) and entities that enable health information exchange (HIE).

I only want to comment briefly on this to say that the challenge in this arena is striking balance between national infrastructure and policies, and local innovation, control, and needs. We want and need a national network and associated policies, but we also recognize that innovation tends to arise from local needs and opportunities. It strikes me that there are parallels to the health care quality measurement environment. Those of us immersed in quality measurement and reporting are trying to strike the balance between the broad use of national consensus-based measures (for example, through the National Quality Forum) and reported nationally (for example, through the Hospital Quality Alliance) while not inhibiting — and in fact, encouraging — the development of new innovative ways to assess health care quality that meet local needs. Health care delivery is local, after all, having at its core the physician-patient relationship; and we strive to measure quality in consistent comparable ways while also supporting innovation in how we assess quality.


In getting to the specifics around using data are used for quality measurement, reporting, and improvement, I’ll draw on my nearly 10 years of experience with Stratis Health, including the current QPIA initiative that I introduced to you earlier.

There are multiple primary sources of data used for quality improvement and patient safety purposes. I’ll briefly outline the benefits and challenges of each, again drawing on the experience of serving as an external change agent for quality improvement.

  • Chart abstraction – benefit is that it’s the most detailed, at-the-bedside source of data about patient care; challenges are that it is retrospective; opportunity for EHRs to improve real-time data collection and analysis; some concern that EHRs will diminish the detail and nuances of individual patients and their care. There are great opportunities in using EHRs for quality and patient safety – by building guidelines into EHRs as logic models and forcing functions, quality can be affected at the bedside during care delivery.
  • Electronic registries – benefit is the ability to view patient populations by condition (e.g., all diabetics) or by treatment (e.g., immunization status); challenge is that electronic registries are not accessible by all providers who would find the information useful (e.g., registries based in primary care that are not accessible or shared with specialties or with hospitals).
  • Internal repositories – many provider organizations in Minnesota, especially large integrated health systems, are challenged by designing internal data warehouses that are efficient and useful for users; they are not able to get data and reports out in ways that are timely and useful for quality improvement purposes.
  • External warehouses – data that are part of local, regional, or national data repositories are useful for benchmarking performance, but the data and reports available often have significant time lag, decreasing their usefulness.
  • Administrative and survey data – claims data, patient satisfaction data, and other survey results are additional data sources that can be used in quality improvement.

A case example – The 40 participating hospitals in the QPIA project indicated early in the project that it would be of significant value to them to have data reports out of the CMS/Medicare QualityNet Exchange national data warehouse (which contains chart-abstracted data on heart failure, AMI, pneumonia, and surgical care). The current processes, approvals, and data use agreements needed to allow data sharing across 5 states, and between the 5 Medicare QIOs and VHA UM were an impediment to our ability to share data to help support care improvement. After many delays, the QIOs, hospitals, and VHA UM finally were able to have the necessary data sharing and confidentiality agreements in place to be able to share and compare data for participating hospitals across the five states. Despite everyone’s agreement that the goals were worthy and active support from CMS, it was a grueling 9-month process. The IOM report from March of 2006 commented on the need to update Medicare’s data policies to reflect an electronic environment; and we’re grateful for the support from CMS to make our QPIA data sharing work, but were certainly slowed down by the current policies and processes in place.

The Committee asked about the various ways data may be used for quality.

Again, drawing on the depth of experience at Stratis Health as an external change agent, there are a number of uses of data for quality improvement purposes:

  • Internal quality improvement and patient safety – comparing one’s own performance over time (an individual physician, a site or a provider organization, and across a health system) or comparing one’s own performance to those of peers or state/regional/national comparisons. These comparisons lead to identification of opportunities for improvement.
  • Peer review – using individual cases to understand sentinel events or near misses, peer learning (typically physicians) occurs.
  • Public/Community health — local, regional, or state profiling of public health needs and opportunities (e.g., prevention, cancer screening)
  • Transparency and accountability — publicly reporting quality performance for purposes of helping patients and consumers be informed decision-makers.
  • Pay for performance – quality performance data are increasingly being used by health plans, by employers, and by state and federal government entities to pay differentially for quality.
  • Research – quality data can contribute to the research and evidence base.

A case example – Minnesota Community Measurement (MNCM) was founded in 2002 and today participates as one of six of Medicare’s Better Quality Information (BQI) national pilot sites. These collaboratives are serving as demonstration sites to pioneer the pooling of private data with Medicare claims data to produce more accurate, comprehensive measures of quality of services at the provider level. MNCM’s mission is to improve care through measurement and reporting, and it supports the work of improvement organizations in Minnesota like Stratis Health and the Institute for Clinical Systems Improvement (ICSI). Its efforts have focused on ambulatory care, and it publishes a report each year showing the performance of medical groups in Minnesota on a variety of measures. Diabetes was the first area reported. As a whole, Minnesota medical groups have shown significant improvement in helping people with diabetes attain improved blood sugar levels and other measures of diabetes control that are proven to reduce the incidence of complications, which include heart disease, strokes, blindness and amputations. The statewide percentage of patients whose diabetes was found to be in optimal control according to medical guidelines increased from 4% in 2004 to 10% in 2006. Credible transparency initiatives can effectively draw the attention of provider organizations to focus on quality improvement, and no doubt these efforts have contributed to Minnesota’s ranking as the top healthiest state in the last 4 years and for 11 of the 17 years of the survey, conducted by United Health Foundation.

Given that my organization is involved with a number of initiatives related to transparency, accountability, and pay for performance, I’d like to comment on these in particular as uses for quality and performance data. In the past 3-5 years, MN has gone from a laggard to a leader in health care transparency. How we are doing collectively on the big picture goals for transparency?

  • Effective at bringing the attention of health care leaders to quality and patient safety, and driving improvement? Yes! (MN Community Measurement is an excellent example of this.)
  • Helping consumers be more informed decision makers and activated patients? Not yet, and much more to be done to understand how quality data can be meaningful to consumers and patients.
  • Effectively helping get us where we want to be as a system (better quality and safety, value, 6 IOM aims)? Too early to tell?

Finally, there are two final issues that I’d like to raise for the Committee. First, a problem with the current design of most electronic medical records is the ability to audit a chart for external peer review or utilization management, requested by Medicare QIOs, health plans, and attorneys during litigation. It’s not clear in an EHR what constitutes the legal medical record – which screens and information are pulled out of the EHR to comprehensively describe a patient and a care episode for the purposes of external review to improve quality? This issue needs to be addressed.

Lastly, Minnesota is a rural state, with nearly 79 Critical Access Hospitals and a rich array of clinics, nursing homes, home health agencies, and other provider organizations serving our rural communities. While rural health can be a model and leader in many areas, there are particular challenges when it comes to transparency and publicly reporting – small provider organizations often do not have the capital and resources to adopt technologies as quickly as their larger and urban counterparts, nor do they have an adequate number of cases to report without explicitly or implicitly identifying patients. Yet rural providers are committed to quality, patient safety, and transparency, and we don’t want to have a two-tier health system result.

I thank you for your time and attention and welcome any questions or comments.

Parts of these comments were created by Stratis Health under a contract with the Centers for Medicare & Medicaid Services (CMS), an agency of the U.S. Department of Health and Human Services (DHHS). The contents do not necessarily reflect CMS policy.