How To | Population Health Management

Address risk factors to bend the cost curve in chronic care

How To | Population Health Management

Address risk factors to bend the cost curve in chronic care

Addressing social and behavioral determinants of health with targeted, intensive interventions drives higher rates of engagement among patients with chronic conditions — significantly reducing costs.

Connecting patients with chronic conditions to healthcare services that address their needs based on social or behavioral factors has been proven to be an effective way to reduce costs of care for these patients.

A recent study in Population Health Management finds that costs of care for such patients were reduced by 11% per year through such efforts.a Meeting just one of the needs related to these factors, commonly referred
to as social determinants of health (SDOH), was shown to reduce care costs by 7% — a sign that even limited interventions can deliver greater value.b

Health plans are beginning to recognize the effects of SDOH, and some acknowledge that cost savings can be achieved from screening for and mitigating SDOH among members who are high users of healthcare.
Yet among many health plans, significant gaps remain in connecting members with resources for addressing these needs.

Treating unmanaged behavioral health issues and addressing SDOH are critical to managing chronic conditions at every stage. Therefore, after identifying members who would benefit from care management, the next step should be to determine whether the members present behavioral and social risk factors likely to overwhelm efforts to engage them in chronic care regimens. With this information, health plans and providers can offer personalized, relationship-oriented support that reduces patient barriers to self-care regimens.

Meeting members’ holistic care needs in this way has been shown to improve health outcomes, reducing inpatient readmissions by as much as 7.9% and delivering average net savings potentially amounting to 12.3%.c

Obtaining data-driven insights for chronic care

Traditional models for risk stratification sort through claims data to identify health plan members who are high users of healthcare resources, usually because they have experienced a high-cost medical event. In contrast, risk stratification models that account for members’ SDOH can aid in predicting these members’ risk of experiencing medical events before the events occur. By expanding care management services to address patient-members’ social needs — such as housing, food and transportation — health plans and providers can promote improved health outcomes and make a substantial, sustained impact in reducing chronic care costs.

Consider a member in her 60s who has recently been diagnosed with heart disease. The member is in denial about her condition, despite recently having undergone angioplasty to clear a clogged artery. When her physician prescribes a medication that strains her budget, she stops refilling the medication.

A review of pharmacy claims data shows the member has not refilled her prescription. Noncompliance with the medication could significantly worsen the member’s heart disease, increasing her risk for heart attack. It also could dramatically increase healthcare costs throughout her life.

The health plan’s care management team receives an alert through a data-driven workflow platform that the member may need medication assistance. A chronic care engagement specialist connects with the member to explore the root cause of the issue. Then, the engagement specialist pairs the member with a social worker, who provides practical support for overcoming the financial barriers to obtaining this medication.

Over the next four to six weeks, the social worker provides the member with concrete problem-solving support via brief, frequent follow-up communications and assesses the member’s progress toward reducing the financial barriers and taking the medication as prescribed. The social worker also contacts pre-identified members of the woman’s social support circle, including family members and a neighbor, to enlist their support and encouragement as well. Within six weeks, the member is fully compliant with medication protocols, and her risk of heart attack decreases.

Using SDOH data to pinpoint members who are at risk for near-term disease progression and to determine the right interventions for social and behavioral support is critical to bending the cost curve in chronic care.

Such data encompasses many factors, including reliability of housing and transportation to socioeconomic status, level of education, employment status, neighborhood and physical environment. For example, SDOH data may reveal that a patient-member lacks safe and affordable housing, is in debt, lives in a food desert or has recently become unemployed. These individuals often feel overwhelmed by their life circumstances, making compliance with chronic care management even more difficult.

Data analysis also may point to behavioral health factors that could inhibit chronic care management, including signs of depression and other mental health conditions and behaviors that pose a detriment to health, such as smoking, alcohol use, poor diet and lack of exercise.

The presence of SDOH factors that could impede a member’s ability to manage his or her own condition is one of the biggest challenges health plans and providers face in managing population health. For example, a study published in 2016 found that patients who lack access to basic resources are more likely than other patients to have depression, diabetes and high blood pressure; are more likely to use the emergency department; and are less likely to show up for their appointments.d The study also found that these patients most commonly indicated difficulties affording care, food and utilities.

