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Health IT Answers | Using Predictive and Prescriptive Insights To Close Care Gaps

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Advancing Health Equity Initiatives Across Special Needs Populations

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.

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MedCity News | Lessons learned from fighting HIV in Africa—and how they apply to Covid-19 in the U.S.

This article was originally published on MedCity News


The HIV and AIDS crisis in Africa was—and still is—devastating for the continent’s population.

Despite only making up 6.2% of the world’s population, Eastern and Southern Africa is home to  54% of all people living with HIV in the world. For almost 20 years, there was essentially no treatment for HIV or AIDS in Africa beyond palliative care. Thanks to investment by international aid organizations, private companies, government sponsorship and advancements in HIV treatment, the situation has improved dramatically in the last two decades.

But now the world faces another public health crisis.

Mark Twain once said, “History doesn’t repeat itself, but it often rhymes.” While the Covid-19 global pandemic isn’t the HIV crisis repeating itself exactly, it sounds a lot like the challenges we faced in Africa in the 1980s and 1990s: using limited resources to tackle an illness no one has ever seen before.

As we enter the third calendar year of the Covid-19 pandemic, there are many lessons learned from the HIV and AIDS crisis in Africa that we can apply to fighting Covid and increasing access to care for everyone. 

When everything is uncertain, data is your biggest ally

One of the most important lessons learned in Africa with HIV—and throughout the Covid-19 pandemic—is that data is your biggest ally. We need data to tell us what’s going wrong and why. But most importantly, we need this information in real time.

When you’re dealing with an epidemic that’s evolving by the second, we can’t use data that’s a week old. We need to know where an outbreak is occurring, as it’s happening, in order to intervene and contain it. And when we know what’s happening right now, we can predict with more accuracy where the next outbreak could occur.

For example, South Africa aims to test eight to 10 million people for HIV each year. Finding these patients is like finding a needle in a haystack. In situations like this, it’s important to use timely data to identify the communities that have the highest number of HIV-positive people. AI technology can help with that, as it allows health facility managers to actively monitor the testing teams’ performance against goals on a daily basis, so resources can be shifted immediately to locations that are showing the highest positive results. When we have all the information we need, we can stretch resources—no matter how limited they may be—for max benefit of patients.

Impactful data is simple data

In order to be actionable, data has to be simple. Our healthcare systems are flush with patient metrics, but turning all those data points into true intelligence is difficult. This information could be locked in complicated spreadsheets, on paper or sealed in one person’s brain.

When you present a complicated dashboard to overwhelmed healthcare workers whose job is to care for the sickest people—managers and frontline workers under extreme stress who haven’t slept in days—you’re asking for the impossible. One big lesson learned is that any technology that adds more work to an already stressed system and its people is unlikely to succeed. The best solutions help providers be more effective while simultaneously making their lives better.

America is currently facing a healthcare burnout crisis thanks to the pandemic. Nearly 1 in 5 healthcare workers have quit their jobs due to the stress of the pandemic. Of those that are still employed, 31% have thought about quitting, including 19% who have thought about leaving healthcare altogether. AI technology can take some of the tedious burden of capturing and interpreting the data off the plates of busy health professionals, allowing them to focus on what really matters, while supporting them to make the right decisions and implement the right interventions in the right way.

You can’t provide relevant care without cultural and social context

During the height of the HIV crisis in Africa, we often saw that patient outcomes were directly tied to the environments and communities in which they lived. We’re seeing that now with Covid in the United States, where racial minorities are experiencing the worst health outcomes. Social determinants of health—such as inclusion, income, food security, housing, environment, access to reliable transportation or even local government policy—can have a massive effect on health outcomes.

Policy and regulatory decisions directly affect these social determinants of health, determining patient outcomes. Nowhere is this more starkly apparent than South Africa. The brutal apartheid system in South Africa institutionalized inequity based on race and the long-term effects are evident in all of the health crises the country faces today. South Africa, as a single country, accounts for 25% of the global total of HIV infections. It also has concurrent major epidemics of tuberculosis, cervical cancer, obesity, diabetes, hypertension, substance abuse and violence.

