Introduction

An increasingly large proportion of the United States population receives medical care in a managed care environment. In this setting, populations are well-defined which allows new and innovative approaches to population health improvement to be developed and initiated. Additional approaches designed to improve the health of populations appear warranted. Despite advances in medical technology, population health improvement has not shown much progress over the past several decades. While life expectancy has increased, quality of life has been on the decline for the population of the United States.

Increased access to medical care per se, is not likely to significantly improve population health. Rather, the combination of changes in the underlying behavioral and environmental risk factors for disease and the appropriate and timely use of medical care services appear to hold more promise. The existing medical care delivery system is largely organized around the provision of acute medical care needs and provides such at a very high quality. However, prevention programs are implemented far less often and effectively, behavioral interventions are considered even less than prevention, and chronic disease care is below par.

The medical care delivery system needs to find a means to effectively reach out to the population it serves, preferably in a proactive manner. Such a system connects the clinical care delivery setting to allied health services thereby effectively creating an expanded care team around the unique needs of individual patients and health plan members. Since managed care organizations (MCOs) serve defined populations, they may be very well suited for a proactively-oriented health improvement system that: 1) can provide the needed resources to the right individual based on health risks and interests, and 2) is capable of monitoring the impact and outcomes of such efforts.

Inside the Box - Outside the Box

Improvements in the delivery of medical care have mainly been based on the interaction between physician and patient at the time of the clinic visit. Here, improvements in medical technology have lead to tremendous progress in the treatment and care of patients and are clearly both important and useful. However, a large part of population health occurs outside the physician’s office. First, this relates to the care provided to those patients who do come in to the office periodically. They are prescribed a clinical care plan and need to adhere to pharmacotherapies and self-medication regimens. Furthermore, they need to make the necessary changes to reduce risks for additional medical care needs related to the disease at-hand or other co-morbidities that might emerge. Second, population health is also related to all those individuals who do not interact with a physician yet are part of the defined population for which the MCO provides medical care. Clearly, any given patient spends more time outside the medical office than in it. Together, increasing patient adherence and compliance to care plans outside of the medical office and promoting opportunities for patients to adopt and maintain healthy lifestyle changes, can significantly improve health outcomes for the population.

System Connectedness

In order for a system to effectively connect the health improvement efforts that stem from “inside the box” as well as “outside the box,” extended care teams need to have the ability to communicate with each other. The physician or nurse needs to have a means of referring a patient to services designed to lower identified behavioral health risks without being worried they may lose the connection with the patient. The allied health professional, e.g. health educator, registered dietitian, or health psychologist, needs to be able to refer to the medical record in order to provide appropriately targeted and high quality health improvement programs. Furthermore, the system needs to safeguard patients’ privacy and confidentiality. Provider notes can be linked to the medical record in both paper and electronic formats and ongoing progress and interaction may be documented to continue to focus on opportunities for support.

Well-developed medical information systems, innovative technology use, and patient-tailored intervention approaches will allow such systems to successfully improve population health. Many of the critical components to design, develop or implement such a system are available in MCO settings today.

Identification of Risk Factors and Target Groups

The identification of target groups for interventions and health improvement programs may be directly related to biological and behavioral risk factors. Biological risk can be derived from information located in the clinical administrative databases. Based on a set of selected diagnosis codes, pharmacy information, laboratory tests and procedure codes, individuals who meet a specified set of inclusion criteria can be identified. This can be done by looking for a specific disease or according to a level of biological risk, e.g., via the use of an index score.

Behavioral risk can be derived using health risk assessment surveys (HRAs). Following the collection of information about behavioral health risks via self-report, individuals who may benefit from specific interventions may be identified. Again, the approach may be specific to a specific behavior or on the basis of a health risk score. This approach has been shown to have acceptable sensitivity and specificity in several studies reported to date.

Proactive Outreach to the Population

Following the identification of the target groups, the system may be oriented to reach out to its population and invite those who might benefit from specific programs to improve their health. The invitation may be presented proactively, in response to a referral from anywhere inside the medical care delivery system or via the use of social marketing techniques. Following patients’ enrollment in health improvement programs, both process and outcome measures must be tracked in several areas including cost, satisfaction, and health-related outcomes. The measurements of these outcomes may be built into the programs as an integral component or may be collected periodicallythrough another means. Regardless of how this tracking is done, both process and outcome measures are crucial for the ongoing quality improvement of program content, delivery, and overall process implementation.

