PHM Glossary: H

Health advocate

A person providing services including, but not limited to, assisting clients or client groups at all levels of care in the healthcare system to make informed choices regarding available options and resources, maximizing clients’ abilities to select among alternate treatment options by facilitating access to relevant information and clarification of clients’ personal or cultural values and preferences. An advocate may work independently, in a medical setting, or on behalf of communities or disease-specific populations.


In the population health setting, health advocates can enable greater autonomy for an individual to make better decisions for improved health and lower cost. In one example, a health advocacy, counseling, and activation program aimed to increase knowledge about coronary heart disease (CHD), social activities, contacts, roles, support, and exercising was effective in reducing depressive symptoms among male CHD patients suffering from a moderate or high amount of depressive symptoms.


Fries, J. F., Koop, C. E., Sokolov, J., Beadle, C. E., Wright, D. (1998). Beyond health promotion: Reducing need and demand for medical care. Health Affairs, 17(2), 70-84.

National Association of Healthcare Advocacy Consultants. Code of ethics.

Salminen, M., Isoaho, R., Vahlberg, T., Ojanlatva, A., Kivela, S. (2005). Effects of a health advocacy, counseling, and activation programme on depressive symptoms in older coronary heart disease patients. International Journal of Geriatric Psychiatry, 20(6), 552-558.

Health Care Management

The term health care management relates to administration of the various and diverse practices and components in a health care system. Those working in this field possess managerial knowledge of the health care industry and its workings. Health care managers administer the business of care delivery in inpatient and outpatient settings, such as clinics, home health care, and hospice. They may also work in related areas—for example, insurance, medical transportation, pharmaceutical firms, physician group practices.

Although health care managers may be employed within the chronic care management community, the term health care management differs considerably from the term chronic care management. The former relates to a category of career and educational pursuits, while the latter is a system of coordinated health care interventions and communications for populations with conditions in which patient self-care efforts are significant. Health care management is about fiscal health while disease management is about physical health and well-being.

See also disease management.


Kongstvedt, P. The Managed Health Care Handbook, 4th ed. Sudbury, MA: Jones and Bartlett (2001).

Health Coaching

Health coaching is the practice of health education and health promotion within an interactive and individualized context, to enhance the well-being of individuals and to facilitate the setting and achievement of their own health and health care-related goals. It is typically performed by a health professional of some type (e.g. nurse, dietitian, pharmacist, respiratory therapist, or social worker).

The objective of health coaching is to empower individuals to actively and optimally manage their health, risk factors, and medical conditions in the short and long term and in accordance with their own preferences based on accurate evidence-based information. Within a population health management context, it is intended to complement, not replace, physician-patient interaction.


Health coaching is an effective modality to achieve sustained behavior change through a structured, supportive partnership between the participant and coach. The coach helps facilitate insights and clarity into the participant’s own values and the personal importance of achieving certain outcomes through inquiry, learning, and personal discovery.

Health coaching is characterized by the presentation of objective information in a manner that is congruent with the current emotional and physical status of the individual and with empathic consideration for the whole person and the context within which he/she is making health and lifestyle decisions. A trusting and professional relationship between the participant and the health coach as advisor, motivator, and mentor is essential to the effective practice of health coaching.

Health coaching occurs in a variety of settings and modalities (e.g. face-to-face interaction, telephonically, via Internet Web sites and correspondence). It may consist of the following elements:

  • Processes and tools for increasing knowledge of health management and treatment options, and the pros and cons and potential outcomes associated with each;
  • Access to tools and information that teach self-management and decision-making skills;
  • Support for setting realistic goals and expectations for improved health outcomes based upon the individual’s situation—health status, level of motivation, resources—and upon the proven effectiveness of treatment options, if any, in question;
  • Facilitation of problem solving to identify and overcome barriers to success; and
  • Assistance in tracking progress.