There are many parallels with marginalized minority communities in the U.S. It’s important that U.S. health systems focus on proactively collecting social determinants of health data from their patients and using that data to identify and reach marginalized populations at risk. When resources are limited, directing them toward people who need them most is good medicine. To do this in practice, use data to understand where the biggest proportion of at-risk patients are, identify what solutions they need and then apply culturally appropriate care.

Creating community-specific messaging

Just like an orchestra isn’t an orchestra without multiple instruments, health systems can’t do their work without partners within the community. They each play a different role, but together can do incredible work. If we’re going to stop the next pandemic, we need to tap into the leaders in the community with the skillsets, access and trust to deliver the important messages and cut through the noise.

We’ve seen this in the U.S. and beyond: it’s hard to decipher whose Covid-19 message is most credible. The cycle of misinformation and fear mongering has caused significant confusion about everything from Covid vaccination and mask wearing to the use of large animal anti-parasite medication for the treatment of Covid.

Preventive partnership are the best medicine in this case. If you spend the time building trust and crafting a consistent message with a network of local community partners, influencers and reputable public health experts, then you will be prepared to activate that network on the day you need it.

That message also needs to connect with the people it’s intended for. When we first started treating HIV in Africa, many populations across the continent did not speak English and some were illiterate or semi-illiterate. In addition, we could be dealing with anything from 20 to 400 different languages in a particular country. Trying to explain the concept of a virus in all these languages to a population whose traditional and religious beliefs often don’t recognize germ theory is impossible.

We learned that you have to work with the communities to generate the messaging that will resonate, then test that message again and again to make sure it has the intended effect. This often looked like using language-agnostic pictures and analogies to explain HIV and treatments in a context they understand. Medical messaging only works if it comes from a trusted source, people understand it and can apply it to their lived experience.

History doesn’t repeat itself, but it often rhymes

While not the same story, the HIV epidemic in Africa and the Covid-19 pandemic experience in America has parallels: uncertainty, limited resources and strained frontline workers, misinformation, and negative political and social impacts.

As we look to the future, we now understand the multiple dynamics at play in the U.S. and we must accept that Covid is here to stay. As healthcare providers, we must lean on data and social context to make informed decisions so that we can address the next pandemic or other health challenges early—or better yet, prevent it from ever occurring.

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HIT Consultant | 3 Ways Artificial Intelligence Can be Used to Improve Health Equity

This article was originally published on HIT Consultant


When I graduated from medical school and took the Hippocratic Oath, I vowed to not just treat the illness on a patient’s medical history form but to treat the person behind the diagnosis. To do this well, clinicians need to understand the whole person and the context in which they live — their race, gender identity, native language, socioeconomic status, or zip code, among other things — to ensure equitable care. According to the CDC, health equity is reached when every person has the opportunity to attain his or her full health potential regardless of social position or other socially determined circumstances.

Yet, health inequities abound in our healthcare systems. Research says that those Americans who live in rural communities have less access to care and subsequently worse health outcomes than those who live in non-rural communities. African American adults are more likely to report they cannot afford to see a doctor, leading to worse health outcomes. African Americans ages 18-49 are twice as likely to die from heart disease than whites. Beyond race and community, even employment status has a great effect on one’s health. Members of the LGBTQ community are twice as likely to be unemployed and uninsured than their straight counterparts, reporting lower health and quality of life.

Healthcare inequities are also a drag on our economic systems. Medicare and Medicaid have an obligation to taxpayers who are paying into the system to help as many people as possible. When there are inequities in the healthcare system, it means that taxpayer dollars aren’t being well spent to impact the people they need to. If a health insurance company’s risk pool is warped toward people who are very sick and don’t have decent access to healthcare, it’s going to make that health plan a lot less profitable, increasing premiums for everyone.

