PHM Glossary: L

Length of Stay (LOS)

The total number of days–measured in multiples of a 24-hr day--that a patient occupies a hospital bed per episode of illness or injury. LOS is calculated as the length of time between the date of admission and the date of discharge. Its calculation can be complex because the actual counting may be based on a 24-hour basis starting at noon or midnight, or it may be calculated on an hourly basis. LOS may also apply to the duration of a stay in a unit of the hospital (e.g., intensive care). Other terms that are sometimes used interchangeably with LOS are days of stay, discharge days, duration of inpatient hospitalization, or inpatient days of stay.


Length of Stay. (2002). McGraw-Hill concise dictionary of modern medicine. The McGraw-Hill Companies, Inc. Retrieved from

Lifestyle Coaching

In the context of population health management programs, lifestyle coaches assist individuals to attain their health, fitness and life goals (e.g., weight loss, healthy eating, exercise, stress management). Lifestyle coaching focuses on reducing a person’s health risks allowing them to function at a higher level with an increased sense of fulfillment and satisfaction.


Lifestyle Coaching Institute. Retrieved from

Lifestyle Risk Factors/Behaviorally Modifiable Risk Factors

A risk factor is a characteristic or behavior associated with an increased risk of mortality or morbidity (e.g., illness, injury, or disease). Some risk factors, such as family history, cannot be controlled. Lifestyle risk factors or behaviorally modifiable risk factors can be controlled by the individual. Examples include tobacco use, low vegetable and fruit consumption, excessive alcohol intake, and physical inactivity.


Risk factor. 2011. Merriam-Webster's medical dictionary. Retrieved from

Risk factor. 2011. Stedman’s medical dictionary (28th ed.). Retrieved from

Linear Regression

Linear regression is a method of modeling a single numerical variable (dependent variable) with one or more predictors (dependent variables) that can be either categorical or numerical. The functional form of the model is as follows:

y = α + β1x1+ β2x2+ . . . + βkx k;

where y is the dependent variable, the x is the independent variable.


A common example of linear regression in population health management is the prediction of future health care expenditures (dependent variable) as a function of several predictors (independent variables) such as demographics, disease states, prior utilization, and prior cost.

Logistic Regression

Logistic Regression allows one to predict a discrete outcome such as group membership from a set of variables that may be continuous, discrete, dichotomous, or a mix. Because of its popularity in the health sciences, the discrete outcome in logistic regression is often disease/no disease.

For example, can presence or absence of hay fever be diagnosed from geographic area, season, degree of nasal stuffiness, and body temperature.

Logistic regression is more flexible than discriminant analysis or regular regression analysis.


UCLA Lecture on Logistic Regression. Retrieved from