Now Is the Time: Data Needs for Improving the Health of the Population

Today we have the vision and understanding of what healthcare should look like.¹ We have innovative mobile technology.²  We have creative models for care delivery.³ However, we still need to develop metrics that will ensure we can reliably and validly demonstrate improved health and cost-effective care⁴—a much greater challenge.

If we cannot do this, do it soon and do it well, we will lose what may be our best opportunity to make a real difference. It won’t be easy. Yet it is a time when, in the immortal words of Pogo, “We are confronted with insurmountable opportunities,” and the time is ripe to document the effectiveness of those care models that actually improve the health of the population.

So, what is the vision? The “Triple Aim”—improved population health through an improved care experience at a reduced or reasonable cost—has been broadly accepted as a framework for the U.S. National Quality Strategy. The Institute for Healthcare Improvement (IHI), in its guide to measuring outcomes, has outlined both measurement principles and procedures organizations can use. These include the need to: 1) define the population; 2) obtain data over time; 3) distinguish between outcome and process measures, and between population and project measures; and 4) understand the value of benchmark or comparison data. The data obtained from the use of solid measures will allow an organization to be confident the care it delivers remains on track with its goals.

This data can also provide information needed to truly become a “learning” organization. A learning organization is one that has the ability to both be informed by the outcomes of its past care and to enable that to improve its future care. A learning focus allows an organization to provide care that is increasingly based on the Institute of Medicine’s six fundamental dimensions of safety, effectiveness, timeliness, efficiency, equitability and patient-centeredness.

The IHI also outlines a model of population health that can be used by an organization as a starting point to determine which specific measures will best assess its progress in meeting the Triple Aim. This framework for measurement⁵ provides causal pathways and a guide to integrating measures. For example, population health is assessed by using mortality measures, common health and functional status and demographic measures. (See Table 1) There are many existing measures of health. Each organization will need to determine which are most congruent with its data collection capabilities and populations. The patient experience is frequently measured using surveys that generally focus on the extent to which the care provided met the six IOM dimensions. Finally, the cost of care is typically measured as cost per patient/member per month or as use of high-cost services (inpatient or outpatient).

Our ability to provide care with innovative mobile applications has never been greater. Currently there are numerous monitoring devices on the market that allow care providers to have remote access to their patients and these patients’ conditions. Health information technology (HIT) has allowed us to be creative, yet safe in our surveillance.

Sadly we have yet to leverage these devices to improve care on a broad basis. Barriers to date include cost, insufficient information on the business case for the use of these devices, and limited understanding and data to determine which patients, conditions and situations represent the most effective use of mobile health (mhealth) applications. Consequently, a part of our measurement agenda needs to be centered on a solid understanding of the optimal use(s) of these developing and exciting new methods.

We do not lack innovative technology. Eric J. Topol, MD, U.S. cardiologist, geneticist, researcher and founder of the Cleveland Clinic Lerner College of Medicine, has described a number of mobile health technologies that allow for patient involvement and partnerships with providers.⁶ He asserts that in a best case, we become “digital doctors,” capable of partnering with patients to leverage existing mobile technology.

We also have an improved understanding of the previously poor coordination that had been a hallmark of care transitions. Using this knowledge, we have developed new models that improve integration of care across the care continuum. These models include, at a broad level, patient-centered medical homes and some newer models such as those of the American Academy of Ambulatory Care Nursing (AAACN), the BOOST Project (www.hospitalmedicine.org/BOOSTCA), hospice revocation avoidance (MedStar Mobile Healthcare) and fall response (Wake County EMS). (See Table 2)

We do not lack promising models of care. There are untold instances of unnecessary pain, expense and flawed care delivery that resulted from our poor past performance in integrating patient care and communicating as patients moved from one setting to another. Now that we have recognized the great need for improved communication and the involvement of a variety of providers—including physicians, nurses, nurse practitioners, pharmacists, EMTs and paramedics, dietitians and social workers—the use of interprofessional teams and mobile surveillance will assume a greater role in care. With the ubiquitous nature of health information technology, we are increasingly able to ensure that the right provider, the right information and the right care decisions combine, at the right time, to serve our patients safely and effectively.

However, we must develop methods and metrics to demonstrate the value of both the mhealth applications and the newer and creative care models. Healthcare outcomes expert Alfred Lewis, in his 2012 book, Why Nobody Believes the Numbers: Distinguishing Fact from Fiction in Population Health Management, argues we have done a poor job measuring the impact of most population health programs. So poor, in fact, that the inherent flaws in most measures are causing losses in credibility, and valuable resources are being diverted wastefully to support programs with little or no claim to improved care. Consequently, if we are to attain our vision of the Triple Aim, each organization that seeks to “move the needle” on population health must absolutely ensure they are adequately informed on what they need to be measuring.

Barriers to rigorous measurement have included poor understanding of what specific measures are needed, limited standardized date and data definitions, lack of clarity in measurement methods and limited common measures of various population outcomes. These have prevented us from collecting and analyzing appropriate, valid and reliable metrics. Examples of specific measures are included in the IHI measurement document. If the needed baseline data is not currently being collected, it must be. If an organization does not currently have the knowledge resources to develop and execute a sound measurement plan, it will need to develop a strategy for obtaining these resources. The IHI guide is a good starting point and lists a variety of other resources.

References

1. Stiefel M, Nolan K. A Guide to Measuring the Triple Aim: Population Health, Experience of Care, and Per Capita Cost. IHI Innovation Series white paper. Institute for Healthcare Improvement. Retrieved Feb. 18, 2014 from www.ihi.org/resources/Pages/IHIWhitePapers/AGuidetoMeasuringTripleAim.aspx.
2. University Hospital Consortium (UHS), Annual Meeting, 2013.
3. Beck E, White L, Goodloe J, Myers B, et. al. Improving population health through innovative alignment of existing mobile health infrastructure. Poster presented at the American Public Health Association Annual Meeting, Boston, MA, Nov 2013.
4. Lewis A. Why Nobody Believes the Numbers: Distinguishing Fact from Fiction in Population Health Management. Hoboken, NJ: John Wiley & Sons. 2012.
5. Evans RG, Stoddart GL. Producing health, consuming health care. Soc Sci Med. 1990;31(12):1347-63.
6. UHC Annual Conference 2013—Eric Topol Highlights. Retrieved Feb. 18, 2014 from www.uhc.edu/55554.htm.

Ida M. Androwich, PhD, RN-BC, FAAN is a Professor of Nursing (and Business) at Loyola University Chicago and teaches graduate courses in Health Care Informatics, Systems, Outcomes Performance Management and Population-based Infection Control. In 2008, she was named Loyola’s Graduate Faculty Member of the Year. She has published and presented nationally and internationally in the area of Health Care Informatics. In addition, she has had funded research related to national efforts to standardize health care vocabularies and has consulted in informatics, terminology and evaluation on federal and foundation grants.