We’d all like to have a crystal ball that would let us see who’s happy with their healthcare insurance plan, who’s going to have challenging clinical outcomes, and who’s going to be compliant with recommended care. Well, the good news is that with population health analytics, that crystal ball is a lot more accessible than you might think.
By integrating big data analytics with population health management, we are able to access both behavioral risk and patient engagement, as well as clinical risk. Better visibility into patient behavior has allowed plans to determine the major barriers to sustained member health, compliance, satisfaction and retention and to subsequently create strategies designed to improve patient engagement and drive better outcomes through greater efficiency.
Looking into my crystal ball, here are the three biggest barriers to member engagement and improved outcomes:
Language and Ethnic Disparity
Plans need to keep close tabs on members who are non-English speakers or where English is not their primary language. A population health management program can help identify members who are at highest risk for adverse outcomes (across clinical, compliance, retention and satisfaction domains), segment those individuals, and identify socioeconomic, demographic, ethnic or health seeking barriers that can impact outcomes.
For instance, a member who is consistently visiting out-of-network doctors may be doing so in order to consult with a physician who speaks his or her language, not knowing that the plan may have doctors that speak their language. If a plan can identify these individuals upfront, they can find and recommend a physician within their network who speaks the member’s language and head off a plan defection.
Population health analytics tell us that:
• Non-English speakers who are in special needs plans are 70% more likely to disenroll than English language speakers;
• Non-English speakers who are higher risk for disenrollment and who have significant out-of-network usage have a 20% higher voluntary disenrollment rate;
• Lower income non-English and non-Spanish speaking members are 10% less likely to get their regular preventive screenings.
Once members are segmented according to their behavioral risk, primary language and barrier to engagement, the plan can begin communicating with these individuals in that language to address their specific barriers. This will go a long way towards ensuring plan loyalty. In a recent blog posting, HealthFirst, a for-profit plan serving a culturally and ethnically diverse population in Downstate New York, said that its biggest challenge in contacting these individuals is making sure that they have translated content. They make a point of reaching out to members in their primary languages — English, Spanish, Chinese dialects, and Russian — so that they can better engage with them and positively influence their health outcomes.
Access
Both proximity to healthcare providers and available transportation also can have a significant impact on member compliance, adherence and retention. Higher risk members that live farther away from their PCP are 15% more likely to leave the plan voluntarily and 8% less likely to get their recommended diabetic screenings
Here again, population health analytics can help. Identifying the higher risk members and then segmenting them by geography can help determine which individuals live the greater distances from their primary care physician and care team (including pharmacy). Health plans can address this barrier directly by recommending providers who are closer or by offering transportation.
Social Support and mobility
Having family and friends around for support is important for individuals of all ages. It’s particularly important for the elderly. Population health analytics show that men living alone are very likely to be admitted and readmitted and can indicate if they have experienced mental health decline. Without a caregiver or spouse, these men are challenged by managing their health and engaging with their primary care provider and care team. With big data population health analytics, these factors can be pinpointed, and plans can designate home healthcare or other resources to help the individuals get the care they need.
Analytics gives us an entirely new way of looking at plan populations and determining each member’s area of greatest risk and need. Identifying these individuals and dealing with their challenges up front will go a long way towards proactively improving quality, satisfaction and retention and influencing better health outcomes.
Saeed Aminzadeh is Chief Executive Officer of Decision Point Healthcare Solutions, the leader in providing engagement analytics solutions to the health care industry.
