In Defense of AHRQ: A Key Component of Healthcare Delivery Science

The Agency for Healthcare Research & Quality (AHRQ) is an unheralded government agency that performs a great deal of healthcare-related research out of proportion to its small size. Just browsing through the AHRQ Web site makes it clear that agency does a great breadth of work for its annual $400 million budget. Yet AHRQ has somehow attracted its share of detractors, including those in control of the House of Representatives budgeting process who propose to abolish the agency in next fiscal year. A divide-and-conquer strategy of increasing federal medical research elsewhere is also concerning.

Eliminating AHRQ would be a profound mistake, especially with the emergence of the new discipline of healthcare delivery science [1], which the American Medical Association (AMA) calls the "third science" of medicine after basic and clinical sciences. It has been obvious for a long time that while the biomedical perspective of disease and its treatment by the healthcare system are important, larger questions loom around the most effective ways to transfer biomedical knowledge into effective, safe, and efficient healthcare delivery. Given the disease-oriented focus of most research from the National Institutes of Health (NIH), the large biomedical research agency of the US government, AHRQ is the main US government funder of research that would fall under the rubric of healthcare delivery science. The AMA has put its weight behind healthcare delivery science through its Accelerating Change in Medical Education Consortium.

AHRQ suffers from a number of challenges. One is that its research focuses on the healthcare system, including areas from healthcare delivery science such as patient safety, change management, and delivering high-value cost-conscious care. There are unfortunately elements of the healthcare system whose interests do not always align with the most effective or efficient care. By the same token, AHRQ also funds research on evidence-based medicine, which helps determine not only what works, but also identifies what does not work. EBM has its detractors, some (though not all) of whom may be invested (financially or otherwise) in specific tests and treatments for diseases. Furthermore, as AHRQ also focuses on patient safety and healthcare system issues, its research may be harder to sell than diseases such as cancer or Alzheimer’s Disease. It is more difficult for there to be "grateful patients" to celebrate a well-designed healthcare system avoiding an error or complication that a patient never knew he or she might suffer. All of these issues were explored well in a recent Washington Post article.

Another challenge for AHRQ is its being a standalone agency within the Department of Health and Human Services (HHS). As such, it is not protected under the umbrella of the larger health-related agencies, such as the NIH or Centers for Disease Control (CDC). A further difficulty for AHRQ is that has always been viewed as being associated with healthcare reform, including its political aspects. As such, it has tended to be viewed with suspicion by political conservatives. (Which to me is rather odd, since conservatives should be the first to point out that markets work best when consumers have information, and few federal agencies produce more high-quality, actionable information than AHRQ.)

One supporter of maintaining AHRQ is Michael Millenson, who recently blogged some criticism of AHRQ but nonetheless made the case for keeping it. I agree with Millenson that AHRQ needs to improve its messaging and perhaps change its name. But instead of Millenson’s suggestion to focus on "translational medicine," I believe that AHRQ should re-describe what it does as healthcare delivery science. Much of what AHRQ already does falls under the umbrella of healthcare delivery science, including areas such as value-based care, quality measurement and improvement, patient safety, and even informatics.

One of the news articles cited above notes that AHRQ comprises 0.1% of the HHS budget. As some of what AHRQ does would likely be transferred to other federal agencies, it is unlikely that eliminating AHRQ would save the government much money. Furthermore, the research AHRQ performs on comparative effectiveness and efficient care might save the government much larger amounts of money in other places, such as the Medicare system. I hope that wiser heads in Washington will prevail and maintain AHRQ and the valuable work it provides.

(Disclaimer: AHRQ funds research of myself and the department I lead at Oregon Health & Science University through its expansive health IT portfolio and its Evidence-Based Practice Center Program, which is part of its larger Effective Healthcare Program.)

References

1. Pronovost, PJ and Goeschel, CA (2010). Viewing health care delivery as science: challenges, benefits, and policy implications. Health Services Research. 45: 1508-1522.

My Choice of a Smartwatch

I am one of those people who is sometimes derisively called an Apple Fanboy. That is, I tend to buy most Apple products, and almost always have the latest iPhone or iPad. This led to people (including myself!) wondering if I would acquire an Apple Watch. What follows is not a product review, but rather my perceptions of smartwatches based on my particular needs.

