As we work together to flatten the curve in the spread of COVID-19, health systems are now using the new COVID-19-specific algorithms and workflows within Arcadia’s population-level platform. Arcadia is supporting the healthcare system in several ways, among them:
On March 20th Arcadia met with its customer base remotely and unveiled a series of tools and strategies for supporting the COVID-19 response with population health. Since then we have been rapidly deploying new stratification algorithms, patient engagement tools, and surveillance analytics on a national scale. The video below is a snippet of our initial Mar 20 presentation. Watch the full video
At the center of our population health strategy to support COVID-19 is a novel set of stratification models to identify patients of highest need and risk. These inform engagement efforts and the allocation of limited primary care and navigation services. Arcadia is rolling these out for all patients with any activity in the last 5 years.
High-to-moderate risk of complications from COVID-19
Patients with chronic or current care needs
Patients that are symptomatic, or diagnosed with COVID-19
Patients that are healthy or otherwise historically unengaged
Cohorts were designed from factors that are believed to elevate a patient’s risk of having serious complications should they become infected with COVID-19. An additional behavioral health stratification was layered in as it can further complicate feelings to isolation and anxiety. Younger populations are generally at lower risk of serious illness or death from COVID-19, while over 50% of members age 80 or older have evidence of a condition that will increase their likelihood of serious complications as it relates to COVID-19.
Arcadia has configured its Outreach and Engage products with content specific to education and triage of COVID-19. The interactive content, currently available in 9 languages, is delivered to patients directly on their mobile device without any pre-configured software. Acting as an extension of the care team, the content guides patients through access information for telehealth and hotlines, provides a personalized interactive COVID symptom checker, and allows health systems to securely capture information about patients’ symptoms and care needs.
As organizations quickly evolve to meet the demands and constraints of COVID-19, patients need consistent, trusted information about access to care and new or relevant services to help them cope with the crisis.
Give patients the most accurate, up-to-date information on COVID-19 symptoms and pair it with “escalation points” to urgent care, emergency room, telehealth visits and hotline numbers.
For symptomatic patients or those at higher risk, provide a regular electronic check-in on evolving symptoms, care needs and social factors.
Now, more than ever, care teams around the country need the support and extenders that population health programs can provide. Access to primary care is severely reduced due to office closures and patients’ fear of getting sick. The most vulnerable of our population risk falling through the cracks during this crisis.
Combining our enhanced stratification algorithms and our broad automated engagement platform, care teams can isolate and focus on complex co-morbid patients at the highest risk of falling through the cracks.
Our care management software has been enhanced to include support for COVID, as well as dozens of high risk medical and social factors.
An automation engine can “escalate” patients to a navigator phone call within minutes of a patient responding to a mobile screener.
You need access to good, fast, trusted data to effectively deploy resources to respond to the pandemic. Arcadia is surveilling systems and diagnostics from all your EHRs, HIEs, labs, claims systems and directly from patients.
Nightly extractions of patient charts including structured, pseudo-structured, and unstructured note data.
Real time alerts of admissions, discharges, and transfers from hospitals and HIEs.
Testing response made available to understand what is happening to patients with an integrated dataset.
Real time additions of data entered by patients via text message surveys or entered by care managers on the phone with a patient.
Arcadia has joined with other private sector organizations to form the COVID-19 Healthcare Coalition, a collaborative private-industry response to the novel coronavirus. Its mission is to save lives by providing real-time learning to preserve healthcare delivery capacity and protect U.S. populations. Each coalition partner is bringing its unique assets, sharing resources and plans, and working together to support those on the front lines in responding to COVID-19. Learn More
An ongoing repository of data on coronavirus cases and deaths in the U.S.:
The COVID-19 outbreak is a human tragedy and has a growing impact on the global economy. This is McKinsey’s evolving take on coronavirus’ business implications.
Pandemics thrive in confusion. Help our nation achieve clarity.
Explore projected hospital bed use, need for intensive care beds, and ventilator use due to COVID-19 based on projected deaths for all 50 US states and District of Columbia.
