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By Dr John Sargent, co-founder of Vantage Technologies and the BroadReach Group

Open any news source today and no doubt you will read about the risks or rewards of Generative AI models like ChatGPT. There seems to be frenzied predictions about how this single technology will change the world for good or evil. My work, over the past 20 years, has been in health equity and how we can harness technology to supercharge health systems and empower healthcare workers to support their patients better. Through this lens, I take a cautiously optimistic, sceptically excited view of this and other emerging technologies. Here is why.

Technologies, data, analytics, artificial intelligence, machine learning and other technologies have been used to manage health systems and population level health outcomes for years. Within Vantage Health Technologies alone, our AI-enabled solutions have been embedded within health systems and supporting healthcare workers around the world for nearly a decade. What we are seeing today is the popularisation of ChatGPT – an exciting AI model – rising to the mainstream. Within healthcare – where lives are on the line – we need to assess these emerging technologies in a thoughtful and balanced manner and apply the most appropriate ones to help us achieve our true north: a world where access to good health enables people to flourish.

1. It is not about technology, it never was

Our first principle must always be: does this improve the lives of patients and increase the healthcare workers’ capacity to provide care? As health tech enthusiasts we must always remember that technology is just a tool in service of that goal, rather than getting caught up in the next shiny new thing. We need to be grounded in this principle and thoughtful in how to avoid the pitfalls and, instead, leverage the potential of the emerging technology. In the case of generative AI, I think both pitfalls and potential exist.

2. Defining ‘Ethical AI

Health data is some of the most sacred and private data we have custodianship of and, combined with fast-evolving AI, presents both terrifying opportunities for misuse and game-changing opportunities for care advancements. We must make it our mission to build trustworthy AI tools as health-tech moves more and more into the mainstream. 

Fundamentally, I think it is important to have a common understanding of the risks, limitations and pitfalls of unleashing AI on health data. The concept of ‘ethical AI’ is currently being widely debated by diverse thought leaders.  For example, Microsoft looks at the topic across six principles: accountability, inclusiveness, reliability and safety, fairness, transparency, and privacy and security. A recent PwC paper prepared for the World Economic Forum defined it as: ‘Ethical Al should promote and reflect the common good such as sustainability, cooperation and openness. As we consider potential development and uses of AI technologies, we will take into account a broad range of social and economic factors, and will proceed where we believe that the overall likely benefits substantially exceed the foreseeable risks and downsides.’  Mira Murati, Chief Technology Officer for OpenAi, in turn says, ‘AI systems are becoming a part of everyday life. The key is to ensure that these machines are aligned with human intentions and values’.

For me, therefore, ethical AI for healthcare means keeping our true north – of patient care – at the forefront, ensuring that we have guardrails in place to protect their health, privacy and integrity so that technologies really can empower human action. As an industry, we should acknowledge that we are in the early phases of what must be a multi-phase approach in implementing these tools into everyday life. Industry collaboration across all types of stakeholders is being led by groups such as the Coalition for Health AI which brings together a community of academic health systems, organizations, and expert practitioners of artificial intelligence (AI) and data science. It is essential to have standards and frameworks in place that are consistently reviewed and updated to ensure adherence and to educate end-users on how to evaluate these technologies to drive their adoption.

3. Garbage in, garbage out

 All data is inherently biased. Data is the process of transforming, capturing and storing information for human consumption, and things can go wrong during any of these steps. As to old adage goes: garbage in, garbage out – and with health data these blind spots can literally be life or death. The quality of the data we collect, and store is critical. How and on what we train our algorithms to mine within the data can also be tainted by oversights, prejudices or errors. Biased and distorted health data can perpetuate discrimination and health inequity in society.

We should all learn how to identify and name the many forms of bias. Neil DeGrasse Tyson in his Masterclass on the types of bias tells us that distortion can be created by wilfulness, but it can also be created by our own limitations of training, knowledge, vocabulary and assumptions. Intentional or not, biases can hurt people. Poor management of data privacy can also hurt people, causing patients to be exposed and potentially exploited.  It is therefore essential to have guardrails and mechanism to clean the data before it is used, so that is accurately represents the population it is describing!

In the case of the commercially ‘free to use’ Generative AI models, this bias can proliferate unchecked. In the example of ChatGPT, its model was trained on digital content found across the internet up until September 2021[1].  So, this would not be suitable data upon which to draw conclusions for today’s health challenges.  For context, the data utilized is from before mass COVID vaccinations were available within many developing health systems. Populations change, data is dynamic, and this particular model has not yet caught up. It simply will not have the nuanced and specific information needed to generate valid conclusions for certain use cases. So, while it can write convincing instructions, the output could be too biased to be valid and ultimately dangerous.

4. Now you are speaking my language

However, add the underlying natural language processing power of the OpenAI’s model within a discreet, validated and secure data set and there is real opportunity for pinpoint efficiency and true empowerment of the health worker. We are exploring this within our own Vantage Workforce Empowerment solution. The AI-enabled solution (developed prior to ChatGPT) is used by thousands of healthcare workers in Africa. Weekly, over 24 000 emails are sent from our system, to guide both frontline and management healthcare workers with their next best actions, personally derived from their health system data. This data is housed securely within Vantage, it is quality checked and managed for reliability.  We are experimenting with Generative AI to enhance this work with more personally crafted language so that its guidance is more precise and empowering for each unique user.

By adding Generative AI natural language capabilities, we can send even more tailored, personalised messages in the tone that we’ve learnt the recipient will respond to best, with the kinds of prompts that resonate with the recipient. Generative AI is exceptionally good at mimicking how humans present written information and can even imbue it with a real sense of confidence and persuasiveness. This could be key to motivating already stretched healthcare workers and guiding them to action with fidelity – and across many languages.

Additionally, we can harness the insights we gain about a patient’s unique circumstances, and craft tailored messages to the patient and their healthcare workers to improve care. We can use everything we know about a patient’s social determinants of health, such as language barriers (e.g., if Spanish or isiZulu is their first language), transport issues (not having a car or money for public transport) or food security (not being able to eat before taking their meds), to tailor solutions for them to keep them healthy. For instance, we use our algorithm to predict when a patient is likely to stop treatment and intervene before it is too late, in language that is likely to resonate with them. This allows us to improve patient retention rates which ensures better health outcomes and lower costs.

 Where to from here?

 AI can help us revolutionise the delivery of very useful “next best action” prompts to health workers and patients, enabling them to make better decisions and save more lives. Generative AI is here to stay – so let’s harness its potential for good to create a world where health equity is a reality.

If we can meet people where they are at, it can be a real game-changer for how we keep cancer, tuberculosis or HIV patients on treatment. If we can keep people on treatment, we can prevent the drug-resistance that can develop when a patient misses a dose.

Going forward, the key is to ensure that these interventions can carry on with proper data and AI management guardrails in place, while never losing sight of our true north – our patients. This is a top priority I will continue to pursue in my conversations with other global health leaders who are committed to AI for Good and who continue to strive, like us, to create a world where access to good health enables people to flourish.

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 MedCity News

by  ERNEST DARKOH

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.