Artificial intelligence (AI) technology and algorithms can help us bridge this health equity gap. But it’s important to remember that AI is not a one-size-fits-all solution. Data is great, but without an application, it’s not useful. AI allows us to use the data to interpret what’s going on with a patient population and prescribe what to do with the data. Here are three ways we can apply this technology to help solve the health inequity problem in America and around the world.

1. Using AI to identify the problem

Health systems are dealing with a seemingly infinite amount of data on massive patient populations. It’s hard to spend time sifting through the data by hand to understand what’s happening within their population. AI technology can help these systems sort through that data to understand exactly where providers should focus to get the best ROI for positive patient outcomes. In a real-world example, a case manager logs onto work on a Monday morning and receives an email with details about their patient, John Doe. An AI-powered algorithm flagged that John Doe has two issues that may impact this ability to manage his diabetes: his current provider isn’t a native Spanish speaker and he currently doesn’t have a vehicle. This means that John Doe, who doesn’t speak English, is facing two serious health inequities that could affect his ability to get the right information and physical access to the clinic for his appointments.

2. Using AI to identify next-best actions

Now that we know the problem, it’s important to take action and solve it. No one wants to spend time analyzing a million charts or rows of data in a spreadsheet. Decision-makers need to know what the issue is, what they need to do and how they need to do it to affect change. By using AI to provide predictive and prescriptive recommendations in a culturally sensitive way, we can bridge the equity gap.

In the John Doe example, the prescriptive recommendations that will improve John’s outcomes include finding John a doctor that speaks Spanish and setting up John with the telehealth services to ensure he has continued care regardless of his transportation challenges. AI allows us to replicate this over millions of patients quickly when compared to doing so by hand. If Amazon can predict which book on the history of World War II I should read next based on my buying history, certainly we can use similar technology to predict what issues will arise for our patients and what we need to do to intervene.

3. Using AI to better allocate limited resources 

Resources are often limited in healthcare. AI technology can help providers make better decisions on where to invest, build and allocate resources more effectively to close the disparities. This type of technology provides a more strategic view that helps managers and executives answer the question, “do I have the right skill sets and resources to meet my health equity challenges?”  If not, do I need to shift certain resources (e.g. Spanish speaking doctors) to other clinics and patients, or do I need to invest in new approaches (e.g. telehealth) or partnerships (e.g. taxi company, local churches) that help me to better treat each patient?

Healthcare is a human issue 

AI can make the entire healthcare system more efficient and effective at identifying and solving these inequity issues. But at the end of the day, healthcare is still a people issue. As a doctor, I was trained to believe that I am in charge of a person’s health. They come to me for a diagnosis and I write the prescription for a medication they need to address it. In reality, 99% of the patient’s life occurs outside the doctor’s office and in their community. In order to improve health equity, we must find ways to partner with the leaders of the communities in which they live.

Medical male circumcision has long been known to be a key tool in reducing the risk of HIV transmission. Now imagine yourself, an outsider, entering a Zulu community in Southern Africa.  No one speaks English and they have a very specific understanding of healthcare. Try to convince a 21-year-old Zulu man to get circumcised for his health — it’s an uphill battle. Who does this 21-year-old man listen to? Most likely his community tribal leaders. A lot of the work the BroadReach Group does today is identifying the local on-the-ground structures, whether they be the tribal or cultural structures, that would influence the community’s decision-making. Essentially choosing the next-best actions informed by behavior science. We then partner with these community groups to craft messaging and create programs to convince the population to take these health steps.

While it may look a little different, we face the same distrust patterns in the U.S., now more so than ever before. How do we convince people that are wary of health systems to see a doctor every year or get vaccinated against COVID-19? Close partnerships with trusted local community leaders.

The healthcare industry can’t solve the equity problem alone — we need partnerships. When healthcare companies partner with the private technology sector, it helps us think outside of the industry about what’s cutting edge — like new AI-driven technology — and how we can apply it to healthcare. When healthcare companies partner with local community leaders, we can effect real change within a hard-to-reach population. Health inequity is a comprehensive problem that covers all of society. We can’t do it alone.

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