Such studies, coupled with providers’ and health plans’ experience, show that efforts to close the gap between unmet behavioral and social needs and a member’s ability to engage in self-care for a chronic condition could dramatically reduce the risk the member’s condition might worsen. It also could substantially reduce costs of care by meeting care needs and preventing disease progression.

Moving from data to action

To mitigate or remove SDOH gaps in care for chronic conditions, care management teams
must identify patient-members most in need of behavioral or social interventions, and determine for them which care manager (e.g., a behavioral health coach, a social worker or a community health worker) would be best suited to help overcome the member’s specific gap.

The teams also should deliver high-touch interventions in a short time via phone, text, an in-person visit or a combination of the three.
By accounting for each member’s needs and communication preferences, health plans and providers can optimize engagement and reduce inefficiencies in member outreach.

For example, when a member expects to communicate with a behavioral health coach a few times a week over a six-week period, the member is more likely to engage in a health-focused initiative because he or she understands the goal (e.g., medication adherence), the time frame for achieving the desired outcome and the solutions that will be used. The member also will be more likely to remain engaged because the model supports the development of one-on-one relationships that build trust, using the communication modality the member prefers. Retention rates under this model are high: 25% to 30%, compared with the industry average of 10% to 20% for traditional care management.e

The first step in the process is building a trusted relationship with the member. For example, if the patient-member has failed to keep appointments with a primary care physician or specialist, the care manager might begin the conversation by saying: “I see that you haven’t been making your appointments. Could we work together to fix the issues making it hard for you to see your primary care physician?”

It may be that the member is experiencing language barriers at the physician’s office, in which case the chronic care support team could arrange for an interpreter to assist the member during appointment scheduling and visits. The interpreter could then relay important information from the visit back to the care manager, who would share the information with the member’s social support circle (or care circle).

Also, in conversing with members, a care manager may find some are unable to make required copayments for their services. The care manager can help these members access financial resources to cover the costs of office visits and, for patients with diabetes, food and supplies needed for compliance with diabetes protocols.

This model draws upon members’ care circles to close chronic condition, self-care and service gaps. The benefits of this approach are corroborated by the John Hopkins Center for Health Equity, which has published research validating the support of family, friends and neighbors significantly improves members’ adherence rates to medical regimens for chronic care conditions. Such adherence starts with keeping their medical appointments and taking their medications and extends to following healthy lifestyle behaviors, such as regular exercise and a healthy diet.

Further, members with strong social support are 50% less likely to die from heart disease than are those who do not have such support, according to research conducted at Brigham Young University.f

With reinforcement of the care plan from the members’ care circles, the desired outcome is more likely to be achieved. In the case of patients with diabetes, for example, lower blood sugar levels can be promoted through support with setting up a medication reminder system.

Designing an effective chronic care management approach

To effectively manage patients with chronic conditions in a way that accounts for SDOH, health plans and providers require access to the following.

The ability to work with large data sets. Critical data for analysis includes claims data, hospital admission/discharge/transfer (ADT) data, member demographic data, assessment data (such as screening tests that may indicate the existence of, or potential for depression) and socioeconomic data. To develop the detailed insight needed for this approach, expertise in advanced predictive modeling is required to identify service gaps and SDOH risk factors that are likely to adversely affect near-term chronic condition outcomes regardless of a patient’s or member’s specific chronic diseases. For example, overcoming barriers that prevent a patient from periodically seeing a cardiologist is likely to improve outcomes of care regardless of whether the patient has heart failure, coronary artery disease or severe hypertension.

A data-driven workflow system for care management. The interdisciplinary care management team will require a workflow platform that helps team members identify and stay focused on the critical goal for each engagement: using co-created tactical solutions to reduce risk factors. Such a platform should be driven by a rules engine that helps team members explore barriers to effective self-care behavior and identify appropriate resources for removing such barriers. The platform also should support matching care manager skill sets to the nature of the barrier and interventions that will be applied. For example, a care manager with expertise in social work should take on cases where the predominant barrier is a lack of access to social services, while a community health worker should be matched with patients who would most benefit from health coaching.