I have two major uses for a wristwatch. The first is that I remain one of those people who looks to my wrist and not my phone when I want to know the time of day. The second is that I am a devoted runner and enjoy tracking my running via GPS devices. I am not one of those “quantified self” types and am not particularly interested in how many steps I take during the day. But I do have fun tracking places I have run, especially when not in Portland. I have had great runs over the years in Singapore; Buenos Aires, Argentina; Bangkok, Thailand; Beijing, China; Jerusalem, Israel; Frankfurt, Germany; Mexico City, Mexico; Cape Town, South Africa; Gabarone, Botswana; Copenhagen, Denmark; Dublin, Ireland; and elsewhere. I also have some favorite routes in Chicago; Washington, DC; and San Francisco. In addition, I have my usual routes in Portland, e.g., for running and cycling.

Based on these two needs, I decided not to purchase an Apple Watch, at least in its first iteration. I see the initial Apple Watch as more of an iPhone accessory than a standalone watch. On the other hand, I have had a succession of Garmin sports watches that have handled my second wristwatch need without needing to be tethered to my iPhone. I am particular enamored with the new Garmin vivoactive watch, which connects to my iPhone via Bluetooth and gets rid of the hassle of earlier Garmin GPS watches that required data transfer via cables or wireless with specific devices needing to be plugged into the computer’s USB port (Ant+). Once the data is transferred to my iPhone, it is then automatically uploaded to the Garmin Web site.

Some have asked, why not just run with your iPhone? I actually occasionally do that, but I do not want to be required to do so. I prefer to have all my GPS tracking done with only a watch, and I have no desire to carry my iPhone each time I want to track a run, especially in inclement weather (such as Oregon rain).

The vivoactive has a number of other interesting features. One is that the watch now actually has a software platform, ConnectIQ, that allows development of apps, such as different watch faces and those aimed at specific sports. (I mainly use my watch for running and cycling, and the built-in apps are fine for that.) The watch also provides notifications (vibration and short display) of those emanating from the phone, such as text messages, incoming calls, and calendar reminders. In short, the vivoactive could be the smartwatch that the Apple Watch should have been, although I have to admit that I may at some point discontinue the notifications from my iPhone, since I do not always want the distraction.

I have not tried any other smartwatches, nor other tracking devices such as the FitBit. I cannot imagine I would find them that useful. I do recognize that newer technologies may come along in the future and change my approach, but for now I am content wear my vivoactive on my wrist and use it to track my runs. (And in case anyone is wondering, I do not own stock in either Garmin or Apple.)

Re-Affirming the National Library of Medicine

Last week, National Institutes of Health (NIH) Director Dr. Francis Collins accepted a report from his Advisory Committee to the Director (ACD) that set forth a strategic vision that affirmed the National Library of Medicine (NLM) as a strategic leader in data science, biomedical informatics, and as a library resource. This report was prompted by the retirement of Dr. Donald A.B. Lindberg as Director of the NLM for over 30 years. I have written before on how important the NLM has been to my career, and I am sure many other informaticians, especially those in academia, can attest likewise.

Input to the report came mainly from a Request for Information (RFI) issued by NIH in February, 2015. My response was among the 650 received by NIH, and was reproduced in a blog posting. Like many of my informatics colleagues, I called on NIH to re-affirm the importance of NLM, and its underlying biomedical and health informatics (BMHI) research and education agenda.

The ACD report put forth six recommendations, which I will list here and interpreted by me in italics:

1. NLM must continually evolve to remain a leader in assimilating and disseminating accessible and authoritative biomedical research findings and trusted health information to the public, healthcare professionals, and researchers worldwide. NLM should continue its role as the world’s premier medical library.
2. NLM should lead efforts to support and catalyze open science, data sharing, and research reproducibility, striving to promote the concept that biomedical information and its transparent analysis are public goods. NLM should expand its library role to advocate for and lead efforts in open data and science.
3. NLM should be the intellectual and programmatic epicenter for data science at NIH and stimulate its advancement throughout biomedical research and application. NLM should the NIH home for data science, including the Big Data to Knowledge (BD2K) program, and biomedical informatics research.
4. NLM should strengthen its role in fostering the future generation of professionals in biomedical informatics, data science, library sciences, and related disciplines through sustained and focused training efforts. NLM should continue its robust education and training activities.
5. NLM should maintain, preserve, and make accessible the nation’s historical efforts in advancing biomedical research and medicine, thereby ensuring that this legacy is both safe and accessible for long-term use. NLM should maintain its role in archiving all aspects of science, including data.
6. New NLM leadership should evaluate what talent, resources, and organizational structures are required to ensure NLM can fully achieve its mission and best allocate its resources. NLM should seek out the most skilled and talented people to pursue its mission and activities.

