Population Health Management Peer Reviewed Journal

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The concept of population health management refers to understanding and managing health outcomes at the population, rather than the individual level. Cousins et al (2002) highlight that risk levels for different ailments and conditions can vary by populations, 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 can be easier to understand underlying causal or contributing factors. From there, tactics can be developed to help deal with those risk factors, addressing them and therefore reducing the risk of that condition among that particular population. Population health management is, therefore, a powerful tool to improve 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 that contributed to cancer than other segments. First, 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 35-49 who had not had a personal history of cancer. Their study then gathered some demographic information, including education level. From this, they were able to determine that education level in particular has an influence on the degree of knowledge that the respondents had regarding lifestyle influences on cancer.

Lifestyle factors are by no means the only contributing factors to whether a women gets breast cancer, but there are lifestyle factors that contribute. 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 do so, and this can explain why some conditions occur more in certain segments of the population. The United States has relatively poor health outcomes for a developed nation, and while there are any number of reasons for this, lifestyle habits among the nation's poor and undereducated are definitely a contributing factor – obesity is in particular a driver of poor health outcomes, and is almost entirely 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 on the level of the individual, that person already has the disease. First, when looking at statistics, outcomes don't change much once a person has something, other than when treatment is improved. Advances in treatment, however, are typically diffused across the developed world quickly, so the United States cannot gain much advantage statistically by innovating treatments. The only true way to improve health outcomes on a broad level, they argue, is by tackling behaviors and knowledge at the population level, to reduce the incidence of disease in the first place.

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The Challenges of Population Health Management

One of the first and most obvious challenges in population health management is that it doesn't 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, but as a piece of the overall health care puzzle, rather than a replacement for treatment-based cared. 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 health system, so there is incentive to run a disease treatment model – healthier populations are less profitable. Second, there is relatively little incentive for population health management when different health care providers operate in the same region, competing against each other for business. None has any incentive to engage in population health management. It is much easier to see the logic of population health management in a country like Canada, or the UK, where the government provides health care. The taxpayer-paid systems have an incentive to reduce illness, as it saves the entire system money. Furthermore, without competition, the health care authorities have no concerns about introducing broad-based education programs, and even training programs. People will still get sick, so doctors will still have jobs, but an entire branch of such a system can easily be set up to improve education about preventable contributing factors to illness. To further this thought, a government-run system can turn to other branches of government for environmental laws, or rules governing other activities, if those support health care objectives. It is difficult to conceive of how such a thing would work in the United States – the trade-offs are entirely different. So Inkelas and McPherson are right to suggest that the US is not in a good position to fully embrace population health management.

Kizer (2015) explores this concept further, looking at how to build clinical integration into a population health management strategy. He notes that care is often fragmented, as a lot of the population suffers from multiple conditions and sees multiple physicians to treat them. This fragmentation of care obfuscates the information that, if possessed, would allow for a higher degree of clarity about each patient individually, but also patients as an aggregate body. He argues that if clinical services were better integrated, outcomes would improve , healthcare would be cheaper, and that such a structure would provide for an opportunity to deliver more preventative care as well.

The logic of this is the current structure focuses on one physician dealing with a patient on one condition; and another physician on a different condition. That approach means that each physician is specifically oriented towards dealing with conditions individually, rather than taking overall patient health as a holistic matter. Restructuring the clinical health delivery model, he argues, provides that opportunity. A physician….....

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Cousins, M., Shickle, L. & Bander, J. (2002) An introduction to predictive modeling for disease management risk stratification. Disease Management. Vol. 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. Vol. 16 (2016) 500-505.

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

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

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