Care manager flow chart

 

 

Tools for high-touch interventions. Digital tools, both social and mobile, are critical for keeping members engaged in chronic care management because they meet patients where they are. Such tools enable care managers to communicate with members by phone, text or email, according to their preference.

Face-to-face interventions also are critical for some patients. Where in-person care management is not possible, a telehealth solution with a face-time element can be effective and appropriate. And social communities, such as networks of members with the same care challenges, can provide the peer-to-peer support that can be hugely valuable in helping members take on the hard work of chronic care management.

Some health plans sponsor apps that members can use to create private social networks by inviting their families, caregivers and friends. Such interventions personalize the care experience, activate additional resources from family and friends and help to build strong relationships that support success.

Using the communication preferences of members’ care circles also is important in keeping them informed and engaged throughout this process.

Typically, a combination of such interventions for keeping members engaged is used. When applied in the real world, such interventions contributed to a 45% increase in member engagement for one health plan and a 24.5% improvement in statin usage among members with coronary artery disease.g

A diverse set of care management experts. There should be a broad range of skill sets among care managers, from registered nurses and pharmacists to managers with expertise in behavioral health, social work services, community outreach, health coaching and addiction. For example, a care manager who specializes in addiction could both provide support for a member undergoing medication-assisted treatment, which is considered the gold standard in treatment for opioid use disorder, and engage
and educate the member’s social circle throughout the treatment and recovery process. This dual-support role strengthens the member’s support network and improves the member’s prospects for recovery.

A proven approach to chronic care success

The issues facing members with chronic care conditions are universal, but there is no one-size-fits-all solution for meeting members’ care management needs. Brief, targeted, high-contact frequency interventions that account for members’ unique SDOH give members the tools, resources and social support they need to manage their chronic care conditions and work collaboratively with their healthcare providers. They also are proven to generate high levels of success critical to sustaining members’ interest and efforts in managing their health appropriately.

Perhaps the most important lesson learned through a data-driven, relationship-centered approach to chronic care management is the importance of recognizing and incorporating members’ communication preferences at each stage. For example, digital techniques for chronic care management should complement, not replace, face-to-face and telephone interactions with vulnerable populations. This point is critical because, although 90% of patients requiring chronic care have access to a cell phone, they may not have access to nutritious food or a bed. For disadvantaged and vulnerable populations, who may be transient and hard to reach, digital communications provide an opportunity to connect with members and to formulate next steps in an action plan of care.

Ultimately, by using SDOH data to advance chronic care management, health plans and providers can arm themselves with a 360-degree view of members’ health needs and the barriers they face in managing their conditions. Access to this information better positions all key stakeholders to design highly effective and efficient approaches to bending the cost curve in chronic care management, bringing the nation’s healthcare system another step closer to achieving the Institute for Health Improvement’s Triple Aim of improved outcomes, a better patient experience and reduced costs. 

Footnotes

a. Pruite, Z., Emechebe, N., Quast, T., Yaylor, P.,  and Bryant, K., “Expenditure Reductions Associated with a Social Service Referral Program,” Population Health Management, Nov. 28, 2018.

b. For purposes of this discussion, we will assume the term social determinants of health also encompasses behavioral factors.

c. Percentages are based on internal analyses performed by HGS AxisPoint Health.

d. Berkowitz, S., Hulburg, A.C., Hong, C., Stowell, B., Tirozzi, K.J., Traore, C.Y., and Atlas, S., “Addressing Basic Resource Needs to Improve Primary Care Quality: A Community Collaboration Programme,” BMJ Quality & Safety, Feb. 18, 2016.

e. Percentages are based on internal analyses performed by HGS AxisPoint Health.

f. Holt-Lunstad, J., Smith, T.B., and Layton, J.B., “Social Relationships and Mortality Risk: A Meta-Analytic Review,” PLOS Medicine, July 27, 2010.

g. Percentages are based on internal analyses performed by HGS AxisPoint Health.

About the Author

Virginia Gurley, MD, MPH,

is chief medical officer, HGS AxisPoint Health, Westminster, Colo.

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