While I am overall highly supportive of the report, I do have a few small quibbles with it. One is the decision to focus on “data science” as opposed to larger BMHI. Data science is certainly an important field, and I am pleased to see NLM recognized as its NIH home for it. However, it would have been more visionary to embrace the optimal use of information to improve individual health, healthcare, public health, and biomedical research, i.e., the larger discipline of BMHI, as the critical mission of the NLM. We cannot have good data science without attention to other aspects of informatics, including but not limited to usability of systems, workflow, and standards and interoperability.

A second concern, related to the first, is the report's modest attention to clinical informatics. While clinical informatics does not represent the entirely of the larger BMHI, NLM is the only US federal research-related entity focused on basic research in clinical informatics, the branch of BMHI that focuses on the use of informatics for patients and in healthcare. The report does call for developing talent in research areas related to the electronic health record and analysis of biomedical text, but these do not represent the entirety of clinical informatics.

A final quibble, although I did not expect it to be addressed, concerns the name of NLM. While I recognize its library function as critically important, many who do not know the breadth of what NLM does may not fully appreciate the work it performs beyond its library role. While I understand it would literally take an act of Congress to change its name, I believe it would be much more logical for NLM to be called something like the National Institute for Biomedical and Health Informatics, with the NLM within it serving its critical library role.

These small issues notwithstanding, I am pleased to see the NLM, and its biomedical and health informatics research and training agenda, endorsed by the report. As such, I believe that the future of the NLM is bright, and now the NIH can get on with hiring the next NLM Director, who will hopefully be guided by the vision of informatics rightfully achieving its value in improving the health of the US and the rest of the world via its information ecosystem.

Informatics is Important When Information Is Important

Many of us in the informatics field, myself included, sometimes believe that the value proposition of informatics is so intuitively obvious that we do not need to explain it to the rest of the world. API-based interoperability? Secondary use of clinical data? Standardized terminology? Their value is so certain that we need not explain it. Not!

However, informatics is in the mainstream of healthcare now, and healthcare recognizes that using data and information to improve processes and outcomes while reducing costs is an essential part of doing business. Clearly there is room for improvement in how operational informatics is being done, but there is no turning back. This means that the priorities for our field are now driven largely by forces external to it. This is not necessarily a bad thing, as we must adapt to play our role optimally for the greater benefit to healthcare.

The main driver for the importance of data and information is changing care delivery models. Some of this can be attributed to the Affordable Care Act (aka, Obamacare), but in reality, healthcare has been changing for some time. The centerpiece of this change is a move away from "volume-based" to "value-based" payment. This is certainly true in the Medicare system, where a goal for the next few years has been established such that the majority of reimbursement will have some modification by quality or value, with half of all payments made through alternative payment models, such as accountable care organizations [1].

By contrast, in the older, volume-based "fee-for-service" model of reimbursement, information is not as important. The physician or the hospital provide their care and are reimbursed for it. Information is mostly important to the extent that all charges are captured.

But in the new value-based payment world, information becomes more important. Whether the physician or hospital is paid under a capitated model or as a bundle for specific diagnoses and/or procedures, there is some element of financial risk on the part of the provider. Especially when combined with a requirement for quality measures, the physician or hospital has incentive to provide the best care at the lowest cost. Information becomes much more important when there is motivation for quality, efficiency, and reduction of complications. The route to that information is through the proper application of informatics.

In this new value-based world, information becomes more important as it allow better management of costs and quality. In an article last year, Bates et al. laid the most important areas for managing high-risk and high-cost patients from the growing volume of data [2]:

• High-cost patients – looking for ways to intervene early
• Readmissions – prevention
• Triage – selecting appropriate level of care, including transfer vs. staying in community
• Decompensation – early detection of patient’s condition worsening
• Adverse events – rapid detection and ability to act
• Treatment optimization – especially for diseases affecting multiple organ systems

This provides a nice list of the priorities for capture and use of information as a driver to increase quality while reducing the cost of care. Informatics is now mainstream, and must become part of the larger healthcare team. It does not mean that our larger visions no longer matter, but rather that we must work with the rest of the system for the betterment of patients.

References

1. Burwell, SM (2015). Setting value-based payment goals - HHS efforts to improve U.S. health care. New England Journal of Medicine. 372: 897-899.
2. Bates, DW, Saria, S, et al. (2014). Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Affairs. 33: 1123-1131.