Population Health Management: Strategies and Challenges

Introduction

The concept of population health management refers to understanding and managing health outcomes at the population level rather than the individual level. Cousins et al. (2002) highlight that risk levels for different ailments and conditions can vary by population, so breaking down a population demographically can help to understand how risk varies. Their study showed that predictive modeling can be used to identify risk levels for different conditions among different populations. This underlying logic is the basis for the concept of population health management. If risk levels vary by population, then it becomes easier to understand underlying causal or contributing factors. From there, tactics can be developed to address those risk factors and therefore reduce the risk of a given condition among a particular population. Population health management is, therefore, a powerful tool for improving health outcomes, because it leads to more preventative approaches rather than treating a condition once it is already occurring.

The Underlying Logic of Population Health Management

Kapp, Oliver, and Simoes (2010) explore the concept of population health management in their study. They surveyed women in Missouri to understand some of the underlying lifestyle factors for cancer. Their study showed, among other things, that certain segments of the population were less aware of lifestyle factors contributing to cancer than other segments. They were able to do this by targeting a fairly narrow segment of the population — women in a certain part of Missouri between the ages of 35 and 49 who had no personal history of cancer. The study then gathered demographic information, including education level, and determined that education level in particular influences the degree of knowledge respondents had regarding lifestyle contributions to cancer.

Lifestyle factors are by no means the only contributors to whether a woman develops breast cancer, but they are nonetheless relevant. The underlying logic of the Kapp study is that some lifestyle factors are preventable. Where there is higher awareness of lifestyle factors that a person can control, they are more likely to act on that knowledge, and this can explain why some conditions occur more frequently in certain segments of the population. The United States has relatively poor health outcomes for a developed nation, and while there are many reasons for this, lifestyle habits among the nation's poor and less-educated are a contributing factor. Obesity in particular is a driver of poor health outcomes and is largely controllable for most people.

Cramm and Nieboer (2016) further explore the issue of population health management. They argue emphatically that disease management is not the solution to public health issues. By tackling disease at the individual level, that person already has the disease. When looking at statistics, outcomes do not change significantly once a person develops a condition, except when treatment is improved. Advances in treatment, however, are typically diffused across the developed world quickly, so the United States cannot gain much statistical advantage by innovating treatments alone. The only true way to improve health outcomes broadly, they argue, is by addressing behaviors and knowledge at the population level to reduce the incidence of disease in the first place.

The Challenges of Population Health Management

One of the first and most obvious challenges in population health management is that it does not work on its own. It must work in conjunction with traditional disease management, because once somebody gets sick, they still need to be treated. Population health management therefore needs to be implemented in a broader, preventative manner — as one piece of the overall healthcare puzzle rather than a replacement for treatment-based care. Inkelas and McPherson (2015) argue that the US healthcare system, as presently designed, is ill-equipped to adopt population health management.

First, it is primarily a for-profit system, creating incentives to run a disease treatment model — healthier populations are less profitable. Second, there is relatively little incentive for population health management when different healthcare providers operate in the same region and compete against each other for business. None has any incentive to engage in population-level prevention. It is much easier to see the logic of population health management in a country like Canada or the United Kingdom, where the government provides healthcare. Taxpayer-funded systems have an incentive to reduce illness, as doing so saves the entire system money. Furthermore, without competition, healthcare authorities have no concern about introducing broad-based education or training programs. People will still get sick, so physicians will still have work, but an entire branch of such a system can be established to improve public awareness of preventable contributors to illness. A government-run system can also turn to other branches of government — for environmental regulations or rules governing other activities — when those support healthcare objectives. It is difficult to conceive of how such coordination would work in the United States, where the trade-offs are entirely different. Inkelas and McPherson are therefore right to suggest that the US is not well-positioned to fully embrace population health management.

Kizer (2015) explores this concept further, examining how to build clinical integration into a population health management strategy. He notes that care is often fragmented, as much of the population suffers from multiple conditions and sees multiple physicians to treat them. This fragmentation of care obscures information that, if consolidated, would allow for greater clarity about each patient individually and about patients as an aggregate group. He argues that if clinical services were better integrated, outcomes would improve, healthcare would be less costly, and the structure would provide greater opportunity to deliver preventative care.

The logic here is that the current structure focuses on one physician addressing one condition per patient, while another physician addresses a different condition. That approach means each physician is oriented toward treating individual conditions rather than viewing overall patient health holistically. Restructuring the clinical health delivery model, Kizer argues, creates the opportunity for a physician to take an interest in a patient's overall health, and in doing so has greater opportunity to educate the patient and coach them toward better lifestyle choices.

References

Cousins, M., Shickle, L., & Bander, J. (2002). An introduction to predictive modeling for disease management risk stratification. Disease Management, 5(3), 157–167.

Cramm, J., & Nieboer, A. (2016). Is disease management the answer to our problems? No! Population health management and disease prevention require management of overall well-being. BMC Health Services Research, 16(2016), 500–505.

Inkelas, M., & McPherson, M. (2015). Quality improvement in population health systems. Healthcare, 3(2015), 231–234.

Kapp, J., Oliver, D., & Simoes, E. (2010). A strategy for addressing population health management. Journal of Public Health Management Practice, 22(5), E21–E28.

Kizer, K. (2015). Clinical integration: A cornerstone for population health management. Journal of Healthcare Management, 60(3), 164–168.