Home Health Care Designing a high-value approach to chronic care management

Designing a high-value approach to chronic care management

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Eighty percent of members’ health is determined by social factors such as housing, economic position, education and social connections, so called social determinants of health. But, most healthcare organizations struggle to use social determinants data to target members for focused intervention, a recent study found.

Even when payers and providers have access to a range of data that can point to the need for targeted social support interventions, they face challenges in effectively engaging members and providers in these support services that improve health and health outcomes.

Some members who would benefit from care management to address chronic conditions at their earliest stages are difficult to locate. They may face lack of stable housing, for example, which can contribute to high levels of stress, impaired coping and weakened social connections.

But all too often, the challenge of locating and engaging members who would benefit from care management interventions is as much about gaining trust and acceptance of assistance as it is about pinpointing their location. When care managers fail to engage members at the first point of contact, the risk that members will avoid future interactions with care managers significantly increases. In the face of identified care gaps, with each avoided phone call, the likelihood of successfully lowering a member’s risk for experiencing a medical event is decreased.

The ability to effectively engage members in care management has emerged as a critical success factor for payers in today’s value-based environment, but it has also proved a formidable challenge. The use of data analytics holds a critical key for unlocking the potential to engage members in holistic care management that yields high value improvements in cost of care, condition management and health outcomes.

Developing a Data-Driven Approach

Determining the right approach for member engagement around care management requires deep understanding not only of the factors that put members’ health at risk, but also the cultural barriers that may inhibit success in establishing a care management relationship.

For example, members who are insured by commercial plans are typically the most difficult to engage because they don’t perceive themselves as having a high level of risk for a medical event. These members also tend to have more limited time than members in other plans. If the care manager fails to articulate the goals of care management, what success will look like, and how long the member can expect to work closely with a care manager, commercially insured members will be less likely to invest in this approach. Their reaction: “I don’t have time for this.”

Low-income members may be burdened with high levels of concern about unpaid medical bills and overall financial vulnerability. They may be reluctant to participate in care management not just out of fear that they won’t be able to afford recommended healthcare services that could improve their health, but also because they may fear that participation could result in a reduction of their benefits.

Cultural and language barriers also are common. Six out of ten Hispanic adults in the United States have had difficulty communicating with a healthcare provider due to a language barrier, a recent poll shows. Lack of trust in physicians or medical care, perceived discrimination, and a deep reluctance to share their medical complaints when speaking with a care professional also can limit the ability to establish an effective care management relationship.

The use of data analytics can better inform a health plan’s approach to engaging members in care management and reducing their risk for a health-related event.

For example, an analysis of consumer data typically used by marketers can shed light on whether a member lives alone, owns a car, or lives in proximity to public transportation. These data points can be constructed into an index that can be used to evaluate the member’s risk for social isolation, which can increase members’ risk for heart attack and stroke. Analysis of consumer data also can point to signs of financial instability, such as high levels of indebtedness, low levels of discretionary income, and a succession of jobs within a short period. Financial instability increases stress, which can have a significant impact on behavioral and physical health. It can also prompt members to delay care, further exacerbating chronic conditions.

Taking a close look at social determinants of health data—including the member’s neighborhood and physical environment—may reveal that a member lacks safe and affordable housing and lives in a food desert. In this way, the member may feel overwhelmed by life circumstances, making adherence with chronic care management not only difficult, but unlikely.

Data analysis can also point to methods of member communication that can yield the highest levels of engagement. For example, highly transient individuals typically maintain access to a mobile phone. Members who lack reliable housing and are difficult to reach via a phone call may be more receptive to communication by text. With this information in hand, care managers can use secure, HIPAA-compliant texting to establish contact with hard-to-reach members, schedule appointments, collect vital healthcare data, and provide access to resources that help meet members’ social and healthcare needs.