AH Home Page. 2006. American Healthways. 9 Feb. 2006

Duke University. Duke University. 9 Feb. 2006

Health Dialog. 2005. 9 Feb. 2006

Palmer, S., “Health Coaching to Facilitate the Promotion of Healthy Behaviour and Achievement of Health-Related Goals.” International Journal of Health Promotion & Education. 41 (3) (2003): 91-93.

University of Ottawa. 9 Feb. 2006

World Health and Healing Collaborative. 2001. 9 Feb. 2006

Health Information Exchange (HIE)

Health information exchange (HIE) refers to the sharing of clinical and administrative data across the boundaries of health care institutions, health data repositories, and states and will create the infrastructure and/or capability for interoperability among health care entities. HIEs translate data from one of several formats into other supported formats; link various systems within a clinical enterprise and send data to those systems; verify the accuracy and conformance of messages to known standards for data communication.


AHRQ – Knowledge Library – ADD CITE

Health Information Technology (HIT)

Health information technology (HIT) refers to the use of a variety of electronic methods for managing information about the health and medical care of individuals and groups of patients. HIT can improve care processes so that patients with acute and chronic conditions receive recommended care, diminishing disparities in treatment and reducing medical errors.


Health Information Technology in the United States: The Information Base for Progress – ADD CITE


Health Information Technology for Economic and Clinical Health (HITECH) Act

Title XIII of the American Recovery and Reinvestment Act (ARRA – PL 111-5) establishes the Office of the National Coordinator for Health Information Technology (ONC) within the Department of Health and Human Services (HHS) to develop and implement programs/initiatives created by the law.

HITECH aims to advance the use of health information technology (HIT) to allow a nationwide electronic exchange for use of health information to improve quality and coordination of care, to encourage use of health IT by doctors and hospitals to exchange patient information, to provide resources to improve quality and coordination, and to strengthen laws to secure patient information.


American Recovery and Reinvestment Act of 2009, Pub. L. no. 111-5, 123 Stat. 115 (2009).


Health Level 7 (HL7)

A set of messaging standards widely used for exchanging data such as orders, results, and patient registration information among disparate healthcare information systems. Newer HL7 standards address clinical document architecture and a reference information model for health care.

The term refers to the seventh, or highest, layer of an international standard for open systems integration, the layer where “content” is transmitted. Also refers to the international organization that maintains the HL7 standards.


Health Level Seven International.

Health Promotion

Health promotion refers to the process of enabling people to increase control over their health and its determinants, and thereby improve their health.

According to Michael O’Donnell, a pioneer in the field, health promotion is the art and science of helping people discover the synergies between their core passions and optimal health, enhancing their motivation to strive for optimal health, and supporting them in changing their lifestyle to move toward a state of optimal health. Optimal health is a dynamic balance of physical, emotional, social, spiritual, and intellectual health.

Lifestyle change can be facilitated through a combination of learning experiences that enhance awareness, increase motivation, and build skills and, most important, through the creation of opportunities that open access to environments that make positive health practices the easiest choice.

See wellness programs.


Nash DB, Reifsnyder J, Fabius RJ, Pracilio VP. Population Health: Creating a Culture of Wellness. Sudbury, MA: Jones & Bartlett Learning, 2011.

O’Donnell MP. Definition of health promotion 2.0: embracing passion, enhancing motivation, recognizing dynamic balance, and creating opportunities. Am J Health Promot. 2009 Sept-Oct;24(1):iv.

Health Risk Assessment (HRA)

A health risk assessment integrates science with self-reported information and refers to the method used to catalog, assess, and estimate the probability of an adverse health effect for an individual and the likely magnitude of the health effect and/or cost of that adverse effect.


Health risk assessment is often used interchangeably with health risk appraisal (HRA). HRA technically refers to the collection instrument only while health risk assessment refers to the overall process that includes use of the HRA instrument. Health risk assessment uses information provided by an individual in a systematic way to estimate the risk of future illness, health care utilization, and expenditures. HRAs can be conducted via questionnaire (paper or Web-based) or via in-person (face-to-face or telephone) interview and can be modified to identify specific risk factors.

