By Amogh Rajan, Product Manager for Vantage Health Technologies Health equity exists when everyone has a fair opportunity to be healthy. Fair opportunity can mean different things and manifests in different ways depending on geography, population composition, comorbidities, prevalence of social determinants of health (SDoH), and more. The topic is all the rage at healthcare conferences or in the news. But what does true person-centered, equitable care look like? Do we, as a community of healthcare professionals, even understand the basic demographics of the populations we are tasked to serve and if not, what should we be doing to facilitate universal access to all? Fortunately, the Centers for Medicare and Medicaid Services (CMS) has grounded us by providing a clear deﬁnition of what it means to improve health disparities. In 2021, CMS laid out its strategic direction for the next decade. At the core of their initiatives is their vision: “A health care system that achieves equitable outcomes through high quality, affordable, person-centered care.” To do this, their strategy focuses on driving accountable care, advancing health equity, supporting innovation that closes care gaps, improving access by addressing affordability, and partnering with others to achieve system transformation. It’s an audacious and important goal that has ﬁnally been clearly deﬁned for our whole industry. But, big audacious goals are often complex and their execution is non-linier. What tangible actions can we make in our industry to harness the best of our people, process and technology to create real progress towards these goals? Getting to know your population We’ve seen ﬁrst-hand how a data-informed approach can bridge the health equity gap. It may seem simple but understanding the unique drivers of your population groups is critical to tailoring impactful care — there is no one size ﬁts all. For example, a 4.5 star health plan that we work with identiﬁed that the impact on primary measures were predominantly driven by non-compliance attributed to their Latinx and Black members. Historically, the approach of reaching out to these populations to drive treatment compliance was no different than the approach the health plan used to improve these outcomes among their White or Asian populations. Research has shined a light on the health equity gap between Latinx, Black and white American populations. It became incredibly important for healths plan to rethink their outreach approach for their Black and Latinx members to drive compliance — ultimately saving the plan vital funding and promoting better health outcomes for the individual. Enter a data-informed approach: 5 steps to address health disparities Changing the outreach approach requires a data-driven focus. To do this, there are 5 key steps: 1. Deﬁne what health inequities look like in your population 2. Identify SMART goals to address 1-3 of these inequities 3. Identify data sets to identify, address and track impact of your actions on health equity 4. Apply data science framework to inform culturally appropriate care delivery 5. Operationalize learnings by enhancing people, process and technology First, you must identify the inequities: are these disparities impacting cost and revenue, or are they patient-focused? Do they cause worse health outcomes, limit access to care, or impact the quality of care for these populations? The health plan must identify which priority was most important. Next, the plan must identify SMART (Speciﬁc, Measurable, Attainable, Relevant and Time-bound) goals based on what health inequity looks like in that organization's population. This can be radically different by population. The SMART goals deﬁne the meaningful, incremental targets that can bring the organization incrementally closer to their health equity objectives. The goal here is incremental progress, since the CMS has stated this is the priority for the next 10 years. Once the problem area is identiﬁed and SMART goals are set, they need to determine which data sources would allow them to identify and track the problem. Without any measurement, they wouldn’t know if they are tracking toward the deﬁned goals. Additionally, publicly available third-party data sources can be used to enhance the existing health plan data set. Through augmented intelligence using the data, the health plan is able to identify the characteristics that are endemic to their targeted population groups — Black and Latinx members. By using third-party consumer data and social determinants of health data, they create ‘member personas’ that tell a bigger picture story about who the people in this demographic are. The member personas get at the motivations behind their members: what motivates them, their family structure, language of origin — all data that can be overlooked in the clinical setting. Based on what the data tells them, they can better understand how their Latinx members differ from the rest of their member population. Finally, the data and personas are put into action. The information in them is used to ask the right questions that enhance the people, process and technology to meet the goals identiﬁed by the health plan. For example: ● People: Do we have bilingual resources who can perform culturally and linguistically appropriate outreach to the Latinx population? ● Process: Do we have the right processes in place to identify, stratify, reach and transform the behaviors leading to adverse outcomes for these members? ● Technology: How can we leverage technology to make some of the above process steps easier for the people who are trying to make an impact in the lives of the people we serve? Why this matters Health inequities are personal and unique to each health plan and their member population. It’s imperative that people are able to access appropriate care in a language they understand, so that they can make the best decisions for themselves and their families. While there’s no silver bullet to solve the issue of health equity, there are simple ways to use data to identify where there is opportunity for your plan to improve disparities in health and health outcomes. Enhancements in people, process and technology tools are the small incremental steps needed to get us to our larger goal of health equity for all. Within the industry, we are challenged to ask ourselves questions about how well we know the population we serve and if we are truly offering culturally appropriate care. We are all at the start of the journey to truly understand how health equity impacts our networks. Please take two minutes to respond to our anonymous survey to share your views, understanding and priorities for health equity.