Nick Stepro, Simon Ioffe. 2020. D3.js, with Illustrator. Data sourced from Commercial, Medicare and Medicaid claims from Arcadia Benchmark Database
The 2017 – 2018 influenza season was the worst on record since the 2009 H1N1 swine flu. The CDC estimates 45 million people nationally caught the flu, with 810,000 hospitalizations and 61,000 deaths.
In 2020, viral outbreaks are on most of our minds. While mortality and transmission rates of the novel coronavirus (COVID-19) far exceeds those of the 2017-2018 flu, a look backwards can still be instructive. Even with its reduced virality, and the presence/distribution of vaccines, the 2017-2018 outbreak was explosive.
Looking at Massachusetts transmissions using medical claims data, we visualize not just the growth rate of total cases but the algae-like “blooms” of infections. A few small clusters at thanksgiving bloom into thousands of cases by new years, each interaction a potential for transmission. For the purposes of illustration, the artist draws a linkage between each new case and the closest person that had a diagnosis four days prior. This simulates the transmission graph as a social network of sorts, and highlights the importance of current calls for “social distancing” as we deal with an ever more deadly virus.
Samir Farooq, Michael Simon, Nick Stepro. 2019. Python, D3.js, with Illustrator. Data sourced from Commercial, Medicare and Medicaid claims from Arcadia Benchmark Database
Care management programs are not one-size fits all. Rather, patients have subtle characteristics requiring tailored patient-centered plans of care. Similarly, the extent to which the patient will benefit from the targeted program varies as well. Identifying those who will be most successful in a given care program takes a combination of medical expertise, research, and mathematical estimation. Arcadia quantifies this “impactability” with learning algorithms that look through hundreds of thousands of past cases, finding shared attributes in those that had the greatest success.
This illustration analyzes about 64,000 patients and their predicted impactability (main diagram) along with their characteristics (sub-diagrams) to answer the question: what characteristics are the strongest indicators of success in complex care management?
Taking the population under a microscope, by applying t-SNE (a dimensionality reduction technique) and subsequently studying the resulting clusters, we find that four distinct groups of patients exist that have high impact scores, and many more small groups. While the largest group is composed of the most predictable at-risk patients (high cost and complex conditions; top left), the other groups show: 1) a unique pattern of avoidable and emergent Emergency Department utilization lead to high impact scores (top right), 2) a particular combination of chronic, psychosocial, and frailty conditions lead to high impact scores (bottom right), and 3) certain socioeconomic traits, along with a set of specific conditions, lead to high impact scores (bottom left).
Arcadia’s impactability algorithm consumes more than thirty specific risk factors – nearly impossible for care managers to keep track of over all patients, but easy for the algorithm to combine into one risk score tailored to care management. This algorithmic assist not only finds more actionable patients, but also lets program coordinators repurpose their valuable time from patient identification to patient engagement and program design.
Jeff Soloman. 2018. D3.js SVG, with Illustrator. Data from Arcadia integration logs, with metadata from leading EHRs and health plan claims data.
The complexity of the data integration challenge in healthcare, is often ethereal, or even escaping. It is near impossible to instinctively grasp the sheer magnitude of data, the complexity of connecting it, and the value of simplifying it. Years of experience in the industry make it easier. But it requires a deep understanding of numbers that have been proven outside of human capabilities to understand. In this work the artist takes an emotional and stunningly beautiful approach to helping the reader see how 2 billion health records across 58 data sources, 3,909 tables, across 1 million patients, and 27 different vendor systems can be simplified.
The artist’s use of a chord diagram helps emphasize the connectivity and relationships between the source data and Arcadia data warehouse. The imbalance between the almost 4 thousand source tables and the 27 destinations creates an emotional connection with the movement and flow of data that even those familiar with a chord diagram may find jarring.
For those who want to look further, the artist shares two other dimensions in the data. The size of each data source is represented as an outer ring, and the size of each of the 3,909 tables in depicted in the bar charts surrounding the edge of the chord diagram.
Through this work, the artist hopes the viewer leaves with a clear understanding of what are otherwise staggeringly incomprehensible amounts of data, and an emotional connection to what happens when you simplify and unify healthcare data.