Lewis C. Robbins, M.D., introduced HRAs in the 1970s as a means of enhancing communications between doctors and patients on the topic of health risks and lifestyle choices. HRA expanded and evolved as more sophisticated health risk estimation methods were developed. Large employee populations were encouraged to complete HRAs with the aim of allowing their employers and health plans to better assess the group's health needs. In the mid-1990s, the Society of Prospective Medicine developed guidelines for HRA users. Today, HRAs are an integral component of population health management programs through participant identification, risk stratification, and assignment to interventions.

Health plans, managed care networks, health management organizations, and large employers may combine data collected via HRAs with national health statistics to project and prioritize group risks and plan health intervention programs. Additionally, some organizations incorporate HRA data into their predictive modeling tools. Health risk assessment may predict risk and the magnitude of that risk on an individual or group basis for comparison with baselines or benchmarks, as well as to predict the need for health management services (e.g., lifestyle management programs, disease management programs, etc.).


HRAs have three standard elements including a questionnaire, risk projection, and assignment to interventions based on the risk. Additionally, educational reports are typically provided to individuals who complete an HRA.

The questionnaire, which is typically completed by the patient or caregiver, collects information about family history, patient age, biometric values (e.g., weight, blood pressure, cholesterol levels), and lifestyle behaviors known to affect health (e.g., diet, tobacco and alcohol use, safety precautions, etc.).

The risk computation compares individual responses to epidemiologic data based on large populations such as Framingham Study population and information held by the National Institutes of Health. Algorithms blend individual risk factors with what is known scientifically about disease precursors (or indicators) such as smoking and sedentary lifestyle in large populations. The relative risk of these indicators is a numerical value that suggests the level of association with a specific chronic disease and costs associated with that disease. Cost data from claims experience may be included in the algorithm.

Based on derived risk status, individuals are identified for appropriate interventions (e.g., lifestyle management, disease management, etc.). This process may incorporate sources of other information such as health care and pharmacy claims data.


Anderson DR, Staufacker MJ. The impact of worksite-based health risk appraisal on health-related outcomes: a review of the literature. Am J Health Promot. 1996 Jul-Aug; 10(6): 499-508.

Care Continuum Alliance. Outcomes guidelines report, vol. 5

Robbins, L. and Hall, L. How to Practice Prospective Medicine. Indianapolis: Methodist Hospital of Indiana (1970).

Anderson DA, Serxner S, Terry PE. Health Assessment. In: O’Donnell MP, ed. Health promotion in the workplace. 3rd ed. Albany, NY: Delmar Thomson Learning; 2002: 218-241.

ACSM’s Worksite Health Handbook – 2nd Edition: A Guide to Building Healthy and Productive Companies (American College of Sports Med) Nicolaas P. Pronk (Editor)

Peterson, K., and Hilles, S. “Guidelines of the Society of Prospective Medicine for Health Risk Appraisal Users.” Handbook of Health Risk Appraisals, 3rd ed. Pittsburgh, PA: Society of Prospective Medicine (1996).

Health Status

Health status is a multi-dimensional concept that incorporates general health and well-being, as well as the risk of illness or death of an individual or population. Health status is measured based on individual self-assessment or self report or according to the types and number of co-morbid conditions and health service utilization. Health status is typically monitored in population health management programs to better understand the particular health care needs, deficiencies, and opportunities in a population.


Because individuals tend to assess their health status based on a range of wellness indicators, self-reported health status provides useful information for population health management programs, including predictive modeling tools to support programs. For example, specialist clinicians may not accurately estimate the health status of patients due to their focus on a specific diagnosis or symptom, with less consideration on a patient’s perception of their overall health status. There is often a discrepancy between the severity of coronary disease resulting from a coronary angiography test and the patient-reported health status. Self-reported information can reveal clues as to the manner in which the patient manages or feels about the illness and quality of life and may help providers select the most appropriate treatment regimens.


Rumsfeld, J. “When Will They Meet?” Circulation; 106:5 (2002).

