In determining the degree of complexity for our patient, Juanita Nelson, we have chosen classifications from the Vector Model of Complexity as defined in the article “Patient Complexity: More Than Comorbidity. The Vector Model of Complexity” by Safford, Allison, and Kiefe (2007). The Vector Model of Complexity “portrays interactions between biological, socioeconomic, cultural, environmental, and behavioral forces as health determinants” (Safford, Allison, & Kiefe, 2007, p.382). We have also included information from the article, “Patient Complexity in Quality Comparisons for Glycemic Control: An Observational Study” by Safford, Brimacombe, Zhang, Rajan, Xie, Thompson, Kolassa, Maney, & Pogach, 2009, to further expand on the biological variable which includes age, comorbid illnesses, and severity of diabetes approximated by insulin treatment. This is to account for the fact that many diabetic patients have multiple medical problems contributing to complexity along the Vector Model biological factor (Safford et al., 2009).

The Vector Model states that the interrelatedness between its axes is a key feature, distinguishing it from previous conceptualizations of the determinants of health. The goal of the Vector Model of Complexity is to propose a conceptual approach to complex patients, demonstrate how this approach promotes achieving congruence between the patient and nurse, and to examine availability of evidence to assess health care quality for the complex patient (Safford, Allison, & Kiefe, 2007). Achieving congruence between patient, nurse, and the health care system as a whole is essential for effective patient and family-centered care and therefore requires all axes of the Vector Model to develop a tailored treatment plan (Safford et al., 2007).

            The Vector Model allows an individual’s level of complexity to vary over time, reflecting the dynamic nature of complexity (Safford, Allison, & Kiefe, 2007). The complexity of a patient can be partially determined by the patient’s number of medical conditions, medications, and number of drug therapy related problems (Patterson, Peek, Bischoff, Heinrich, & Scherger, 2002). Our patient, Juanita Nelson, has biological complexity as evidenced by her multiple conditions: diabetes mellitus, atrial fibrillation, congestive heart failure, coronary artery disease, hypertension, bronchoalveolar lung cancer, hypothyroidism, depression, gastroesophageal reflux disease (GERD), cholelithiasis (gallstones), increased blood lipids, and chemodectomas in her lungs. A combination of multiple health issues therefore contributes to the deterioration of the patient’s health resulting in failure to thrive (Patterson et al., 2002). Juanita is a complex patient in regards to a specific biological factor; diabetes. Therefore she must make considerable efforts to modify diet and exercise, and to monitor her blood glucose levels (Safford et al., 2007). Once incorporated into her lifestyle, these health behaviors tend to diminish complexity over time.

Juanita lives in a small town with a population of less than one thousand people. There is no access to a hospital, but the community does have access to a primary health care team. Therefore the environment she lives in makes accessibility an issue, resulting in an increase in complexity. In order to maintain an optimal state of health, Juanita would require access to members of the health care team and resources that are vital to providing the assistance needed upon discharge from hospital (Stamler & Yiu, 2008). Iton (2008) believes that the community in which one lives "remains an important context wherein individual decisions about health behaviors may be constrained by limited access to opportunities and amenities, and negative social messages that reinforce unhealthy individual behaviors” (p.337). In reference to Juanita’s community, the health care team (doctors and nurses at Royal University Hospital) will have to examine the usual lifestyle patterns of her community, health attitudes of the population, and access to appropriate health care services. Areas of concern will be around access to health care providers who can give appropriate diabetic teaching and education regarding methods of controlling hypertension and maintaining a heart healthy lifestyle.

A patient’s behavior and availability of psychosocial support mechanisms may directly impact clinical decision-making (Safford, Brimacombe, Zhang, Rajan, Xie, Thompson, Kolassa, Maney, & Pogach, 2009). Complexity is also introduced along the behavioral axis, because diabetes imposes considerable self care demands. These self care demands can be especially difficult for patients who lack social support, contributing complexity along the socioeconomic vector (Safford et al., 2009). To obtain better glycemic control, Juanita will require adequate support from family members to maintain compliance with her diet and exercise regime. Juanita is married and has children but adequate support given by these family members is limited due to the barriers in distance and expense. Along with socioeconomic factors, cultural influences may result in an unhealthy diet and mistrust in an unfamiliar approach to health care. Cultural complexity therefore influences socioeconomic, environmental, and behavioral complexity (Safford, Allison, & Kiefe, 2007).

  A patient may exhibit complexity along any of several axes of the Vector Model of Complexity, which are interrelated in subtle ways (Safford, Allison, Kiefe, 2007). If the multiple forces of complexity are not addressed, they become barriers to congruence between the patient and health care provider (Safford et al., 2007). Juanita is having difficulty achieving recommended targets for her comorbidities due to her recent cognitive impairment (confusion, onset of hallucinations, and persistent pain) and inability to follow direction. Therefore due to her limited accessibility in her community, lack of physical ability due to deterioration and length of hospital stay, Juanita Nelson is a complex patient.


Iton, A.B. (2008). The ethics of the medical model in addressing the root causes in health disparities in local public health practice. Journal of Public Health Management and Practice, 14(4), 335-339.

Patterson, J., Peek, C.J., Bischoff, R., Heinrich, R., & Scherger, J. (2002). Mental health professionals in medical settings: A primer. W.W. Norton & Co.

Safford, M., Allison, J., & Kiefe, C. (2007). Patient complexity: More than comorbidity. The vector model of complexity. Journal of Internal Medicine, 22(3), 382-390.

Safford, M., Brimacombe, M., Zhang, Q., Rajan, M., Xie, M., Thompson, W., Kolassa, J., Maney, M., & Pogach, L. (2009). Patient complexity in quality comparisons for glycemic control: An observational study. Implementation Science, 4(2).

Stamler, L., & Yiu, L. (2008). Community health nursing- a Canadian perspective (2nd Ed.). Toronto: Pearson Education Canada.

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