Strategies for Effective Engagement

A data-driven approach to care management enables health plans to design focused, personalized interventions that support adherence and care gap closure within short timeframes—with engagement rates as high as 20 percent. There are four ways payers can use data to develop a holistic approach to care management that yields high levels of engagement while improving health outcomes and reducing costs.

Focus not only on clinical and claims data, but also data that point to behavioral and social risk factors. Traditional models for risk stratification sort through claims data to identify members who exhibit high utilization of care resources—usually after experiencing a high-cost medical event. In contrast, risk-stratification models that take into account members’ social and behavioral determinants of health can predict members’ risk of experiencing a medical event before the event takes place. By targeting these members for focused intervention, health plans are better able to design a holistic care management approach that supports improved health outcomes prior to development of high-cost disease progression.

Use data analysis to determine which care managers are best suited to assist individual members. For example, if social determinants of health data reveal a member is struggling with high levels of debt, pair the member with a social worker who can address the member’s health needs, as well as the financial limitations that could impact the member’s ability to stick to a care regimen. When claims data point to missed prescription refills, a nurse or pharmacist may be ideally suited for care intervention and support. Meanwhile, a health coach or community health worker may be the best choice for a patient who could benefit from lifestyle change coaching.

Health plans should also consider pairing members with community health workers of similar backgrounds to bridge cultural and language barriers and establish trust early in the relationship.

Deploy a data-driven workflow system for care management. A workflow platform that helps staff stay focused on the critical goal for each engagement—”What is the care gap at hand that we’re trying to address?”—is key to success. Such a platform should be driven by a decision support rules engine that helps team members identify barriers specific to the targeted care gap and identify appropriate solutions and resources to support success. Ideally, the initial care gap assessment should take just 15 minutes, which is possible when the workflow platform helps keep the care manager tightly focused on the objectives at hand. When initial appointments last as long as 45 minutes—typical among care management programs—members are more likely to disengage because the value to them and their health is not clear, and they may also feel as though their time has not been respected.

It’s also critical that care managers explicitly state the goals of the service and the length of time members can expect to take part in the program from the start of the first visit. For example, when members know they will be in communication with a behavioral health coach a few times a week over a six-week period, they are more likely to engage in a health-focused initiative because they know the goal (e.g., medication adherence), the timeframe in which the desired outcome will be achieved, and the solutions that will be put in play.

When members are difficult to locate or engage, rely on data-driven insight to adjust the approach. It’s possible to use phone number verification to help track down a difficult-to-reach member, but there is an art to maintaining communication and engagement after the point of contact. Consider the following tactics:

  • When a member isn’t answering phone calls, but data analysis confirms the phone number is correct, communication via text or email is a great way to break the ice. Be careful to keep messages brief and to the point: “It seems you haven’t been making your primary care appointments. Could I help you resolve the issues that are making it hard for you to see your primary care physician?”
  • Seek to involve a family member, neighbor, or friend in the care management process. With reinforcement of the care action plan from the member’s support circle, the desired outcome is more likely to be achieved.
  • After the first visit, schedule a follow-up appointment and send reminders via text. When members schedule an appointment, they are more likely to continue to engage with you.
  • Avoid using “should-isms.” Instead of saying, “You should be getting your prescriptions refilled,” try using questions like, “What would make it easier for you to get your prescriptions refilled?” Also, work to incorporate “we” statements such as “Let’s see if we can set up reminders for a refill,” or “Let’s see if we can get you a discount on these medications.” Doing so reinforces the positive, solution-oriented nature of the relationship and helps members feel as though they have an ally.

Now that we have a greater understanding of the significant impact of social determinants on member health, health plans are acknowledging the urgent need for developing and implementing strategies that incorporate these factors into the overall care plan. Addressing social determinants of health can help to reduce unnecessary costs while significantly improving member engagement and health outcomes. Using data-driven insight is a key component in helping members overcome the social, behavioral, and cultural barriers that put their health at risk, while also developing strong relationships for improved health and value.

 

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