Terms Beginning with ‘H’. 2003. Manitoba Centre for Health Policy. 16 Feb. 2006

Rumsfeld, J. “Health Status and Clinical Practice: When Will They Meet?” 2002. American Heart Association. 16 Feb. 2006


HEDIS (Healthcare Effectiveness Data and Information Set) includes a standardized survey tool used by more than 90 percent of America’s health plans to measure performance on important dimensions of care and service experiences that evaluates plan performance in areas such as customer service, access to care, and claims processing. HEDIS is sponsored, supported, and maintained by the National Committee for Quality Assurance (NCQA). Health plans also use HEDIS results internally to identify and focus their improvement efforts.


HEDIS 2009. National Committee for Quality Assurance.

Health Insurance Portability and Accountability Act of 1996

The administrative simplification provisions of the Health Insurance Portability and Accountability Act of 1996 (HIPAA, Title II) require the Department of Health and Human Services to establish national standards for electronic health care transactions and national identifiers for providers, health plans, and employers. It also addresses the security and privacy of patients’ medical records and other health data. Developed by the Department of Health and Human Services, these new standards provide patients with access to their medical records and expanded control over how their personal health information is used and disclosed. Adopting these standards will improve the efficiency and effectiveness of the nation's health care system by encouraging the widespread use of secure electronic data interchange in health care. State laws providing additional protection are not affected by this law.


Centers for Medicare and Medicaid Services. 14 Dec. 2005. Department of Health and Human Services. 16 Feb. 2006

HIPAA Home Page. 18 Oct. 2005. 16 Feb. 2006

Electronic Code of Federal Regulations. 21 July 2005. 16 Feb. 2006

Hybrid Model (Predictive Model)

A hybrid is the combination of two or more different things, aimed at achieving a particular objective or goal. A model is a simplified version of something complex used in analyzing and solving problems or making predictions. A hybrid (predictive) model in healthcare attempts to predict a disease-related outcome (such as adverse clinical events, hospitalizations, or costs) by using several modeling methodologies in cooperation or competition.

Hybrid modeling is generally used to predict outcomes for a specific data set; the model it produces is not intended for use on multiple, unrelated data sets. Therefore, hybrid models may be the best predictors for the data on which the model was developed but do not generalize well to other sets. In general, the state of a hybrid system is defined by the values of the continuous variables and a discrete control mode. The state changes either continuously, according to a flow condition, or discretely according to a control graph. Continuous flow is permitted as long as so-called invariants hold, while discrete transitions can occur as soon as given jump conditions are satisfied. Discrete transition may be associated with events.


Like all predictive models, a hybrid model attempts to predict who will acquire a disease, a disease-related adverse event, a change from one health state to another (increasing disease severity), or cost. But instead of using a single methodology to do so, it incorporates several methodologies in fitting a model to a specific data set. For example, the data may be modeled using a combination of regression, correlation, neural nets, decision/classification trees, Markov chains, and other data mining techniques. These methodologies can be used sequentially and in parallel, and the ultimate model that emerges does so from both cooperation and competition among the various methodologies.

Unlike models developed with pure or straightforward methodologies, hybrid models are generally used to fit a specific data set – for example, to predict health care costs in 2005 based on data for health plan members in 2003 and 2004 (as usual, such data may be supplied via claims, demographics, test results, patient input, or the medical record).

Hybrid models have also been called meta models, because they combine models for best results.

The output from a hybrid predictive model is a rule set customized to a specific population for a specific time period. To avoid overfitting the model to the data, the hybrid model is tested against a holdback (validation) data set taken from the same data set from which the development set was drawn (as is conventionally done in all forms of modeling).

Advantages of hybrid predictive modeling include potentially greater accuracy against a specific population (data set) compared with models developed for more generic use and the potential advantage arising from using multiple methods of prediction.

Disadvantages include nongeneralizability of results and greater expertise required to perform the modeling. The modeler must understand not only many modeling and data mining methodologies, but how to combine them to optimize the results. A new model must be built with each data set.