This article was originally published on Health IT Answers by John Sargent Healthcare in the United States is often better at treating disease than preventing it. Chronic diseases are the leading causes of illness, disability, death and rising healthcare costs in the country. Lifestyle health issues like obesity, high blood pressure and blood sugar, poor diet, and smoking are linked to more than $730 Billion in healthcare spending in the U.S. As many as six in 10 U.S. adults live with a chronic disease. But these lifestyle diseases don’t exist in a bubble. The circumstances in which a person lives has a direct impact on health outcomes. Social determinants of health metrics (SDOH) like income levels, food insecurity, education and housing status accounts for 30–55% of health outcomes. In addition to being the root of a patient’s health challenges, these SDOH can also be a barrier to receiving the cultural and contextually-appropriate care that a person needs. It’s in the healthcare system’s best interest to close the care gap. When providers can identify patients who may be at risk for stopping disease treatment, the impact could be a matter of life or death. When Medicare payers identify patients with potential negative health outcomes and flags them to providers, it saves both taxpayer dollars and patient lives. By using a combination of predictive and prescriptive next best action insights, providers can close the gap in care for individual patients. To do this, healthcare systems leverage a combination of data sources — clinical data, patient surveys, SDOH data, and consumer and behavioral data sets — and apply artificial intelligence (AI) techniques to create those insights. Predictive and prescriptive insights defined Predictive analytics use historical data to create static models that predict future outcomes. In a real world example, predictive models can be used to prepare a hospital for an influx of new disease; something we experienced often during the peak months of the COVID-19 pandemic. These predictive models analyze past and current infection numbers to identify when a new wave of infection will begin, giving a hospital system a head start to prepare with adequate PPE, staffing plans and protocols. Arguably the most important aspect of using predictive analytics is knowing what to do with the information. It’s one thing to understand which patients are at higher risk of developing type 2 diabetes based on their bloodwork. It’s another to know what the next best actions are for that specific patient and their lifestyle. Predictive insights identify which patients are at risk and prescriptive insights recommend a set of interactions specifically for those patients. For instance, a predictive insight might tell us that Member X is at risk for not complying with their treatment based on a few critical factors, such as lack of reliable transport to their doctor, their distrust in the medical system or because English isn’t their primary language. Based on this information, a specific set of next best actions are recommended to target these specific factors to encourage care compliance. Generating insights through AI and data AI techniques like machine learning take predictive analytics to the next level. Machine learning algorithms build predictive models using sample data. As the model is fed more data, the algorithms learn and improve on their own from patterns in the data. Self-reported patient data is often combined with third-party data sets, such as publicly-available national SDOH data or from companies that collect consumer data. Each of these data sets are never 100% accurate on their own, but when combined, they create a more complete picture in which to uncover patterns and generate insights. We believe that it is critical to include a wide range of data sets including clinical, SDOH, consumer data, behavioral data and self-reported data when building these predictive models. This allows us to obtain a better whole-person view of each patient, understand what could potentially prevent them from having positive health outcomes (e.g. they are living in a rural area, are not fluent in English, have transportation issues and are distrustful of the health system) and close the care gap by prescribing a set of interactions specific to that patient’s circumstances (e.g. to ensure that they stay on their medications or find a provider who speaks their native language). Using predictive and prescriptive insights to close the care gap I’ve seen how predictive models and next best action insights can make a difference in some of the most at-risk communities. One of the predictive machine learning models we run in Africa tracks patients who are on HIV antiretroviral therapy (ART) in order to identify which patients are at high risk for dropping out of the healthcare system. All of the predictive analytics models are built on large multi-year longitudinal patient data sets (e.g. 500,000 patients): data that identifies who dropped out of the healthcare system, what their CD4 count was, where they lived, etc. Based on the model which builds the correlations and identifies patients at increased risk for dropping out of care, the providers are notified and take proactive actions with the high risk patients. These interactions are fed back into the model, which uses this information to continually improve upon itself. The results are impressive. One program experienced a 36% increase in retention of high-risk patients. Looking ahead: Using data to create more personalized next best actions We are living in a data-driven and data-rich world — from our smartphones in our pockets, to our Apple Watches and FitBits on our wrists, to the purchases we make online. As we look ahead 10 to 15 years, I predict that this type of personal data will be used more often to gain an even more personalized picture of individual patients’ health. If doctors had access to the objective data on a patient’s resting heart rate, sleep patterns, how many steps they’re taking in a day — which is all currently taken via subjective self-reporting surveys — we could build more accurate predictive models to identify at-risk patients and the next best actions needed to meet that patient’s personal needs in the moment. Like all conversations around big data, especially healthcare data, privacy is paramount. Apple is advancing this technology with their recent update to iOS 15, which allows users to share their health data with others, such as family members or their doctor via the Health app. It’s a step toward closing the gap between patients and their providers. Combining this data with SDOH, we can not only create models that predict illness or disease progression, but create a personalized action plan for each patient, delivered to them in the context of their real life. Equitable healthcare is possible and data and AI will get us there.
“There’s not enough focus at all on what it actually takes to scale up vaccination programs for adults primarily, who, by the way, we really don’t have global models for vaccinating an entire adult population in a prolonged period,” said Ernest Darkoh, the co-founder of BroadReach Group, a health care company working to expand Africa’s health system. “It is not something to be backing away from right now.” Read more