Partners for Better Health Program

As part of the innovative Partners for Better Health (PBH) program at HealthPartners, telephonic behavior change support counseling has been linked to clinical care delivery. Using systems to review on-line medical record information, document patient interaction, and collect appropriate process and outcome measures, telephone-based counselors provide behavior change counseling support to HealthPartners members in areas such as weight management, physical activity, smoking cessation, stress management, back health, cholesterol management, and other.

The PBH Phone Line staff receives direct referrals from physicians and clinic nurses. These referrals are for patients needing to make changes in specific behaviors that underlie their biological risks. For example, patients with diabetes may need to increase their level of physical activity or manage their weight better. The PBH Phone Line counselor schedules a series of appointments with the patient and documents the interactions as well as program results in the medical record. Clinical staff may review these notes at any time, especially prior to the patient’s follow-up clinic visits.

As part of a larger weight management initiative, two HealthPartners clinics referred patients directly to the PBH Phone Line service. At the end of six months, patients who selected behavior modification only (no weight loss medications) had received an average of nine 15-minute phone calls and lost approximately 14 pounds of body weight. Those patients who did select weight loss medications in addition to phone-based counseling support lost an average of 25 pounds. Among a variety of other process and outcomes measures, every patient that was referred to the program over the course of this time period (N = 136) was asked about their health status at the time of enrollment and at 6 months. The 12-item Health Status Questionnaire (HSQ-12) version 2.0 (Health Outcomes Institute, Bloomington, MN, 1994), was used as the data collection instrument. At the end of six months of counseling (i.e., an average of 9 times 15 minutes = 2.25 hours of counseling), significant improvements in the physical functioning (+16.6%) and the health perception (+18.8%) domains were observed. No significant changes in any of the four domains of social functioning, bodily pain, energy/fatigue, or mental health were observed. Furthermore, satisfaction rates of both patients and providers exceeded the 90th percentile for services offered, overall quality, and degree to which this service helped the patients.

Conclusion

It is possible to develop a system that connects the various parts of the medical care delivery system with allied health services and provides a proactive health improvement opportunity for the population served. Such a system can have a positive effect upon health outcomes, satisfaction, and, when implemented in a cost-conscious way, is likely to show a positive economic benefit as well. This type of program can be designed, developed, and implemented as an integral part of already existing medical information data bases and communication technologies. The initial outcomes data of the PGH program demonstrate positive results. However, the true excitement of this approach lies in its potential to: 1) provide health improvement opportunities to individuals who, based on their unique medical and health risks, may benefit from the available programs and 2) do so in a proactive manner that reaches the entire population as opposed to only those who present to the clinical care setting.

Selected References

1. McGinnis JM, Foege WH. Actual causes of death in the United States. JAMA 1993; 270:2207-12.

2. Pronk NP, Boyle RB, O’Connor PJ. The association between physical fitness and diagnosed chronic disease in health maintenance organization members. Am J Health Promot; in press.

3. US Dept of Health and Human Services. Healthy People 2000: National Health Promotion and Disease Prevention Objectives. Washington, DC: US Dept of Health and Human Services; 1991. DHHS publication PHS 91-50212.

4. Pelletier KR. A review and analysis of the health and cost-effective outcome studies of comprehensive health promotion and disease prevention programs at the worksite: 1993-1995 update. Am J Health Promot 1996; 10:380-8.

5. Gilmer TP, O’Connor PJ, Manning WG, Rush WA. The cost to health plans of poor glycemic control. Diab Care 1997; 20: 1847-53.

6. Prochaska JO, Velicher WF. The transtheoretical model of health behavior change. Am J Health Promot 1997; 12: 38-48.

7. Boyle R, O’Connor P, Pronk NP, Tan, A. Stages of change for physical activity, diet, and smoking among HMO members with chronic conditions. Am J Health Promot, 1998; 12: 170-5.

8. O’Connor PJ, Rush WA, Rardin KA, Isham G. Are HMO members willing to engage in two-way communications to improve health? HMO Practice 1996; 10: 17-19.

9. Pronk NP, O’Connor PJ, Isham G, Hawkins C. Building a patient registry for implementation of health promotion initiatives: Targeting high-risk individuals. HMO Practice 1997; 11: 43-46.

10. O’Connor PJ, Rush WA, Pronk NP. Database systems to identify biological risk in managed care organizations: Implications for clinical care. J Ambulatory Care Manage 1997; 20: 17-23.

11. Pronk NP, O’Connor PJ. Systems approach to population health management. J Ambulatory Care Manage 1997; 20: 24-31. n