The following is a guest article by Nate Maslak, Founder & CEO at Ribbon Health.
Personalization is embedded into many aspects of our lives, from individualized show recommendations on our Netflix accounts to AI-driven product recommendations based on our shopping habits. And consumers have come to expect and demand this personalization. Recent McKinsey research shows that 71% of consumers expect companies to deliver personalized interactions.
This expectation is expanding into healthcare. Personalized healthcare meets people where they are as an individual with unique behaviors, medical histories and life circumstances. Patients who receive a personalized experience may have multiple specialists treating each of their needs individually, such as an endocrinologist for hormone therapy or a sports medicine doctor for a knee injury. The ability to seek treatment from a doctor specializing in your specific medical need is beneficial and improves health outcomes. Yet, an intrinsic outcome of this personalization is data fragmentation — data points from different doctors, platforms and tools stored in multiple systems, completely disconnected from each other. In this world of data fragmentation, healthcare organizations must prioritize creating ecosystems where data can be centralized and untangled to empower patients to make personalized and informed care decisions.
Personalized Healthcare in Action
Personalized healthcare considers two stakeholders: the patient and the healthcare system treating them. Every patient is an individual with a unique health history, life experience and social determinants of health that impact their physical and mental well-being. To provide a personalized care experience that considers all these factors, healthcare organizations must collect the right data and take steps to understand the whole person behind the patient.
Personalized healthcare can take many different forms. The COVID-19 pandemic accelerated telehealth adoption in a way that initially wasn’t considered feasible by providers and payers pre-2020. A 2021 report by the Department of Health and Human Services estimates that the number of medical visits via telehealth grew from 840,000 in 2019 to 52.7 million in 2020. Patients can now receive more flexible and personal care to meet their circumstances — like overcoming a lack of reliable transportation or navigating preexisting conditions that make in-person doctor appointments risky — keeping them engaged in the healthcare system and improving their health outcomes. Other personalization tools, like health trackers and wearables, allow patients to receive personalized medical recommendations and treatments based on their actual health metrics. Ultimately, personalization increases patient engagement and improves outcomes.
Why Fragmentation Results from Personalization
As healthcare becomes more personalized, data naturally becomes fragmented. Because care is often decentralized across provider networks, specialists and hardware technology like Apple Watches and FitBits, essential pieces of data are lost when systems aren’t talking to each other. This could be data like lab results from a patient’s rheumatologist not being sent to their primary care doctor. It could be provider-specific data, like details on a provider’s specialties or office phone number, that is outdated and inaccurate across payer databases.
Let’s consider a real-world example outside of healthcare. Imagine a college campus has one dining hall that serves one meal, chicken caesar salad. Every student on campus dines in this one dining hall every night, eating chicken caesar salad again and again. When friends meet for dinner, there’s no confusion about which dining hall they’re going to; no fragmentation. Now, consider that the college has decided to open six new on-campus dining halls, each serving a unique menu. If you’re a vegetarian or dairy intolerant, you’re in luck — you now have choices and can personalize your experience to fit your health circumstances. However, these additional options lead to fragmentation. When the same group of friends meets at the dining hall, there needs to be more specificity about which location they’re referencing. They may need to share details such as the dining hall’s name, address or menu to identify which location they intend to meet at. Otherwise, the group becomes lost and disjoined with fragmented information.
Now apply this same principle to the millions of care decisions that are made every year, informed by millions of data points. Data fragmentation in healthcare can cause similar confusion for providers and payers, ultimately negatively affecting patient outcomes.
Data, Fragmentation, and the Power for Good
Data, especially in an increasingly personalized healthcare system, is inevitably siloed. Healthcare providers maintain different systems than payers, as do government entities like Medicare and Medicaid. While data fragmentation brings significant challenges, the data generated in our healthcare system is one of the most valuable assets to the healthcare industry to drive affordable, accessible and high-quality healthcare. Innovative technology enables us to centralize data from various places into one source of truth, make sense of it and surface personalized patient recommendations.
Artificial intelligence, supported by machine learning, can navigate these incredible data stores across multiple warehouses to analyze data patterns and relationships in ways that matter most to patients and care navigators. Health systems can understand patient or provider data from many sources, enriched by software, rather than trying to augment and consolidate fragmented data silos. Specialization and focus on getting the most important insights from inherently fragmented data requires artificial intelligence to constantly add expansive and accurate data sources to pull from in order to drive meaningful insights to support patient care decisions.
In practice, there are many ways that applying artificial intelligence to fragmented data silos improves patient care. AI can analyze data from thousands of heart disease patients across a population to identify patterns of treatment response, getting us closer to optimizing personalized treatments. Innovative digital health companies are creating solutions to manage this fragmentation, such as a tool that consolidates data from clinical audio notes and turns it into actionable care plans, freeing doctors to spend more time with their patients. AI can analyze patient health record data across multiple systems, such as age, previous health experiences and self-reported SDOH information, to provide preventative care recommendations or interventions that are personalized to each patient.
Healthcare interoperability is the ultimate goal. But we cannot discuss the value of creating personalized healthcare while simultaneously saying it can only happen without fragmentation. Personalization in modern healthcare can only exist with some level of data fragmentation. And thanks to new innovations like AI models, we can make sense of this fragmented data for patient benefit.
About Nate Maslak
Nate Maslak is the co-founder and CEO at Ribbon, a data platform fueling healthcare enterprises with actionable provider information to transform how care decisions are made. After consulting for healthcare companies at McKinsey, Nate built and ran the Identity Graph predictive analytics product and business at Datalogix to help drive an acquisition by Oracle for more than $1.2 billion in 2014. He was recently named to Business Insiders 30 Under 40 Changing the Healthcare Industry in 2022. Outside of Ribbon, you can find Nate trying to eat his way through the NYC boroughs. Nate received his MBA from Harvard Business School and BS from Washington University in St. Louis.
